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

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.

An empirical investigation of patients’ acceptance and resistance toward the health cloud: The dual factor perspective

“…Health IT has great potential to improve the quality of care and patient safety, but this benefit is not always being realized because many health IT efforts encounter difficulties or fail altogether. Many of these failures and problems can be traced back to user resistance (Bartos, Butler, & Crowley, 2011). Resistance is not quite equivalent to non-usage because non-usage may imply that potential adopters are simply unaware of a new technology or are still evaluating the technology prior to its adoption, while resistance implies that the technology has been considered and rejected by these users (Bhattacherjee & Hikmet, 2007a).…”

Social and personal normative influences on healthcare professionals to use information technology: towards a more robust social ergonomics

“…2009). Yet, there is almost no research on the social forces that shape health IT use, much less EHR use, by clinicians, and the existing research is limited (for exceptions, see, e.g., Bartos et al in press , Fung et al . 2009, Van Akkeren and Rowlands 2007).…”

Electronic health records: research into design and implementation

“…As axiomatic as this statement may be, there is hardly any work explicitly aiming to understand the social phenomena surrounding EHR use or the social consequences that an EHR may have on the implementing organisation or on the wider community. Nevertheless, there is some evidence that the decision to use an EHR is greatly dependent on the social influence of colleagues and superiors, [14][15][16] and there is ample evidence that EHRs transform social structures and processes within organisations. For example, social roles change when a physician must do more data entry or make more decisions than with a paper-based system; the physician takes on tasks that used to be delegated to other staff, changing the physician's role in the team.…”