AMIA Annu Symp Proc - Creating and Evaluating a Dynamic Study Randomization and Enrollment Tool within a Robust EHRs.

Tópicos

{ studi(2440) review(1878) systemat(933) }
{ model(3404) distribut(989) bayesian(671) }
{ data(2317) use(1299) case(1017) }
{ ehr(2073) health(1662) electron(1139) }
{ patient(2315) diseas(1263) diabet(1191) }
{ clinic(1479) use(1117) guidelin(835) }
{ patient(2837) hospit(1953) medic(668) }
{ motion(1329) object(1292) video(1091) }
{ error(1145) method(1030) estim(1020) }
{ cost(1906) reduc(1198) effect(832) }
{ decis(3086) make(1611) patient(1517) }
{ bind(1733) structur(1185) ligand(1036) }
{ extract(1171) text(1153) clinic(932) }
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{ state(1844) use(1261) util(961) }
{ model(2656) set(1616) predict(1553) }
{ medic(1828) order(1363) alert(1069) }
{ signal(2180) analysi(812) frequenc(800) }
{ sampl(1606) size(1419) use(1276) }
{ gene(2352) biolog(1181) express(1162) }
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{ first(2504) two(1366) second(1323) }
{ activ(1138) subject(705) human(624) }
{ patient(1821) servic(1111) care(1106) }
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{ can(981) present(881) function(850) }
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{ health(1844) social(1437) communiti(874) }
{ structur(1116) can(940) graph(676) }
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{ use(1733) differ(960) four(931) }
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{ activ(1452) weight(1219) physic(1104) }
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{ detect(2391) sensit(1101) algorithm(908) }

Resumo

Randomized trials are difficult to perform in resource-limited settings. We developed a Randomization and Enrollment Tool (RET) within a live EHRs which automated enrollment, randomization, and data-collection in support of robust EHRs-based randomized interventions. We describe an observational assessment of RET which we piloted at three Kenyan HIV clinics for a decision support trial. We manually evaluated RET's adequacy and accuracy in its core functions. RET enrolled 327/6626 patients, 100% meeting criteria based on EHRs data. Human reviews reveal that only 250 patients (76.5%) should have been enrolled as the EHRs contained inaccurate data for the other 77 (23.4%). 23 eligible patients were also missed through sole reliance on EHRs data. 18 (5.5%) RET-enrolled patients never received the intervention because of missed appointments. An automated randomization tool has potential to reduce human and financial costs of conducting EHRs-based randomized trials, but remains vulnerable to data quality and workflow limitations.

Resumo Limpo

random trial difficult perform resourcelimit set develop random enrol tool ret within live ehr autom enrol random datacollect support robust ehrsbas random intervent describ observ assess ret pilot three kenyan hiv clinic decis support trial manual evalu ret adequaci accuraci core function ret enrol patient meet criteria base ehr data human review reveal patient enrol ehr contain inaccur data elig patient also miss sole relianc ehr data retenrol patient never receiv intervent miss appoint autom random tool potenti reduc human financi cost conduct ehrsbas random trial remain vulner data qualiti workflow limit

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