Healthcare 2020: Thoughts & Trends

Stoicism believes in forces driving the cosmos and humanity. So do I. In our industry, those forces shape trends and they are underlying every advance in healthcare, life sciences and technology.

Trends are vectors driving an entire ecosystem forward and creating opportunities for innovation. Here, I am expressing thoughts about contemporary issues and advances in healthcare, and I am trying to decode trends driving sciences and industry in 2020 and beyond.

1- Cost Pressure and Value Based Care

All healthcare systems, either in poor or prosperous countries, suffer from escalating costs. In some places like the USA, these rising costs aren’t even balanced by a clear improvement of health or lifespan. The traditional fee-for-services reimbursement model, which fuels the majority of healthcare systems, is undoubtedly reaching its limits. This cost pressure will be aggravated by the recent breakthrough in drug development. Highly effective therapies that cost hundreds of thousand dollars are now available and this is just the beginning… Within a liberal and capitalist world, the majority if not all stakeholders are incentivized by “crude” numbers. The ever-growing number of care providers, healthcare structures, costly medical devices and imaging equipments, combined with the growing number of people eligible for care through better accessibility and insurance coverage, will undoubtedly bankrupt the system. This is a global and pressing issue with already many failed attempts to tackle it. Some will limit the installation of healthcare providers, thus limiting the prescription power, while others will decrease the reimbursement rates or deny coverage for some patients. We have seen every possible scenario to address this “cost disease” with few successes. New generation of doctors rely more and more on advanced and costly tests to perform their duties, medicine relies on sophisticated tools to fulfil its mission, and the old-fashioned (and cost-effective) purist clinician is a vanishing species. One approach to address this cost pressure, and seems to be a consensus, is value based care (VBC). Paying for the value-added instead of the service provided. Paying care providers based on health outcomes rather than the volume of interventions.

It seems attractive and intuitive, however I have doubts on our ability to define, quantify and measure value.

It will certainly work for some diseases in some ecosystems, but it can’t be a universal solution.

My second concern is related to the biopharmaceutical and medical devices industry’s incentives and profitability. It is not impossible that these stakeholders are profitable and thriving thanks to the fee-for-services reimbursement model. What will happen if these industrial stakeholders realise that their growth and valuation is endangered by VBC ? This will constitute a major misalignment of incentives and could reduce the ability of our societies to innovate. The same concern applies to physicians. Physicians are, at least partly, incentivized by money. Doctors are paid for the work they perform. And some are pretty well paid, even if the health outcomes of their patients are not so good. Don’t get me wrong, I am not saying that some specialists are bad doctors, I am only pointing out that some specialties deal with weaker and sicker patients. Let’s take neurosurgery as an illustrative (and oversimplified) example. One of the highest paid specialties in Switzerland and North America, and one with the worst health outcomes because of the nature of the conditions treated. Now imagine a reimbursement model that pays well only for good clinical outcomes at 3, 6 or 12 months. The once highly paid neurosurgeons will become the least paid physicians and the residency application for neurosurgery might dwindle, putting the future of the specialty at risk.

What could be an alternative or a complementary solution to address this cost pressure ?

I believe one of the most effective ways to reduce cost is to invest in the education of physicians and care providers, train them more efficiently and faster, review and adapt every clinical guideline to make it lean and get inspiration from low income countries.

I also strongly believe that we can optimise better our human resources (yes, human, not artificial). I would invest heavily in up-training nurses and non-physician healthcare providers to deliver independently some aspects of care.

This includes up-training radiology technicians to report conventional radiographs and some cross sectional imaging (non-enhanced head CT, traumatic knee MRI…), perform ultrasounds in an emergency setting, up-training optometrists to perform basic ophthalmologic examinations, care and followups, up-training nurses to perform clinical consultations, basic surgical procedures and discharge patient on the front line. We should approach this re-shaping and up-training strategy with open mindedness and a holistic vision, regardless of dogmatic thinking, medical specialties lobbying, FOMR (Fear Of Missing Revenue) and turf wars.

2- Personalized Medicine

A second vigorous trend is Personalized or Precision Medicine. “Ensuring the right treatment for the right patient at the right time”; perfectly articulated by Roche, which developed a robust personalized healthcare strategy powered by technology.

Medicine has been the discipline of the “averages” (in the statistical sense). When we analyse diseases, we analyse them at the population level. When we analyse tumor samples, we analyse the average “DNA soup” coming from millions of individual cells. And finally, when we treat a patient, we prescribe a therapy to treat a diagnostic class, not a patient.

Today, the advances in the molecular and biological understanding of human physiology and diseases allow us to draw new lines between patients and not only between diseases groups. The blockbuster era is slowly replaced by personalized treatments, especially in oncology. Less than three years ago, the FDA approved the first tissue-agnostic cancer drug; a drug prescribed to patients based on specific genetic abnormalities rather than the location of the tumor in a specific organ. We are now able to cluster patients affected by the same “disease” into different molecular subtypes, and groups with distinct clinical outcomes and responses to therapies.

Therapies are also engineered for specific patients. Cell therapies are the perfect example of this phenomenon.

In a not so distant future, hospitals will become drug factories. Patients will be admitted, their cells collected and engineered on-site, to be re-injected as a curative treatment.

Bio-pharmaceutical companies involved in these therapeutic areas will be forced to reinvent themselves, moving from the current conventional factory model to distributed & platform-type drug development companies, offering manufacturing services within hospitals, close to the patient, and developing “processes first” rather than “products first”.

The concept of personalized medicine is the child of the Human Genome Project (1990–2003) and is based on the notion that through the understanding of the genetic code underlying health and disease states, we will be able to diagnose and treat diseases better and more precisely. The Human Genome Project was launched in 1990 and today we are realising that decoding the genome isn’t enough to fully understand the mechanisms of diseases and deliver truly transformative cures. We are just at the beginning of the human biology discovery endeavour.

3- Deeper Understanding and Finer Engineering of Biology

The major bottleneck in drug discovery & development and in medicine in general is the lack of understanding of the biological processes underlying health and disease states. I would like to remind here that last centuries’ major discoveries in therapeutics were mainly driven by randomness, luck and serendipity. When you do not understand the mechanisms behind a disease, you are like a blindfolded watchmaker trying to fix a broken complex Swiss watch. The better the understanding of biological processes, the higher the odds of developing effective cures.

Recent technology advances support a better understanding of biological processes. Advances in microscopy and live cell imaging, single cell analysis, genomics, epigenomics, transcriptomics & proteomics, combined with high throughput scalable technologies and computerized data and statistical analysis will enable a granular vision of biological processes and disease’s mechanisms.

These technological tools will help scientists going beyond the “mere” identification of discrete gene variations, and enabling a systematic analysis of multiple and intricate pathophysiological & physiological processes, hopefully identifying relevant and actionable drug targets.

The Human Genome Project mentioned above (1990–2003) brought us the first generation of biotechnology breakthrough and companies, but it is just the first chapter of this biotech journey. The granular understanding of biological processes underlying diseases will bring us the next generation of miracles.

I am not expecting that these progresses will roll out as smoothly as they are conceptualised and articulated in this article … For each advance in the understanding of a physiological mechanism, for each new selective modification of the genome and for each adjustment of a molecular pathway, we will realise how ignorant we are and how broad and complex is human biology. We might also activate deleterious mechanisms, potentially inducing new forms of possibly irreversible and nature-altering adverse events.

The path of deeper understanding and finer engineering of human biology we are taking is highly promising but hazardous and unpredictable, and we are certainly about to open a Pandora box.

4- Digital & Data

Digital technologies and advanced data analytics are prime trends in healthcare and life sciences in 2020.

We are currently living on a fertile soil for advanced digital data analytics thank to multiple enablers working in synergy to make a better use of knowledge:

• maturing electronic health record adoption
• widespread penetration of IT tools and consumer electronics
• wearables & sensors
• high performing data and statistical analytics
• growing computational power
• cloud technology
• educated workforce
• open-source ecosystems
• new entrants with fresh blood
• success stories in healthcare
• significant investments

Digital tools and data analytics allow a deeper understanding of diseases and health states at the patient level. The widespread adoption of smartphones and Apps, the growing use of sensors and the growing openness of the industry and regulators, enable us to draw precise phenotypic profiles for a multitude of diseases at the individual level.

With the granular exploration of human physiology as described above, and a near-continuous stream of digital and real world data, we are flooded by an infinite amount of information making robust multi-dimensional and high throughput data analysis essential to exploit this knowledge and generate actionable insights.

Data is also at the foundation of one of the most promising technologies in healthcare today: Artificial Intelligence (AI). Its performances are intimately tied to the quality of the data they are fed with. High quality curated datasets lead to powerful and accurate algorithms. It is not surprising that we have seen tangible results of AI applications in non-healthcare industries, because their data have been digitized and structured long before healthcare ! Same for life sciences and drug R&D; as chemical and experimental data are largely digitized, AI is accelerating drug discovery by mining thousand if not millions of data points to identify new drug candidates at an unprecedented pace. In hospitals and care delivery settings, the progresses are still lagging because of the lower level of digitalization, the heterogeneity of workflows and the complexity of data extraction and curation compared to other industries.

Hence, I strongly advise healthcare institutions to engage in structuring and curating their clinical data, either directly or through collaborations. This is the most valuable initiative a hospital can perform to fulfil its innovation ambitions.

Co-developing AI tools for specific tasks isn’t appealing enough because it remains narrow, local, hardly scalable and with a lower return on investment for healthcare facilities. On the other hand, structuring the clinical data and electronic health records and putting in place a framework and governance to exploit it, share it with other healthcare facilities and external stakeholders, will return significant clinical and monetary value in a near future. Building multi-modal databases covering clinical, molecular, biological, radiological, pathological and digital data will accelerate the pace of discoveries and unveil precious correlation and causation, laying the ground for personalized care and a more efficient development of drugs.

Talking about data in healthcare without discussing patient privacy and data ownership would be insufficient.

Data ownership remains a grey zone and many health technology companies are building their business models on the razor’s edge.

I believe data belongs to the patient, unless he says otherwise. I also believe that patients, if asked, will allow the secondary use of their data for research purposes, even if orchestrated by commercially driven startups. Having said that, data access remains the most challenging aspect of a health technology project. Even when regulation allows it after proper anonymization, healthcare facilities are not ready or willing to share data with external stakeholders.

There is a false belief that clinical data are a precious asset. Structured, curated and contextualised datasets are indeed precious (and rare) assets. Raw clinical data as they exist currently in the EHR within hospitals are useless for ambitious projects, and the effort and investment needed to make them ready for exploitation are significant and underestimated.

Data is also the strongest asset of Health Technology and AI companies today. Not the algorithms, not (anymore) the talents, nor the IP or the business model. Some companies have already been very successful in converting clinical data into insights and entrepreneurial successes. To name just the most famous, Flatiron Health and Foundation Medicine have built their success by collecting and structuring high quality oncology patients data. When assessing a healthcare AI company, one should analyse how they are accessing the data and how they can build around it a defensibility over the long run. Paige AI is the archetypal venture-backed company built on a privileged and defensible access to high quality clinical datasets. By securing an exclusive (and controversial) access to Memorial Sloan Kettering Cancer Center pathological data, they secured a near-unbeatable edge in the field of clinical decision support in pathology (microscopic analysis of tissues and tumors) and the lucrative oncology health technology business.

5- What else ? a Brief Pot Pourri…

The more I write, the more I see trends… and time is limited. So here is a list of final thoughts and trends that are shaping the industry.

New “non-conventional” entrants in healthcare such as Amazon, Walmart, JP Morgan, Apple and Google are an established trend, without having yet seen convincing results.

The adoption of advanced technology beyond EHR by care provider is also a slowly but steadily growing trend enabling an efficient and distributed provision of care and undoubtedly reducing costs.

Patient centricity is everywhere.

The workplace is more toxic than ever, adding deadly burden not only on worker but also on society as a whole. This has been, rightly so, coined as Social Pollution.

Cyber security is still a discreet trend in healthcare, and occupies an astonishingly low place in the priorities list of healthcare stakeholders… until carnage occurs.

Miniaturisation, robotisation and automatisation of laboratory tools, medical devices and manufacturing processes is a major trend.

Last but not least, interoperability of data is acknowledged as a major roadblock for innovation in healthcare but still lacks structural and political support. A governmental push to align stakeholders and break the deleterious status quo of data hijacking is needed.

The flow of thoughts is laminar and I am far from covering all the current trends and forces in healthcare, life sciences and technology… However, I have to close my laptop now… So I suggest to continue the discussion on Twitter !

Amine Korchi MD is a medical doctor specialized in medical imaging and image-guided interventions, and Venture Partner at Fusion Partners, a Corporate Venturing and Innovation Strategy Firm. He advises Corporates, Startups and Investment Funds in the field of healthcare, life sciences and technology.