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  • Neurosurgery is a discipline that diagnoses and treats a range of injuries and disorders of the brain and the central nervous system
  • For millennia the speciality was dominated by forms of craniotomies, which are procedures to remove portions of the skull to gain access to brain disorders
  • In the early and mid-20th century visual, guidance and radiation technologies disrupted the treatment of some brain disorders by introducing less- and non-invasive procedures to the discipline
  • At the beginning of the 21st century, a flurry of rapidly developing innovative technologies including, augmented reality, artificial intelligence (AI), robotics and genomic and cellular therapies, are accelerating the trajectory of neurosurgery towards a less- and non-invasive speciality
 
Brain disorders and the changing nature of neurosurgery
 
Populations throughout the world are growing and aging, the prevalence of age-related disabling neurological disorders is increasing, and healthcare systems are facing large and escalating demands for treatment, rehabilitation, and support services for such disorders. According to the most recent Global Burden of Disease (GBD) Study, neurological disorders are the leading cause of disability and the second leading cause of death in the world.
 
The total annual global burden of traumatic brain injury alone is ~US$400bn and in the US, ~16% of households are affected by brain impairment, with many individuals requiring 24-hour care. This suggests that often several family members are involved in the caregiving process, and some are juggling the responsibilities of caregiving, child rearing and employment simultaneously.
 
The scarcity of established modifiable risks for most of this vast and rapidly growing neurological burden suggests that innovations are required to develop efficacious prevention and treatment strategies. This Commentary describes some of these, especially those that have changed or have the potential to change neurosurgery, by making therapies less- and non-invasive, and hold out the prospect of improving patient outcomes and lowering healthcare costs.
 
Neurosurgery is a medical speciality concerned with diagnosing and treating a range of disorders and injuries of the brain and central nervous system (CNS) in patients of all ages. These include tumours of the brain and CNS, infections of the CNS, pituitary tumours and neuroendocrine disorders, traumatic brain injury, cerebral aneurysms and stroke, hydrocephalus and other conditions that affect the flow of cerebrospinal fluid, degenerative spine disorders, Parkinson’s disease, Alzheimer’s, epilepsy, spina bifida, and psychiatric disorders.

Treating brain conditions is complex and challenging. This is partly because the brain is one of the best protected organs of the human body. It is encased in the bones of the skull, covered by the meninges, which consist of three membranes and cushioned by cerebrospinal fluid (CSF). It is also protected by the blood-brain barrier (BBB), which is a network of blood vessels and tissue comprised of closely spaced cells, which shield the brain from toxic substances in the blood, supply brain tissue with nutrients, and filter harmful compounds from the brain back into the bloodstream. The BBB limits the ability of therapeutics to be effectively delivered to the brain and thereby complicates the treatment of CNS disorders. Further, the brain does not feel pain because there are no nociceptors (a sensory receptor for painful stimuli) located in its tissue, which often makes diagnosis of neuro disorders late when treatment becomes more challenging and costly, and survival less likely.

Such factors partly explain why neurology and neurosurgery have been slower than some other specialities to take advantage of new and evolving technologies. However, this is changing. Over the past five decades, progress in three-dimensional (3D) visualization, miniaturisation, digital technology, robotics, computer assisted manipulation, radiation therapy, early diagnosis of cancer, and precision medicine, have contributed to improvements in the diagnosis, prognosis, and prevention of some neurological conditions and started to transform neurosurgery towards less- and non-invasive procedures that efficaciously execute complex challenges, eliminate mechanistic errors, reduce operating times, and improve patient outcomes.
 
Further, the growing significance of applying artificial intelligence (AI) and machine learning techniques to pre-, intra- and post-operative clinical data introduces the possibility of a new suite of medical services that have the potential to enhance patient outcomes and reduce costs by improving diagnosis, planning and the rehabilitation of patients. And more recently, there are growing synergies between neurosurgery and gene and cellular therapies, which promise to accelerate personalized, non-invasive treatments for a range of neuro disorders.
 
In this Commentary
 
This Commentary is divided into 9 sections. Section 1 provides a brief history of neurosurgery, which has its genesis in ancient times when a form of craniotomy (surgical removal of a portion of the skull) was practiced and note the difference between craniotomy and craniectomy. Section 2 describes how, in the mid-20th century, neurosurgery took ~4 decades to pivot when Lars Leksell, a Swedish surgeon, introduced a stereotactic guided device that permitted the accurate positioning of probes to treat small targets in the brain, which were not amenable to conventional surgery. Shortly afterwards Leksell developed ‘stereotactic radiotherapy’, which formed the basis the Gamma Knife®, a device that provides non-invasive surgeries for a range of brain disorders. Section 3 details how advances in magnification, illumination, and the development of fibreoptics contributed to less-invasive endoscopic neurosurgeries, which facilitated a range of brain disorders to be treated through a small burr hole in the skull. Previously such procedures would have required a craniotomy. This section also notes the rapid development of endovascular neurosurgery, which uses tools that pass-through blood vessels to diagnose and treat diseases and conditions of the brain rather than using open surgery. Today, neuro-endovascular surgery is the most practiced therapeutic approach for a range of vascular conditions affecting the brain and spinal cord and is positioned to grow further over the next decade. Section 4 suggests howneurosurgery has benefitted from a range of rapidly developing 21st century technologies including: augmented reality, artificial intelligence (AI), robotics and genomic and cellular therapies. All help to increase less- and non-invasive neurosurgical procedures and contribute to advancing personalized therapies that improve patient outcomes and lower costs. Section 5 provides some insights into the life of a neurosurgeon through the lens of Henry Marsh, an English neurosurgeon who, between 2014 and 2022, published three candid memoirs, which chronicle his career, describe daily challenges and frustrations of the speciality and explain how neurosurgical units have changed the way they are organized and run. Sections 6 briefly mentions the increasing prevalence of dementias. Although outside the direct realm of neurosurgery, the scale and speed of their growth are likely to have an indirect impact on it. Section 7 introduces traumatic brain injury (TBI), a condition caused by a blow to the head and suffered by millions. The section describes the gold standard management of severe TBI and flags a pressing need to develop a non-invasive modality for managing the condition. Section 8 notes the frustration of neurosurgeons with the late diagnosis of brain tumours and describes well-resourced global endeavours to detect a wide range of cancers from a single blood test in asymptomatic people. Takeaways follow in Section 9 and suggest that a significant proportion of neurological disorders, which previously were treated with craniotomies, are now treated with either less- or non-invasive procedures. With the speed at which technology and biomedical science are developing, the only direction of travel for neurosurgery is towards non-invasive procedures.
 
Section 1
History
 
Neurosurgery has a long history with its genesis in Mayan civilizations ~1500 BCE, who practiced cranial deformations that included flattening frontal skull bones. During the Egyptian era, when mummification started to be practiced ~2,500 BCE, embalmers did not use a form of craniotomy to gain access to the brain. Instead, they used hooked instruments to remove the brain through the nose: a prototype of modern transsphenoidal surgery, which is a common procedure today for removing tumours of the pituitary gland. Rather than opening the skull with a traditional craniotomy, the physician reaches the tumour through the nasal passages and the sphenoid sinus.
 
In ancient Peru Inca surgeons practiced an early form of craniotomy referred to as trepanation, which used a scraping technique to penetrate the skull. Such procedures were performed on adult men to treat injuries suffered during combat. A version of this procedure called a trephination was also practiced in Egyptian and Roman times and performed on individuals who had experienced head traumas. The approach entails making a hole in the skull to relieve the build-up of intracranial pressure (ICP) caused by brain oedema (swelling) and is described by Hippocrates in the Greek era. The first known neurosurgery in Greece took place ~1900 BCE in Delphi when skull trephinations were probably performed for religious reasons. Later, the technique was recommended by Galen during the Roman period for people who had suffered a traumatic brain injury (TBI) in battle. From ~500 to ~1500 AD, the rise of religion and war resulted in many craniocerebral traumas, which contributed to the early development of neurosurgery as a distinct specialty.
 
Similar trephination procedures were performed during the American Revolutionary War, which secured American independence from Great Britain, and culminated in the Declaration of Independence on July 4, 1776. During the war soldiers suffered TBIs after being hit on the head with the butt of a rifle. Although the treatment for severe TBI is similar today, (see Section 7) the main difference is that the surgical instruments used in the 18th century were not powered. About 132 years later, in 1909, Theodore Kocher, a Swiss physician and Nobel Laureate in Medicine was the first person to systematically describe a decompressive craniectomy procedure for severe TBI patients. A craniectomy is different to a craniotomy. The latter is a surgical procedure in which a section of the skull is removed to expose the brain and is performed to treat various neurological conditions, or when an injury or infection has occurred in the brain. A craniectomy involves a different surgical technique and is used on people suffering severe TBI to relieve brain oedema. In such a procedure the bone fragment removed may not be replaced immediately and is either replaced during a subsequent surgery or discarded in favour of a future reconstruction using an artificial bone.

 
Section 2
Stereotactic surgery
 
For millennia, a form of craniotomy dominated what we now know as neurosurgery. During the 20th century advances in medical science paved the way for the introduction of less- and non-invasive modalities to treat brain disorders (see below). A landmark event occurred at the beginning of the 20th century with the introduction of stereotactic surgery, which makes use of three-dimensional (3D) coordinates to locate and treat lesions in the brain. The method was first reported in the May 1908 edition of Brain, by two British surgeons Victor Horsley, and Robert Clarke. The device they described became known as the Horsley-Clarke apparatus, and was used to study the cerebellum in animals by enabling accurate electrolytic lesioning to be made in the brain of a monkey. It took ~40 years before the technique was introduced to humans following the publication of a seminal paper by Ernest Spiegel and Henry Wycis,  in the October 1947 edition of Science. Spiegel was a Vienna trained neurologist who moved to Temple Medical School in Philadelphia, which in 2015 was renamed the Lewis Katz School of Medicine. Wycis was one of Spiegel’s students who became a neurosurgeon. By the time they published their 1947 paper, they had performed several neurosurgeries and there had been sufficient advances in neurophysiology, pneumoencephalography, radiology, and electrophysiology for them to design a device like the Horsley-Clarke apparatus, which was fixed to a patient’s head by means of a plaster cast and was accurate enough to be used in human stereotactic surgery. Spiegel’s and Wycis’s surgical innovations attracted attention from physicians internationally, but there were no commercial stereotactic frames and neurosurgeons were obliged to design and manufacture their own. A pivotal moment occurred in 1947, when Lars Leksell, a Swedish physician and Professor of Neurosurgery at the Karolinska Institute, in Stockholm, visited Wycis in Philadelphia and afterwards designed a lightweight titanium head frame to provide the basis for stereotactic surgery, which he described in a 1949 paper entitled, ‘A stereotaxic apparatus for intracerebral surgery’.
 

The Gamma Knife®   
In the early 1950s, Leksell and Börje Larsson, a biophysicist from the University of Uppsala, Sweden, were convinced that agents other than cannulas and electrodes could be used to eradicate pathologies in the brain, and combined a source of radiation with a stereotactic guiding device. This led to the development of a non-invasive device, which Leksell used to perform the first radio-neurosurgical procedure and discovered that a single dose of radiation could successfully destroy deep brain lesions. He called this technique “stereotactic radiosurgery”, which, in 1968, led to the first stereotactic Gamma Knife® that used a focused array of intersecting beams of gamma radiation to treat lesions within the brain. Its success encouraged Leksell to use the device over the ensuing decade in functional brain surgeries to treat intractable pain and movement disorders. Leksell’s radio surgical device used Cobalt-60 (a synthetic radioactive isotope) as a radiation source. The basic physics that drives stereotactic radiosurgery today is substantially the same. It focuses ~200 tiny beams of radiation on a target in the brain with submillimetre accuracy. Although each beam has little effect on the brain tissue it passes through, a strong dose of radiation is delivered to the place where the beams meet.
 
Over time, the Gamma Knife® has been refined and enhanced and its efficacy and safety have been well established. Today, the Gamma Knife® provides a non-invasive operative system for a range of brain disorders, including small to medium size tumours, vascular malformations, epilepsy, and nerve conditions that cause chronic pain. Before its introduction such disorders were treated by surgeries, which involved craniotomies. In 1987, the Gamma Knife® was introduced into the US and installed at the Universities of Pittsburgh and Virginia. Although it took decades to achieve regulatory approval and be widely used throughout the world, the Gamma Knife® represents a significant technological advance in neurosurgery. Unlike craniotomies the device provides painless procedures that do not require anaesthesia, treatments take just one session, and patients can return to normal activities almost immediately. The Gamma Knife® is ~90% successful in killing or shrinking brain tumours, and today, there are ~300 Gamma Knife® sites worldwide, which each year treat >60,000 patients.
 
Neurosurgeon Ranjeev Bhangoo, Clinical Director for neurosurgery at King’s College Hospital, London, UK likens the Gamma Knife® to, “an umbrella, that sits above the patient’s head, rather like the old-fashioned hair dryers in women’s hair salons, but much bigger and more complex”, and stresses that the procedure, “is not painful. Forget any notion of surgery: there’s no knife, there’s no operating theatre. It’s done with the patient awake: you walk in, have your treatment, and walk out.” See videos.

 

What is Gamma Knife Radiosurgery?
 

Is Gamma Knife Radiosurgery painful?

 
Section 3
Endoscopic and endovascular neurosurgery
 
Neuroendoscopy
Neurosurgery pivoted again in the 1990s when disorders that would normally require opening the skull began to be treated less invasively through a small burr hole. Improved magnification, miniaturization, and illumination of lenses and the development of fibre optics facilitated an endoscopic surgical procedure to treat hydrocephalus, a condition in which cerebrospinal fluid (CSF) abnormally accumulates in the brain. There is currently no prevention or cure for the condition, but it can be managed with surgery. The procedure includes creating an opening in the floor of the third ventricle using an endoscope (a thin, flexible, tube-like imaging instrument with a small video camera on the end) placed within the ventricular system through a burr hole in the skull. In the late 1990s, neuro-endoscopy expanded to treat lesions outside the ventricular system and the endoscopic endonasal approach was established as a technique that allowed surgeons to go through the nose to operate on areas at the front of the brain and top of the spine.

Since the early use of the endoscopic procedures for treating intrasellar pituitary adenomas, the approach has been expanded to treat a range of skull base lesions. Today, skull base surgery is undertaken to remove both noncancerous and cancerous growths, and abnormalities on the underside of the brain or the top few vertebrae of the spinal column. Because this is such a difficult area to see and reach, skull base surgery has been advantaged by endoscopic procedures where surgeons insert instruments through natural openings in the skull - the nose or mouth - or by making a small hole just above the eyebrow. This type of surgery requires a team of specialists that may include ear, nose, and throat (ENT) surgeons, maxillofacial surgeons, neurosurgeons, and radiologists. Before endoscopic skull base surgery was developed, the only way to remove growths in this area of the body was by making an opening in the skull. In some cases, today, this type of surgery may be still needed.

Recent advances in endoscope design have produced equipment that is smaller and more efficient, with improved resolution and brighter illumination, than earlier models. Such developments, combined with surgeon enthusiasm, have contributed to the expansion of neuro-endoscopy to treat a range of neuro disorders including intracranial cysts, intraventricular tumours, skull base tumours, craniosynostosis (a birth defect in which the bones in a baby's skull join too early), degenerative spine disease, hydrocephalus and a rare benign tumour called hypothalamic hamartoma.
 
Neuro-endoscopic surgery causes minimal damage to normal structures, carries a lower rate of complications, shortens hospital stays, minimizes cosmetic concerns associated with many neurosurgical conditions and improves patient outcomes. It is positioned to take advantage of further miniaturization of cameras and optical technology, innovations in surgical instrumentation design, and further innovation in navigation and robotics systems.
 

Endovascular neurosurgery
Another innovation that has developed over the past five decades is endovascular surgery. The term ‘endovascular’ means ‘inside a blood vessel’. Endovascular neurosurgery uses tools that pass-through blood vessels to diagnose and treat diseases and conditions of the brain rather than using open surgery. The genesis of endovascular neurosurgery is credited to Professor Alfred Luessenhop, an American physician at Georgetown University Hospital in Washington DC, who, in 1964, carried out the first embolization of a cranial arteriovenous malformation and the first intracranial arterial catheterization to occlude an aneurysm. Over the past 60 years, endovascular neurosurgery has developed and has become a subspeciality. Today, >50% of cerebral aneurysms are treated through this minimally invasive approach.
 
Neuro-endovascular surgery has become the most practiced therapeutic approach for the majority of vascular conditions affecting the brain and spinal cord. It is used more frequently than open surgery for the management of complex vascular conditions, with high rates of safety and efficacy. The expansion of endovascular techniques into the treatment of stroke, the third highest cause of death in the US, has provided meaningful benefits to large numbers of patients worldwide. Further, with populations throughout the world aging neuro-endovascular techniques are poised to become one of the most necessary and important treatment modalities within neurosurgery.
 
With age our brains shrink, which causes a space to develop between the surface of our brain and its outermost covering. This increases the possibility that a knock to the head of a person >60 will result in a brain blood vessel rupturing and bleeding: a subdural hematoma. Research suggests that, “significant numbers occur after no significant antecedent trauma”, and could be the result of “an inflammatory process occurring at the level of the dural border cell”. A chronic version of this disorder can manifest itself within weeks of the first bleeding in which blood accumulates. With aging populations, chronic subdural hematoma (cSDH), is a condition predicted to become one of the most common neurosurgical conditions in the near-term future and expected to be treated with neuro-endovascular techniques.
 
Further, minimally invasive neuro-endovascular procedures are now commonly used to repair cerebral aneurysms, which are weak or thin spots on arteries in the brain that balloon and fill with blood. A bulging aneurysm can put pressure on brain tissue, and may also burst or rupture, spilling blood into the surrounding tissue (brain haemorrhage). Today most brain aneurysms are treated minimally invasively with neuro-endovascular techniques, which means an incision in the skull is not required. Instead, the surgeon guides a catheter or thin metal wires through a large blood vessel in the patient’s groin to reach the brain, using contrast dye to identify the problematic blood vessel. The aneurysm is then sealed off from the main artery, which prevents it growing and rupturing. In the US ~6.5m people are living with an unruptured brain aneurysm. The annual rate of rupture is ~10 per 100,000: ~30,000 Americans suffer a brain aneurysm rupture each year. Ruptured cerebral aneurysms are fatal in ~50% of cases and those who survive, ~66% suffer some permanent neurological deficit. Each year, there are ~0.5m deaths worldwide caused by brain aneurysms and ~50% are <50years.

 
Section 4
Evolving technologies affecting neurosurgery

At the beginning of the 21st century scientific and technological advances are again changing the face of neurosurgery. This section briefly describes four such changes.
 

Neurosurgery and augmented reality
Neurosurgery relies on visualization and navigational technologies and makes liberal use of computed tomography (CT) and magnetic resonance imaging (MRI) scans during preoperative planning and intraoperative surgical navigation. More recently, augmented reality (AR) applications have been used to complement more conventional visualization and navigational technologies to enhance neurosurgery. AR can bring digital information into the real environment and is beginning to play an increasing role to help neurosurgeons train, as well as plan and perform complex surgical procedures. In June 2020, surgeons atJohns Hopkins University successfully carried out a spinal fusion surgery for the first time in the US using xvision™, an FDA approved AR device for spine surgery developed by Augmedics Inc., a Chicago based company, which went public in 2020 through a reverse merger with Malo Holdings. Xvision™ allows surgeons to “see” the patient's anatomy through skin and tissue as if they have X-ray vision, to accurately navigate instruments and implants during surgical spine procedures. Each year, there are ~1.62m instrumented spinal procedures performed in the US, the majority of which are undertaken using a freehand technique, which can lead to suboptimal results.  
 

Neurosurgery and artificial intelligence
Such heavy use of advanced imaging and guidance technologies creates a vast amount of clinical data during a patient’s neurosurgical journey. It is not altogether clear how effectively pre-, intra-, and post-operative clinical patient data are collected and analyzed to enhance surgical procedures and patient outcomes. An article in the August 2021 edition of the journal Neuroscienceentitled, ‘Neurosurgery and Artificial Intelligence’, suggests that the collection and analysis of such data are beginning to happen. Over the past decade, AI techniques applied to data collected during patients’ neurosurgical journeys have enhanced diagnoses and prognostic outcomes and contributed to post operative care and the rehabilitation of patients. Being able to predict prognosis, identify potential postoperative complications, and track rehabilitation are enhanced with AI applications. The future suggests that the symbiotic relationship between AI and neurosurgery, which today is in its infancy, is positioned to grow. This will not only help AI to develop better and more robust algorithms but will provide opportunities for MedTechs to gain access to new revenue streams by providing enhanced patient services.
 
Robotics
Linked to medical imaging and navigation technologies is the increasing use of surgical robotics. However, neurosurgery has been slower than other specialties to incorporate robotics into routine practice owing to the anatomical complexity of the brain and the spatial limitations inherent in neurosurgical procedures. Notwithstanding, the first documented use of a robot-assisted surgical procedure was in neurosurgery. In 1985 Yik San Kwoh and colleagues, at the Memorial Medical Center in Long Beach, California, used an Unimation Programmable Universal Machine for Assembly (PUMA) 200 (which was originally designed for General Motors’ factories) to perform a CT-guided stereotactic biopsy of a brain lesion. Although discontinued, the PUMA 200 is considered the predecessor of current surgical robots.  There are now several robotic systems that have gained regulatory approval for cranial surgery. These include Zimmer Biomet’s ROSA ONE Brain, which obtained FDA approval in 2012 for intracranial applications, and Renishaw’s Neuromate robotic system, which was granted approval by the FDA in 2014. The former has been used extensively in the treatment for epilepsy, and the latter provides surgeons with five degrees of freedom for use in stereotactic applications. Robotics is a fast-moving discipline, which together with AI and machine learning, is positioned to impact neurosurgery in the near to medium term.
  

Neuro-pharmaceuticals and Trojan horses
There are growing synergies between neurosurgery, gene, and cellular therapies. However, the BBB, which plays a significant role in controlling the influx and efflux of biological substances essential for the brain to operated effectively, makes it extremely difficult to effectively deliver drugs to the brain. Over the past three decades, many biologics (medications developed from blood, proteins, viruses, or living organisms) have entered brain and CNS clinical studies. However, they have not gained FDA approval mainly because they did not have effective mechanisms to deliver neuro-pharmaceuticals across the BBB. Instead, the clinical trials were predicated upon a variety of BBB avoidance strategies. Cerebrospinal fluid (CSF) injections are the most widely practiced approach that delivers drugs to the brain by attempting to bypass the BBB. However, this results in limited drug penetration to the brain because of the rapid export of CSF from the brain back into the bloodstream. Future drug or gene-based neuro-pharmaceuticals will need to be accompanied by advances in BBB delivery vehicles.
 
Currently, there are numerous scientific endeavours to devise innovative and effective ways to deliver gene therapies across the BBB to the brain. Success in this regard will mean that genomic and cellular therapies will increasingly have the potential to work synergistically with neurology and neurosurgery to provide non-invasive, personalized care for a range of brain disorders including Alzheimer’s, Parkinson’s, spinal muscular atrophy, spinocerebellar ataxia, epilepsy, Huntington’s disease, stroke, and spinal cord injury. Endeavours are underway to re-engineer biologic drugs as brain-penetrating neuro-pharmaceuticals using BBB molecular Trojan horse technologies. This approach employs genetically engineered molecular Trojan horses (proteins), which carry genes across the BBB to have a therapeutic impact on brain disorders. The future development of neuro-pharmaceuticals linked to effective means to deliver these across the BBB are together positioned to reduce the need for interventional neuro therapies, but this may take some time.

 
Section 5
A perspective: life as a neurosurgeon
 
Three memoirs by Henry Marsh, an English neurosurgeon who treated a range of brain disorders over a 40-year career at a leading neurosurgical unit in London, provide insights into the human dramas that occur in a busy modern hospital. Marsh studied Politics, Philosophy and Economics (PPE) at Oxford University before starting medical school at the Royal Free Hospital in London. In 1984, he became a Fellow of the UK’s Royal College of Surgeons and in 1987, was appointed a consultant neurosurgeon at the Atkinson Morley Regional Neurosciences Centre at St George’s Hospital in London, where he spent his entire career.
 
Marsh’s first book is an unflinching memoir entitled, Do No Harm: Stories of Life, Death and Neurosurgery, which was published in 2014, and describes, with compassion and candour, challenging professional experiences filled with risk and imminent death. The book opens with the sentence, “I often have to cut into the brain and it's something I hate doing.” His first operation as a neurosurgeon was to treat a cerebral aneurysm. Forty years ago, this would have required opening the skull to access the brain. The procedure had a profound impact on Marsh, who commented, “What could be finer than to be a neurosurgeon. The operation involved the brain, the mysterious substrate of all thought and feeling, of all that was important in human life: a mystery, it seemed to me, as great as the stars at night and the universe around us.”
 
Marsh describes the difficult decisions, which neurosurgeons and patients regularly must make that change lives forever. He recalls moments of celebration and gratification when complex operations go well, and candidly recounts some of the more undesirable outcomes and slips of the hand that result in devastating outcomes. Marsh liked working with American neurosurgeons and came to “love their optimism, their faith that any problem can be solved if enough hard work and money is thrown at it, and the way in which success is admired and respected and not a cause for jealously”. He found the attitudes of American surgeons, “a refreshing contrast to the weary and knowing scepticism of the English”. However, after visiting hospitals in the US he expressed some scepticism about “the extremes to which treatments can sometimes be pushed” and wondered whether American physicians and patients “have yet to understand the famous American dictum that ‘death is optional’, was meant as a joke”. Tellingly, Marsh notes that “sometimes doctors admit their mistakes and ‘complications’ to each other, but are reluctant to do so in public, especially in countries that have commercial, competitive healthcare systems.” 
 
Marsh’s second memoir, Admissions: A Life in Brain Surgery, was published in 2017 two years after he retiredfrom his full-time job in England to work pro bono in Ukraine and Nepal. A documentary of his work in Ukraine, The English Surgeon, won an Emmy award. Marsh uses ‘Admissions’ to take an inventory of his life, which makes the book an even more introspective memoir than his first. He compares the challenges of working in troubled, impoverished countries like Nepal with his experience as a neurosurgeon in wealthy nations like the UK and US. The excesses of American medicine intrigued Marsh and he comments, “only in America have I seen so much treatment devoted to so many people with such little chance of making a useful recovery.” But he also expresses disillusionment with the administrative red tape in the English National Health System (NHS), which he maintains has eroded the authority and status of surgeons. In his final years working as a surgeon in St George’s Hospital in London he bemoans, “The feeling that there was something special about being a doctor had disappeared.” Marsh’s true love was patients and neurosurgery and at the end of his career, he was spending less time with patients and more time in meetings justifying his judgements and familiarizing himself with the latest UK government’s targets and edicts, which led him to say, “doctors need regulating, but they need to be trusted as well. It is a delicate balance, and it is clear to me that in England the government has got it terribly wrong”. 
 
Marsh suggests that patients’ fear encourages surgeons to exaggerate their competence and knowledge to “shield our patients from the frightening reality they often face”.  Over time, Marsh suggests, surgeons tend to believe the exaggerated versions of themselves. But the best un-learn their self-deception and come to accept their shortcomings and learn from their mistakes. “We always learn more from failure . . . . . . Success teaches us nothing,” Marsh writes.
 
Marsh’s third memoir,And Finally: Matters of Life and Death, was published in August 2022 and is very British, full of self-deprecation and dominated by the news that he is diagnosed with incurable prostate cancer. Marsh describes the sudden reversal of roles, from omniscient and omnipotent neurosurgeon to humble patient and provides descriptions of the ebbs and flows of his therapeutic journey, which gives valuable insights into how medicine in England works.
 
All three books bear witness to the fact that neurosurgery is a stressful and demanding profession, which requires extensive training, stamina, a high degree of manual dexterity, excellent hand-eye coordination, exquisite precision, extraordinary attention to detail, an ability to rapidly gather and process complex information to resolve challenging problems, compassion and empathy for patients, communication skills and teamwork. Unlike other surgical disciplines, a relatively small mistake can lead to “appalling disability”, coma, and death. According to research published in the October 2014 edition of Surgical Neurology International, ~25% of neurosurgical errors can be prevented or reduced with the increased use of evolving technologies, some of which are described in this Commentary.
 

Changes in the organization of neurosurgical units 
During Marsh’s 40-year career there were changes in the way neurosurgical units were organized and run; particularly the development of subspecialities among physicians and the use of multidisciplinary team approaches to clinical challenges. Much of Marsh’s career reflected a time when neurosurgeons worked in relative isolation and treated a wide range of neurosurgical conditions that presented in their clinics. Today, most neurosurgeons have a primary interest in a subspeciality such as epilepsy, neurovascular surgery, spinal surgery, the excision of tumours etc., and a secondary interest, which they share with colleagues. This tends to facilitate cross referral of patients among a team of physicians and improves patient care and the training of health professionals. In the operating room (OR) neurosurgeons work with other physicians, anaesthetists, trainee doctors, theatre nurses, and medical students. Outside the OR they collaborate with radiologists who use a range of diagnostic tools, including CT, MRI scans, and cerebral angiographies, which are used to detect abnormalities in blood vessels such as aneurysms, blockages, and bleeding. These neuroimaging technologies and neurosurgery have become inseparable. Neurosurgeons also work with neurologists, oncologists, ophthalmologists, and paediatricians. In 2017, Bob Carter, head of neurosurgery at the Massachusetts General Hospital, in the US, appreciated the interconnections between several clinical disciplines that care for people with neurological disorders and merged his neurosurgery department with the departments of neurology, psychiatry, and neuroradiology. While sub specialisms and teamwork have made an impact on the organization of neurosurgical units, new and emerging technologies have expanded the repertoire of neurosurgeons.
 

Awake brain procedures
Marsh specialised in operating on the brain while the patient is awake. This aspect of his work was the subject of a BBC documentary, Your Life in Their Hands. Awake brain procedures are usually performed when a lesion is located near the frontal lobes responsible for motor skills and speech. In the video below, Ranjeev Bhangoo describes the procedure, “It’s a technique where the patient is awake during the brain surgery. The patient is neither in pain nor suffering. When we make a cut in the skin and raise a trapdoor in the skull the patient is completely asleep. We wake them up after that point and the good news is the brain itself doesn’t feel pain. So, you can do this operation without the patient being in any distress or pain. It’s an unusual situation and the patient is prepared for it beforehand. The reason why you might want to do an awake craniotomy is because in some situations, tumours are close to critical structures of the brain that control speech or movement. While we have good maps of the brain and we have image guidance, they’re not precise enough. You want the patient to be talking to you and you want to be stimulating bits of the brain to see precisely where speech is so that you can avoid those areas and do the same with movement, you want to see the patient moving his or her arm or leg while you’re stimulating bits of their brain. So, we use an awake craniotomy when we’re operating near to what we call ‘eloquent’ areas of the brain that, if damaged, would produce a devastating deficit such as problems with speech or movement”. See video.
 

When and why is an awake craniotomy performed?

 
Section 6
The increasing burden of dementias on healthcare systems and economies
 
As populations age and live longer so dementia conditions increase. Alzheimer's, which effects parts of the brain that control thought, memory, and language, is the most common dementia in Western societies. It is a progressive disorder that begins with mild memory loss and leads to a loss of the ability to carry on a conversation and respond to your environment. In the three decades between 1990 and 2019, the global incidence of Alzheimer’s and other dementias increased by ~148%. In 2022, there were >6.5m Americans living with the condition: ~73% >65 and ~66% of these women, but this simply may be due to women living longer. By 2050, it is projected that ~13m Americans will suffer from dementia, which is expected to kill 1 in 3 seniors; that is more than breast and prostate cancers combined.
 
According to the World Health Organization (WHO), there are ~55m people with dementia globally, and >60% are living in low- and middle-income countries (LMIC). Age is the most significant risk factor: the likelihood of Alzheimer’s doubles every 5 years after you reach 65. But also, dementias appear to be increased by conditions that damage the heart and blood vessels, which include heart disease, diabetes, stroke, high blood pressure and high levels of cholesterol. As the proportion of older people in populations increase in nearly every country, people living with dementias are expected to rise to ~78m by 2030 and 139m in 2050. There is no cure for Alzheimer's, and treatments tend to fall to neurologists.  Drug therapies include galantamine, rivastigmine, and donepezil, which are cholinesterase inhibitors (also known as anti-cholinesterase, are chemicals that prevent the breakdown of the neurotransmitter acetylcholine) that are prescribed for mild to moderate Alzheimer's symptoms and may help reduce or control some cognitive and behavioural symptoms. Also, there are non-drug options.  Although outside the direct realm of neurosurgery, the scale and speed of the growth of Alzheimer’s and other dementias are likely to indirectly impact neurosurgery by increasing the burden on over-stretched healthcare systems. Under such circumstances, it seems reasonable to assume that there will be increased pressure on neurosurgery to become less resource intense, which means less invasive and less costly while improving patient outcomes.
 
Section 7
Traumatic brain injury

On Thursday 29th September 2022, Tua Tagovailoa, the Miami Dolphins’ quarterback received a head injury during a match against the Cincinnati Bengals and was stretchered off. Four days earlier he left the field after receiving another head injury while playing against the Buffalo Bills. He was then checked for a concussion and cleared and came back onto the field in the third quarter. Subsequently, the NFL Players Association exercised its right to remove the independent neurological expert who was involved in the decision to clear Tagovailoa to return to the Buffalo Bills game after being evaluated for a traumatic brain injury (TBI). This raises the significance of injuries to the brain and the challenges of accurately assessing their severity and adequately treating them.
 
TBI is as an alteration in brain function pathology by a sudden trauma, causing damage to the brain. Each year, the condition affects ~69m individuals worldwide. Symptoms can be mild, moderate, or severe, depending on the extent of the damage: annually ~5.5m severe cases are recorded globally. The epidemiology of the disorder is challenging because, in low-resourced regions of the world, where the prevalence of TBI is believed to be high, data are poor. According to the World Health Organization (WHO), ~90% of deaths due to head injuries occur in low- and middle-income countries (LMICs), where ~85% of the global population live and where the standards of care are patchy. TBI not only causes health loss and disability for individuals and their families, but also represents a costly burden to healthcare systems and economies through lost productivity and high healthcare costs. The total annual global burden of TBI is ~US$400bn.
 
Since the beginning of the 20th century, our knowledge and understanding of the pathophysiology of brain oedema (swelling) in head trauma patients has increased and today decompressive craniectomy is a recognised procedure for severe TBI to mitigate intracranial hypertension and its impact on clinical outcomes. One of the largest clinical studies, which sought to determine the efficacy of decompressive craniectomies for TBI patients, was the RESCUEicp trial: findings of which were published in the September 2016 edition of the New England Journal of Medicine. The study was carried out over a 10-year period, between 2004 and 2014, on 408 randomly assigned patients, 10 to 65 years of age, and concluded that, “At 6 months, decompressive craniectomy in patients with traumatic brain injury and refractory intracranial hypertension resulted in lower mortality and higher rates of vegetative state, lower severe disability, and upper severe disability than medical care”. 
 
In the US, TBI is a leading cause of death and disability. Each year, ~1.5m Americans sustain a TBI, ~50,000 die from the insult, ~230,000 are hospitalized and survive, and ~90,000 experience the onset of long-term disability. According to the US Centers for Disease Control and Prevention, ~5.3m Americans (~2% of the population) are living with disability as a result of a TBI. In 2010, the economic impact of TBI in the US was estimated to be ~US$77bn in direct and indirect costs. Each year in the UK ∼1.4m patients attend hospital following head injury and TBI is the most common cause of death for people in the UK <40 years.
  
Gold standard monitoring of intracranial pressure
There is no cure for severe TBI, and the gold standard management is to monitor intracranial pressure (ICP), caused by brain oedema (swelling). Current clinical guidelines for raised ICP levels suggest thresholds, usually between 20 and 25 millimetres of mercury (mmHg), at which treatment is recommended to either prevent or reduce further damage to the brain. The device used to monitor ICP is an intraventricular catheter system that requires drilling a burr hole in the skull to insert a catheter and placing it in a cavity (ventricle) in the brain, which is filled with cerebrospinal fluid (CSF). This is then connected to an extra-ventricular drain (EVD) that measures ICP. Such systems are accurate and reliable, but also, they are health-resource-intensive modalities, which run a risk of haemorrhage and infection.

Challenges with gold standard monitoring
According to research findings published in the January 2017 edition of the Journal of Neurosurgery, haemorrhage is a common complication of an EVD placement. Among the cases in which patients underwent imaging after a placement procedure, haemorrhage was found in 94 (21.6%). Another study, of 246 EVDs placed in 218 patients over a 30-month period and published in the November 2014 edition of Interdisciplinary Perspectives on Infectious Diseases, reported the cumulative incidence of EVD-related infections to be 8.3%. Further, because of the dearth of qualified neurosurgeons in under-resourced regions of the world, EVD systems are not widely available in LMIC, where the incidence rates of TBI are understood to be high and increasing.

Non-invasive ICP monitoring
Numerous alternatives to invasive gold standard ICP monitoring are in development, but none have established a valid place within a daily clinical setting. A review paper published in the December 2020 edition of the journal Neurotrauma, entitled “Non-Invasive Techniques for Multimodal Monitoring in Traumatic Brain Injury: Systematic Review and Meta-Analysis”, stresses the significance of monitoring ICP and brain oxygenation continuously in severe TBI patients, and suggests that the “two most prominent and widely used technologies for non-invasive monitoring in TBI are near-infrared spectroscopy [a form of photoplethysmography (PPG)] and transcranial Doppler”. Researchers conclude that, “both techniques could be considered for the future development of a single non-invasive and continuous multimodal monitoring device for TBI”.

Transcranial Doppler (TCD) ultrasonography is a non-invasive, painless ultrasound technique that uses high-frequency sound waves to measure cerebral blood flow velocity that may correlate with ICP. Research suggests that in ~15% of cases the ultrasound waves are unable to penetrate the patients’ skulls, and measurement is prone to intra- and inter- observer variability and accuracy. As the TCD system for measuring ICP non-invasively is encountering challenges, so near infra-red spectroscopy is gaining significance. This is a form of PPG technology, which is an uncomplicated, inexpensive, non-invasive, and convenient optical measurement that has the potential of being used at the site of injuries to quickly assess the severity of the head trauma. In the recent case of Tagovailoa, such a non-invasive ICP measurement device could have been applied on the playing field. Over the next decade, expect PPG technology to impact neurosurgery by potentially providing more accurate triaging and further disrupting the gold standard of care for severe TBI patients.
 
Section 8
Brain cancer and early diagnostics

We mentioned the Gamma Knife’s® ability to treat some brain tumours and suggested that patients have benefitted significantly from its use. The first successful modern brain tumour excision was performed in 1878 by William Macewen, a pioneering Scottish surgeon, at the Glasgow Royal Infirmary. At the beginning of the 20th century, contributions by Americans started with Harvey Cushing, who is generally recognised as the father of modern neurosurgery. Working at the John’s Hopkins Hospital in Baltimore, Cushing introduced meticulous documentation of the clinical and pathological details of cerebral tumours and devised several surgical techniques for operating on the brain that became the foundation of neurosurgery as an autonomous surgical discipline. In 1912, he discovered an endocrinological syndrome caused by a malfunction of the pituitary gland, which is named after him: Cushing’s disease.
 
The prognosis for a brain tumour is dependent upon its type, location, size and time of diagnosis, growth and how much can be surgically removed or treated. Factors including age and general wellbeing as well as some recognised genetic factors also influence prognosis. Poor prognosis for brain cancers is perpetuated by the lack of cost-effective, accurate tests that can be used in a primary care setting to diagnose the conditions. This means that a large proportion of brain cancers are diagnosed too late for current treatments to be effective. However, in recent years there have been advances made in detecting brain cancers early and this is expected to significantly improve prognosis.
 
Although there are >120 different types of brain tumours, lesions and cysts, your chances of developing brain cancer is <1%. Brain tumours account for ~90% of all primary central nervous system (CNS) tumours. In 2020, >0.3m people worldwide were diagnosed with a primary brain or spinal cord tumour. According to the Annual Report of the US Central Brain Tumor Registry, >84,000 Americans were diagnosed with a primary brain tumour in 2021. The US National Cancer Institute, suggests ~0.6% of Americans will develop brain cancer in their lifetime and the 5-year survival rate for those that do is only ~33%. This year, >4,000 Americans <15, are expected to be diagnosed with a brain or CNS tumour. In the UK, each year ~16,000 people are diagnosed with a brain tumour and ~ 60,000 people are living with a brain tumour.
 
The causes of brain tumours are not fully understood and occur because of an abnormal growth of brain cells or cells in the brain’s supporting tissues, which can damage the brain, threaten its function and result in death. Some tumours may occur around the edge of the brain and press on certain parts of it, while others can be more diffuse and grow among healthy tissue. In the video below, neurosurgeon Christopher Chandler, who leads the Paediatric and Adolescent Neurosurgical Service at King’s College Hospital, London explains that, “A brain tumour is an uncontrolled growth of a bunch of cells where the ‘off’ switch is missing. This means that there’s nothing telling these cells to stop growing, so they grow and divide. As this uncontrolled mass, or tumour, grows it displaces brain tissue and causes pressure on the surrounding brain. If you don’t remove the tumour or stop it from growing, it will grow so large that it causes critical pressure on the surrounding structures of the brain, which eventually, if untreated, can kill the patient.” See video.  

 
What is a brain tumour?
 
The Holy Grail
Neurosurgeons are frustrated by the fact that brain cancers are often diagnosed late. This is because brain tumours often present with non-specific symptoms and are therefore challenging to diagnose. In the video below, neurosurgeon Ranjeev Bhangoo explains the reasons for a brain tumour to be diagnosed late. “Firstly, the symptoms are non-specific: tiredness, headache, poor concentration - maybe not finding your keys as well as you use to – the sort of thing that can happen to any of us when we’re tired. The classic thing of having a fit or collapsing occur, but they’re unusual. Your GP is only likely to see just one or two brain tumour cases in his or her whole career. . . . Now, if you do get a scan, the chances of you having a brain tumour are incredibly rare. So, just because a neurologist has organized a scan, you mustn’t get worried because it’s very unlikely that you’ll have a brain tumour. But ultimately, through some path or other, you have a scan, usually a CT scan, which is a form of X-ray, which is quick and safe and if there is a tumour it will show. At that point, what will normally happen is that your doctor will refer you to a neurosurgeon”.    
 
How are brain tumors diagnosed?
 
Technologies positioned to reduce neurosurgeons’ frustration with late diagnosis of brain cancers are quick, easy-to-use, and inexpensive blood tests that can diagnose cancer early. Such tests fall into four general categories: (i) complete blood count used to evaluate your overall health and detect a wide range of disorders, (ii) biomarkers, which are molecules found in your blood and other body fluids that can indicate specific cancers, (iii) blood protein testing that measures the amount of protein in your blood to diagnose cancer, and (iv) circulating tumour cell tests, which look for tumour cells that are shed from a tumour and are now circulating through your bloodstream.
 

Detecting brain cancers early
Two recent examples of simple diagnostic blood tests are reported in the August 2022 edition of Clinical Cancer Research and the October 2019 edition of Nature Communications. In the former paper, scientists at Massachusetts General Hospital (MGH) report findings of a study, which detected the presence of brain cancers early by identifying pieces of tumour cells’ genetic material - mRNA - that circulate in your blood. The test, which has a sensitivity of 72.8% and a specificity of 97.7% can characterize brain tumours and monitor their status after treatment. According to Leonora Balaj, a co-senior author, and assistant professor of Neurosurgery at Harvard Medical School, “There is a real need to make brain tumor diagnosis less invasive than the current technique of tissue biopsy. This research demonstrates that it is now feasible to diagnose a brain tumor via a blood test for one of the most common mutations detected in brain tumors”. Findings of the latter paper suggest that certain brain cancers may be detected early from a simple blood test using PPG technology, which has been used in hospital settings since the 1980s to monitor heart rate and relative blood volume. Today, the technology is used in a wide range of commercially available medical devices, as well as smartwatches (the Apple version is an FDA approved medical device) and fitness trackers, for measuring oxygen saturation, blood pressure and cardiac output, assessing autonomic function and detecting peripheral vascular disease. Previously we described how PPG technology is positioned to provide a non-invasive means to monitor ICP in TBI patients.

The 2019 Nature paper describes how PPG easily, cheaply, and accurately identified asymptomatic people with suspected brain cancer. In the first instance, the technology was used on a retrospective cohort of 724 people, which included those with primary and secondary cancers as well as control participants without cancer. PPG was employed to identify biomarkers from patients’ blood samples and a machine learning algorithm was trained to identify specific biomarkers with cancer present. The algorithm was then used on a sample of 104 random participants and brain cancer was detected in 12. The PPG test revealed a sensitivity of 83.3% and a specificity of 87%. According to Matthew Baker, from the University of Strathclyde, Scotland, the paper’s lead author, “This is the first publication of data from our clinical feasibility study, and it’s the first demonstration that our blood test works in the clinic.
 

A global endeavour
These two studies are part of a well-resourced global endeavour to develop an affordable, simple, point-of-care, blood test, which detects cancer before any symptoms occur. Today, biomedical advances move at a much faster pace than medical technology did in the 1950s and 60s when Lars Leksell was developing minimally invasive stereotactic radiosurgery procedures to accurately locate and remove brain tumours. For example, in ~7 years since its foundation in 2015, GRAIL, a US biomedical start-up backed by Jeff Bezos and Bill Gates, has become a global leader in a ground-breaking multi-cancer, early detection, blood test, Galleri®, which has the potential to detect >50 types of cancers before they are symptomatic. This is achieved by looking for abnormal DNA shed from cancer cells in the blood, called cell-free DNA (cfDNA). The Galleri® test uses genetic sequencing technology and artificial intelligence (AI) to scan for patterns of chemical changes in the cfDNA that come from cancer cells but are not found in healthy cells. If validated, the GRAIL test will provide a simple, cheap, non-invasive means to identify a range of cancers in asymptomatic people when they are more likely to respond positively to therapy.
 

Large UK clinical study
In May 2019, the GRAIL Galleri ® blood test was granted US FDA Breakthrough Device designation. The test is only available commercially in the US but is rapidly gaining provenance in other regions of the world. For example, in September 2021, NHS England launched a massive clinical study for Galleri® and set up ~150 mobile clinics in convenient locations across the country to recruit ~140,000 participants. In July 2022, participants were invited to attend two further appointments spaced ~12 months apart. Findings from the study are expected to confirm the accuracy of the test in asymptomatic participants and lead to its regulatory approval. Although Galleri® is the first of its kind to be trialled on such a scale in the UK, it is not the only player and cfDNA is not the only technology.
 

Guardant Health
Another US biotech developing capabilities to detect a range of cancers early from a simple blood test is Guardant Health. Founded in 2011, the company is now ~US$6bn Nasdaq traded global enterprise with annual revenues ~US$110m. In April 2022, Guardant presented new data at the American Association for Cancer Research Annual Meeting. Findings suggested that the company’s investigational next-generation Guardant SHIELD™ assay has the capacity to analyse ~20,000 epigenomic biomarkers that help to detect a broad range of solid tumours using a single blood test. Guardant’s co-CEO, Amir Ali Talasaz said: “These positive results show that the next-generation Guardant SHIELD multi-cancer assay provides sensitive detection of early-stage cancers with the ability to identify the tumor tissue of origin with high accuracy”.
 
Section 9
Takeaways

For millennia neurosurgery, which has its roots in ancient civilizations, was dominated with forms of craniotomies, which opened the skull to access cerebral disorders. In the 20th century the speciality pivoted and introduced less- and non-invasive procedures to deal with a range of brain and CNS conditions. However, the introduction of these were slowed by the fact that the brain is such a well-protected organ and they took nearly half a century to gain regulatory approval and enter the clinic. At the beginning of the 21st century biomedical research is advancing at such a pace and it positioned to significantly transform neurosurgery towards a less- and non-invasive modality. Further, in the next two decades expect gene and cell therapies to substantially increase their influence as treatments for neurodisoders. Over the past three decades novel neuro-pharmaceuticals have been constantly in clinical trials but failed to receive regulatory approval because they did not have an efficatious mechanism to deliver the therapeutics across the BBB. Today, there are a myriad of novel vehicles under development, which are expected to effectively smuggle 21st century pharmaceuticals across the BBB. These are being advanced in parallel to the drugs, and together are positioned to significantly disrupt traditional neurosurgical procedures over the next two decades.  
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  • The core business of medical technology companies (MedTechs) has been manufacturing and marketing physical devices
  • Physical devices will continue to be a substantial part of their business, but on their own, are unlikely to deliver high growth rates, which are more likely to come from artificial intelligence (AI) data driven strategies that improve patient outcomes
 
The impact of big data, artificial intelligence, and machine learning on the medical technology industry
 
James Carville, an American strategist, who played a leading role in Bill Clinton winning the 1992 presidential race, insisted that the campaign focus on the economy and coined the phrase “It’s the economy, stupid”. If Carville was asked today for a winning long-term growth strategy for medical technology companies, might he say, “It’s big data, stupid”?
 
This Commentary suggests that while physical products have been the backbone of MedTech companies in the past, they are unlikely to contribute significantly to future growth rates, which are more likely to come from artificial intelligence (AI) driven big data innovations, which create new solutions that improve patient journeys and outcomes.
 
In this Commentary
 
This Commentary describes the meaning of ‘big data’ in a healthcare context, explains ‘the data universe’ and stresses not only its immense volume, but also its variety, and the phenomenal speed at which the data universe is growing. Today, most industries leverage big data and AI techniques to create innovative offerings that drive growth and enhance competitive advantage. However, with few exceptions, traditional MedTechs have been relatively slow to collect and analyse a wide range of health, medical and lifestyle data which have the potential to provide innovative software offerings that improve patients’ therapeutic journeys and complement physical products. This is partly because the industry must adhere to strict regulations and partly because many medical technology companies lack the necessary capabilities and mindsets to collect and leverage big data. Most have business models that tweak legacy physical products and accept growth rates of ~5% as the ‘new normal’. We provide a brief history of big data and AI business strategies mainly to underline that these are relatively new. It was only in the early 2000s that electronic health records (EHR) began to replace paper-based patient records, which were stored in numerous filing cabinets in healthcare silos. It was not until ~2015 that EHRs became standard practice and researchers started to apply algorithms to EHRs and other data to detect patterns and make predictions that could improve diagnoses and treatments, enhance patient outcomes, and reduce healthcare costs. The increased use of big data and AI techniques in healthcare raises important cybersecurity concerns and trust issues because health professionals and patients do not understand how algorithms arrive at their conclusions and actions. Cybersecurity concerns are addresses by a range of encryption techniques and security protocols. Trust in algorithms has been helped by the development of  ‘explainable AI’, which is software that describes the essence of algorithms in easily understood terms. However, more work is still needed in these two areas. We introduce cloud and cloud services together with an explanation why these have experienced such rapid growth across all industries in recent years. The cloud makes it easier to store and access big data via the internet from anywhere in the world. Cloud services provide security for big data as well as a range of management and analytical tools that help to transform data into revenue generating software offerings. For MedTech companies, the cloud and cloud services provide the basis for more efficacious R&D. The medical technology industry has become bifurcated between companies that leverage AI driven big data strategies to enhance growth rates and those that predominantly focus on legacy physical product offerings and settle for lower growth rates. Over the past decade the nature of the medical technology industry has changed; partly because of AI big data strategies supported by the cloud computing and a large and rapidly growing range of open-source, easy-to-use AI tools. This has given small companies a competitive advantage. The Commentary concludes by describing a few of these small MedTechs with disruptive digital products that target large, rapidly growing, underserved market segments.       
 
Big data and healthcare

Big data are comprised of a wide range of information collected from multiple sources that surpasses the traditionally used amount of storage, processing, and analytical power and is unmanageable using conventional software tools. In healthcare settings, big data include hospital records, medical records of patients, results of medical examinations, and data generated by traditional medical devices as well as various biomedical and healthcare tools such as genomics, wearable biometric sensors, and smartphone apps. Biomedical research also generates data relevant for the medical technology industry.
 
The data universe

The massive amount of data, which is generated from the entirety of the internet is referred to as the ‘data universe’. It is not only its volume that makes this special, but it is also the variety of the data and the phenomenal speed at which the universe is growing. The International Data Corporation (IDC) estimated that the data universe grew from ~130 exabytes in 2005 to >40,000 exabytes in 2020.  To put this in perspective: 1 gigabyte of data is 1bn bytes (18 zeros after the 1 or 230 bytes), and 1 exabyte is equal to 1bn gigabytes.
Data generated healthcare innovations

In the past, collecting and interpreting vast quantities of data was not feasible, partly because computer systems were relatively small and did not generate much data, and partly because technologies to manage big data were underdeveloped. Fast forward to the present, and businesses across most industries now generate enormous amounts of data. Organizations apply AI and machine learning (ML) techniques to these data to create innovative product offerings to access new revenue streams with significant growth potential. Such technologies, combined with health-related big data, can positively impact the medical technology industry by generating novel diagnostics and treatments for patients, streamlining the process of medical record keeping and developing more personalized and responsive care plans that improve patient journeys and outcomes.

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The new rapidly evolving AI data driven healthcare ecosystem

Despite the potential commercial advantages of AI data driven diagnostic and therapeutic solutions, many traditional MedTechs have been slow to collect health and lifestyle data from multiple sources to develop software offerings, which complement their legacy physical products. One notable exception is Philips Healthcare. In the early 2000s, the company was challenged by new entrants to the market who were successfully leveraging information from health wearables and other sources to create and market AI data driven offerings. At the 2016 annual conference of the American Healthcare Information and Management Systems Society (HIMSS) in Chicago, Jeroen Tas, a Philips executive, said, “We are in the midst of one of the most challenging times in healthcare history, facing growing and aging populations, the rise of chronic diseases, global resource constraints, and the transition to value-based care. These challenges demand connected health IT solutions that integrate, collect, combine, and deliver quality data for actionable insights to help improve patient outcomes, reduce costs, and improve access to quality care”.
 
Philips had the mindset and resources to respond positively to this rapidly changing ecosystem. In 2017 the company appointed Tas as its Chief Innovation & Strategy Officer, tasked with launching a suite of big data AI driven solutions, the IntelliVue® patient monitors, which support the growing demands of health professionals to provide quality care and improved outcomes for an expanding population of older, sicker patients with fewer resources. These monitoring solutions seamlessly connect big data, AI technology and patients to support health professionals to manage patients as they transition through their care journeys. In 2016, Philips and Masimo, a medical technology company specializing in non-invasive AI data driven patient monitoring devices, entered a multi-year business partnership involving both companies’ innovations in patient monitoring. Philips agreed to integrate Masimo's measurement technologies into its IntelliVue® monitors, to help clinicians assess patients’ cerebral oximetry and ventilation status. The outcome of the collaboration was the launch of a new suite of patient solutions, called Connected Care, which give healthcare providers the ability to monitor patients more effectively and reduce costs.
 
The bifurcation of the MedTech market

In addition to large MedTechs such as Philips and Masimo, there are hundreds of small companies developing AI driven big data offerings aimed at improving patient outcomes. The reasons for many traditional companies’ slowness to fully leverage big data and AI applications are partly because medical devices are required to comply with stringent regulatory guidelines and partly because of the lack of capabilities. The different responses have bifurcated the industry. On the one hand there are traditional MedTechs, which predominantly focus on existing customers and market legacy physical offerings in slow growing markets. On the other hand, there are many small companies and a few very large medical technology corporations, which have embraced AI driven big data patient-centric solutions.
 
A brief history

Big data has its genesis in the 1950s and 1960s when scientists and mathematicians began exploring the possibility of using computers to process large amounts of data to make intelligent decisions. This led to the development of technologies such as the first neural networks, which laid the foundation for modern Deep Learning. In the 1980s, researchers at IBM popularized the concept of big data to describe the process of collecting and analyzing large amounts of data, which empowered organizations to gain insights from information that previously was too complex to process. The 1990s saw the development of AI and ML, which enabled computers to learn from data and make decisions without the need for explicit programming. By the early 2000s, AI-based algorithms empowered machines to learn from data and make predictions. Many organizations, across a range of industries, saw the commercial opportunities of this and acquired capabilities to collect, store and analyse large amounts of information to identify patterns and trends that were previously impossible to detect.  Without large amounts of data, AI and ML techniques are less effective, which is significant for healthcare and the medical technology industry.
 
Big data in healthcare

AI driven big data strategies are becoming increasingly important in healthcare. This is because AI techniques applied to masses of health-related information can improve patient care, enable more effective decision-making, reduce costs, identify new treatments, explore new markets, and create more efficient healthcare systems. Further, such applications can provide more accurate and timely diagnoses, as well as insights into how various treatments affect different people. As increasing amounts of health information become available, and data handling techniques improve, so traditional MedTech companies will have opportunities to boost their growth by complementing their physical devices and volume-based care with digital assets and personalised care.
 
Paper-based mindset

Until recently health professionals were responsible for most of the different types of data associated with a patient’s treatment journey, which included medical histories, known allergies, medical and clinical narratives, images, laboratory examinations, and other private and personal information. Until the early 2000s these data were recorded on paper and stored in filing cabinets across numerous healthcare departments. It was not until 2003 that the US Institute of Medicine used the term ‘electronic health records(EHR). By 2008, only ~10% of US hospitals were using EHRs, which increased to ~80% by 2015. As EHRs became standard practice across multiple providers and data interoperability issues were resolved, the provision of healthcare improved, and medical errors and healthcare costs were reduced. Currently, the American National Institutes of Health (NIH) is inviting 1m people from diverse backgrounds across the US to help build a comprehensive big data set, which can be used to learn more about how biology, environment and lifestyles affect health in the expectation of discovering new ways to treat and prevent disease.
 
Trust and medical algorithms
 
As AI driven big data applications have increased, so trust in algorithms has been raised as an issue. This has been a major concern in healthcare. To address this challenge, explainable AI, has been developed. This is an AI technology that explains decisions and actions made by algorithms in a way that is easily understood by health professionals and patients. Explainable AI has helped to create trust in algorithms by providing a level of transparency, understanding and accountability. Further, incorporating feedback from medical professionals, patients, and other stakeholders into the development of medical algorithms has also helped to build trust. However, this entails collecting a wider variety of data than many healthcare companies are used to.
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Have diversified medical technology companies blown their competitive advantage?
Big data healthcare strategies and security
 
With the increasing number of big data and AI healthcare solutions, cybersecurity has become a concern. Reducing this involves using technologies such as data encryption, secure cloud computing (see below), and authorization protocols to protect data stored in large databases. Additionally, organizations may use AI-driven applications to monitor their systems to find anomalies, detect malicious activity and unauthorized access to sensitive, personal information. To ensure the security of healthcare data, organizations also employ measures such as risk assessments, incident response plans, and regular security training of their staff.
Cloud storage and services

Since the early 1990s, big data have benefitted from cloud storage, which makes it easier to store and access data over the internet and helps businesses to become more efficient and productive. It also offers organizations scalability, more control over their data and reduced costs. Organizations can: (i) easily increase their storage capacity as their data needs grow, (ii) access their data from anywhere in the world, and (iii) stop investing in expensive local storage devices. Further, cloud storage is becoming more secure, with encryption and other security measures making it safer to store data.
 
Companies moving their data from local storage devices to the cloud is more than just a simple transfer process and can be a complex, multi-year journey. Any organization that has accumulated several legacy databases and infrastructures will have to develop and manage a hybrid architecture to transfer the data. However, once in place and shared among stakeholders, cloud-based platforms can assist in unlocking clinical and operational insights at scale while speeding up innovation cycles for continuous value delivery. In combination with a secure and interoperable network of connections to hospital systems, cloud-based solutions represent an opportunity for healthcare leaders to unlock the value of data generated along the entire patient journey, from the hospital to the home. By turning data into insights at scale, it is possible to empower healthcare professionals by helping them to deliver personalized care, improved patient outcomes and lower costs.
 
The cloud also offers an increasing number of computing services. These are provided by companies such as Amazon Web Services, Google Cloud Platform, Microsoft Azure, IBM Cloud, Oracle Cloud, and Rackspace Cloud. The services include: (i) Infrastructure-as-a-Service (IaaS), which provides users with access to networks, storage, and computing resources, (ii) Platform-as-a-Service (PaaS) helps users to develop, run, and control applications without the need to manage infrastructure, (iii) Software-as-a-Service (SaaS), provides access to a variety of applications, (iv) Data-as-a-Service (DBaaS), gives users access to several types of databases, and (v) Serverless Computing enables users to run code without needing to provision or manage servers. Such services are expected to continue growing and help to transform healthcare. The provision of cloud computing services in healthcare makes medical record-sharing easier and safer, automates backend operations and facilitates the creation and maintenance of telehealth apps. The increasing use of data and cloud services by MedTech companies helps to break down data silos and develop evidence-based personalized solutions for a connected patient journey. In 2020, the healthcare cloud computing market was valued at ~US$24bn, and it is expected to reach ~US$52bn by 2026, registering a CAGR of >14% during the forecast period. Major drivers of cloud services include the increasing significance of AI driven big data applications.
 
Changes the nature of R&D

Further, the cloud can change and speed up R&D. The starting point for MedTech R&D should be evolving patient needs and affordability. Healthcare-compliant cloud platforms offer a flexible foundation for the rapid development and testing of AI driven big data solutions created by cross functional teams working across an entire life cycle of an application: from development and testing to deployment. This changes medical technology companies’ traditional approach to R&D by transforming it into short cycles undertaken by multiple stakeholders. This modus operandi is replacing traditional lengthy and expensive R&D often carried out in an organisational silo and constrained by annual budgeting cycles. This often means that a significant length of time passes before an innovation gets into the hands of health professionals and patients for testing. Digital health solutions, on the other hand, can be tested by physicians and patients early in their development and improved features quickly added.   
 
Free and easy to use AI and ML software libraries

In the early 2000s, when AI and ML were in their infancy, companies needed data engineers with advanced mathematical capabilities to build complex AI systems. Today, this is unnecessary because of the development of simplified AI and ML libraries such as PyTorch and Tensorflow. These are free, easy to use, open-source, scalable AI, and ML packages, which reduce the need for data engineers to have advanced mathematical skills to build effective software health solutions. PyTorch, released in 2016,  was developed by Facebook and then Meta AI, and is now part of the Linux Foundation. The technology is known for its ease of use and flexibility, making it favoured by developers who want to rapidly prototype and experiment with new ideas. Its tools support graphics processing, which is popular with deep learning medical imaging strategies that involve training large, complex models on big data. TensorFlow was developed by the Google Brain team and originally released in 2015 for internal use.  It is a highly scalable library for numerical computations and allows its users to build, train and deploy large-scale ML models. Both platforms have become significant open-source tools for AI and ML due to their ability to support the development and training of complex models on large datasets. They have been widely adopted by researchers and developers throughout the world and are regularly used in a variety of applications relevant to the medical technology industry. Significantly, they give smaller MedTechs a competitive advantage. 
 
Disruptive effects of AI driven big data strategies

The development and availability of big data and predictive AI help small medical technology companies enter markets, grow, and strengthen their competitive positions, which has the potential to change market dynamics. Over the past decade, several large medical technology companies have experienced their markets dented by small companies, which have successfully used open-source AI applications to leverage big data. For example, Philips Healthcare’s market was affected by the emergence of innovative offerings developed by new entrants using cloud computing services and big data from medical wearables. Above we described how Philips robustly responded to this and became a market leader in AI data-driven patient monitoring technology. Siemens Healthineers’ market share suffered from small MedTechs with innovative AI driven offerings. Further, the rise of digital imaging technology caused GE Healthcare’s market share to shrink. These vast companies have since developed AI driven big data strategies and bounced back. However, traditional MedTechs that fail to leverage big data and AI capabilities risk being left behind in an increasingly competitive digitalized industry.
 
Small MedTechs using big data and AI

Examples of small MedTechs that leverage big data, AI, and ML techniques to capture share of large underserved fast-growing market segments include Brainomix, which was spun out of Oxford University, UK, in 2010 and serves the stroke market. Iradys, a French start-up specialising in interventional neuroradiology. Elucid, a Boston, US-based MedTech founded in 2013, which has developed innovative technology that supports the clinical adoption of coronary computed tomography angiography, and Orpyx Medical Technologies, a Canadian company that provides sensory insoles for people living with diabetes. These are just a few examples of small agile companies that collectively have helped to bifurcate and disrupt segments of the medical technology industry by developing offerings predicated upon big data, AI and ML that deliver faster, more accurate diagnoses to ensure that patients get the treatment they need, when they need it.

Brainomex’s lead product offering is a CE-marked e-Stroke platform, which has been developed using data from images sourced across 27 countries including the UK, Germany, Spain, Italy, and the US and provides fast, effective and accurate analysis of brain scans that expedite treatment decisions for stroke patients. The platform has been adopted across multiple healthcare systems throughout the world, and for the past two years, England’s National Health Service (NHS) has been using the technology on suspected stroke patients. Early-stage analysis of the technology predicated on >110,000 patients suggests that eStroke can reduce the time between presenting with a stroke and treatment by ~1 hour and is associated with a tripling in the number of stroke patients recovering with no or only slight disability - defined as achieving functional independence - from 16% to 49%. With this disease, time is of the essence because after a stroke, each minute that passes without treatment leads to the death of ~2m neurons (nerve cells in the brain), which cause permanent damage. It can be challenging for health professionals to determine whether stroke patients need an operation or drugs, because the interpretation of brain scans is complicated and specialist doctors are required. Sajid Alam, stroke consultant at a large regional hospital in the UK, (Ipswich Hospital), reflected: “As a district general hospital, we don’t have ready access to dedicated neuroradiologists to interpret every stroke scan. Having Brainomix’s AI software gives us more confidence when interpreting each scan.

Intradys is a French start-up, which develops algorithms that combine ML and mixed reality to empower interventional neuroradiologists and help them enhance the care of stroke patients. Orpyx Medical Technologies provides sensory insoles for people living with diabetes who have developed peripheral neuropathy to help prevent foot ulcers. The insoles collect data on pressure, temperature, and steps and give feedback to the wearer and healthcare professionals. Elucid is a Boston-based MedTech founded in 2013. The company’s offerings are predicated on big data, AI, and ML to provide fast and precise treatments that improve the outcomes of patients with cardiovascular disease and reduce healthcare costs. Heart attack and stroke are primarily caused by unstable, non-obstructive plaque (the buildup of fats, cholesterol, and other substances in and on the artery walls) that often goes undiagnosed and untreated. Current non-invasive testing cannot visualize the biology deep inside artery walls where heart disease develops. Elucid’s lead offering is an FDA-Cleared and CE-marked non-invasive software to quantify atherosclerotic plaque.
 
Takeaways
 
The potential benefits for medical technology companies that leverage AI driven big data strategies include: (i) improved diagnoses and treatments, (ii) enhanced patient journeys and outcomes, (iii) cost savings, (iv) a better understanding of stakeholders’ needs, (v) superior decision-making, (vi) more effective products and services, and (vii) increased competitive advantage. Big data strategies may also be used to uncover insights from large datasets to develop predictive models that can automate repetitive tasks, optimize care processes, free up resources for healthcare professionals to focus on providing care, and staying ahead of the competition by providing greater insights into customer trends and needs. Medical technology companies that do not leverage AI driven big data strategies to develop innovative products for growth and competitive advantage potentially risk: (i) falling behind the competition in terms of product innovation, (ii) missing out on key market opportunities, as data-driven insights can help identify new trends and customer needs, (iii) struggling to keep up with the changing pace of technological change, as staying ahead of the competition requires a deep understanding of the latest developments in data-driven product development and (iv) losing the trust of customers, as they may be wary of MedTechs that do not use advanced technologies to develop their product offerings. Future significant growth for medical technology companies is more likely than not to come from AI driven big data strategies. Start collecting data.
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  • MedTechs have built proficiencies to successfully create and market physical devices predominantly for the US and Western European markets
  • To remain relevant in the rapidly changing healthcare ecosystem they will need to develop advanced digital and data capabilities and increase their penetration of Asian markets, which will present challenges for most of them
  • Will companies be forced to decide whether to remain hardware manufacturers or become software enterprises, or can they look both ways and prosper?
  • Given the rate of market changes, the next 5 years represent a window of opportunity for traditional MedTechs to pivot and transform their strategies and business models
 
Can elephants be taught to dance?
MedTech’s strategic challenges
 
MedTechs are at a crossroad of manufacturing physical devices and developing software solutions. Both aim to deliver value by enhancing patient outcomes while reducing costs. Can these two scenarios co-exist, or will industry leaders be forced to choose one or the other?
 
For decades, many companies have displayed a deep-rooted reluctance to transform their business models and adopt digitalization strategies and have used M&A activity to become bigger. This suggests that a significant proportion of MedTech leaders are likely to manage increased competition and changing healthcare ecosystems by accelerating M&A activities, which are familiar to them and require no significant change. However, such activities alone will not future-proof companies. Over the next five years, “informed” MedTechs will benefit by shifting away from their current business models that depend on developing and selling physical products predominantly to hospitals in the US and Western Europe and move toward providing patient-centric software solutions as partners in dynamic, connected international healthcare ecosystems.
 
M&A activity to enhance scale

For decades, M&A activities have helped MedTechs to acquire mature assets to tuck into their existing sales and distribution channels. More than anything, this has assisted them to increase their scale, while optimising their portfolios, reducing competition, and improving profits. Over the past decade, when Western markets became more uncertain, monetary policy tightened, technologies advanced, and global economic growth slowed, MedTechs responded by exploiting the fall in the cost of capital to increase their M&A activities with the main purpose of increasing their scale: bigger was generally perceived by industry leaders to be better.
 
Before the COVID-19 pandemic crisis, 2020 was expected to be a strong year for MedTech’s M&A. However, the disruptive impact of the coronavirus outbreak slowed the industry’s M&A performance, and between July 2019 and June 2020, M&A expenditures plunged by 60% compared to the previous 12-month period. Activity returned in Q3, 2020, and today, although high asset valuations and increasing cost of capital have impacted M&A transactions and re-focused attention on organic growth, there are signs that a M&A buyer’s market is developing, but with a difference.
 
The difference is a significant number of M&A transactions do not appear to be focussed entirely on acquiring scale. While there are still some advantages to increasing scale, there are disadvantages, which include having to integrate and service more customers, more employees, and more institutional investors, and this often contributes to strategic rigidities.

 
The demise of scale

The significance of scale was first elaborated in 1937 by Nobel economics laureate Ronald Coase in his seminal paper, The Nature of the Firm, and ~50 years later, repeated by Michael Porter in his book, Competitive Advantage. Both Coase and Porter suggested that scale gained from reducing the ratio of overhead to production would increase the power of firms in markets. In 2013, Rita McGrath challenged this thesis in, The End of Competitive Advantage, by suggesting that bigger was not necessarily better. According to McGrath, in an increasingly high-tech environment, more important than size, is whether enterprises have access to technical capabilities, which can drive top-line growth in dynamic market settings.


Recapitalized MedTech’s M&A firepower
 
According to a 2020 report on the state of the MedTech industry, published by EY, a consulting firm, between July 2019 and June 2020, MedTechs took advantage of low interest rates, and financing levels more than doubled to a record US$57.1bn compared to the previous 12 months; with >40% resulting from debt financing. Thus, as we emerge from restrictions imposed by the COVID-19 pandemic, there is a lot of liquidity in the market and larger MedTechs have significant M&A firepower. Will they use this to become bigger, or will they use their capital to make strategic investments in new technologies and to penetrate large rapidly growing Asian markets?
M&A driving a shift to digital health

In H1,2021, the MedTech sector recorded a total of 33 M&A deals, up from 25 in the whole of 2020. There is some evidence to suggest that some companies in the sector are using their renewed M&A firepower to acquire high growth digital and AI opportunities that can be integrated into their existing product offerings to provide access to new revenue streams and help companies pivot away from being solely dependent upon manufacturing physical devices. We briefly describe four such deals.
 
In January 2020, as the first COVID-19 case was reported in the US, Boston Scientific paid US$0.925bn for Preventice, a developer of mobile health solutions and remote monitoring services, which connect patients and caregivers. Its digitally enabled service has the potential to reduce healthcare costs and improve patient outcomes. In February 2020,  Medtronic, acquired, for an undisclosed sum, Digital Surgery, a London-based privately-held pioneer in surgical AI, data and analytics. The acquisition is expected to accelerate Medtronic’s plans to incorporate AI and data into its laparoscopic and robotic-assisted surgery platforms. In December 2020 Philips acquired BioTelemetry for US$2.8bn. BioTelemetry is a US-based provider of remote cardiac diagnostics and monitoring, with offerings in wearable heart monitors and AI-based data analytics and services. The deal provides Philips with the capability to expand its remote monitoring business outside of hospitals and into lower cost day-care settings and patients’ homes. One of the largest healthcare deals of 2020 was Teladoc’s US$18.5bn acquisition of Livongo, a remote patient monitoring company, founded in 2014, to build a cloud-based diabetes management programme, linking a person’s glucose monitor to personalized coaching to help control blood sugar levels. In 2019, just one year before Teladoc’s acquisition, Livongo IPO’d at a valuation of US$355m, and expanded its products and services to cover high blood pressure and behavioural health with an ultimate goal of leveraging digital medicine to address “the health of the whole person”. 
 
These four acquisitions are from market segments, which run parallel to traditional medical devices and are often perceived by some MedTech executives to be competitors destined to be controlled by giant tech companies such as Apple, Huawei, and Samsung. However, given the rate at which technology is developing, the speed at which MedTech and pharma are converging, and the renewed liquidity in the market, it might be more efficacious for MedTechs to view such specialised digital health companies as partners rather than competitors.
 
Technologies helping MedTechs to develop actionable solutions

Today, many new biomedical technologies are being developed and benefit from continuous miniaturization, enhanced battery life, cost reductions and increasing data storage capacity. One such technology is photoplethysmography (PPG), a non-invasive, uncomplicated, and inexpensive optical measurement method that employs a light source and a photodetector to calculate the volumetric variations of blood circulation. PPG is employed in smartphones and wearables that are used by billions of people worldwide. There is a large and growing global research endeavour to develop more effective and sophisticated PPG algorithms that could be attached to traditional, non-active medical devices and implants to provide accurate and reliable real time monitoring of a wide range of conditions.
 
Outside of specific health monitoring technologies, few MedTechs collect, store, and analyse data generated by their existing traditional devices and implants, and even fewer use such data to facilitate real time, monitoring of conditions. However, some companies are beginning to transform their dumb devices into intelligent ones to gain access to new revenue streams. For example Zimmer-Biomet’s smart” knee, utilizes a biosensor [an analytical device that uses natural biological materials to detect and monitor virtually any activity or substance] to generate self-reports on patient activity, recovery, and treatment failures, without the need for physician intervention and dependence upon patient compliance. 
 
According to Roger Kornberg, Professor of Structural Biology at Stanford University and Nobel Laureate for Chemistry, “the excitement of biosensors pertains to their microscopic size and the ease with which they can transmit wirelessly in real time information about responses to treatment from an implantable device within the body”. [See video below].
 
A fast-growing field of AI is tiny machine learning (TinyML), which has the capability to perform on-device, real time, sensor data analytics at extremely low power, typically in the mW [one thousandth of a watt] range and below. The technology is expected to make always-on use-cases economically viable and accelerate the transformation of dumb devices and implants into smart ones.

 
 
Changing traditional R&D models
 
In their search for innovative healthcare solutions, MedTechs might consider increasing their R&D spend and reorganizing their R&D function. MedTech’s R&D spend, as a percentage of revenues, has slowed compared to levels the industry recorded prior to the 2007 financial crash. Overall, the industry tends to allocate more of its capital to share buybacks and investor dividends than to R&D. This strategy may please shareholders in the short term, but it suggests some uncertainty among industry leaders about how to invest for growth in the longer term and could have a medium- to long-term potential downside. 
 
Further, a significant percentage of R&D spend goes on tweaking existing products rather than creating new ones. Given that the future of the industry is dependent upon innovation, it seems reasonable to suggest that, as competition increases and markets tighten, MedTechs will need to consider increasing their R&D resources and capabilities to develop innovative technologies that provide improved actionable solutions across entire patient journeys.

Unlocking value from R&D innovations might require a different culture and new operating models to the ones that tend to prevail today. Instead of lengthy R&D cycles fixed on the launch of a physical product, it could be more beneficial to focus on developing minimum-viable patient-centric solutions, which research teams can deploy early, test, learn from and enhance. Moreover, R&D strategy sessions might benefit by including a mandatory question: “In the near- to medium-term, are there any evolving technologies likely to disrupt a specific market segment important to our company?”.

 
The potential of innovative technologies to disrupt markets
 
To illustrate the significance of this question, consider traumatic brain injury (TBI), which each year affects ~69m individuals worldwide. There is no cure for the condition, and the cornerstone of its management is to monitor intracranial pressure (ICP). [Pressures >15 millimetres of mercury (mm Hg) are considered abnormal, and ICP >20 mm Hg is deemed pathological]. An ICP monitor is expected to be easy to use, accurate, reliable, reproducible, inexpensive and should not be associated with either infection or haemorrhagic complications. Currently, the gold-standard is to drill a small burr hole in the skull, insert a catheter and place it in a cavity [ventricle] in the brain, which is filled with cerebrospinal fluid (CSF). Such an invasive intraventricular catheter system is accurate and reliable, but it is also a health-resource-intensive modality, which runs a risk of haemorrhage and infection. Recent advances in PPG and other technologies have accelerated research developing non-invasive techniques to continuously measure and monitor ICP, which in the medium-term, could replace the gold standard and avoid drilling a hole in a traumatised patient’s skull.   
  
Pros and cons of the COVID-19 crisis

One beneficial outcome for MedTechs of the COVID-19 crisis has been the change in regulatory norms, which favour innovation. In the US, the FDA reduced barriers to market entry for new devices by increasing its emergency use authorization (EUA), which fast-tracks the availability of medical devices. Also, at the onset of the pandemic, the EU deferred for one year the implementation of its Medical Device Regulation (MDR), which governs the production and distribution of medical devices in Europe. In mid 2021, when governments began removing the outstanding legal restrictions imposed to reduce the impact of the third wave of the COVID-19 pandemic, some MedTechs, which had invested in remote communication strategies, chose to build on the changes they had made and invest further in digitalization AI strategies, while many others reverted to their labour-intensive supply channels. According to a June 2021 Boston Consulting Group (BCG) study, “On average, MedTech companies are still spending two to three times more on selling, general, and administrative (SG&A) expenses (as a percent of the costs of goods sold) than the typical technology or industrial company”.
 
A potential disadvantage for MedTechs of the COVID-19 pandemic is that it can lead to an excessive focus on short-term challenges and put off addressing longer-term strategic threats.
 
MedTech executives have never had it so good

Why are some companies reluctant to transform their strategies and business models?

We suggest that a deep-rooted resistance to change results from MedTechs “never having it so good” over a long period. Indeed, for several decades before the global economic crisis in 2007 and 2008, the medical device market was buoyed by limited competition, benign reimbursement policies, aging populations, and a slower pace of technological change compared to today. These factors promoted double-digit growth rates, investor confidence, and solid valuations. This fostered a sense of security among C suites and encouraged “business as usual” agendas, which tended to focus on sharpening legacy products, legacy business models, legacy forms of market access and pricing and legacy capabilities.
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Who should lead MedTech?

The 2007-8 financial crisis only inflicted a short-lived blow to the industry and most companies bounced back relatively quickly. Throughout the decade that followed, MedTechs maintained solid financial performance, steady growth, investor confidence and robust valuations. Many enterprises across the industry ended 2019 in a strong position, with some trading at 52-week highs and the industry overall growing revenues at ~6%.
In 2020, the COVID-19 pandemic threw some segments of the industry off course by a substantial reduction in elective care. However, by 2H 2021, most MedTechs had recovered, albeit their annual growth in revenues did not recapture the heights of the early years of the 21st century.
 
MedTechs became like elephants

It seems reasonable to suggest that decades of commercial success shaped the mindsets of industry leaders and resulted in MedTechs becoming like elephants. In 1990, James Belasco published, Teaching the Elephant to Dance, in which he likened organizations to elephants. The book describes how trainers shackled young elephants to a stake securely embedded in the ground so that they could not move away despite their efforts. By the time the elephants became fully grown and had the strength to pull the stakes out of the ground, they were so conditioned they did not move and remained in position even though most were no longer tethered to the stakes. The author uses this analogy to warn how companies can become stuck in obsolete working practices, which are obstacles to their future commercial success.

In 1993, IBM, the world’s largest manufacturer of mainframe computers, had become “an elephant” continuing to produce hardware appliances when the industry was embracing software solutions. IBM, which had posted a US$8bn loss, appointed Lou Gerstner, an executive from outside the computer industry, to turn the company around. Nine years later, IBM had become one of the world's most admired companies. In a book published in 2002, entitled, Who Says Elephants Can't Dance?, Gerstner described how he successfully changed IBM from a maker of hardware to a service orientated company.
 
A 5-year window of opportunity
 
A doubt as to whether many traditional MedTechs can be taught to dance was sewn in a 2021 BCG study cited above, which suggested that enterprises “do not yet have the capabilities in place to develop and implement a next-generation, omnichannel commercial model”. Ten years from now, the MedTech market is projected to be significantly different to what it is today, and what it has been for the past four decades. However, it seems reasonable to assume that because of its size and growth rate, [~US$0.5tn, growing at a compound annual growth rate (CAGR) of ~6% and projected to reach US$0.75tn by 2030], many industry leaders will not feel any pressing need to transform their strategies and business models in the short-term.

However, with a rapidly changing healthcare ecosystem, it seems reasonable to suggests that, to remain relevant after 2030, MedTechs will need to use the next five years as a window of opportunity to prepare solutions that enable them to focus on entire patient treatment pathways, create best-in-class distributive services, and develop digital marketing and sales capabilities that help to expand their influence beyond selling hardware. This will require targeting the “right” market segments, developing the “right” solutions, funding in the “right” R&D, creating the “right” playbooks; and recruiting, retaining, and developing the “right” people with the “right” capabilities.

 
From restricted staged events to real time distribution

Companies are rich reservoirs of clinical data and expertise, but the data tend to be kept in silos and distributed intermittently to a limited number of clinicians and providers at “staged” events. Digital technologies can unlock these assets and facilitate real time, online marketing, self-service portals, and virtual engagements; all of which can provide physicians and providers with unprecedented access to knowhow that can help improve the quality of care and reduce costs. However, shifting to such a distributed care model to drive profitability requires developing a digital, remote, marketing and sales force, which is supported by data analytics, virtual demonstrations, automated call reporting, and AI-supported coaching tools.
 
The reduction of obstacles to data rich digital distributed care strategies

While distributed computing and communications systems have significantly enhanced a wide range of commercial organizations, they have yet to take root in MedTech settings, despite data sharing being critical in modern clinical practice and medical research. A challenge for MedTechs is to engage in data sharing that reconciles individual privacy and data utility. This will entail universally agreed AI and machine learning practices. Although there are sophisticated technologies that can help with this, MedTech’s management and information systems’ personnel may not be prepared to effectively reconcile these competing interests and push for universal data standards. According to a US National Institute of Health report, “The lack of technical understanding, the lack of direct experience with these new tools, the lack of confidence in their management, the lack of a peer group of successful adopters (except for a few academic medical organizations), and uncertainties about reasonable risks and expectations all leave conservative organizational managers hesitant to make decisions”. 
 
While the mindsets of some industry leaders appear to be obstacles to change, other obstacles to transformative business models have been reduced. For instance, privacy is now less of an obstacle for data-rich strategies than it once was. Increasingly, patients show a willingness for their clinical and personal data to be used anonymously in the interest of improving healthcare. Further, regulators’ attitudes towards data are changing.  In September 2021 the FDA published its AI enabled devices that are marketed in the US, which embrace the full scale of approvals from 510(k) de Novo authorizations to Premarket (PMA) approvals. The FDA’s initiative comes at a time of continued growth in AI enhanced digital offerings that contribute to a variety of clinical spheres, and the increasing number of companies seeking to enter this space. There are ~130 algorithms approved for clinical use in the US and Europe.
 
A recent report from Frost & Sullivan, a US market research company, suggests that although in the near-term, traditional medical devices will continue to make up the bulk of the market, after 2024, they are expected to grow at only a CAGR of ~2%. By contrast, digitally enhanced medical devices, and algorithms, which facilitate managing patients remotely and non-intrusively, are expected to grow at a CAGR >14% and reach US$172bn by 2024.

 
The shift to low-cost settings

Over the next five years, as technology advances, populations age, healthcare costs escalate, patient expectations continue to rise, and markets tighten, we can expect the shift away from hospitals to outpatient settings and other lower-cost venues to accelerate. This move to a distributed care model is a headwind for traditional MedTechs, whose principal focus is provider systems rather than patients, and a tailwind for new players entering the market unencumbered by legacy supply chains, costs, and infrastructures. According to an EY 2020 study, ~70% of start-ups in the diagnostics segment have products applicable to the point-of-care setting.
 
Corporate venture funds

To help traditional MedTechs dance leaders of medium sized, well capitalized enterprises might consider copying the world’s largest MedTechs and create corporate venture capital (CVC) funds to invest in tech-savvy start-ups. While 7 of the top 10 MedTechs by sales have venture arms, many company leaders shy away from investing in early-stage, unproven technologies. However, CVC funds offer traditional corporates access to innovations and scarce science, technology, engineering, and mathematics (STEM) skills, which are necessary to capture and analyse data, deliver enhanced care, and drive biomedical R&D with the potential to improve patient outcomes and lower costs.
 
The latest giant MedTech to launch a CVC fund is Intuitive Surgical. In Q4 2020, the company started disbursing capital from its initial US$100m venture fund to start-ups developing digital tools and precision diagnostics, with an emphasis on minimally invasive care. Intuitive is the world’s largest manufacturer of robotic surgical systems for minimally invasive surgery. Since its lead offering, the da Vinci Surgical System, received FDA approval in 2000, it has been used by surgeons in all 50 US states, ~67 countries worldwide and has performed >8.5m procedures.

In the first three quarters of 2020, CVCs participated in investment rounds worth US$1.2bn, which amounted to >25% of the total venture funding the sector raised. The lion’s share went to products and solutions that address digital therapies, telehealth, and treatments for low-cost settings. Such technologies are positioned to continue receiving significant funding in 2022 and beyond. A 2021 study by Deloitte, a consulting firm, suggests that MedTech start-ups, unencumbered by legacy products and practices have capabilities, which stretch beyond traditional devices that support episodic care, and focus on distributed solutions, which address the full patient journey: from diagnosis to rehabilitation. The study also maintains that technologies employed by these enterprises are getting smarter, with ~70% of them including digital AI capabilities.
 
Further, MedTechs with CVC arms might consider allowing their digital business functions to operate within a different organizational framework, giving them greater decision-making authority and enhanced freedoms.

 
Asia Pacific MedTech markets

Before closing let us briefly draw attention to the increasing significance of the emerging Asia Pacific MedTech markets. For the past 4 decades, industry leaders were not obliged to seriously consider penetrating markets outside the US and Western Europe because ~70% of global MedTech revenues came from the US and Western Europe. However, as Western markets tighten, and become increasingly competitive, attention is moving East towards Asia.

Over two decades ago, a handful of giant MedTechs began investing in Asia, but most companies in the sector preferred not to risk navigating such unfamiliar healthcare territories. An early investor in the region was Medtronic, which, since ~2000, has achieved significant growth from a multi-faceted strategy that included exporting innovative products from the US to China, establishing R&D facilities in China to design products specifically for the needs of the Chinese market, crafting partnerships with Beijing to educate patients in under-served therapeutic areas, and acquiring domestic Chinese MedTech companies.

Because of the current political stand-off between the two countries, such a China strategy is not so feasible as it has been over the past two decades. However, it is worth bearing in mind that Asia is comprised of 48 countries with a combined population of ~5bn, which is projected to reach 8.5bn by 2030, [~60% of the world’s population], with 1 in 4 people >60. In 2020, ~2bn Asians were members of the middle class, and by 2030, this demographic is projected to grow to ~3.5bn. Moreover, health insurance coverage in the region is expanding. By contrast, the middle classes in the US and Western Europe are smaller and growing at lower rates. According to the Pew Research Center in 2018, ~52% of the 258m US adults (>18 years) was considered middle class. The dynamics of the Asian middle class is driving a large and rapidly growing Asian MedTech market, which is on the cusp of eclipsing Europe to become the world’s second largest regional market, growing at a CAGR of ~9%.

Further, the region has become an important source of technological innovation. For example, in 2020, its digital health market was valued at ~US$20bn and projected to grow at a CAGR of ~21% until 2027, when its value is expected to be ~US$80bn. Despite its complexities and unfamiliarity, Asia represents a substantial opportunity for MedTechs. However, for Western enterprises to succeed in Asian markets they will require in depth local knowhow, long term commitments, agility, innovation, and robust strategies that can prosper under fiercely competitive conditions.  

 
Takeaways

MedTechs have built capabilities to develop, launch, market and sell physical devices. With some notable exceptions, few have the capabilities necessary to drive significant growth from digitalization and data strategies. Sharpening traditional commercial procedures and practices alone is unlikely to significantly increase growth, especially when competitors and new entrants have business models that are more effective, promote better patient outcomes and provide greater value to healthcare systems.  

MedTechs could play a significant role in the transformation of healthcare, but not without risks and some significant changes to the way they operate. Over the next five years, as competitive pressures increase, industry leaders have a window of opportunity to pivot. Here are six strategic questions that might help in this regard:
  1. Should we support significant investments in digitalization, and data analytics to improve our supply chains and R&D endeavours to convert dumb devices and implants into smart ones?
  2. What are the top three actionable innovations that we can develop in the near-term to provide access to new revenue streams?
  3. What are the top three technologies likely to disrupt our product offerings in the near- to medium-term and what should we do about them?
  4. Can we remain a hardware manufacturer while developing significant software solutions that embrace entire patient journeys or must we choose between manufacturing and software?
  5. How do we create valuable solutions that enhance patient journeys from data?
  6. How are global markets changing in ways that are not reflected in our company’s discussions?
The answers to these questions will help to shape a corporation’s strategy, and inform M&A and CVC activities, “must have” capabilities, desired partnerships, R&D spend and agendas, and the type of business models to pursue. All critical for teaching elephants to dance.
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  • Digitalization, big data, and artificial intelligence (AI) are transformational technologies poised to shape the future of MedTech companies over the next decade
  • Fully embracing these technologies and integrating them in all aspects of a business will likely lead to growth, and competitive advantage while treating them as peripheral add-ons will likely result in stagnation and decline
  • MedTech executives’ analogue mindsets and resource constraints prevent them from fully embracing transformational technologies
  • There are also potential pushbacks from employees, patients, providers and investors
  • Notwithstanding, there are unstoppable structural trends forcing governments and payers throughout the world to oblige healthcare systems to leverage digitalization, big data, and AI to help reduce their vast and escalating healthcare burdens
  • Western MedTechs are responding to the rapidly evolving healthcare landscape by adopting transformational technologies and attempting to increase their presence in emerging markets, particularly China
  • To date, MedTech adoption and integration of digitalization, big data, and AI have been patchy
  • To remain relevant and enhance their value, Western MedTechs need to learn from China and embed transformational technologies in every aspect of their businesses
 
Unleashing MedTech's Competitive Edge through Transformational Technologies
Digitalization, Big Data, and AI as Catalysts for MedTech Competitiveness and Success
 
 
In the rapidly evolving landscape of medical technology, the integration of digitalization, big data, and artificial intelligence (AI) [referred to in this Commentary as transformational technologies] has emerged as a pivotal force shaping the future of MedTech companies.  Such technologies are not mere add-ons or peripheral tools but will soon become the lifeblood that fuels competition and enhances the value of MedTechs. From research and development (R&D) to marketing, finance to internationalization, and regulation to patient outcomes, digitalization, big data, and AI must permeate every aspect of medical technology businesses if they are to deliver significant benefits for patients and investors. To thrive in this rapidly evolving high-tech ecosystem, companies will be obliged to adapt to this paradigm shift.
 
Gone are the days when traditional approaches would suffice in the face of escalating complexities and demands within the healthcare industry. The convergence of transformational technologies heralds a new era, where innovation and success are linked to the ability to harness the potential of digitalization, big data, and AI. MedTech companies that wish to maintain and enhance their competitiveness must recognize the imperative of integrating these technologies across all facets of their operations. From improving their R&D processes by utilizing advanced data analytics and predictive modeling, to optimizing internal processes through automation and machine learning algorithms. Embracing such technologies opens doors to enhanced marketing strategies, streamlined financial operations, efficacious legal and regulatory endeavours, seamless internationalization efforts, and the development of innovative offerings that cater to the evolving needs of patients, payers, and healthcare providers.
 
This Commentary aims to stimulate discussion among MedTech senior leadership teams as the industry's competitive landscape continues to rapidly evolve, and the fusion of digitalization, big data, and AI becomes not only a strategic advantage but a prerequisite for survival in an era defined by data-driven decision-making, personalized affordable healthcare, and a commitment to improving patient outcomes.
 
In this Commentary

This Commentary explores digitalization, big data, and AI in the MedTech industry. It presents two scenarios: one is to fully embrace these technologies and integrate them into all aspects of your business and the other is to perceive them as peripheral add-ons. The former will lead to growth and competitive advantage, while the latter will result in stagnation and decline. We explain why many MedTechs do not fully embrace transformational technologies and suggest this is partly due to executives’ mindsets, resource constraints and resistance from employees, patients, and investors. Despite these pushbacks, the global healthcare ecosystem is undergoing an unstoppable transformation, driven by aging populations and significant increases in the prevalence of costly to treat lifetime chronic conditions. Western MedTechs are responding to structural shifts by adopting transformational technologies and increasing their footprints in emerging markets, particularly China. To date, company acceptance of AI-driven strategies has been patchy. We suggest that MedTechs can learn from China and emphasize the need for organizational and cultural change to facilitate the comprehensive integration of transformational technologies. Integrating these technologies into all aspects of a business is no longer a choice but a necessity for companies to stay competitive in the future.
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Transformational technologies in MedTech

Digitalization in the MedTech industry involves adopting and integrating digital technologies to improve healthcare delivery, patient care, and operational efficiency. It transforms manual and paper-based processes into digital formats, enabling electronic health records, connected medical devices, telemedicine, and other digital tools. This allows for seamless data exchange and storage, improving clinical processes, decision-making, and patient empowerment. Big data in the MedTech industry refers to the vast amount of healthcare-related information collected from various sources. It includes structured and unstructured data such as patient demographics, clinical notes, diagnostic images, and treatment outcomes. Big data analysis identifies patterns, correlations, and trends that traditional methods may miss. They aid medical research, drug discovery, personalized medicine, clinical decision support, evidence-based care, population health management, and public health initiatives. Data privacy, security, and ethical use are crucial considerations. Artificial Intelligence (AI) in the MedTech industry uses computer algorithms to simulate human intelligence. AI analyzes medical data to identify patterns, make predictions, and improve diagnoses, treatment plans, and patient outcomes. It assists in medical imaging interpretation, personalized medicine, and patient engagement. In R&D, AI accelerates the development of devices and the discovery of new therapies and has the capacity to analyze scientific literature and molecular data. The technology serves as a tool to augment healthcare professionals' expertise and support decision-making.
With the proliferation of large language AI models (LLM) and to borrow from a recent essay by Marc Andreeseen - an American software engineer, co-author of Mosaic, [one of the first widely used web browsers] and founder of multiple $bn companies - everyone involved with medical technology, including R&D, finance, marketing, manufacturing, regulation, law, international etc., “will have an AI assistant/collaborator/partner that will greatly expand their scope and achievement. Anything that people do with their natural intelligence today can be done much better with AI, and we will be able to take on new challenges that have been impossible to tackle without AI, including curing all diseases.”

Two scenarios

We suggest there are only two scenarios for MedTechs: a company that fully embraces transformational technologies and one that does not. The former, will benefit from strengthened operational efficiencies, improved patient outcomes, and enhanced innovations, which will lead to increased market share and investor confidence. By leveraging digital technologies, such as remote monitoring devices, telemedicine platforms, LLMs, and machine learning, a company will be able to offer more personalized, effective and affordable healthcare services and solutions. An enterprise that integrates these technologies into their strategies and business models will, over time, experience improved growth prospects, increased revenues, and potentially higher profitability. These factors will contribute to a positive perception in the market, leading to an increase in company value. MedTechs that fail to fully embrace digitalization, big data, and AI will face challenges in adapting to the rapidly evolving healthcare landscape. They will struggle to remain competitive and relevant in a market that increasingly values transformational technologies and data-driven approaches. As a result, such companies will experience slower growth, lower market share, and limited investor interest, which will lead to a stagnation or decline in their value.
 
The analogue era's influence on MedTechs

If the choice is so stark, why are many MedTechs not grabbing the opportunities that transformational technologies offer? To answer this question let us briefly remind ourselves that the industry took shape in an analogue era, which had a significant effect on how MedTech companies evolved and established themselves. During the high growth decades of the 1980s, 1990s, and early 2000s, the medical technology industry operated with limited access to the technologies that have since radically changed healthcare. The 1980s marked a period of advancements, which included the widespread adoption of medical imaging such as computed tomography (CT) scans and magnetic resonance imaging (MRI). These modalities provided detailed visualizations of the human body, supporting more accurate diagnoses. Medical devices like pacemakers, defibrillators, and implantable cardioverter-defibrillators (ICDs) were developed and improved the treatment of heart conditions. The 1990s witnessed further advancements, with a focus on minimally invasive procedures. Laparoscopic surgeries gained popularity, allowing surgeons to perform operations through small incisions, resulting in reduced patient trauma and faster recovery times. The development of laser technologies enabled more precise surgical interventions. The decade also saw the rise of biotechnology, with the successful completion of the Human Genome Project and increased emphasis on genetic research. The early 2000s saw the emergence of digital transformation in some quarters of the medical technology industry. Electronic medical records (EMRs) began to replace paper-based systems, increase data accessibility and upgrade patient management. Telemedicine, although still in its nascent stages, started connecting healthcare providers and patients remotely, overcoming geographical barriers. Robotics and robotic-assisted surgeries gained traction, enabling more precise and less invasive procedures. During these formative decades, the medical technology industry focused on enhancing diagnostic capabilities, improving treatment methods, and streamlining healthcare processes. The industry had yet to witness the transformational impact of digitalization, big data and AI that would emerge in subsequent years, enabling more advanced analytics, personalized medicine, and interconnected healthcare systems.
 
From analogue to digital

During these formative analogue years, MedTechs experienced significant growth and expansion, where innovative medical technologies changed healthcare practices and improved patient outcomes. Companies thrived by leveraging their expertise in engineering, biology, and clinical research and developed medical devices, diagnostic tools, and life-saving treatments. For MedTechs to experience similar growth and expansion in a digital era, they must fully harness the potential of transformational technologies, and to achieve this, there must be a receptive mindset at the top of the organization.
 
According to a recent study by Korn Ferry, a global consulting and search firm, the average age of CEOs in the technology sector is 57, and the average age for a C-suite member is 56. Thus, as our brief history suggests, many MedTech executives advanced their careers in a predominantly analogue age, prior to the proliferation of technologies that are transforming the industry today. Thus, it seems reasonable to suggest that this disparity in experience and exposure colours the mindsets of many MedTech executives, which can lead to them underestimating and under preparing for the significant technological changes that are set to reshape the healthcare industry over the next decade. Senior leadership teams play a pivotal role in developing the strategic direction of companies and driving their success. Without a proactive mindset shift, these executives may struggle to fully comprehend the extent of the potential disruptions and opportunities that digitalization, big data, and AI bring.
 
By embracing such a mindset shift, senior leadership teams could foster a culture of innovation and agility. But they must recognize the urgency of preparing for a future fueled by significantly different technologies from those they might be more comfortable with. Such urgency is demonstrated by a March 2023 Statista report, which found that in 2021, the global AI in healthcare market was worth ~US$11bn, but forecasted to reach ~US$188bn by 2030, increasing at a compound annual growth rate  (CAGR) of ~37%. As these and other facts (see below) suggest, the integration of digitalization, big data, and AI has already begun to redefine healthcare delivery, patient engagement, and operational efficiency and is positioned to accelerate in the next decade. To remain competitive and relevant in this rapidly evolving high-tech world, MedTechs must foster a culture of openness to change and innovation. Leaders should encourage collaboration, both internally and externally, and create cross-functional teams that bring together expertise from various domains, including AI and data analytics. This multidisciplinary approach facilitates the integration of transformational technologies into all aspects of the business, ensuring that the organization remains at the forefront of the evolving industry.

 
Implementation and utilization

Limited resources, such as budgets and IT infrastructure, can hinder the adoption and utilization of digitalization, big data, and AI, especially for smaller companies. Compliance with healthcare regulations like HIPAA and GDPR adds complexity and can slow down technology implementation. Resistance to change from employees, healthcare providers, and patients also poses challenges. Fragmented and unstandardized healthcare data limit the effectiveness of AI-driven strategies. The expertise gap can be bridged through collaboration with academic institutions and technology companies. Demonstrating the tangible benefits of digitalization, big data and AI is essential to address concerns about return on investments (ROI). Strategic planning, resource investment, collaboration, and cultural change are necessary for the successful implementation and utilization of transformational technologies in MedTech companies. 
 
Organizational and cultural changes

MedTechs must embrace agility and innovation to harness the potential benefits from transformational technologies. This requires fostering a culture that encourages risk-taking and challenges conventional practices. Creating cross-functional teams and promoting collaboration nurtures creativity and innovative solutions. Transitioning to data-driven decision-making involves establishing governance frameworks, ensuring data quality, and leveraging analytics and insights from big data. Talent development and upskilling are crucial, necessitating training programmes to improve digital literacy and add analytics skills. Collaboration and partnerships with external stakeholders facilitate access to cutting-edge technologies. Enhancing patient experiences through user-friendly interfaces and personalized solutions is essential. Investing in agile technology infrastructure, including cloud computing and robust cybersecurity measures is necessary. MedTechs must navigate complex regulatory environments while upholding ethical considerations, transparency, and patient consent to gain credibility and support successful technology adoption.
 
Investors

A further potential inhibitor to change is MedTech investors who may harbour conservative expectations that tend to discourage companies from taking risks, such as fully embracing and integrating digitalization, big data, and AI across their entire businesses. This mindset also can be traced back to the formative analogue decades on the 1980s, 1990s, and early 2000s when investors became accustomed to growing company valuations. During that time, most MedTechs catered to an underserved, rapidly expanding market largely focussed on acute and essential clinical services in affluent regions like the US and Europe, where well-resourced healthcare systems and medical insurance compensated activity rather than patient outcomes. However, the landscape has since undergone a radical change. Aging populations with rising rates of chronic diseases have significantly increased the demands on over-stretched healthcare systems, which have turned to digitalization, big data, and AI in attempts to reduce their mounting burdens. These shifting dynamics now demand a more forward-thinking approach, but investor expectations often remain fixed on a past traditional model, which impedes the adoption and full integration of transformational technologies into MedTech enterprises.

To overcome investor conservatism and reluctance to embrace transformational technologies requires a concerted effort by MedTechs to demonstrate the tangible benefits of these technologies on the industry. Companies can focus on providing evidence of improved patient outcomes, increased efficiency, cost savings, and competitive advantages gained through the integration of digitalization, big data, and AI. Engaging in open and transparent communications with investors, showcasing successful case studies, and highlighting the long-term potential and sustainability of a technology-driven approach can help shift investor expectations and encourage a more receptive attitude towards risk-taking and innovation.
Global structural drivers of change

For decades, Western MedTechs have derived comfort from the fact that North America and Europe hold 68% of the global MedTech market share. These wealthy regions have well-resourced healthcare systems, which, as we have suggested, for decades rewarded clinical activity rather than patient outcomes, and MedTech’s benefitted by high profit margins on their devices, which contributed to rapid growth, and enhanced enterprise values. Today, the healthcare landscape is significantly different. North America and Europe are experiencing aging populations, and large and rapidly rising incidence rates of chronic diseases in older adults. Such trends are expected to continue for the next three decades and have forced governments and private payers to abandon compensating clinical activity and adopt systems that reward patient outcomes while reducing costs. This shift has put pressure on healthcare systems to adopt transformational technologies to help them cut costs, increase access, and improve patient journeys. MedTech companies operating in this ecosystem have no alternative but to adapt. Their ticket for increasing their growth and competitiveness is to adopt and integrate digitalization, big data, and AI into every aspect of their business, which will help them to become more efficient and remain relevant.
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Most developed economies are experiencing aging populations, which affect everything from economic and financial performance to the shape of cities and the nature of healthcare systems. Let us illustrate this with reference to the US. According to the US National Council on Aging, ~56m Americans are ≥65 and this cohort is projected to reach ~95m by 2060. On average, a person ≥65 is expected to live another 17 years. Older adult Americans are disproportionately affected by costly to treat lifetime chronic conditions such as cancer, heart disease, diabetes, respiratory disorders, and arthritis. ~95% of this older adult cohort have at least one chronic disease, and ~80% have two or more. Multiple chronic disorders account for ~66% of all US healthcare costs and ~93% of Medicare spending.

According to a May 2023, Statista report, the US spends more on healthcare than any other country. In 2021, annual health expenditures stood at US$4.2trn, ~18% of the nation’s Gross Domestic Product (GDP). The demographic trends we described in the US are mirrored in all the principal global MedTech markets. Many of which, particularly Japan, are also experiencing shrinking working age populations resulting from a decline in fertility rates, and curbs on immigration. This shrinkage further impacts a nation’s labour force, labour markets, and tax receipts; all critical for resourcing and paying for healthcare services.
 
MedTechs’ response to structural changes

Western MedTechs’ response to these structural challenges have been twofold: (i) the adoption of transformational technologies, which contribute to lowering healthcare costs, improving innovation, and developing affordable patient-centric services and solutions and (ii) targeting emerging markets as potential areas for growth and development. As we have discussed the first point, let us consider briefly the second. Decades ago, giant MedTechs like Johnson and Johnson (J&J), Abbott Laboratories and Medtronic established manufacturing and R&D centres in emerging economies like Brazil, China, and India, where markets were growing three-to-four times faster than in developed countries. Notwithstanding, many MedTechs, were content to continue serving wealthy developed regions - the US and Europe - and either did not enter, or were slow to enter, emerging markets. More recently, as a response to the trends we have described, many MedTechs are either just beginning or accelerating their international expansions. However, such initiatives might be too late to reap the potential commercial benefits they anticipate. Establishing or expanding a footprint in emerging economies is significantly more challenging today than it was two decades ago. 

For instance, two decades ago, China lacked medical technology knowhow and experience and welcomed foreign companies’ participation in its economy. Today, the country has evolved, enhanced its technological capacity and capabilities, and is well positioned to become the world’s leading technology nation by 2030. No longer so dependent on foreign technology companies, the Chinese Communist Party (CCP) raised barriers to their entry. In 2017, government leaders announced the nation's intention to become a global leader in AI by putting political muscle behind growing investment by Chinese domestic technology companies, whose products, services and solutions were used to improve the country's healthcare systems. Over decades, the CCP committed significant resources to developing domestic STEM skills, and research to achieve “major technological breakthroughs” by 2025, and to make the nation a world leader in technology by 2030, overtaking its closest rival, the US. According to a 2023 AI Report from the Stanford Institute for Human-Centered Artificial Intelligence, in 2021, China produced ~33% of both AI journal research papers and AI citations worldwide. In economic investment, the country accounted for ~20% of global private investment funding in 2021, attracting US$17bn for AI start-ups. The nation’s AI in the healthcare market is fueled by the large and rising demand for healthcare services and solutions from its ~1.4bn population, a large and rapidly growing middle class, and a robust start-up and innovation ecosystem, which is projected to grow from ~US$0.5bn in 2022 to ~US$12bn by 2030, registering a CAGR of >46%. 

>4 years ago, a HealthPad Commentary described how a Chinese internet healthcare start-up, WeDoctor, founded in 2010, bundles AI and big data driven medical services into smart devices to help unclog China’s fragmented and complex healthcare ecosystem and increase citizens’ access to affordable quality healthcare. The company has grown into a multi-functional platform offering medical services, online pharmacies, cloud-based enterprise software for hospitals and other services. Today, WeDoctor owns 27 internet hospitals, [a healthcare platform combining online and offline access for medical institutions to provide a variety of telehealth services directly to patients], has linked its appointment-making system to another 7,800 hospitals across China (including 95% of the top-tier public hospitals) and hosts >270,000 doctors and ~222m registered patients. It is also one of the few online healthcare providers qualified to accept payments from China's vast public health insurance system, which covers >95% of its population. WeDoctor, like other Chinese MedTechs, has expanded its franchise outside of China and has global ambitions to become the “Amazon of healthcare”. China’s investment in developing and increasing its domestic transformational technologies and upskilling its workforce has made the nation close to technological self-sufficiency and has significantly raised the entry bar for Western MedTechs wishing to establish or extend their presence in the country.

China's progress in AI and digital healthcare underscores the urgent need for Western MedTechs to adopt and implement these technologies. To remain relevant and survive in a rapidly changing global healthcare ecosystem, Western MedTechs might do well to learn from China's endeavours in leveraging AI, big data, and digitalization to drive innovation, enhance competitiveness, and ultimately contribute to the transformation of the global healthcare landscape. Notwithstanding, be minded of the ethical concerns Western nations have regarding China’s utilization of big data and AI in its healthcare system and its potential to compromise privacy and individual rights due to the CCP's extensive collection and analysis of personal health data.

 
Takeaways

Digitalization, big data, and AI are transformational technologies that have the power to influence the shape of MedTech companies over the coming decade, and their potential impact should not be underestimated. Fully embracing these technologies and integrating them into every aspect of a business is necessary for growth and competitive advantage. On the other hand, treating them as peripheral add-ons will likely lead to stagnation and decline. However, the path towards their full integration in companies is not without its challenges. MedTech executives, hindered by their analogue mindsets and resource constraints, often struggle to fully embrace the potential of digitalization, big data, and AI. Moreover, there may be pushbacks from various stakeholders including employees, patients, healthcare providers, and investors. These concerns and resistances can impede the progress of transformation within the industry. Nonetheless, governments and payers across the globe are being compelled by unstoppable structural trends to enforce the utilization of digitalization, big data, and AI within healthcare systems. The large and escalating healthcare burdens facing economies throughout the world leave them with little choice but to leverage these technologies to reduce costs, improve patient access and outcomes. In response to the rapidly evolving healthcare landscape, Western MedTechs are making efforts to adopt transformational technologies and expand their presence in emerging markets, particularly China. They recognize the need to stay ahead of the curve and adapt to the changing demands of the industry. However, the adoption and integration of digitalization, big data, and AI by companies thus far have been inconsistent and patchy. To remain relevant and enhance their value, Western MedTechs, while being mindful of ethical concerns about China’s use of AI-driven big data healthcare strategies, might take cues from their Chinese counterparts and embed these transformational technologies in every aspect of their businesses. The transformative impact of digitalization, big data, and AI on MedTech companies cannot be overstated. While challenges and resistance may arise, the inexorable drive towards leveraging these technologies is unstoppable. MedTech companies should shed their analogue mindsets and resource constraints and fully embrace the potential of these transformational technologies.
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  • MedTech growth strategies have taken advantage of low interest rates and cheap money to debt finance acquisitions of near adjacent companies with existing tried and tested products
  • This allowed companies to expand their product portfolios, geographic reach, and customer bases
  • Many MedTechs preferred such a growth strategy to investing in R&D to develop disruptive technologies that maybe outside their immediate field of interest
  • These technologies include 3D bioprinting, robotics, virtual reality, biometric devices and wearables, digital therapeutics, and telemedicine
  • All are patient-centric software driven technologies rather than hardware devices that serve the needs of hospitals
  • All are positioned to influence the shape of healthcare systems over the next decade
  • Many MedTech R&D investments are devoted to making small improvements to legacy products that prioritize the interests of large healthcare organizations over the needs of patients
  • Traditional MedTech M&A-driven growth strategies that have benefitted from an era of low interest rates and cheap money may now be challenged in the current period of higher interest rates, stagnate growth and rapidly evolving disruptive healthcare technologies.
  
Healthcare disrupters
 
On March 10, 2023, the Silicon Valley Bank (SVB) collapsed after a series of ill-fated investment decisions triggered a run on its assets. It was the largest bank failure since the 2008 financial crisis and the second largest in US history. The demise of SVB triggered a subsequent free fall in the shares of the Silvergate Bank, the Signature Bank, and the First Republic Bank. Then, on March 17, Credit Suisse shares crashed. Despite a US$54bn lifeline from theSwiss National Bankon  March 19, the bank collapsed and was ‘acquired’ by UBS for ~US$3bn. This banking crisis could create a weakness in corporate balance sheets more generally. Especially in MedTechs that have borrowed heavily in an era of low interest rates and cheap money, and now might be challenged by higher rates, economic stagnation, and rapidly advancing software driven healthcare technologies. These include, 3D bioprinting, robotics, virtual reality (VR), biometric devices and wearables, digital therapeutics, and telemedicine. All are positioned to influence the shape of healthcare over the next decade by: (i) changing the way healthcare is delivered, (ii) improving patient outcomes, (iii) lowering healthcare costs, (iv) increasing access to care, and (v) creating new business models as value shifts from hardware to software. Should the banking collapse be a warning to traditional MedTechs whose preferred growth strategies have been debt financed acquisitions of near adjacent companies with physical product offerings optimised for hospitals?
 
In this Commentary

This Commentary explores the potential vulnerability of some MedTechs that have taken advantage of the recent period of low interest rates and cheap money to pursue growth strategies dominated by the acquisition of near adjacent companies, and have not balanced this with investments in innovative technologies. These may not fit neatly into their existing product portfolios and business models but are positioned to have a significant influence on the medical technology industry and healthcare systems over the next decade. Such technologies include: 3D bioprinting, robotics, virtual reality (VR), biometric devices and wearables, digital therapeutics, and telemedicine. Before describing these, we briefly outline the causes of the recent banking crisis and suggest how it might signal a weakness in corporate balance sheets more generally.
 
The demise of SVB

Founded in 1983, headquartered in Santa Clara, California, USA, SVB was the preferred bank of the large and rapidly growing tech sector, and it quickly grew to become the 16th largest bank in America. Tech companies used SVB to hold their cash for payroll and other business expenses, which resulted in a significant inflow of deposits. Banks only keep a portion of such deposits as cash and invest the rest. Like many other banks, SVB invested billions in long-dated US government bonds. [Bonds are debt obligations, where an investor loans a sum of money (the principal) to a government or company for a set period, and in return receives a series of interest payments (the yield). When the bond reaches its maturity, the principal is returned to the investor]. Bonds have an inverse relationship with interest rates; when rates rise, bond yields and prices fall. During the past decade of historically low interest rates, bonds became a preferred investment vehicle. SVB’s problem arose when central banks throughout the world increased rates to curb inflation, partly caused by the hike in energy prices following the Ukraine war. For instance, in 2022, the American Federal Reserve raised interest rates seven times; from ~0 to 4.5%. As interest rates rose, SVB’s large bond portfolio lost money and the bank was forced to sell its bonds at a loss. On March 8, SVB announced a US$1.75bn capital raise to plug the gap caused by the sale of its loss-making bonds. This alerted customers to SVB’s financial challenges. They started withdrawing their deposits, which triggered a run on the bank.
MedTech growth strategies

Sudden hikes in interest rates may sound alarm bells for some traditional MedTechs that have pursued debt financing to acquire near adjacent companies rather than invest in R&D to develop disruptive technologies and innovative offerings. While R&D is a critical component of the industry, it is a complex and costly process, which often takes years to yield a product that can be marketed and generate revenue. By contrast, M&A activity allows companies to acquire existing products and technologies that have already been developed and tested, which reduces the risk and uncertainty of R&D. Further, with the industry becoming increasingly competitive, MedTechs need to achieve scale and market share to remain relevant. This can be achieved by the acquisition of near adjacencies, which allows acquirers to quickly expand their product portfolios, geographic reach, and customer base.

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Have diversified medical technology companies blown their competitive advantage?


Can elephants be taught to dance?

 
The recent era of low interest rates and cheap money reinforced debt financed acquisitions as a growth strategy. Between 2011 and 2021, there were 2,365 M&A deals in the MedTech industry globally. However, to the extent that MedTechs focussed their acquisitions on near adjacencies, they may have missed out on acquiring innovative technologies positioned to reshape the industry over the next decade. This is because disruptive technologies often come from outside a company's core business and may not be immediately obvious to its leaders. Further, indebted companies facing high interest rates, might feel obliged to increase their revenues, which could result in them doubling down on cost cutting and optimizing their legacy products rather than investing in innovative R&D to drive revenue growth. Companies that adopt such business models could be at risk of having a dearth of technologies to drive future growth in a significantly more competitive healthcare ecosystem and challenging financial markets.
 
Disruptive technologies

The disruptive technologies we mention above shift the needle from hardware to software, from the needs of organizations to the needs of patients. While most of these are in their infancy, they all have the potential to transform healthcare in the next decade by providing new treatments for a variety of diseases and injuries, advancing drug development, enabling personalized medicine, reducing healthcare costs and improving medical training and surgical procedures. Let us explore these in a little more detail.

3D bioprinting

Three dimensional (3D) bioprinting is a relatively new technology, which involves the creation of 3D structures using living cells and holds promise for the future of regenerative medicine. The technology is an additive manufacturing process like 3D printing, which uses a digital file as a design to print an object layer by layer. However, 3D bioprinters print with cells and biomaterials, creating organ-like structures that let living cells multiply.

In 1999, a group of scientists at the Wake Forest Institute for Regenerative Medicine led by Anthony Atala, a bioengineer, urologist, and pediatric surgeon, created the first artificial organ with the use of bioprinting. Soon afterwards, bioprinting companies like Cellink (Sweden), Allevi (Italy), Regemat (Spain), and RegenHU (Switzerland) evolved. In 2010, Organovo, a biotech company founded in 2007 and based in San Diego, California, USA, introduced the first commercial bioprinter capable of producing functional human tissues that mimic key aspects of human biology and disease. In 2014, the company was the first to successfully engineer commercially available 3D-bioprinted human livers and kidneys. In 2019, researchers at Rensselaer Polytechnic Institute, New York, USA developed a way to 3D bioprint living skin, complete with blood vessels. Also in 2019, researchers at Tel Aviv University in Israel announced the creation of a 3D bioprinted heart using a patient's own cells. Today, 3D bioprinting is used to create a wide range of tissues and organs, including skin, bone, cartilage, liver, and heart tissue.
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One of the most promising applications of 3D bioprinting is the creation of replacement organs using a patient's own cells. This could potentially eliminate the need for organ donors and reduce the risk of rejection. The technology also can be used to create complex tissues and structures, such as blood vessels, skin, and bone, which could be useful for patients with severe burns or injuries, as well as those with degenerative diseases. Further, 3D bioprinting can be used to create realistic models of human tissues for drug development and testing, which could help to reduce the cost and time associated with drug development, as well as reduce the need for animal testing. 3D bioprinting could enable the creation of customized implants and prosthetics that are tailored to a patient's unique anatomy.

According to findings of a 2023 report by MarketsandMarkets, in 2022, the global 3D bioprinting market was ~US$1.3bn, and expected to grow at a compound annual growth rate (CAGR) of ~21% and reach >US$3bn by 2027.
Robotics

Medical and surgical robotics have a relatively short history. The first robot-assisted surgical system, the PUMA 560, [Programmable Universal Machine for Assembly], was developed in 1985 by the engineering firm Unimation, and used to perform a neurosurgical biopsy. A decade later, in 1994, the FDA approved the first robotic system for laparoscopic surgery, the Automated Endoscopic System for Optimal Positioning (AESOP), which was superseded in 2001 by the ZEUS Robotic Surgical System. In the late 1990s and early 2000s, researchers began exploring miniature in vivo robots for minimally invasive procedures. In 2000, the first robotic system designed for spinal surgery, SpineAssist, was developed by Mazor Robotics, an Israeli company, which Medronic’s acquired in 2018. In the mid-2000s, researchers began developing robots for use in orthopaedic surgery. Perhaps the biggest influence on robotic surgery was made by  Intuitive Surgical, an American company founded in 1995. Intuitive developed the da Vinci Surgical System, which was approved by the FDA in 2000 and quickly became the most widely used surgical robot in the world. It has been used in millions of procedures across a wide range of specialities. Today, Intuitive Surgical is a Nasdaq traded company with a market cap of >US$84bn, annual revenues >US$6bn and >12,000 employees.
Medical and surgical robotics continue to evolve, with new technologies and applications being developed all the time. Such technologies offer the potential for more precise, efficient, and less invasive procedures, reduced operating times, improved accuracy, and fewer surgical complications. Demand for surgical robotics is increasing as are investments in robotic surgery companies and an increasing number of hospitals around the world are investing in robots. In the US, >250 hospitals use surgical robots for complex operations. Europe has also seen an increase in the number of hospitals that utilize robots for medical purposes. In 2016, there were over 7,000 medical robots in use globally, today there are >20,000.


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According to a Verified Market Research report, in 2021 the global market for medical robots was ~US$11bn and is expected to reach ~US$35bn by 2030. Scientists are developing the next generation of microbots, which are small enough to seamlessly travel through the human body performing repairs.
 
Virtual reality

The use of virtual reality (VR) in healthcare has been growing rapidly in recent years, but its history only dates from the early 1990s, when the first VR applications in healthcare focused on pain management and distraction therapy. In the late 1990s and early 2000s, researchers began exploring the use of VR for a wider range of medical applications, including surgical simulation, medical education, and mental health therapy. In recent years, the technology has been used in pain management, physical therapy, treatment of phobias and anxiety disorders, and to improve quality of life for hospice patients. During the Covid-19 pandemic, VR was used to help healthcare workers train for and cope with the challenges of the pandemic, as well as to provide virtual healthcare visits to patients who were unable to receive in-person care.

VR healthcare start-ups have attracted attention from major players. For example, in February 2020, Medtronic acquired UK start-up Digital Surgery for >US$300m. Founded in 2013 by two former surgeons, Digital Surgery first made waves with an app to help train surgeons using a database of common procedures. It also developed VR software to train doctors as well as AI tools for surgeons in the operating room. OxfordVR is also a British VR start-up. Founded in 2017 by Daniel Freeman, Professor of Clinical Psychology at Oxford University, the company is focused on mental health applications and has successfully automated psychological therapy. Users are guided by a virtual coach instead of a real-life therapist, which allows the treatment to reach significantly more patients. Another notable VR start-up is Firsthand Technology, founded in 2016 and headquartered in California, USA.  The company's flagship product is a VR distraction therapy (VRDT) that offers immersive experiences designed to distract patients from the discomfort and anxiety associated with medical procedures. The company's offerings demonstrate the importance of addressing the psychological and emotional factors that impact health and well-being. In January 2020, Pear Therapeutics, a leader in digital prescriptions acquired Firsthand.

Over the next decade, expect VR to improve medical/surgical training by providing immersive, realistic simulations for medical students and health professionals, allowing them to practice procedures and techniques in a safe and controlled environment. In addition to helping patients to reduce pain and anxiety during medical procedures, VR can help to overcome barriers to care, such as distance and mobility, by providing virtual healthcare visits and remote monitoring of patients. Also, the technology is positioned to improve surgical planning. By providing surgeons with 3D models of patients' anatomy, allowing for more precise surgical planning, and reducing the risk of complications. Further, it can be used in physical therapy to improve patient engagement and motivation, leading to faster recovery times.

According to a 2021 Verified Market Research report, the VR healthcare market was valued at ~US$3bn in 2019, and is projected to reach ~US$57bn by 2030.
 
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Biometric devices and wearables

Biometric devices and wearable technologies aim to empower people with granular data that leads to actionable healthcare insights. It gives people the ability to collect their own health data and report them in a digital format to physicians, thus eliminating the need for in-person appointments for simple check-ups. Insurers and providers have also bought into wearable devices, relying on data collected from them to inform personalized health plans. Corporations too have adopted them to encourage healthy habits among employees working from home.
The use of biometric devices and wearables in healthcare has a relatively short, but influential history. In the early 2000s, the first commercial monitors were introduced, which allowed athletes to track their heart rates during exercise. The technology can provide a wealth of data about a patient's health, allowing healthcare providers to tailor treatment plans to individual patients, monitor chronic disorders, detect changes in real-time and intervene expeditiously. Biometric devices and wearables can help to detect early signs of illness or disease and can help patients to take a more active role in their own health and wellness. The technology has the potential to reduce the cost of care by enabling remote monitoring, preventing hospital readmissions, and reducing the need for in-person visits. Further, it can provide researchers with large amounts of patient data to facilitate AI-driven research into disease prevention and treatment.
 
One successful biometric device company is Fitbit, which was founded in 2007 and is headquartered in San Francisco, California, USA. Fitbit offers a range of wearable devices that track physical activity, heart rate, sleep patterns, and other biometric data. The company’s products include smartwatches, activity trackers, and wireless headphones that integrate with its mobile app and web-based platform to provide users with personalized health and fitness insights. The company has developed partnerships with insurers and healthcare providers to use its products as part of employee wellness programmes. Since its founding, the company has sold >120m devices. In 2019, Fitbit was acquired by Google for US$2.1bn, which is a testament to the value of biometric data and the potential of wearables to transform healthcare.
 
The Apple Watch is the other market leader. Its first edition, launched in 2010, included features for tracking physical activity, heart rate, and other health metrics. An upgraded version, released in April 2015, helped to establish the health tracking market, which led to the mass adoption of wearable technologies. From the outset, the Apple Watch was conceptualized as a device that would help people stay connected in less invasive ways than with smartphones. Each iteration since its inception has increased the watch’s focus on improving health and wellbeing. In 2018, it was approved by the FDA as a medical device capable of alerting users to abnormal heart rhythms. Today there are ~150m Apple Watch users.
 
Another leader in the wearable sensor market is Abbott Laboratories, which provides a range of services for diabetes and cardiology. In November 2018, the company received FDA clearance for its FreeStyle Libre, a glucose reader smartphone app. Oura Health, a Finnish company founded in 2013, has launched a health wearable product in the form of a small ring that tracks activity, heart rate, body temperature, respiratory rate, and sleep data. As the technology continues to evolve, biometric devices and wearables are likely to play an increasing role in healthcare by helping people to participate in their own health and wellness, improving medical outcomes, and reducing healthcare costs.
 
According to findings from a 2019 ResearchandMarkets report, the wearable health technology industry is projected to see a CAGR >25% between 2020-2027, and annual sales are expected to reach ~US$60bn by 2027.
 
Digital Therapeutics
 
Digital therapeutics (DTx) are software-based interventions that aim to prevent, manage, or treat medical conditions by modifying patients’ behaviours. The therapeutics are delivered through mobile apps, virtual reality, or digital platforms. Their use in healthcare is growing, and the history of DTx can be traced back to the late 1990s when the first digital intervention for substance abuse was developed. In the early 2000s, a few digital interventions were introduced to manage chronic conditions such as diabetes and hypertension. However, it was not until the 2010s when the use of DTx started to gain momentum, driven by technological advances, the growing prevalence of chronic diseases, and the need for more cost-effective healthcare solutions.
 
In the November 2020 edition of Scientific America, DTx were ranked in the top-10 emerging technologies, which have demonstrated an ability to prevent and treat a variety of chronic conditions. In September 2017, Pear Therapeutics digital software programme, reSET, became the first FDA-approved DTx for substance use disorders (SUD) involving alcohol, cocaine, marijuana, and stimulants. According to the US Centers for Disease Control and Prevention (CDC) >40m Americans, ≥12 years presented with SUDs in 2022. In 2020, Pear received FDA clearance for Somryst, an insomnia therapy app. The company has a pipeline of DTx offerings for a wide range of conditions, including multiple sclerosis, epilepsy, post-traumatic stress disorder and traumatic brain injury. In 2020, the FDA approved EndeavorRx, which is produced by Boston based Akili Inc and is the first DTx delivered as a video game for children with attention deficit hyperactivity disorder (ADHD). Omada Health, is another digital therapeutics start-up, founded in 2011 and headquartered in California, USA, which provides personalized coaching and support to individuals with chronic health conditions.

Given that DTx are evidence-based and personalized, they can be tailored to meet the unique needs of each patient. This individualized approach can lead to enhanced patient outcomes and improved quality of life. DTx are often more cost-effective than traditional therapies, as they eliminate the need for in-person visits and reduce the need for expensive medications. This could help to lower healthcare costs. Digital therapeutics can be accessed from anywhere, any time and on any device, making them particularly useful for patients in remote or underserved regions. This could help to improve access to healthcare for millions of people. DTx can be integrated with other healthcare technologies, such as wearables, mobile health apps, and electronic health records, to provide a comprehensive approach to healthcare. This could lead to improved coordination of care and better health outcomes. Further, DTx could bring about a shift in treatment paradigms and change the way we approach chronic diseases: instead of relying solely on medications, patients could use digital therapeutics to manage their conditions and improve their overall health.

The FDA has created a new classification for digital therapeutics, which is likely to make it easier for more DTx solutions and services to obtain regulatory approval. In a 2020 survey of MedTech leaders by Deloitte, a consulting firm, 63% of respondents agreed that DTx will have a significant impact on the industry over the next 10 years. A report by Grand View Research, suggested that the global digital therapeutics market was valued at US$4.20bn in 2021, and is estimated to grow at a CAGR of ~26% from 2022 to 2030. 

 
Telemedicine

The practice of using telecommunications and information technologies to provide remote medical services, has a history dating back to the early 20th century. In 1924, the first radiologic images were transmitted by telephone between two towns in West Virginia, USA. In the 1950s and 1960s, the technology began to advance, and the first video consultation between a patient and a physician was conducted. In the 1970s, NASA began using telemedicine to provide medical care to astronauts in space. In 2001, the Indian Space Research Organization successfully linked large city hospitals and healthcare centres in remote rural areas. With the development of the internet in 1990s, remote healthcare exchanges became more widespread, particularly in rural areas where access to medical services were limited. In 1993, the American Telemedicine Association (ATA) was founded to promote the use of the technology. Since then, telemedicine has continued to evolve and expand.
The Covid-19 pandemic led to a surge in telemedicine usage as healthcare providers looked for ways to provide care while minimizing in-person contact. Based on a survey by McKinsey, a consulting firm; before the pandemic in 2019, ~11% of US patients used telehealth services. After COVID, that number had grown to ~50%. Some estimates suggest that during the height of the pandemic, the number of telemedicine appointments increased by 5,000%. According to McKinsey’s, 76% of US consumers report that they are interested in using telehealth in the future as a way to complement in-person physician visits.In August 2020, digital health history was made with the merger of two of the largest publicly traded virtual care companies Teladoc and Livongo. The former, a multi-billion-dollar market leader in telemedicine founded in 2002, and the latter, a multi-billion-dollar market leader in remote patient monitoring. The deal created a US$38bn entity, which was the market’s first full-stack virtual health company. Today, virtual health is a rapidly growing field, and combines virtual physician visits, remote patient monitoring, chatbots, algorithms, and analytics.
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Over the next decade, AI-powered telemedicine tools are likely to become more prevalent, helping to streamline and automate many aspects of the care delivery process, such as triage, diagnosis, and treatment plans. Remote patient monitoring technologies are likely to become more advanced and widespread, allowing healthcare providers to monitor patients’ health and vital signs remotely, which can improve outcomes and reduce hospitalizations. Expect healthcare providers to increasingly work as part of virtual care teams, collaborating with other health professionals, including specialists, to deliver care to patients in real-time, regardless of location. Telemedicine will continue to improve access to care, particularly for underserved populations such as those in rural and remote areas, and those with limited mobility or poor transportation options. The technology will also facilitate more personalized and patient-centred care, as providers will be able to tailor care plans to the specific needs and preferences of individual patients.

According to a report by MarketResearchFuture, the current global telemedicine market size is valued at ~US$67bn and is expected to reach >US$405bn by 2030, exhibiting a compound annual growth rate of >22%.

 
Takeaways

We have described six evolving software driven technologies positioned to significantly influence healthcare systems in the next decade. Note that all are software driven and focused on patients to make care more personalized and sensitive to specific needs of individuals. Such technologies are in stark contrast to traditional medical devices, which overwhelmingly are physical devices designed to serve hospitals, rather than individual patients. Such a focus can lead to a lack of innovation, higher costs for patients, lower quality of care, and less personalized treatment options. A shift towards technology optimized to deliver patient-centered care is necessary to improve the quality of healthcare and ensure that patients receive the best possible outcomes. From our analysis it is not altogether clear whether traditional MedTechs are well positioned to achieve this.
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