A method and knowledge base for automated inference of patient problems from structured data in an electronic medical record
- Adam Wright1,2,3,
- Justine Pang1,
- Joshua C Feblowitz1,2,
- Francine L Maloney2,
- Allison R Wilcox2,
- Harley Z Ramelson1,2,3,
- Louise I Schneider1,2,
- David W Bates1,2,3
- 1Department of General Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA
- 2Information Systems, Partners HealthCare, Boston, Massachusetts, USA
- 3Harvard Medical School, Boston, Massachusetts, USA
- Correspondence to Adam Wright, Brigham and Women's Hospital, 1620 Tremont St, Boston, MA 02115, USA;
- Received 22 October 2010
- Accepted 25 April 2011
- Published Online First 25 May 2011
Background Accurate knowledge of a patient's medical problems is critical for clinical decision making, quality measurement, research, billing and clinical decision support. Common structured sources of problem information include the patient problem list and billing data; however, these sources are often inaccurate or incomplete.
Objective To develop and validate methods of automatically inferring patient problems from clinical and billing data, and to provide a knowledge base for inferring problems.
Study design and methods We identified 17 target conditions and designed and validated a set of rules for identifying patient problems based on medications, laboratory results, billing codes, and vital signs. A panel of physicians provided input on a preliminary set of rules. Based on this input, we tested candidate rules on a sample of 100 000 patient records to assess their performance compared to gold standard manual chart review. The physician panel selected a final rule for each condition, which was validated on an independent sample of 100 000 records to assess its accuracy.
Results Seventeen rules were developed for inferring patient problems. Analysis using a validation set of 100 000 randomly selected patients showed high sensitivity (range: 62.8–100.0%) and positive predictive value (range: 79.8–99.6%) for most rules. Overall, the inference rules performed better than using either the problem list or billing data alone.
Conclusion We developed and validated a set of rules for inferring patient problems. These rules have a variety of applications, including clinical decision support, care improvement, augmentation of the problem list, and identification of patients for research cohorts.
Funding This work was supported by a grant from the Partners Community HealthCare Incorporated (PCHI) System Improvement Grant Program. PCHI was not involved in the design, execution or analysis of the study or in the preparation of this manuscript.
Competing interests None.
Ethics approval This study was approved by the Partners HealthCare Institutional Review Board.
Provenance and peer review Not commissioned; externally peer reviewed.