Healthcare Diagnostics is a Recession-Proof Sector
Written by DigitalDx Fellows Melania Antoszko, Isha Godara, Alex Behrens
Introduction
The Healthcare Diagnostics sector refers to the use of technology to provide early and accurate diagnostics of various kinds of illnesses. Companies operating in the diagnostics industry, and we use those of DigitalDx Ventures portfolio for the purpose of this analysis, are focusing on using advanced technology, data, and artificial intelligence to provide less invasive, accurate, and early diagnosis for illness. Investments, industry trends, and where the smart money is going, reflect this transformation.
Healthcare and Recession
The main industries that performed well during the financial 2008 recession and the current COVID-19 crisis include staple goods, discount stores, and healthcare. Some examples of such companies are Walmart, Dollar Tree, and Amgen Inc, respectively. In this paper, we will be focusing further on the companies operating in the healthcare sector. This sector includes diagnostics, pharma, health insurance, biotech, and more…
Some of the reasons why healthcare stocks are recession-proof include:
- Everyone gets sick, even (or moreso) during a recession or pandemic. Companies experiencing growth in the healthcare sector are finding innovative solutions to burning problems such as using robotics for surgery. For investors, there is a strong incentive to invest early in companies developing innovative technologies as returns can be incredibly high.
- As the population is aging in most of the developed countries, the elderly are facing various comorbid symptoms and chronic diseases. This is leading to a continuous and increasing demand for healthcare services regardless of what’s happening on a world scale, and strong performance in times of recession or a global pandemic as we are currently experiencing.
- Artificial Intelligence is now being embraced in many areas, but none more so than healthcare. Now, with more data than they know what to do with, health care practitioners are embracing technologies to help them with various elements of patient care such as diagnostics, surgeries, monitoring, etc. This adoption has had a huge financial impact on the companies in the healthcare sector providing technological tools for holistic patient care.
COVID-19 has created a very real health threat around the globe and the financial markets have stumbled. However, for some stocks, the effect has been the opposite. Not surprisingly, the similarity in these stocks is that they all belong to the healthcare sector and the companies involved are working in innovative diagnostics methods and the discovery of new drugs. Some of the companies such as CODX, MRNA and REGN’s stock performance has been 1889.5%, 33%, and 23.8% respectively. The current health epidemic has brought attention to the need for research and development in healthcare to cope with current health-related challenges and the ones that may arise in the future. Due to this, and the role AI is now playing in the insights into clinical data, imaging, and speed to market, it is safe to assume that the healthcare sector will be a very financially secure place for the next 5–10 years.
AI and Diagnostics
Within the last ten years, there has been a phase change in the way that diagnosis is done. Previous to this era, most medical information was analog with handwritten notes on patient charts and individual processing by a doctor. In fact, our most powerful data collection tools were doctors. Due to this analog nature of diagnostic data, it was nearly impossible to collect and categorize without breaking HIPPA procedures. Fast forward to today, a new paradigm of diagnosis technology has been enabled by advances in AI. New machine learning models are iterative and able to improve diagnostic accuracy simply by receiving more data. Each new patient that an AI diagnostic model interacts with has the opportunity to improve the model. This turns machine learning applications focused on specific diagnoses into flywheels; the more patients that get diagnosed, the more accurate the diagnosis model becomes.
The AI diagnostics space has two main trends, imaging and pattern matching. The first, imaging, uses advances in computer vision technology to replace expensive sensors in areas such as oncology and optical diagnoses. The value added by this technology is tremendous. Not only does computer vision enable more reliable diagnoses, but it enables a scale that was not before possible. In effect, the limiting factor of an imaging diagnosis has changed from a doctor’s eyeball to a camera’s resolution, multiplied by a number any one person could not review in a lifetime. Additionally, of course, cameras are a lot easier to scale than doctors.
One company in the DigitalDX portfolio, Optina Diagnostics, uses a specific computer vision technique called hyperspectral imaging to diagnose Alzheimer’s up to ten years before symptoms of brain deterioration occur. The data generated by the imaging is then plugged into a machine learning model that identifies beta amyloid and tao buildup in the brain. These digital biomarkers form the basis of the Alzheimer’s diagnosis. Optina has over 90% accuracy compared to traditional test of 60-70% and costs a little over 10% of the cost of traditional tests.
The other recent trend is traditionally qualitative diagnosis methods becoming digitized. New machine learning techniques are driving this revolution in pattern matching technologies. In mental health, diagnoses moving from qualitative methods to quantitative is especially apparent.
Trayt, a Digital DX portfolio company, is an embodiment of this trend. Trayt uses a digital platform to diagnose neurological disorders. The platform is the perfect example of an AI flywheel. After Trayt diagnoses a patient, the system will continue to track the patent across the treatment process. If a patent shows unexpected symptoms or responds poorly to a treatment, Trayt will factor that into their model so each new patient has a compounding effect on accuracy.
So not only does Trayt learn from its mistakes and improve outcomes for patients, it expands the market of mental health diagnosis into the many more nuances of the diseases, by including comorbidity data into the stratification of illness to better target treatment. As it stands now, mental health diagnoses are expensive, backlogged, and prone to human judgment. Just due to the nature of the digital diagnosis platform, Trayt alleviates these fears, meaning that mental health diagnoses can reach more patients and intervene earlier than ever before.
Industry Trends
Healthcare diagnostics is a vital area in healthcare that has the potential to help improve the patient’s health outcomes and reduce costs. In the US, it has been estimated that earlier detection of certain cancers could save around $26 billion USD per year.
Diagnostics can also contribute to better health outcomes. The Health Distributors Association has found that appropriate diagnostic testing saves 56,000 patients from adverse health effects and saves 34,000 lives by ensuring more targeted treatment and intervention (HIDA, n.d.)
The market for Diagnostics enhanced by AI is growing rapidly. In 2019, the global market for AI diagnostics was valued at $378.4 million and is expected to grow to $1.98 billion by 2024 at a CAGR of 39.3%.
Drivers
Preventative care will continue to drive the diagnostics sector as payors seek to solve their rising cost structures and patients demand better quality of care. Additional externalities such as better medical adherence will create a positive feedback loop around health. Apart from core patient health, innovation around preventative care will be powered by alternative diagnostic techniques, made possible by large data sets, that can then be used more pervasively in the world.
Increasing spending on healthcare in the United States is expected to positively affect expenditure on industry services (IBISWorld, 2020). American health spending is projected to grow at an average rate of 5.5% per year for 2018–27 and to reach nearly $6.0 trillion USD by 2027.
Another driver of the diagnostics sector is the aging population. The number of individuals aged 50 and older is anticipated to grow at an annualized rate of 1.6% over the five years to 2019. There will likely be a higher demand for laboratory testing due to the older demographic’s high prevalence of chronic illnesses such as diabetes, congestive heart failure, chronic obstructive pulmonary disease, and arthritis, which will require frequent monitoring and testing by healthcare providers (IBISWorld, 2019).
According to the World Health Organization, chronic disease prevalence is expected to rise by 57% by the year 2020. As the middle class grows, people are adopting a more sedentary lifestyle. Increasing awareness of chronic diseases is estimated to drive the global chronic disease management market growth in the future. Diagnostics and home monitoring will be able to deliver services to improve patient health at a relatively lower and affordable cost.
Opportunities
In this age of Big Data and AI, the diagnostic industry has many opportunities to grow and as it does, issues like inconsistent data will be greatly reduced by technology. Facing physical limitations, many diagnostic applications that use radiological image can use AI in a way that is ingestible for search, analytics, and other technology and data solutions (Joanna Zhu, The Financial Upside of Hard-Science Female Founded Companies). AI can instantly ingest product images to consolidate and combine inconsistent data, classify products using standardized taxonomy, generate contextual identifiers and tags, and represent visual information (organs, cells). Through AI, issues of inconsistent data can be minimized.
Furthermore, data can be used by healthcare providers and diagnostic companies to detect patterns of diseases and make it available to health information providers who can use it to provide patients with personalized care. Machine learning is particularly well-suited for big-data analysis and is responsible for the bulk of today’s AI research, investment, and product development. Machine learning is particularly well-suited for medical diagnoses for the same reasons it became useful in the first place: an abundance of clinical data and cheap computing power has allowed researchers to train ML to instantly recognize patterns and pathologies that humans are incapable of detecting quickly.
In emerging markets, there are many new opportunities for diagnostic companies. As Grand View Research has found, emerging markets such as China and India are demanding healthcare services because the unmet clinical testing needs are so great. This has resulted in lucrative opportunities for the growth of the laboratory testing market in these countries.
Exits
Based on the SVB Healthcare Investments and Exits 2020 report, the US healthcare venture fundraising set a record high in 2019, reaching $10.7 billion USD, a 10% increase over the previous year. While Series A investment in the dx/tools sector decreased, the number of deals increased. Recently, dx/tools companies have exited through issuing stocks by an Initial Public Offering (IPO) as M&A activity decreases.
Despite mixed pre-money IPO step-ups, both Twist (.6x) and 10X (2.6x) showed strong post- IPO performances (Twist +57% and 10X +100%).
Conclusion
It has become evident that diagnostics companies serve two key purposes. First, they are a good financial investment even during economic downturns because people require efficient healthcare solutions and the innovative diagnostic companies provide early investors an opportunity to make significant gains in the future. Secondly, developed countries are experiencing an aging population often accompanied by chronic illnesses. The use of innovative tools such as AI in diagnostics will help in providing early, accurate diagnosis and efficient monitoring of the patients leading to better health care outcomes for them.
