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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.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: identifier: NCT02577198.
dc.publisherAmerican Medical Association (AMA)
dc.rightsAttribution 4.0 International
dc.subjectLength of Stay
dc.subjectHospital Mortality
dc.subjectEvidence-Based Medicine
dc.subjectDecision Support Systems, Clinical
dc.subjectHospital Information Systems
dc.subjectMiddle Aged
dc.subjectHospitals, General
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.
prism.publicationNameJAMA Netw Open
dc.contributor.orcidLiberati, Elisa [0000-0003-4981-1210]
rioxxterms.typeJournal Article/Review

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