Using Artificial Intelligence to Make Use of Electronic Health Records Less Painful-Fighting Fire With Fire
JAMA Netw Open . 2021 Jul 1;4(7):e2118298. doi: 10.1001/jamanetworkopen.2021.18298.
Richard J Baron
Sometimes it takes a computer to solve problems created by a computer. The travails of using electronic health records (EHRs) are well known and widely experienced. Literature identifies EHRs both as a major time sink for clinicians1 and as a major driver of burnout and frustration.2 Electronic health records have improved many things at the point of care, especially when used within the same practice or system. Being able to see trended laboratory or vital sign data in tabular or graphic format, seamless prescription ordering, and consistent access to concurrent data, such as telephone calls, generated in real time by colleagues in practice are all definite improvements over the old days of paper records. However, one thing that is clearly worse: dealing with scanned copies of old records. They are unindexed, unlabeled, and, once uploaded as unidentified PDFs, completely inscrutable. One waits for the PDF to load, hoping against hope that one is not about to see a fax cover sheet or a copy of an insurance preauthorization. The time and energy demanded for review is quite high, and one is tempted to ignore them altogether.
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