title: Use of electronic healthcare records to identify complex patients with atrial fibrillation for targeted intervention.
abstract: BACKGROUND
  Practice guidelines recommend anticoagulation therapy for patients with atrial fibrillation (AF) who have other risk factors putting them at an elevated risk of stroke. These patients remain undertreated, but, with increasing use of electronic healthcare records (EHRs), it may be possible to identify candidates for treatment.
  
  
  OBJECTIVE
  To test algorithms for identifying AF patients who also have known risk factors for stroke and major bleeding using EHR data.
  
  
  METHODS
  We evaluated the performance of algorithms using EHR data from the Partners Healthcare System at identifying AF patients and 16 additional conditions that are risk factors in the CHA 2 DS 2 -VASc and HAS-BLED risk scores for stroke and major bleeding. Algorithms were based on information contained in problem lists, billing codes, laboratory data, prescription data, vital status, and clinical notes. The performance of candidate algorithms in 1000 bootstrap resamples was compared to a gold standard of manual chart review by experienced resident physicians.
  
  
  RESULTS
  : Physicians reviewed 480 patient charts. For 11 conditions, the median positive predictive value (PPV) of the EHR-derived algorithms was greater than 0.90. Although the PPV for some risk factors was poor, the median PPV for identifying patients with a CHA 2 DS 2 -VASc score ≥2 or a HAS-BLED score ≥3 was 1.00 and 0.92, respectively.
  
  
  CONCLUSIONS
  We developed and tested a set of algorithms to identify AF patients and known risk factors for stroke and major bleeding using EHR data. Algorithms such as these can be built into EHR systems to facilitate informed decision making and help shift population health management efforts towards patients with the greatest need.
authors.affiliation: Division of Pharmacoepidemiology and Pharmacoeconomics, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
authors.forename: Shirley V
authors.initials: SV
authors.lastname: Wang
authors.affiliation: Division of Pharmacoepidemiology and Pharmacoeconomics, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
authors.forename: James R
authors.initials: JR
authors.lastname: Rogers
authors.affiliation: Division of Pharmacoepidemiology and Pharmacoeconomics, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
authors.forename: Yinzhu
authors.initials: Y
authors.lastname: Jin
authors.affiliation: Division of General Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
authors.forename: David W
authors.initials: DW
authors.lastname: Bates
authors.affiliation: Division of Pharmacoepidemiology and Pharmacoeconomics, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
authors.forename: Michael A
authors.initials: MA
authors.lastname: Fischer
mesh_terms: D000465:Algorithms; D000925:Anticoagulants / Q000009:adverse effects / Q000627:therapeutic use; D001281:Atrial Fibrillation / Q000150:complications / Q000175:diagnosis* / Q000188:drug therapy; D057286:Electronic Health Records; D006470:Hemorrhage / Q000209:etiology; D006801:Humans; D009323:Natural Language Processing; D018570:Risk Assessment / Q000379:methods; D012307:Risk Factors; D020521:Stroke / Q000209:etiology
chemical_list: D000925:Anticoagulants
keywords: algorithms; anticoagulation; chronic disease; natural language processing; outcomes; quality improvement; stroke