Chronic disease is now the default, not the exception. Yet millions of patients with diabetes, hypertension, and kidney disease are either undiagnosed or poorly documented in the record, even when they are regularly touching the health system. 

And what that means for care, coding, and AI Electronic health records (EHRs)—often still called EMRs —were supposed to give us a complete, computable picture  of each patient.  In reality, when you look closely at problem lists, diagnosis codes, and multimorbidity measures, a tough pattern emerges:  The typical EHR view misses a huge chunk of chronic-condition complexity — easily 30–50%, depending on how you measure it.  Studies show that:  Problem list completeness for comm

(and What Providers Should Demand Instead)   Large language models (LLMs) are suddenly everywhere in healthcare.  Vendors promise auto-generated notes , “one-click” HCC documentation, and “AI scribes” that quietly listen in the background. Early studies and pilots show that AI scribes can reduce time spent on documentation and improve perceived efficiency and satisfaction, but they also raise questions about accuracy, safety, and over-reliance. PMC.NCBI.NIH For clinicians exh

Beyond “1.0” – how risk scores reshape care, staffing, and revenue Most clinicians know that RAF scores  matter for Medicare Advantage and other value-based contracts. Fewer have a clear mental model of what a RAF score actually is  and what it means for day-to-day clinical and operational decisions.  In under 10 minutes, here’s how to think about RAF like a vital sign for your clinic —not just a finance metric.  1. RAF in one sentence  CMS uses risk adjustment  to translate