Screening for undiagnosed atrial fibrillation using an electronic health record‒based clinical prediction model: clinical pilot implementation initiative
To our knowledge, this study is the first of its kind to demonstrate clinical application of an electronic health record-based AF predictive model. The newly documented diagnoses, however, did not solely result from implementation of UNAFIED. This non-invasive, inexpensive approach could be adopted by other sites wishing to proactively screen patients at elevated risk for AF. Other sites should verify the model's performance in their own settings and ensure compliance with evolving regulatory requirements where applicable.
Authors
- Randall W. Grout (ORCID: https://orcid.org/0000-0002-2911-6782)
- Sara A. Hart (ORCID: https://orcid.org/0000-0001-9793-0420)
- Baely M. DiRenzo
- Asad J. Torabi (ORCID: https://orcid.org/0000-0002-1011-0209)
- Chase King (ORCID: https://orcid.org/0000-0002-6242-9473)
- Todd A. Walroth (ORCID: https://orcid.org/0000-0001-6861-6408)
- Mohammad Ateya (ORCID: https://orcid.org/0000-0002-9345-4049)
- Richard J. Kovacs (ORCID: https://orcid.org/0000-0001-7870-4313)
- Susan Sporrer
- Joshua Rajkumar
Institutions
- Regenstrief Institute (US)
- Pfizer (United States) (US)
- Franciscan Health Indianapolis (US)
- Eskenazi Health Foundation (US)
- Eskenazi Health
- Indiana University School of Medicine
- Indiana University – Purdue University Indianapolis (US)
Publication Details
- Journal
- BMC Medical Informatics and Decision Making
- Published
- 2024-12-18
- DOI
- https://doi.org/10.1186/s12911-024-02773-z
- Citations
- 3
- Primary Topic
- Atrial Fibrillation Management and Outcomes
- Type
- article
- Field-Weighted Citation Impact
- 1.21
Funders
- Pfizer