{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T17:56:52Z","timestamp":1782842212857,"version":"3.54.5"},"reference-count":25,"publisher":"JMIR Publications Inc.","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JMIR Med Inform"],"abstract":"Abstract<\/jats:title>\n \n Background<\/jats:title>\n With the capability to render prediagnoses, consumer wearables have the potential to affect subsequent diagnoses and the level of care in the health care delivery setting. Despite this, postmarket surveillance of consumer wearables has been hindered by the lack of codified terms in electronic health records (EHRs) to capture wearable use.<\/jats:p>\n <\/jats:sec>\n \n Objective<\/jats:title>\n We sought to develop a weak supervision\u2013based approach to demonstrate the feasibility and efficacy of EHR-based postmarket surveillance on consumer wearables that render atrial fibrillation (AF) prediagnoses.<\/jats:p>\n <\/jats:sec>\n \n Methods<\/jats:title>\n We applied data programming, where labeling heuristics are expressed as code-based labeling functions, to detect incidents of AF prediagnoses. A labeler model was then derived from the predictions of the labeling functions using the Snorkel framework. The labeler model was applied to clinical notes to probabilistically label them, and the labeled notes were then used as a training set to fine-tune a classifier called Clinical-Longformer. The resulting classifier identified patients with an AF prediagnosis. A retrospective cohort study was conducted, where the baseline characteristics and subsequent care patterns of patients identified by the classifier were compared against those who did not receive a prediagnosis.<\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n \n The labeler model derived from the labeling functions showed high accuracy (0.92;\n F<\/jats:italic>\n 1<\/jats:sub>\n -score=0.77) on the training set. The classifier trained on the probabilistically labeled notes accurately identified patients with an AF prediagnosis (0.95;\n F<\/jats:italic>\n 1<\/jats:sub>\n -score=0.83). The cohort study conducted using the constructed system carried enough statistical power to verify the key findings of the Apple Heart Study, which enrolled a much larger number of participants, where patients who received a prediagnosis tended to be older, male, and White with higher CHA\n 2<\/jats:sub>\n DS\n 2<\/jats:sub>\n -VASc (congestive heart failure, hypertension, age \u226575 years, diabetes, stroke, vascular disease, age 65-74 years, sex category) scores (\n P<\/jats:italic>\n <.001). We also made a novel discovery that patients with a prediagnosis were more likely to use anticoagulants (525\/1037, 50.63% vs 5936\/16,560, 35.85%) and have an eventual AF diagnosis (305\/1037, 29.41% vs 262\/16,560, 1.58%). At the index diagnosis, the existence of a prediagnosis did not distinguish patients based on clinical characteristics, but did correlate with anticoagulant prescription (\n P<\/jats:italic>\n =.004 for apixaban and\n P<\/jats:italic>\n =.01 for rivaroxaban).\n <\/jats:p>\n <\/jats:sec>\n \n Conclusions<\/jats:title>\n Our work establishes the feasibility and efficacy of an EHR-based surveillance system for consumer wearables that render AF prediagnoses. Further work is necessary to generalize these findings for patient populations at other sites.<\/jats:p>\n <\/jats:sec>","DOI":"10.2196\/51171","type":"journal-article","created":{"date-parts":[[2024,4,10]],"date-time":"2024-04-10T03:24:16Z","timestamp":1712719456000},"page":"e51171-e51171","source":"Crossref","is-referenced-by-count":4,"title":["Scalable Approach to Consumer Wearable Postmarket Surveillance: Development and Validation Study"],"prefix":"10.2196","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1349-8237","authenticated-orcid":false,"given":"Richard M","family":"Yoo","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1510-7567","authenticated-orcid":false,"given":"Ben T","family":"Viggiano","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1230-367X","authenticated-orcid":false,"given":"Krishna N","family":"Pundi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9316-5768","authenticated-orcid":false,"given":"Jason A","family":"Fries","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6835-1139","authenticated-orcid":false,"given":"Aydin","family":"Zahedivash","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9945-3629","authenticated-orcid":false,"given":"Tanya","family":"Podchiyska","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5312-4451","authenticated-orcid":false,"given":"Natasha","family":"Din","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9385-7158","authenticated-orcid":false,"given":"Nigam H","family":"Shah","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1010","published-online":{"date-parts":[[2024,4,4]]},"reference":[{"issue":"20","key":"R1","doi-asserted-by":"publisher","first-page":"1909","DOI":"10.1056\/NEJMoa1901183","article-title":"Large-scale assessment of a smartwatch to identify atrial fibrillation","volume":"381","author":"Perez","journal-title":"N Engl J Med"},{"issue":"19","key":"R2","doi-asserted-by":"publisher","first-page":"1415","DOI":"10.1161\/CIRCULATIONAHA.122.060291","article-title":"Detection of atrial fibrillation in a large population using wearable devices: the Fitbit Heart Study","volume":"146","author":"Lubitz","journal-title":"Circulation"},{"issue":"9","key":"R3","doi-asserted-by":"publisher","first-page":"1359","DOI":"10.1093\/jamia\/ocaa137","article-title":"Clinical evaluation and diagnostic yield following evaluation of abnormal pulse detected using Apple Watch","volume":"27","author":"Wyatt","journal-title":"J Am Med Inform Assoc"},{"issue":"6","key":"R4","doi-asserted-by":"publisher","first-page":"1040","DOI":"10.1093\/jamia\/ocac009","article-title":"Will Apple devices\u2019 passive atrial fibrillation detection prevent strokes? estimating the proportion of high-risk actionable patients with real-world user data","volume":"29","author":"Feldman","journal-title":"J Am Med Inform Assoc"},{"key":"R5","unstructured":"Device classification under section 513(F)(2)(De Novo). 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