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Ceiling effect in EMR system assimilation: a multiple case study in primary care family practices

Overview of attention for article published in BMC Medical Informatics and Decision Making, April 2017
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Title
Ceiling effect in EMR system assimilation: a multiple case study in primary care family practices
Published in
BMC Medical Informatics and Decision Making, April 2017
DOI 10.1186/s12911-017-0445-1
Pubmed ID
Authors

Marie-Claude Trudel, Josianne Marsan, Guy Paré, Louis Raymond, Ana Ortiz de Guinea, Éric Maillet, Thomas Micheneau

Abstract

There has been indisputable growth in adoption of electronic medical record (EMR) systems in the recent years. However, physicians' progress in using these systems has stagnated when measured with maturity scales. While this so-called ceiling effect has been observed and its consequences described in previous studies, there is a paucity of research on the elements that could explain such an outcome. We first suggest that in the context of EMR systems we are in presence of a "tiered ceiling effect" and then we show why such phenomenon occurs. We conducted in-depth case studies in three primary care medical practices in Canada where physicians had been using EMR systems for 3 years or more. A total of 37 semi-structured interviews were conducted with key informants: family physicians (about half of the interviews), nurses, secretaries, and administrative managers. Additional information was obtained through notes taken during observations of users interacting with their EMR systems and consultation of relevant documents at each site. We used abductive reasoning to infer explanations of the observed phenomenon by going back and forth between the case data and conceptual insights. Our analysis shows that a ceiling effect has taken place in the three clinics. We identified a set of conditions preventing the users from overcoming the ceiling. In adopting an EMR system, all three clinics essentially sought improved operational efficiency. This had an influence on the criteria used to assess the systems available on the market and eventually led to the adoption of a system that met the specified criteria without being optimal. Later, training sessions focussed on basic functionalities that minimally disturbed physicians' habits while helping their medical practices become more efficient. Satisfied with the outcome of their system use, physicians were likely to ignore more advanced EMR system functionalities. This was because their knowledge about EMR systems came almost exclusively from a single source of information: their EMR system vendors. This knowledge took the form of interpretations of what the innovation was (know-what), with little consideration of the rationales for innovation adoption (know-why) or hands-on strategies for adopting, implementing and assimilating the innovation in the organization (know-how). This paper provides a holistic view of the technological innovation process in primary care and contends that limited learning, satisficing behaviours and organizational inertia are important factors leading to the ceiling effect frequently experienced in the EMR system assimilation phase.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 103 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 103 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 25 24%
Student > Ph. D. Student 10 10%
Student > Bachelor 9 9%
Student > Doctoral Student 5 5%
Researcher 4 4%
Other 17 17%
Unknown 33 32%
Readers by discipline Count As %
Nursing and Health Professions 14 14%
Medicine and Dentistry 14 14%
Business, Management and Accounting 11 11%
Social Sciences 8 8%
Computer Science 4 4%
Other 14 14%
Unknown 38 37%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 22 April 2017.
All research outputs
#20,414,746
of 22,965,074 outputs
Outputs from BMC Medical Informatics and Decision Making
#1,814
of 2,001 outputs
Outputs of similar age
#269,868
of 310,204 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#32
of 34 outputs
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