The Pain of Recording Patient Risk Factors as Illuminated by Apixio (Part 1 of 2)

Posted on October 27, 2016 I Written By

Andy Oram is an editor at O'Reilly Media, a highly respected book publisher and technology information provider. An employee of the company since 1992, Andy currently specializes in open source, software engineering, and health IT, but his editorial output has ranged from a legal guide covering intellectual property to a graphic novel about teenage hackers. His articles have appeared often on EMR & EHR and other blogs in the health IT space. Andy also writes often for O'Reilly's Radar site (http://oreilly.com/) and other publications on policy issues related to the Internet and on trends affecting technical innovation and its effects on society. Print publications where his work has appeared include The Economist, Communications of the ACM, Copyright World, the Journal of Information Technology & Politics, Vanguardia Dossier, and Internet Law and Business. Conferences where he has presented talks include O'Reilly's Open Source Convention, FISL (Brazil), FOSDEM, and DebConf.

Many of us strain against the bonds of tradition in our workplace, harboring a secret dream that the industry could start afresh, streamlined and free of hampering traditions. But history weighs on nearly every field, including my own (publishing) and the one I cover in this blog (health care). Applying technology in such a field often involves the legerdemain of extracting new value from the imperfect records and processes with deep roots.

Along these lines, when Apixio aimed machine learning and data analytics at health care, they unveiled a business model based on measuring risk more accurately so that Medicare Advantage payments to health care payers and providers reflect their patient populations more appropriately. Apixio’s tools permit improvements to patient care, as we shall see. But the core of the platform they offer involves uploading SOAP notes, usually in PDF form, and extracting diagnostic codes that coders may have missed or that may not be supportable. Machine learning techniques extract the diagnostic codes for each patient over the entire history provided.

Many questions jostled in my mind as I talked to Apixio CTO John Schneider. Why are these particular notes so important to the Centers for Medicare & Medicaid Services (CMS)? Why don’t doctors keep track of relevant diagnoses as they go along in an easy-to-retrieve manner that could be pipelined straight to Medicare? Can’t modern EHRs, after seven years of Meaningful Use, provide better formats than PDFs? I asked him these things.

A mini-seminar ensued on the evolution of health care and its documentation. A combination of policy changes and persistent cultural habits have tangled up the various sources of information over many years. In the following sections, I’ll look at each aspect of the documentation bouillabaisse.

The financial role of diagnosis and risk
Accountable care, in varying degrees of sophistication, calculates the risk of patient populations in order to gradually replace fee-for-service with payments that reflect how adeptly the health care provider has treated the patient. Accountable care lay behind the Affordable Care Act and got an extra boost at the beginning of 2016 when CMS took on the “goal of tying 30 percent of traditional, or fee-for-service, Medicare payments to alternative payment models, such as ACOs, by the end of 2016 — and 50 percent by the end of 2018.

Although many accountable care contracts–like those of the much-maligned 1970s Managed Care era–ignore differences between patients, more thoughtful programs recognize that accurate and fair payments require measurement of how much risk the health care provider is taking on–that is, how sick their patients are. Thus, providers benefit from scrupulously complete documentation (having learned that upcoding and sloppiness will no longer be tolerated and will lead to significant fines, according to Schneider). And this would seem to provide an incentive for the provider to capture every nuance of a patient’s condition in a clearly code, structured way.

But this is not how doctors operate, according to Schneider. They rebel when presented with dozens of boxes to check off, as crude EHRs tend to present things. They stick to the free-text SOAP note (fields for subjective observations, objective observations, assessment, and plan) that has been taught for decades. It’s often up to post-processing tools to code exactly what’s wrong with the patient. Sometimes the SOAP notes don’t even distinguish the four parts in electronic form, but exist as free-flowing Word documents.

A number of key diagnoses come from doctors who have privileges at the hospital but come in only sporadically to do consultations, and who therefore don’t understand the layout of the EHR or make attempts to use what little structure it provides. Another reason codes get missed or don’t easily surface is that doctors are overwhelmed, so that accurately recording diagnostic information in a structured way is a significant extra burden, an essentially clerical function loaded onto these highly skilled healthcare professionals. Thus, extracting diagnostic information many times involves “reading between the lines,” as Schneider puts it.

For Medicare Advantage payments, CMS wants a precise delineation of properly coded diagnoses in order to discern the risk presented by each patient. This is where Apixio come in: by mining the free-text SOAP notes for information that can enhance such coding. We’ll see what they do in the next section of this article.