Automatically detecting problem list omissions of type 2 diabetes cases using electronic medical records.

Jennifer A. Pacheco*, Will Thompson, Abel Kho

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

9 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)1062-1069
Number of pages8
JournalAMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium
Volume2011
StatePublished - 2011

ASJC Scopus subject areas

  • Medicine(all)

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