Automated problem list generation and physicians perspective from a pilot study

Murthy V. Devarakonda, Neil Mehta, Ching Huei Tsou, Jennifer J. Liang, Amy S. Nowacki, John Eric Jelovsek

Research output: Contribution to journalArticlepeer-review

27 Scopus citations


Objective An accurate, comprehensive and up-to-date problem list can help clinicians provide patient-centered care. Unfortunately, problem lists created and maintained in electronic health records by providers tend to be inaccurate, duplicative and out of date. With advances in machine learning and natural language processing, it is possible to automatically generate a problem list from the data in the EHR and keep it current. In this paper, we describe an automated problem list generation method and report on insights from a pilot study of physicians’ assessment of the generated problem lists compared to existing providers-curated problem lists in an institution's EHR system. Materials and methods The natural language processing and machine learning-based Watson1

Original languageEnglish (US)
Pages (from-to)121-129
Number of pages9
JournalInternational Journal of Medical Informatics
StatePublished - Sep 2017


  • Electronic health records
  • IBM Watson
  • Longitudinal patient records
  • Machine learning
  • Natural language processing
  • Problem list

ASJC Scopus subject areas

  • Health Informatics


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