This article reports the results of a study that follows a multi-year, pragmatic clinical trial in a real world, community based primary care. What started as a quality project evolved to include the development and deployment of Artificial Intelligence (AI) decision support to guide medication choices when treating hypertension (HTN). Results show that primary care physicians significantly improved HTN outcomes as compared to the national average of success.

All patients with a hypertension diagnosis were tracked across three years–including the COVID pandemic period. Of the 13, 441 HTN patients 94% had a blood pressure “at goal” (i.e. less than 140/90)–as of their last clinician visit. The last published study of US blood pressure control which occurred prior to the pandemic was 44%.

Because the use of AI in primary care is novel as of this writing, the concept of AI is often unfamiliar to many practicing clinicians and medical group leaders. This paper defines a threshold between simpler versions of decision support for clinicians versus AI level decision support. The paper also distinguishes between sources of AI including machine learning and other forms of AI that do not involve machine learning.

AI in medicine

AI is suited to a wide variety of use cases in healthcare (e.g., basic science research, genetics, epidemiology, pharmacological innovation, diagnosis and treatment). This project involves AI as a clinical decision support.

Not all decision support qualifies as AI. To date, most computer-based decision support in primary care has been based upon basic computations and “if/then” logic. Electronic Health Records (EHRs) are programmed to remind practitioners that an A1c or colonoscopy is overdue, for example. An EHR may point out that a patient with hypertension has not yet had an ACE/ARB prescribed. In some EHRs, a person must create the reminder and in other instances, the computer system is programmed to track one or more variable and send a message about the result. Generally speaking, basic decision support is not dynamic.

AI begins when decision support is dynamic in that its actions or recommendations are based upon multiple–potentially hundreds or more–variables, each with potentially many different values. Changes in a value in even one variable can result in changes to the entire decision model and result. Thus, the relationship of the variables is dynamic – as one changes, the entire solution can change. For example, the AI solution for recommending precise HTN medications includes over 300 million permutations, and for Heart Failure with Reduced Ejection Fraction (HFrEF) solution there are over 850 million permutations. This is far beyond the capabilities of “if/then” logic in part because no one could map all those decision points.

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