Using computational modeling to transform nursing data into actionable information

Judith A. Effken, Barbara B. Brewer, Anita Patil, Gerri S. Lamb, Joyce A. Verran, Kathleen M. Carley

Research output: Contribution to journalArticle

11 Scopus citations

Abstract

Transforming organizational research data into actionable information nurses can use to improve patient outcomes remains a challenge. Available data are numerous, at multiple levels of analysis, and snapshots in time, which makes application difficult in a dynamically changing healthcare system. One potential solution is computational modeling. We describe our use of OrgAhead, a theoretically based computational modeling program developed at Carnegie Mellon University, to transform data into actionable nursing information. We calibrated the model by using data from 16 actual patient care units to adjust model parameters until performance of simulated units ordered in the same way as observed performance of the actual units 80% of the time. In future research, we will use OrgAhead to generate hypotheses about changes nurses might make to improve patient outcomes, help nurses use these hypotheses to identify and implement changes on their units, and then measure the impact of those changes on patient outcomes.

Original languageEnglish (US)
Pages (from-to)351-361
Number of pages11
JournalJournal of Biomedical Informatics
Volume36
Issue number4-5
DOIs
StatePublished - Jan 1 2003

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Keywords

  • Actionable information
  • Computational modeling
  • Data
  • Information
  • Organizational change
  • Patient outcomes
  • Patient safety outcomes

ASJC Scopus subject areas

  • Computer Science Applications
  • Health Informatics

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