Evaluating the Accuracy of Existing EMR Data as Predictors of Follow-up Providers
- aDepartment of Biomedical Informatics, University of Utah, Salt Lake City, UT
- bDepartment of Family and Preventive Medicine, University of Utah, Salt Lake City, UT
- cIntermountain Healthcare, Salt Lake City, UT
- Correspondence: Jacob S. Tripp, Department of Biomedical Informatics, University of Utah School of Medicine, 26 South 2000 East, Suite 5700 HSEB, Salt Lake City, UT 84112-5750 (Email: jacob.tripp{at}hsc.utah.edu)
- Received 11 February 2008
- Accepted 14 August 2008
Abstract
In order to evaluate the accuracy of existing EMR data in predicting follow-up providers, a retrospective analysis was performed on six months of data for inpatient and ED encounters occurring at two hospitals, and on related outpatient data. Sensitivity and Positive Predictive Value (PPV) were calculated for each of eight predictors, to determine their effectiveness in predicting follow-up providers. Our findings indicate that access to longitudinal patient care records can improve prediction of which providers a patient is likely to see post-discharge compared to simply using Primary Care Provider data from admissions records. Of the predictors evaluated, a patient's past appointment history was the best predictor of which providers they would see in the future (PPV = 48% following inpatient visits, 35% following emergency department visits). However, even the best performing predictors failed to predict more than half of the follow-up providers and might generate many “false” alerts.
Footnotes
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This research was funded under National Library of Medicine Training Grant No. T15LM07124.
