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

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          Abstract

          Background

          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.

          Methods

          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.

          Results

          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.

          Conclusions

          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.

          Electronic supplementary material

          The online version of this article (doi:10.1186/s12911-017-0477-6) contains supplementary material, which is available to authorized users.

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          Most cited references29

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          Effect of clinical decision-support systems: a systematic review.

          Despite increasing emphasis on the role of clinical decision-support systems (CDSSs) for improving care and reducing costs, evidence to support widespread use is lacking. To evaluate the effect of CDSSs on clinical outcomes, health care processes, workload and efficiency, patient satisfaction, cost, and provider use and implementation. MEDLINE, CINAHL, PsycINFO, and Web of Science through January 2011. Investigators independently screened reports to identify randomized trials published in English of electronic CDSSs that were implemented in clinical settings; used by providers to aid decision making at the point of care; and reported clinical, health care process, workload, relationship-centered, economic, or provider use outcomes. Investigators extracted data about study design, participant characteristics, interventions, outcomes, and quality. 148 randomized, controlled trials were included. A total of 128 (86%) assessed health care process measures, 29 (20%) assessed clinical outcomes, and 22 (15%) measured costs. Both commercially and locally developed CDSSs improved health care process measures related to performing preventive services (n= 25; odds ratio [OR], 1.42 [95% CI, 1.27 to 1.58]), ordering clinical studies (n= 20; OR, 1.72 [CI, 1.47 to 2.00]), and prescribing therapies (n= 46; OR, 1.57 [CI, 1.35 to 1.82]). Few studies measured potential unintended consequences or adverse effects. Studies were heterogeneous in interventions, populations, settings, and outcomes. Publication bias and selective reporting cannot be excluded. Both commercially and locally developed CDSSs are effective at improving health care process measures across diverse settings, but evidence for clinical, economic, workload, and efficiency outcomes remains sparse. This review expands knowledge in the field by demonstrating the benefits of CDSSs outside of experienced academic centers. Agency for Healthcare Research and Quality.
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            IBM computer usability satisfaction questionnaires: Psychometric evaluation and instructions for use

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              Effects of clinical decision-support systems on practitioner performance and patient outcomes: a synthesis of high-quality systematic review findings.

              To synthesize the literature on clinical decision-support systems' (CDSS) impact on healthcare practitioner performance and patient outcomes. Literature search on Medline, Embase, Inspec, Cinahl, Cochrane/Dare and analysis of high-quality systematic reviews (SRs) on CDSS in hospital settings. Two-stage inclusion procedure: (1) selection of publications on predefined inclusion criteria; (2) independent methodological assessment of preincluded SRs by the 11-item measurement tool, AMSTAR. Inclusion of SRs with AMSTAR score 9 or above. SRs were thereafter rated on level of evidence. Each stage was performed by two independent reviewers. 17 out of 35 preincluded SRs were of high methodological quality and further analyzed. Evidence that CDSS significantly impacted practitioner performance was found in 52 out of 91 unique studies of the 16 SRs examining this effect (57%). Only 25 out of 82 unique studies of the 16 SRs reported evidence that CDSS positively impacted patient outcomes (30%). Few studies have found any benefits on patient outcomes, though many of these have been too small in sample size or too short in time to reveal clinically important effects. There is significant evidence that CDSS can positively impact healthcare providers' performance with drug ordering and preventive care reminder systems as most clear examples. These outcomes may be explained by the fact that these types of CDSS require a minimum of patient data that are largely available before the advice is (to be) generated: at the time clinicians make the decisions.
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                Author and article information

                Contributors
                +44(0)20 78486606 , Talya.porat@kcl.ac.uk
                brendan.delaney@imperial.ac.uk
                o.kostopoulou@imperial.ac.uk
                Journal
                BMC Med Inform Decis Mak
                BMC Med Inform Decis Mak
                BMC Medical Informatics and Decision Making
                BioMed Central (London )
                1472-6947
                2 June 2017
                2 June 2017
                2017
                : 17
                : 79
                Affiliations
                [1 ]ISNI 0000 0001 2322 6764, GRID grid.13097.3c, Department of Primary Care and Public Health Sciences, , King’s College London, ; 3rd floor Addison House, Guy’s Campus, London, SE1 3QD UK
                [2 ]ISNI 0000 0001 2113 8111, GRID grid.7445.2, Department of Surgery and Cancer, , Imperial College London, ; London, UK
                Author information
                http://orcid.org/0000-0002-6410-2028
                Article
                477
                10.1186/s12911-017-0477-6
                5457602
                28576145
                11c5d98d-f3ee-4f9a-aa53-7eff74021054
                © The Author(s). 2017

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                History
                : 3 February 2017
                : 25 May 2017
                Funding
                Funded by: Seventh Framework Programme (BE)
                Award ID: 247787
                Award Recipient :
                Categories
                Research Article
                Custom metadata
                © The Author(s) 2017

                Bioinformatics & Computational biology
                decision support systems,diagnosis,diagnostic error,usability,usefulness,acceptability,patient satisfaction,electronic health record,cognitive engineering

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