AMIA Annu Symp Proc - A method to compute treatment suggestions from local order entry data.

Tópicos

{ decis(3086) make(1611) patient(1517) }
{ use(976) code(926) identifi(902) }
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{ data(3008) multipl(1320) sourc(1022) }
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{ model(3404) distribut(989) bayesian(671) }
{ imag(2675) segment(2577) method(1081) }
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{ health(3367) inform(1360) care(1135) }
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{ ehr(2073) health(1662) electron(1139) }
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{ signal(2180) analysi(812) frequenc(800) }
{ group(2977) signific(1463) compar(1072) }
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{ structur(1116) can(940) graph(676) }
{ high(1669) rate(1365) level(1280) }
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{ drug(1928) target(777) effect(648) }
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{ activ(1452) weight(1219) physic(1104) }

Resumo

Although clinical decision support systems can reduce costs and improve care, the challenges associated with manually maintaining content has led to low utilization. Here we pilot an alternative, more automatic approach to decision support content generation. We use local order entry data and Bayesian networks to automatically find multivariate associations and suggest treatments. We evaluated this on 5044 hospitalizations of pregnant women, choosing 70 frequent order and treatment variables comprising 20 treatable conditions. The method produced treatment suggestion lists for 15 of these conditions. The lists captured accurate and non-trivial clinical knowledge, and all contained the key treatment for the condition, often as the first suggestion (71% overall, 90% non-labor-related). Additionally, when run on a test set of patient data, it very accurately predicted treatments (average AUC .873) and predicted pregnancy-specific treatments with even higher accuracy (AUC above .9). This method is a starting point for harnessing the wisdom-of-the-crowd for decision support.

Resumo Limpo

although clinic decis support system can reduc cost improv care challeng associ manual maintain content led low util pilot altern automat approach decis support content generat use local order entri data bayesian network automat find multivari associ suggest treatment evalu hospit pregnant women choos frequent order treatment variabl compris treatabl condit method produc treatment suggest list condit list captur accur nontrivi clinic knowledg contain key treatment condit often first suggest overal nonlaborrel addit run test set patient data accur predict treatment averag auc predict pregnancyspecif treatment even higher accuraci auc method start point har wisdomofthecrowd decis support

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