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dc.contributor.authorMoja, Lorenzo
dc.contributor.authorPolo Friz, Hernan
dc.contributor.authorCapobussi, Matteo
dc.contributor.authorKwag, Koren
dc.contributor.authorBanzi, Rita
dc.contributor.authorRuggiero, Francesca
dc.contributor.authorGonzález-Lorenzo, Marien
dc.contributor.authorLiberati, Elisa G
dc.contributor.authorMangia, Massimo
dc.contributor.authorNyberg, Peter
dc.contributor.authorKunnamo, Ilkka
dc.contributor.authorCimminiello, Claudio
dc.contributor.authorVighi, Giuseppe
dc.contributor.authorGrimshaw, Jeremy M
dc.contributor.authorDelgrossi, Giovanni
dc.contributor.authorBonovas, Stefanos
dc.date.accessioned2020-09-11T23:31:05Z
dc.date.available2020-09-11T23:31:05Z
dc.date.issued2019-12-02
dc.identifier.issn2574-3805
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/310187
dc.description.abstractImportance: Sophisticated evidence-based information resources can filter medical evidence from the literature, integrate it into electronic health records, and generate recommendations tailored to individual patients. Objective: To assess the effectiveness of a computerized clinical decision support system (CDSS) that preappraises evidence and provides health professionals with actionable, patient-specific recommendations at the point of care. Design, Setting, and Participants: Open-label, parallel-group, randomized clinical trial among internal medicine wards of a large Italian general hospital. All analyses in this randomized clinical trial followed the intent-to-treat principle. Between November 1, 2015, and December 31, 2016, patients were randomly assigned to the intervention group, in which CDSS-generated reminders were displayed to physicians, or to the control group, in which reminders were generated but not shown. Data were analyzed between February 1 and July 31, 2018. Interventions: Evidence-Based Medicine Electronic Decision Support (EBMEDS), a commercial CDSS covering a wide array of health conditions across specialties, was integrated into the hospital electronic health records to generate patient-specific recommendations. Main Outcomes and Measures: The primary outcome was the resolution rate, the rate at which medical problems identified and alerted by the CDSS were addressed by a change in practice. Secondary outcomes included the length of hospital stay and in-hospital all-cause mortality. Results: In this randomized clinical trial, 20 563 patients were admitted to the hospital. Of these, 6480 (31.5%) were admitted to the internal medicine wards (study population) and randomized (3242 to CDSS and 3238 to control). The mean (SD) age of patients was 70.5 (17.3) years, and 54.5% were men. In total, 28 394 reminders were generated throughout the course of the trial (median, 3 reminders per patient per hospital stay; interquartile range [IQR], 1-6). These messages led to a change in practice in approximately 4 of 100 patients. The resolution rate was 38.0% (95% CI, 37.2%-38.8%) in the intervention group and 33.7% (95% CI, 32.9%-34.4%) in the control group, corresponding to an odds ratio of 1.21 (95% CI, 1.11-1.32; P < .001). The length of hospital stay did not differ between the groups, with a median time of 8 days (IQR, 5-13 days) for the intervention group and a median time of 8 days (IQR, 5-14 days) for the control group (P = .36). In-hospital all-cause mortality also did not differ between groups (odds ratio, 0.95; 95% CI, 0.77-1.17; P = .59). Alert fatigue did not differ between early and late study periods. Conclusions and Relevance: An international commercial CDSS intervention marginally influenced routine practice in a general hospital, although the change did not statistically significantly affect patient outcomes. Trial Registration: ClinicalTrials.gov identifier: NCT02577198.
dc.format.mediumElectronic
dc.languageeng
dc.publisherAmerican Medical Association (AMA)
dc.rightsAttribution 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectHumans
dc.subjectLength of Stay
dc.subjectHospital Mortality
dc.subjectEvidence-Based Medicine
dc.subjectDecision Support Systems, Clinical
dc.subjectHospital Information Systems
dc.subjectAged
dc.subjectMiddle Aged
dc.subjectHospitals, General
dc.subjectItaly
dc.subjectFemale
dc.subjectMale
dc.subjectElectronic Health Records
dc.subjectPractice Patterns, Physicians'
dc.subjectPrecision Medicine
dc.subjectOutcome Assessment, Health Care
dc.titleEffectiveness of a Hospital-Based Computerized Decision Support System on Clinician Recommendations and Patient Outcomes: A Randomized Clinical Trial.
dc.typeArticle
prism.issueIdentifier12
prism.publicationDate2019
prism.publicationNameJAMA Netw Open
prism.startingPagee1917094
prism.volume2
dc.identifier.doi10.17863/CAM.57273
rioxxterms.versionofrecord10.1001/jamanetworkopen.2019.17094
rioxxterms.versionVoR
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserved
rioxxterms.licenseref.startdate2019-12-02
dc.contributor.orcidLiberati, Elisa [0000-0003-4981-1210]
dc.identifier.eissn2574-3805
rioxxterms.typeJournal Article/Review
cam.issuedOnline2019-12-02


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Attribution 4.0 International
Except where otherwise noted, this item's licence is described as Attribution 4.0 International