The anatomy of decision support during inpatient care provider order entry (CPOE): empirical observations from a decade of CPOE experience at Vanderbilt

  • Authors:

  • Randolph A. Miller;Lemuel R. Waitman;Sutin Chen;S. Trent Rosenbloom

  • Affiliations:

  • Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN;Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN;Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN;Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN

  • Venue:
  • Journal of Biomedical Informatics
  • Year:
  • 2005

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Abstract

The authors describe a pragmatic approach to the introduction of clinical decision support at the point of care, based on a decade of experience in developing and evolving Vanderbilt's inpatient "WizOrder" care provider order entry (CPOE) system. The inpatient care setting provides a unique opportunity to interject CPOE-based decision support features that restructure clinical workflows, deliver focused relevant educational materials, and influence how care is delivered to patients. From their empirical observations, the authors have developed a generic model for decision support within inpatient CPOE systems. They believe that the model's utility extends beyond Vanderbilt, because it is based on characteristics of end-user workflows and on decision support considerations that are common to a variety of inpatient settings and CPOE systems. The specific approach to implementing a given clinical decision support feature within a CPOE system should involve evaluation along three axes: what type of intervention to create (for which the authors describe 4 general categories); when to introduce the intervention into the user's workflow (for which the authors present 7 categories), and how disruptive, during use of the system, the intervention might be to end-users' workflows (for which the authors describe 6 categories). Framing decision support in this manner may help both developers and clinical end-users plan future alterations to their systems when needs for new decision support features arise.