J Am Med Inform Assoc - Factors influencing alert acceptance: a novel approach for predicting the success of clinical decision support.

Tópicos

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Resumo

CKGROUND: Clinical decision support systems can prevent knowledge-based prescription errors and improve patient outcomes. The clinical effectiveness of these systems, however, is substantially limited by poor user acceptance of presented warnings. To enhance alert acceptance it may be useful to quantify the impact of potential modulators of acceptance.METHODS: We built a logistic regression model to predict alert acceptance of drug-drug interaction (DDI) alerts in three different settings. Ten variables from the clinical and human factors literature were evaluated as potential modulators of provider alert acceptance. ORs were calculated for the impact of knowledge quality, alert display, textual information, prioritization, setting, patient age, dose-dependent toxicity, alert frequency, alert level, and required acknowledgment on acceptance of the DDI alert.RESULTS: 50,788 DDI alerts were analyzed. Providers accepted only 1.4% of non-interruptive alerts. For interruptive alerts, user acceptance positively correlated with frequency of the alert (OR 1.30, 95% CI 1.23 to 1.38), quality of display (4.75, 3.87 to 5.84), and alert level (1.74, 1.63 to 1.86). Alert acceptance was higher in inpatients (2.63, 2.32 to 2.97) and for drugs with dose-dependent toxicity (1.13, 1.07 to 1.21). The textual information influenced the mode of reaction and providers were more likely to modify the prescription if the message contained detailed advice on how to manage the DDI.CONCLUSION: We evaluated potential modulators of alert acceptance by assessing content and human factors issues, and quantified the impact of a number of specific factors which influence alert acceptance. This information may help improve clinical decision support systems design.

Resumo Limpo

ckground clinic decis support system can prevent knowledgebas prescript error improv patient outcom clinic effect system howev substanti limit poor user accept present warn enhanc alert accept may use quantifi impact potenti modul acceptancemethod built logist regress model predict alert accept drugdrug interact ddi alert three differ set ten variabl clinic human factor literatur evalu potenti modul provid alert accept or calcul impact knowledg qualiti alert display textual inform priorit set patient age dosedepend toxic alert frequenc alert level requir acknowledg accept ddi alertresult ddi alert analyz provid accept noninterrupt alert interrupt alert user accept posit correl frequenc alert ci qualiti display alert level alert accept higher inpati drug dosedepend toxic textual inform influenc mode reaction provid like modifi prescript messag contain detail advic manag ddiconclus evalu potenti modul alert accept assess content human factor issu quantifi impact number specif factor influenc alert accept inform may help improv clinic decis support system design

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