Can an electronic health record system be used for preconception health optimization?

To explore the potential of an integrated outpatient electronic health record (EHR) for preconception health optimization. An automated case-finding EHR-derived algorithm was designed to identify women of child-bearing age having outpatient encounters in an 85-site, integrated health system. The algorithm simultaneously cross-referenced multiple discrete data fields to identify selected preconception factors (obesity, hypertension, diabetes, teratogen use including ACE inhibitors, multivitamin supplementation, anemia, renal insufficiency, untreated sexually transmitted infection, HIV positivity, and tobacco, alcohol or illegal drug use). Surveys were mailed to a random sample of patients to obtain their self-reported health profiles for these same factors. Concordance was assessed between the algorithm output, survey results, and manual data abstraction. Between 8/2010-2/2012, 107,339 female outpatient visits were identified, from which 29,691 unique women were presumed to have child-bearing potential. 19,624 (66 %) and 8,652 (29 %) had 1 or ≥2 health factors, respectively while only 1,415 (5 %) had none. Using the patient survey results as a reference point, health-factor agreement was similar comparing the algorithm (85.8 %) and the chart abstraction (87.2 %) results. Incorrect or missing data entries in the EHR encounters were largely responsible for discordances observed. Preconception screening using an automated algorithm in a system-wide EHR identified a large group of women with potentially modifiable preconception health conditions. The issue most responsible for limiting algorithm performance was incomplete point of care documentation. Accurate data capture during patient encounters should be a focus for quality improvement, so that novel applications of system-wide data mining can be reliably implemented.