Methodological Review: Ranked Levels of Influence model: Selecting influence techniques to minimize IT resistance

Authors:
Christa E. Bartos;Brian S. Butler;Rebecca S. Crowley
Affiliations:
Health and Community Systems, University of Pittsburgh School of Nursing, Pittsburgh, PA, USA;Katz Graduate School of Business, University of Pittsburgh, Pittsburgh, PA, USA;Department of Biomedical Informatics, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
Venue:
Journal of Biomedical Informatics
Year:
2011

Citing 2
Cited 1

Perceptions of personal power and their relationship to clinician's resistance to the introduction of computerized physician order entry

Perceptions of personal power and their relationship to clinician's resistance to the introduction of computerized physician order entry
A multilevel model of resistance to information technology implementation

MIS Quarterly

Guest Editorial: Biomedical Complexity and Error

Journal of Biomedical Informatics

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Abstract

Implementation of electronic health records (EHR), particularly computerized physician/provider order entry systems (CPOE), is often met with resistance. Influence presented at the right time, in the right manner, may minimize resistance or at least limit the risk of complete system failure. Combining established theories on power, influence tactics, and resistance, we developed the Ranked Levels of Influence model. Applying it to documented examples of EHR/CPOE failures at Cedars-Sinai and Kaiser Permanente in Hawaii, we evaluated the influence applied, the resistance encountered, and the resulting risk to the system implementation. Using the Ranked Levels of Influence model as a guideline, we demonstrate that these system failures were associated with the use of hard influence tactics that resulted in higher levels of resistance. We suggest that when influence tactics remain at the soft tactics level, the level of resistance stabilizes or de-escalates and the system can be saved.