I'm an American physician. I've practiced in a couple of specialties and know the insurance billing drill (or should I say game) pretty well. Medical record sy

I'm an American physician. I've practiced in a couple of specialties and know the insurance billing drill (or should I say game) pretty well. Medical record systems, as other comments point out, have been constructed for the benefit of administrators, recording clinical data is mainly to support administrative needs. In reality manifestations of a given illness vary continuously across a wide spectrum. It means patients with the same diagnosis have differing sets of symptoms and course of illness. As I like to say it, "no two patients have exactly the same disease." Official disease classification schemes embody the "splitter" model which attempts to fit continuous data into discrete categories. Of course sharp-edged distinctions suit purposes like billing and other management operations. However diagnosis is often ambiguous, mixed or multiple, but no matter what categorical diagnoses must be assigned. Unsurprisingly doctors are biased to select the choices with the greatest reimbursement, and "stretching" criteria to cover the patient's condition (or vice versa) is not at all uncommon. (Also, there's not assigning diagnoses where it might negatively impact payment.) EHR clinical data follows the discrete assumptions of the system design. This can create an impedance mismatch between clinician observations and data input. This may be troublesome, for example, in specialties (behavioral health) where data is complex with many overlapping subtle but meaningful variations. I've often received records after a patient has been hospitalized. More than not it's difficult to understand the course of treatment as there's no coherent summary or narrative description provided. The "pile of data" literally transcribed from the EHR isn't very useful to human readers. To be sure information like lab reports, etc., are good to have, but the marginalized human-to-human element is troublesome. EHR clinical data failing to serve ML purposes could only point to problematic EHR system design. Though I've been aware of EHR limitations, I wouldn't have guessed about ML issues. The article taught me something about the problem that exists. Now it remains to be seen what can or will be done about it.