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PRIME - PRocess modelling in ImpleMEntation research: Can this process be an avenue to increase the acceptance of CDS alerts?


According to Rob Kling et al, “ the history of discourses about appropriate forms of computerization is littered with utopian visions that do not effectively engage the complexities of the social worlds of the likely users of new technologies.”[1] This is not a free and empty statement. The expert system Mycin and many other computer systems can be seen as fitting this description. It seems appropriate that Clinical Informatics gives due consideration to the complexity of clinicians’ cognitive processes, needs and work environments. Although electronic health records (EHR) and clinical decision support (CDS) are viewed as holding the promise for increased healthcare quality and cost containment, their use is still not optimal in the US. The most advanced CDS cannot unfortunately help any institution achieve its desired financial benefit, if a significant proportion of its cost-saving alerts are overridden by its clinicians. Implementation Research, conducted within psychological theory frameworks, is being used to identify behavioral factors to target with interventions aimed at increasing the acceptance of evidence based findings, and changing clinical practice processes [2]. For the purpose of this article, implementation research is defined as the scientific study of methods to promote the integration of research findings and evidence-based interventions into practice and EHR use.

Clinical Decision Support and Evidence-Based Medicine (EBM)

EBM constitutes the knowledge base that instructs the development of CDS systems. Specifically, reactive alerts and reminders are intended to help enforce standard of care [3]. However, clinicians often reject their notifications [4-7]. Even safety alerts are overridden 49 to 96% of the time [4]. These statistics are staggering, but easy to understand when the reasons for the overrides are examined. EBM guidelines are not always accepted by individual physicians or practice groups. Many authors [8-9] have reported physicians’ non-adherence to practice guidelines as being associated with various factors such as:

  • Lack of awareness or familiarity
  • Physicians attitudes
  • Lack of agreement
  • Self-Efficacy
  • Lack of evidence of efficacy
  • Limited usefulness for individual patients
  • Threat to the autonomy of doctor/patient relationship
  • Limited resources
  • External barriers

Individual, organizational, even geographical variables affect adherence to some EBM guidelines, thus the CDS systems that they support.

Potential utility of PRIME in launching EHR/CDS

Reminders and Alerts in CDS do not indeed seem sufficient to ensure that clinicians adhere to evidence-based guidelines. Iterative alerts may be annoying. However, most of the factors listed above, including lack of agreement, attitudes, lack of familiarity, just to name a few, suggest that physicians do not always reject alerts simply because they are annoyed. Moreover, Ash et al. reported in 2006 that physicians perceive these CDS alerts as nuisance. “They don’t want to be told how to practice, they don’t want a system to suggest practice.” This brings into question this phenomenon known as “alert fatigue.” It seems more appropriate to conduct a PRIME, prior to launching an EHR. An EHR project is not completed overnight. It is certainly possible to assess the acceptability of the content of CDS. A simple question would be whether the end-users agree with the evidence on which the system alerts are based. This step can help identify psychological and behavioral factors to target. Walker et al.2 suggest an implementation research for evidence based practice, which appears applicable to EHR implementation. Various psychological theories have constructs that explain behaviors in terms of factors that are conducive to change [10]. Constructs such as behavioral intention, perceived behavioral control, attitude toward the behavior, and subjective norms are specific to the Theory of Planned Behavior (TPB). EHR implementation efforts can be pursued in the TPB theoretical framework. This can be achieved through the following steps:

•Identify whether clinicians intend to use the envisioned EHR/CDS; •Identify clinicians opinions about the evidence based contents intended as support of the CDS; •Estimate the impact of individual beliefs and perceptions on the strength of their motivation to use the EHR/CDS; and •Identify which belief has the strongest impact on motivation.

These above elements can help predict the factors that are likely to increase or decrease motivation for EHR/CDS use. There seem to be no single psychological theory that addresses or explains completely human behaviors. They all have some limitations. It has been argued that TPB ignores factors such as power relationships, social reputation, and the possibility that risk behavior may involve more than one person [10].

A health care organization is a system, i.e. a collection of parts (or subsystems) integrated to accomplish an overall health care goal. Health care organizations have inputs, processes, outputs and outcomes, with ongoing feedback among these various elements. As such, with the PRIME approach, healthcare organizations should also be analyzed from a systems theory perspective. The work of Joan Ash et al, [11] reported elements such as power, control, and autonomy, which from a system theory perspective cannot also be ignored.

Click on the following link to view a flowchart depicting the steps of a theory-based implementation model [1]

Cited Literature

1.Rob Kling, Geoffrey W. McKim: Not just a matter of time: Field differences and the shaping of electronic media in supporting scientific communication. JASIS 51(14): 1306-1320 (2000)

2.Walker A, Grimshaw J, Johnston M, Pitts N, Steen N, Eccles M. PRIME - PRocess modelling in ImpleMEntation research: selecting a theoretical basis for interventions to change clinical practice. BMC Health Services Research 2003;3(1):22.

3.Carter, Jerome H. (ed.) Electronic Health Records, 2nd Edition. 2008, American College of Physicians

4.Van Der Sijs H, et al: Turning off frequently overridden drug alerts: Limited opportunities for doing it safely. Journal of the American Medical Informatics Association, July/August 2008, 15(4):439-448

5.Weingart SN, Toth M, Sands DZ, Aronson MD, Davis RB, Phillips RS. Physicians’ decisions to override computerized drug alerts in primary care. Arch Intern Med 2003;163(21):2625–31.

6.Ash J, Berg M, Coiera E. Some unintended consequences of information technology in health care: The nature of patient care information system-related errors. J Am Med Inform Assoc. 2003;11:104 –12.

7.Galanter, et al. "A trial of Automated Decision Support Alerts for Contraindicated Medications Using Computerized Physician Order Entry." J Am Med Inform Assoc. 2005; 12:269-274.

8.Cohen, A., Stavri, P., Hersh W. (2004). A categorization and analysis of the criticisms of evidence-based medicine. International Journal of Medical Informatics, 73: 35‐43.

9.Cabana, M., Rand, C., et al. (1999). Why don't physicians follow clinical practice guidelines? A framework for improvement. Journal of the American Medical Association, 282: 1458‐1465.

10.Conner M. Sparks P: The theory of planned behaviour and health behaviours. In predicting Health Behaviour: Edited by: Conner M. Norman P. Buckingham: Open University Press: 1996:121-162

11.Ash JS, Sittig DF, Campbell E, Guappone K, Dykstra RH. An unintended consequence of CPOE implementation: shifts in power, control, and autonomy. AMIA Annu Symp Proc. 2006;:11-5

Submitted by Yves Vimegnon, MD, MPH