AMIA Annu Symp Proc - Data-driven order set generation and evaluation in the pediatric environment.

Tópicos

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{ patient(2837) hospit(1953) medic(668) }
{ model(2656) set(1616) predict(1553) }
{ data(2317) use(1299) case(1017) }
{ signal(2180) analysi(812) frequenc(800) }
{ cost(1906) reduc(1198) effect(832) }
{ group(2977) signific(1463) compar(1072) }
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{ gene(2352) biolog(1181) express(1162) }
{ data(3008) multipl(1320) sourc(1022) }
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{ survey(1388) particip(1329) question(1065) }
{ estim(2440) model(1874) function(577) }
{ decis(3086) make(1611) patient(1517) }
{ process(1125) use(805) approach(778) }
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{ detect(2391) sensit(1101) algorithm(908) }

Resumo

Order sets as part of the Computerized Provider Order Entry (CPOE) system can improve care delivery through allowing faster and easier physician order entry guided by known best practices. This study examines current utilization patterns of order sets and "a la carte" orders in a pediatric environment with a preliminary investigation of methods to automate the creation and modification of order sets using historical ordering data. We examine the current usage of order sets associated with Asthma Minor and Appendectomy Minor patients to understand how physicians are utilizing order sets, and how order set usage is associated with the time of ordering and characteristics of order sets. K-means clustering was applied to orders to generate evidence-based order sets that are learned from historical hospital data. We demonstrate that coverage rate of order sets and ordering efficiency can be increased through modifications of existing sets and creation of new sets.

Resumo Limpo

order set part computer provid order entri cpoe system can improv care deliveri allow faster easier physician order entri guid known best practic studi examin current util pattern order set la cart order pediatr environ preliminari investig method autom creation modif order set use histor order data examin current usag order set associ asthma minor appendectomi minor patient understand physician util order set order set usag associ time order characterist order set kmean cluster appli order generat evidencebas order set learn histor hospit data demonstr coverag rate order set order effici can increas modif exist set creation new set

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