Making Health Care Human Again
THE CURRENT U.S. HEALTH CARE PICTURE is pretty bleak: more than 12 million serious diagnostic errors each year, a third of the $3.6 trillion spent attributed to waste, reduction in life expectancy for what will be three years in a row, and peak levels of physician burnout, depression, and suicide. That’s all happening at a time when there is more medical data per individual than ever, imagined with wearable sensor physiology, scan anatomy, DNA sequencing, gut microbiome biology, just to name a few layers. Enter deep-learning A.I., with neural networks that will impact every type of clinician, from helping to accurately read scans, slides, skin lesions, eyegrounds, and more, to health systems, promoting the use of remote monitoring that ultimately obviates the need for regular hospital rooms, and at the consumer level, by providing a virtual medical coach to better manage or even prevent diseases. It’s still early in the integration of A.I. into medical practice, with far more hype than validation. But it’s our best shot to deal with all of the formidable challenges: to use the wealth of data to reduce errors and waste, and the gift of time to markedly improve the clinician-patient relationship. — Eric Topol, MD, is the founder and director of the Scripps Research Translational Institute and author of the forthcoming book Deep Medicine.
Outsmarting Your Doctor
IN JUST THE PAST few years, there have emerged credible if still-in-the-works A.I.-powered technologies that can read radiology scans, identify tumors and track the spread of cancer, detect eye conditions using retinal imaging, flag dangerously abnormal potassium levels via a “bloodless blood test”, and otherwise assist with the tricky business of diagnosing, or even predicting, disease. Historically, diagnostic error rates have been put at 5% to 20%, though the rate is higher for some conditions, while the health care system is strained by doctor shortage and burnout—some things A.I. may be able to treat. —Erika Fry
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Reinventing Drug R&D
THE MEDICINE BUSINESS IS FILLED WITH TWISTS OF FATE. A drug may appear safe for humans in early studies with small groups of patients only to crash and burn in spectacularly expensive fashion in a large-scale clinical trial. In fact, return on investment for the largest biopharmaceutical companies in the U.S. fell to a dismal 3.2% in 2017, according to Deloitte. Which is why American companies like BERG and Roivant Sciences and U.K.-based Exscientia want to harness the power of A.I. to better deploy resources. BERG has partnered with major drugmakers like AstraZeneca and Sanofi Pasteur to use clinical data fed through an algorithm to identify promising biological targets for drugs and molecules that may be able to treat diseases like Parkinson’s. Sanofi is also analyzing huge amounts of data to gain a deeper understanding of why certain flu vaccines are effective for some people but not for others (a critical public health question considering last year’s devastating flu season). A.I. as a central medicine-making tool is still in its early stages. But the promise is clear: Being able to funnel pharma R&D efforts to the most promising targets can avoid a huge waste of time and money and, hopefully one day, lead to a more streamlined drug development process that benefits companies and patients alike. —Sy Mukherjee
Reversing Disease
AMERICA’S HEALTH care system has been criticized for favoring triage over cheaper, proactive approaches—and businesses pay the price in lost productivity and skyrocketing health care costs. Virta Health CEO Sami Inkinen is taking a different tack, using A.I. to prevent patients at risk for diabetes from developing the full-blown disease and, in early trials, even reversing Type 2 diabetes through its purely digital platform. Virta aims to shift customers’ lifestyles by connecting them with coaches who give them personalized recommendations on diet and other factors. It also provides digitally connected tools to measure blood sugar, ketones, blood pressure, and weight. Using a patient’s anticipated blood sugar and weight improvement, clinicians can prioritize patients in their hourly workflow. Virta is not alone: IBM’s Watson Health unit and medtech giant Medtronic are collaborating on an app called Sugar.IQ that offers similar tools. —Sy Mukherjee
Date: November 7, 2018
Source: Fortune
