J Clin Monit Comput - Connecting the dots: rule-based decision support systems in the modern EMR era.

Tópicos

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{ implement(1333) system(1263) develop(1122) }
{ record(1888) medic(1808) patient(1693) }
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{ risk(3053) factor(974) diseas(938) }
{ patient(2837) hospit(1953) medic(668) }
{ data(1737) use(1416) pattern(1282) }
{ care(1570) inform(1187) nurs(1089) }
{ general(901) number(790) one(736) }
{ studi(1119) effect(1106) posit(819) }
{ data(2317) use(1299) case(1017) }
{ gene(2352) biolog(1181) express(1162) }
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{ network(2748) neural(1063) input(814) }
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{ learn(2355) train(1041) set(1003) }
{ concept(1167) ontolog(924) domain(897) }
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{ extract(1171) text(1153) clinic(932) }
{ method(1557) propos(1049) approach(1037) }
{ data(1714) softwar(1251) tool(1186) }
{ design(1359) user(1324) use(1319) }
{ control(1307) perform(991) simul(935) }
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{ system(1050) medic(1026) inform(1018) }
{ import(1318) role(1303) understand(862) }
{ model(2341) predict(2261) use(1141) }
{ visual(1396) interact(850) tool(830) }
{ perform(1367) use(1326) method(1137) }
{ blood(1257) pressur(1144) flow(957) }
{ spatial(1525) area(1432) region(1030) }
{ health(3367) inform(1360) care(1135) }
{ model(3480) simul(1196) paramet(876) }
{ ehr(2073) health(1662) electron(1139) }
{ model(2656) set(1616) predict(1553) }
{ age(1611) year(1155) adult(843) }
{ signal(2180) analysi(812) frequenc(800) }
{ group(2977) signific(1463) compar(1072) }
{ data(3008) multipl(1320) sourc(1022) }
{ time(1939) patient(1703) rate(768) }
{ patient(1821) servic(1111) care(1106) }
{ use(2086) technolog(871) perceiv(783) }
{ can(981) present(881) function(850) }
{ analysi(2126) use(1163) compon(1037) }
{ structur(1116) can(940) graph(676) }
{ high(1669) rate(1365) level(1280) }
{ cancer(2502) breast(956) screen(824) }
{ use(976) code(926) identifi(902) }
{ use(1733) differ(960) four(931) }
{ result(1111) use(1088) new(759) }
{ survey(1388) particip(1329) question(1065) }
{ estim(2440) model(1874) function(577) }
{ activ(1452) weight(1219) physic(1104) }
{ method(1969) cluster(1462) data(1082) }
{ method(2212) result(1239) propos(1039) }

Resumo

The intensive care unit (ICU) environment is rich in both medical device and electronic medical record (EMR) data. The ICU patient population is particularly vulnerable to medical error or delayed medical intervention both of which are associated with excess morbidity, mortality and cost. The development and deployment of smart alarms, computerized decision support systems (DSS) and "sniffers" within ICU clinical information systems has the potential to improve the safety and outcomes of critically ill hospitalized patients. However, the current generations of alerts, run largely through bedside monitors, are far from ideal and rarely support the clinician in the early recognition of complex physiologic syndromes or deviations from expected care pathways. False alerts and alert fatigue remain prevalent. In the coming era of widespread EMR implementation novel medical informatics methods may be adaptable to the development of next generation, rule-based DSS.

Resumo Limpo

intens care unit icu environ rich medic devic electron medic record emr data icu patient popul particular vulner medic error delay medic intervent associ excess morbid mortal cost develop deploy smart alarm computer decis support system dss sniffer within icu clinic inform system potenti improv safeti outcom critic ill hospit patient howev current generat alert run larg bedsid monitor far ideal rare support clinician earli recognit complex physiolog syndrom deviat expect care pathway fals alert alert fatigu remain preval come era widespread emr implement novel medic informat method may adapt develop next generat rulebas dss

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