Understanding technology adoption in clinical care: Clinician adoption behavior of a point-of-care reminder system
Section snippets
Summary of research results
What was known before the study?
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Clinical decision support systems can enhance the clinical performance in a variety of areas.
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Many CDSS fail in real-life installations regardless of the technological quality of the application.
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Research on the effects of CDSS on user adoption and organization workflows in realistic settings is lacking.
What the study has added to the body of knowledge?
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Introduced a new statistical methodology drawn from the social sciences for analyzing revealed behavior versus
Clinical reminder system
Clinical reminder system (CRS) uses patients’ medical status data to provide “just-in-time” reminders to clinicians at the point of the care consistent with the latest evidence-based medicine guidelines for chronic disease and preventive care management. CRS is made available to physicians and clinic staff via desktop computers installed in every examination room of the clinic. The application integrates the hospital's administrative, laboratory, and clinical records systems into a single
Distinct user adoption groups
Our application of DTA identified three distinct user adoption types. The developmental trajectories and the group compositions are depicted in Fig. 2. Solid and dashed lines denote the observed and predicted trends, respectively. Observed data values are computed as the mean use rate of users assigned to each of these groups identified by estimation. Predicted values are computed using DTA model coefficient estimates. Numbers above each trendline denote the percentage of residents that
Methods
The mixed methods model we have used in this paper has enabled us to successfully distinguish distinct groups of CRS users, to validate the results of statistical analysis with actual user comments, and to finally identify key user concerns that have enabled system redesign towards more efficient and more effective use.
Results
Comments and related system data related to iterative advisories indicate that information gathering and reminder-generating functions of the system, particularly in situations
Conclusion
In this study, we assess clinician users’ acceptance and adoption of a clinical reminder system. We apply a mixed methods approach that combines quantitative methods to identify distinct developmental trajectories of user adoption with qualitative instruments to examine the causal processes of such adoption behaviors.
We find that a significant level of resistance exists to the use of the reminder system; a large proportion of users demonstrated a consistently low or decreasing level of usage
References (21)
- et al.
Computerizing guidelines to improve care and patient outcomes: the example of heart failure
J. Am. Med. Inform. Assoc.
(1995) - et al.
Improving preventive care by prompting physicians
Arch. Intern. Med.
(2000) - et al.
Effects of computer-based clinical decision support systems on physician performance and patient outcomes
JAMA
(2000) - et al.
Clinical decision support systems for the practice of evidence-based medicine
JAMIA
(2001) Protocol-based computer reminders, the quality of care, and the non-perfectability of man
N. Engl. J. Med.
(1975)- et al.
Enhancement of clinicians’ diagnostic reasoning by computer-based consultation: a multisite study of 2 systems
JAMA
(1999) - et al.
Evaluation of user acceptance of a clinical expert system
JAMIA
(1994) Evaluating informatics applications—some alternative approaches: theory, social interactionism and call for methodological pluralism
Int. J. Med. Inf.
(2001)Evaluating informatics applications—clinical decision support systems literature review
Int. J. Med. Inf.
(2001)- et al.
Practice based, longitudinal, qualitative interview study of computerized evidence-based guidelines in primary care
Br. Med. J.
(2003)
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