↓ Skip to main content

What hinders the uptake of computerized decision support systems in hospitals? A qualitative study and framework for implementation

Overview of attention for article published in Implementation Science, September 2017
Altmetric Badge

About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (93rd percentile)
  • High Attention Score compared to outputs of the same age and source (81st percentile)

Mentioned by

blogs
1 blog
twitter
57 tweeters
facebook
1 Facebook page

Citations

dimensions_citation
19 Dimensions

Readers on

mendeley
133 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
What hinders the uptake of computerized decision support systems in hospitals? A qualitative study and framework for implementation
Published in
Implementation Science, September 2017
DOI 10.1186/s13012-017-0644-2
Pubmed ID
Authors

Elisa G. Liberati, Francesca Ruggiero, Laura Galuppo, Mara Gorli, Marien González-Lorenzo, Marco Maraldi, Pietro Ruggieri, Hernan Polo Friz, Giuseppe Scaratti, Koren H. Kwag, Roberto Vespignani, Lorenzo Moja

Abstract

Advanced Computerized Decision Support Systems (CDSSs) assist clinicians in their decision-making process, generating recommendations based on up-to-date scientific evidence. Although this technology has the potential to improve the quality of patient care, its mere provision does not guarantee uptake: even where CDSSs are available, clinicians often fail to adopt their recommendations. This study examines the barriers and facilitators to the uptake of an evidence-based CDSS as perceived by diverse health professionals in hospitals at different stages of CDSS adoption. Qualitative study conducted as part of a series of randomized controlled trials of CDSSs. The sample includes two hospitals using a CDSS and two hospitals that aim to adopt a CDSS in the future. We interviewed physicians, nurses, information technology staff, and members of the boards of directors (n = 30). We used a constant comparative approach to develop a framework for guiding implementation. We identified six clusters of experiences of, and attitudes towards CDSSs, which we label as "positions." The six positions represent a gradient of acquisition of control over CDSSs (from low to high) and are characterized by different types of barriers to CDSS uptake. The most severe barriers (prevalent in the first positions) include clinicians' perception that the CDSSs may reduce their professional autonomy or may be used against them in the event of medical-legal controversies. Moving towards the last positions, these barriers are substituted by technical and usability problems related to the technology interface. When all barriers are overcome, CDSSs are perceived as a working tool at the service of its users, integrating clinicians' reasoning and fostering organizational learning. Barriers and facilitators to the use of CDSSs are dynamic and may exist prior to their introduction in clinical contexts; providing a static list of obstacles and facilitators, irrespective of the specific implementation phase and context, may not be sufficient or useful to facilitate uptake. Factors such as clinicians' attitudes towards scientific evidences and guidelines, the quality of inter-disciplinary relationships, and an organizational ethos of transparency and accountability need to be considered when exploring the readiness of a hospital to adopt CDSSs.

Twitter Demographics

The data shown below were collected from the profiles of 57 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 133 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 133 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 22 17%
Unspecified 21 16%
Researcher 20 15%
Student > Ph. D. Student 18 14%
Other 14 11%
Other 37 28%
Unknown 1 <1%
Readers by discipline Count As %
Unspecified 33 25%
Medicine and Dentistry 31 23%
Nursing and Health Professions 19 14%
Engineering 8 6%
Computer Science 8 6%
Other 33 25%
Unknown 1 <1%

Attention Score in Context

This research output has an Altmetric Attention Score of 39. 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 19 March 2019.
All research outputs
#453,036
of 13,727,342 outputs
Outputs from Implementation Science
#109
of 1,397 outputs
Outputs of similar age
#17,081
of 268,765 outputs
Outputs of similar age from Implementation Science
#2
of 11 outputs
Altmetric has tracked 13,727,342 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 96th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,397 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 13.4. This one has done particularly well, scoring higher than 92% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 268,765 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 93% of its contemporaries.
We're also able to compare this research output to 11 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 81% of its contemporaries.