J Am Med Inform Assoc - Improving patient safety via automated laboratory-based adverse event grading.

Tópicos

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Resumo

The identification and grading of adverse events (AEs) during the conduct of clinical trials is a labor-intensive and error-prone process. This paper describes and evaluates a software tool developed by City of Hope to automate complex algorithms to assess laboratory results and identify and grade AEs. We compared AEs identified by the automated system with those previously assessed manually, to evaluate missed/misgraded AEs. We also conducted a prospective paired time assessment of automated versus manual AE assessment. We found a substantial improvement in accuracy/completeness with the automated grading tool, which identified an additional 17% of severe grade 3-4 AEs that had been missed/misgraded manually. The automated system also provided an average time saving of 5.5 min per treatment course. With 400 ongoing treatment trials at City of Hope and an average of 1800 laboratory results requiring assessment per study, the implications of these findings for patient safety are enormous.

Resumo Limpo

identif grade advers event ae conduct clinic trial laborintens errorpron process paper describ evalu softwar tool develop citi hope autom complex algorithm assess laboratori result identifi grade ae compar ae identifi autom system previous assess manual evalu missedmisgrad ae also conduct prospect pair time assess autom versus manual ae assess found substanti improv accuracycomplet autom grade tool identifi addit sever grade ae missedmisgrad manual autom system also provid averag time save min per treatment cours ongo treatment trial citi hope averag laboratori result requir assess per studi implic find patient safeti enorm

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