Saturday, October 27, 2018

Non-Diagnosis: An Unappreciated but Critical Role for AI in Healthcare

Guest Blogger

William M. Sage
AI can help people understand and improve their health without forcing them along the “final common pathway” into the paid medical mainstream. 

Better health information technology has been a consensus goal of health policy experts for roughly two decades, with AI (“artificial” or “augmented” intelligence) the latest example of a potentially disruptive innovation in the informatics domain.  In particular, AI’s potential to improve diagnostic speed and accuracy has created palpable excitement in radiology and pathology for cancer detection and other clinical applications.  In 2015, the Institute of Medicine (now called the National Academy) devoted an entire consensus report titled “Improving Diagnosis in Health Care” to reducing diagnostic errors – an effort that continued an influential series of Academy critiques of the safety and quality of healthcare. 

It seems unobjectionable to argue that more accurate diagnosis will lead to more effective treatment.  As the IOM observed, “Getting the right diagnosis is a key aspect of health care -- it provides an explanation of a patient's health problem and informs subsequent health care decisions.“  However, diagnosis releases a cascade of additional effects that have largely gone unnoticed.  The IOM report failed even to acknowledge these “social meanings” of diagnosis, such as replacing uncertainty with explanation, inferring moral culpability or blamelessness, suggesting communicability or lack thereof, and creating or constraining opportunities for education, employment, insurance, and the like. 

Perhaps most importantly, the act of diagnosis channels the measurement and modification of health into conventional medical pathways, including an assurance and perhaps even an amplification of payment within the existing system.  A dispassionate assessment of why most health care information is produced, recorded, and exchanged has been lost in the enthusiasm for AI and similar innovations.  On the list: professional traditions such as the physician’s “H&P” (history and physical) and “SOAP notes” (subjective and objective data followed by the physician’s assessment and plan), clinical performance aids such as test results and consultation reports, and documentation to avoid inferences of professional negligence (malpractice).  But these are decidedly partial explanations.  More than anything else, the US healthcare system collects the information it needs to collect in order to get paid.

Threshold conditions must be met for payment to issue.  For health professionals and facilities, these include state licensing and receipt of Medicare provider credentials; for drugs, medical devices, and similar tangible technologies, they include FDA or other regulatory approval as well as certification of coverage from public and private insurers. 

But the sina qua non for payment remains the individual patient encounter, in which one or more diagnoses are rendered by a physician and one or more treatments planned or administered.  Each diagnosis is represented by a code (currently ICD-10) as is each billable treatment (CPT, which remains a proprietary set of designations owned and profitably licensed by the American Medical Association).  In many instances, pairing diagnosis with intervention triggers an avalanche of “claims” by providers and suppliers through “insurance” intermediaries that may or may not bear financial risk but ultimately receive a cut of the transactions they process.  This “final common pathway” exerts a powerful effect on the character and cost of the medical care system, including its reactive posture, its deference to physician authority, its technological bias, its profound inequities, and its colossal waste.  Consider the ironic vernacular for claims payment, “reimbursement”—a term that connotes happenstance and volunteerism rather than industrial structure and commercial competition even as healthcare spending approaches one-fifth of national economic output.

If one views diagnosis as the gateway to payment, even highly effective AI can induce frictions.  For example, applying unsupervised deep learning to sleep disorders not only confirmed that established categories of brain activity had an objective basis, but also drastically reduced the time, staffing, and equipment needed to perform and interpret sleep studies.  Unsurprisingly, sleep-related AI found few supporters among existing sleep specialists, who perceived a threat to their authority and revenue.  By contrast, sleep-related AI did find a market niche helping pharmaceutical companies perform at low cost the sleep studies sometimes required by the FDA as a condition of product approval.

This example illustrates a broader point.  The final common pathway of medical payment is professionally entrenched and safeguarded by regulatory battlements that are politically challenging to surmount.  Even in the medium term, therefore, informational innovations such as AI can improve the existing healthcare system only to the extent that system chooses to be improved. 

What AI can do, perhaps uniquely, is help people delay and sometimes avoid entering the final common pathway of medical payment while nonetheless offering reliable guidance about risk and mitigation of illness.  Put differently, for AI to be a truly revolutionary medical presence, it must enable “non-diagnosis.”  I do not limit the term to avoiding false positive results, which is a probable but not certain effect of incorporating AI into conventional diagnostics -- sensitive AI-based diagnostics may increase false positives in the short term insofar as real findings may nevertheless be clinically unimportant.   Rather, I use “non-diagnosis” as the elucidation of health-relevant information apart from the coding, workflow, claims, and associated payment that are the expected consequences of traditional diagnosis.

Non-diagnosis enables non-treatment – an inversion of the IOM quote offered above.  Decisions guided by AI-based risk analysis will mix conventional medical care with over-the-counter and informal therapies, lifestyle modification, and continued monitoring.  In this respect, AI may have unique potential to bridge the gap between medical care and health.  Because of professional, regulatory, and payment conventions, health IT innovators honor the conventional differentiation of medical devices from consumer health technologies such as “wearables” (Fitbit), even if both incorporate decision-support.  The distinction between the two has always been something of a fiction, and increasingly seems paradoxical from a policy perspective: shouldn’t health be the measurable outcome of medical care?
In sum, AI may well improve medical care, but its greater impact could be to serve “people” rather than “patients” – removing many health needs and the means to address them from an overly medicalized, professionally dominated, and claims-driven approach to gathering and acting on information.  That would be an even more dramatic change.

William M. Sage is James R. Dougherty Chair for Faculty Excellence, School of Law, and Professor of Surgery and Perioperative Care, Dell Medical School, at The University of Texas at Austin. You can reach him by e-mail at wsage at

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