A pilot study of distributed knowledge management and clinical decision support in the cloud

Brian E. Dixon, Linas Simonaitis, Howard S. Goldberg, Marilyn D. Paterno, Molly Schaeffer, Tonya Hongsermeier, Adam Wright, Blackford Middleton

Research output: Contribution to journalArticle

40 Scopus citations

Abstract

Objective: Implement and perform pilot testing of web-based clinical decision support services using a novel framework for creating and managing clinical knowledge in a distributed fashion using the cloud. The pilot sought to (1) develop and test connectivity to an external clinical decision support (CDS) service, (2) assess the exchange of data to and knowledge from the external CDS service, and (3) capture lessons to guide expansion to more practice sites and users. Materials and methods: The Clinical Decision Support Consortium created a repository of shared CDS knowledge for managing hypertension, diabetes, and coronary artery disease in a community cloud hosted by Partners HealthCare. A limited data set for primary care patients at a separate health system was securely transmitted to a CDS rules engine hosted in the cloud. Preventive care reminders triggered by the limited data set were returned for display to clinician end users for review and display. During a pilot study, we (1) monitored connectivity and system performance, (2) studied the exchange of data and decision support reminders between the two health systems, and (3) captured lessons. Results: During the six month pilot study, there were 1339 patient encounters in which information was successfully exchanged. Preventive care reminders were displayed during 57% of patient visits, most often reminding physicians to monitor blood pressure for hypertensive patients (29%) and order eye exams for patients with diabetes (28%). Lessons learned were grouped into five themes: performance, governance, semantic interoperability, ongoing adjustments, and usability. Discussion: Remote, asynchronous cloud-based decision support performed reasonably well, although issues concerning governance, semantic interoperability, and usability remain key challenges for successful adoption and use of cloud-based CDS that will require collaboration between biomedical informatics and computer science disciplines. Conclusion: Decision support in the cloud is feasible and may be a reasonable path toward achieving better support of clinical decision-making across the widest range of health care providers.

Original languageEnglish (US)
Pages (from-to)45-53
Number of pages9
JournalArtificial Intelligence in Medicine
Volume59
Issue number1
DOIs
StatePublished - Sep 1 2013

Keywords

  • Clinical decision support systems
  • Computer-Assisted Decision Making
  • Information dissemination
  • Knowledge management
  • Log file analysis
  • Medical informatics
  • Preventive health services
  • Qualitative analysis

ASJC Scopus subject areas

  • Artificial Intelligence
  • Medicine (miscellaneous)

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    Dixon, B. E., Simonaitis, L., Goldberg, H. S., Paterno, M. D., Schaeffer, M., Hongsermeier, T., Wright, A., & Middleton, B. (2013). A pilot study of distributed knowledge management and clinical decision support in the cloud. Artificial Intelligence in Medicine, 59(1), 45-53. https://doi.org/10.1016/j.artmed.2013.03.004