title: Automatically detecting problem list omissions of type 2 diabetes cases using electronic medical records.
abstract: As part of a large-scale project to use DNA biorepositories linked with electronic medical record (EMR) data for research, we developed and validated an algorithm to identify type 2 diabetes cases in the EMR. Though the algorithm was originally created to support clinical research, we have subsequently re-applied it to determine if it could also be used to identify problem list gaps. We examined the problem lists of the cases that the algorithm identified in order to determine if a structured code for diabetes was present. We found that only just over half of patients identified by the algorithm had a corresponding structured code entered in their problem list. We analyze characteristics of this patient population and identify possible reasons for the problem list omissions. We conclude that application of such algorithms to the EMR can improve the quality of the problem list, thereby supporting satisfaction of Meaningful Use guidelines.
authors.affiliation: Northwestern University Feinberg School of Medicine, Chicago, IL, USA.
authors.forename: Jennifer A
authors.initials: JA
authors.lastname: Pacheco
authors.forename: Will
authors.initials: W
authors.lastname: Thompson
authors.forename: Abel
authors.initials: A
authors.lastname: Kho
mesh_terms: D000465:Algorithms; D003924:Diabetes Mellitus, Type 2 / Q000175:diagnosis*; D057286:Electronic Health Records / Q000592:standards; D006801:Humans; D062527:Meaningful Use; D008501:Medical Records, Problem-Oriented / Q000592:standards