AMIA Annu Symp Proc - A successful model and visual design for creating context-aware drug-drug interaction alerts.

Tópicos

{ medic(1828) order(1363) alert(1069) }
{ model(2656) set(1616) predict(1553) }
{ design(1359) user(1324) use(1319) }
{ algorithm(1844) comput(1787) effici(935) }
{ research(1218) medic(880) student(794) }
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{ concept(1167) ontolog(924) domain(897) }
{ risk(3053) factor(974) diseas(938) }
{ patient(2837) hospit(1953) medic(668) }
{ studi(2440) review(1878) systemat(933) }
{ care(1570) inform(1187) nurs(1089) }
{ system(1050) medic(1026) inform(1018) }
{ activ(1138) subject(705) human(624) }
{ use(1733) differ(960) four(931) }
{ inform(2794) health(2639) internet(1427) }
{ system(1976) rule(880) can(841) }
{ patient(2315) diseas(1263) diabet(1191) }
{ surgeri(1148) surgic(1085) robot(1054) }
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{ record(1888) medic(1808) patient(1693) }
{ health(3367) inform(1360) care(1135) }
{ state(1844) use(1261) util(961) }
{ data(2317) use(1299) case(1017) }
{ age(1611) year(1155) adult(843) }
{ signal(2180) analysi(812) frequenc(800) }
{ group(2977) signific(1463) compar(1072) }
{ sampl(1606) size(1419) use(1276) }
{ first(2504) two(1366) second(1323) }
{ intervent(3218) particip(2042) group(1664) }
{ time(1939) patient(1703) rate(768) }
{ patient(1821) servic(1111) care(1106) }
{ use(2086) technolog(871) perceiv(783) }
{ analysi(2126) use(1163) compon(1037) }
{ health(1844) social(1437) communiti(874) }
{ structur(1116) can(940) graph(676) }
{ high(1669) rate(1365) level(1280) }
{ cancer(2502) breast(956) screen(824) }
{ use(976) code(926) identifi(902) }
{ drug(1928) target(777) effect(648) }
{ result(1111) use(1088) new(759) }
{ implement(1333) system(1263) develop(1122) }
{ estim(2440) model(1874) function(577) }
{ decis(3086) make(1611) patient(1517) }
{ activ(1452) weight(1219) physic(1104) }
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{ method(2212) result(1239) propos(1039) }
{ detect(2391) sensit(1101) algorithm(908) }

Resumo

Evaluating the potential harm of a drug-drug interaction (DDI) requires knowledge of a patient's relevant co-morbidities and risk factors. Current DDI alerts lack such patient-specific contextual data. In this paper, we present an efficient model for integrating pertinent patient data into DDI alerts. This framework is designed to be interoperable across multiple drug knowledge bases and clinical information systems. To evaluate the model, we generated a set of contextual DDI data using our local drug knowledge base then conducted an evaluation study of a prototype contextual alert design. The alert received favorable ratings from study subjects, who agreed it was an improvement over traditional alerts and was likely to support clinical management and save physician time. This framework may ultimately help reduce alert fatigue through the dynamic display of DDI alerts based on patient risk.

Resumo Limpo

evalu potenti harm drugdrug interact ddi requir knowledg patient relev comorbid risk factor current ddi alert lack patientspecif contextu data paper present effici model integr pertin patient data ddi alert framework design interoper across multipl drug knowledg base clinic inform system evalu model generat set contextu ddi data use local drug knowledg base conduct evalu studi prototyp contextu alert design alert receiv favor rate studi subject agre improv tradit alert like support clinic manag save physician time framework may ultim help reduc alert fatigu dynam display ddi alert base patient risk

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