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The impact of a diagnostic decision support system on the consultation: perceptions of GPs and patients

Overview of attention for article published in BMC Medical Informatics and Decision Making, June 2017
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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (88th percentile)
  • High Attention Score compared to outputs of the same age and source (97th percentile)

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2 news outlets
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7 X users
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1 Facebook page

Citations

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46 Dimensions

Readers on

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171 Mendeley
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Title
The impact of a diagnostic decision support system on the consultation: perceptions of GPs and patients
Published in
BMC Medical Informatics and Decision Making, June 2017
DOI 10.1186/s12911-017-0477-6
Pubmed ID
Authors

Talya Porat, Brendan Delaney, Olga Kostopoulou

Abstract

Clinical decision support systems (DSS) aimed at supporting diagnosis are not widely used. This is mainly due to usability issues and lack of integration into clinical work and the electronic health record (EHR). In this study we examined the usability and acceptability of a diagnostic DSS prototype integrated with the EHR and in comparison with the EHR alone. Thirty-four General Practitioners (GPs) consulted with 6 standardised patients (SPs) using only their EHR system (baseline session); on another day, they consulted with 6 different but matched for difficulty SPs, using the EHR with the integrated DSS prototype (DSS session). GPs were interviewed twice (at the end of each session), and completed the Post-Study System Usability Questionnaire at the end of the DSS session. The SPs completed the Consultation Satisfaction Questionnaire after each consultation. The majority of GPs (74%) found the DSS useful: it helped them consider more diagnoses and ask more targeted questions. They considered three user interface features to be the most useful: (1) integration with the EHR; (2) suggested diagnoses to consider at the start of the consultation and; (3) the checklist of symptoms and signs in relation to each suggested diagnosis. There were also criticisms: half of the GPs felt that the DSS changed their consultation style, by requiring them to code symptoms and signs while interacting with the patient. SPs sometimes commented that GPs were looking at their computer more than at them; this comment was made more often in the DSS session (15%) than in the baseline session (3%). Nevertheless, SP ratings on the satisfaction questionnaire did not differ between the two sessions. To use the DSS effectively, GPs would need to adapt their consultation style, so that they code more information during rather than at the end of the consultation. This presents a potential barrier to adoption. Training GPs to use the system in a patient-centred way, as well as improvement of the DSS interface itself, could facilitate coding. To enhance patient acceptability, patients should be informed about the potential of the DSS to improve diagnostic accuracy.

X Demographics

X Demographics

The data shown below were collected from the profiles of 7 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 171 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 171 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 23 13%
Student > Master 23 13%
Other 13 8%
Student > Ph. D. Student 13 8%
Student > Bachelor 9 5%
Other 34 20%
Unknown 56 33%
Readers by discipline Count As %
Medicine and Dentistry 43 25%
Computer Science 14 8%
Nursing and Health Professions 10 6%
Psychology 7 4%
Social Sciences 6 4%
Other 27 16%
Unknown 64 37%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 18. 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 02 March 2020.
All research outputs
#1,780,391
of 23,312,088 outputs
Outputs from BMC Medical Informatics and Decision Making
#92
of 2,024 outputs
Outputs of similar age
#36,469
of 318,319 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#2
of 48 outputs
Altmetric has tracked 23,312,088 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 92nd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 2,024 research outputs from this source. They receive a mean Attention Score of 4.9. This one has done particularly well, scoring higher than 95% 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 318,319 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 88% of its contemporaries.
We're also able to compare this research output to 48 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 97% of its contemporaries.