Appl Clin Inform - Analysis of electronic medication orders with large overdoses: opportunities for mitigating dosing errors.

Tópicos

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Resumo

CKGROUND: Users of electronic health record (EHR) systems frequently prescribe doses outside recommended dose ranges, and tend to ignore the alerts that result. Since some of these dosing errors are the result of system design flaws, analysis of large overdoses can lead to the discovery of needed system changes.OBJECTIVES: To develop database techniques for detecting and extracting large overdose orders from our EHR. To identify and characterize users' responses to these large overdoses. To identify possible causes of large-overdose errors and to mitigate them.METHODS: We constructed a data mart of medication-order and dosing-alert data from a quaternary pediatric hospital from June 2011 to May 2013. The data mart was used along with a test version of the EHR to explain how orders were processed and alerts were generated for large (>500%) and extreme (>10,000%) overdoses. User response was characterized by the dosing alert salience rate, which expresses the proportion of time users take corrective action.RESULTS: We constructed an advanced analytic framework based on workflow analysis and order simulation, and evaluated all 5,402,504 medication orders placed within the 2 year timeframe as well as 2,232,492 dose alerts associated with some of the orders. 8% of orders generated a visible alert, with ? of these related to overdosing. Alerts presented to trainees had higher salience rates than those presented to senior colleagues. Salience rates were low, varying between 4-10%, and were lower with larger overdoses. Extreme overdoses fell into eight causal categories, each with a system design mitigation.CONCLUSIONS: Novel analytic systems are required to accurately understand prescriber behavior and interactions with medication-dosing CDS. We described a novel analytic system that can detect apparent large overdoses (=500%) and explain the sociotechnical factors that drove the error. Some of these large overdoses can be mitigated by system changes. EHR design should prospectively mitigate these errors.

Resumo Limpo

ckground user electron health record ehr system frequent prescrib dose outsid recommend dose rang tend ignor alert result sinc dose error result system design flaw analysi larg overdos can lead discoveri need system changesobject develop databas techniqu detect extract larg overdos order ehr identifi character user respons larg overdos identifi possibl caus largeoverdos error mitig themmethod construct data mart medicationord dosingalert data quaternari pediatr hospit june may data mart use along test version ehr explain order process alert generat larg extrem overdos user respons character dose alert salienc rate express proport time user take correct actionresult construct advanc analyt framework base workflow analysi order simul evalu medic order place within year timefram well dose alert associ order order generat visibl alert relat overdos alert present traine higher salienc rate present senior colleagu salienc rate low vari lower larger overdos extrem overdos fell eight causal categori system design mitigationconclus novel analyt system requir accur understand prescrib behavior interact medicationdos cds describ novel analyt system can detect appar larg overdos explain sociotechn factor drove error larg overdos can mitig system chang ehr design prospect mitig error

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