Healthcare information systems: data mining methods in the creation of a clinical recommender system

  • Authors:

  • L. Duan;W. N. Street;E. Xu

  • Affiliations:

  • Department of Management Sciences, Henry B. Tippie College of Business, University of Iowa, Iowa City, IA, USA;Department of Management Sciences, Henry B. Tippie College of Business, University of Iowa, Iowa City, IA, USA;College of Natural Resources, University of California-Berkeley, Berkeley, CA, USA,College of Arts and Sciences, University of Virginia, Charlottesville, VA, USA

  • Venue:
  • Enterprise Information Systems
  • Year:
  • 2011

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Abstract

Recommender systems have been extensively studied to present items, such as movies, music and books that are likely of interest to the user. Researchers have indicated that integrated medical information systems are becoming an essential part of the modern healthcare systems. Such systems have evolved to an integrated enterprise-wide system. In particular, such systems are considered as a type of enterprise information systems or ERP system addressing healthcare industry sector needs. As part of efforts, nursing care plan recommender systems can provide clinical decision support, nursing education, clinical quality control, and serve as a complement to existing practice guidelines. We propose to use correlations among nursing diagnoses, outcomes and interventions to create a recommender system for constructing nursing care plans. In the current study, we used nursing diagnosis data to develop the methodology. Our system utilises a prefix-tree structure common in itemset mining to construct a ranked list of suggested care plan items based on previously-entered items. Unlike common commercial systems, our system makes sequential recommendations based on user interaction, modifying a ranked list of suggested items at each step in care plan construction. We rank items based on traditional association-rule measures such as support and confidence, as well as a novel measure that anticipates which selections might improve the quality of future rankings. Since the multi-step nature of our recommendations presents problems for traditional evaluation measures, we also present a new evaluation method based on average ranking position and use it to test the effectiveness of different recommendation strategies.