BMC Med Inform Decis Mak - Advancing clinical decision support using lessons from outside of healthcare: an interdisciplinary systematic review.

Tópicos

{ decis(3086) make(1611) patient(1517) }
{ ehr(2073) health(1662) electron(1139) }
{ research(1085) discuss(1038) issu(1018) }
{ studi(2440) review(1878) systemat(933) }
{ inform(2794) health(2639) internet(1427) }
{ research(1218) medic(880) student(794) }
{ process(1125) use(805) approach(778) }
{ sampl(1606) size(1419) use(1276) }
{ drug(1928) target(777) effect(648) }
{ system(1976) rule(880) can(841) }
{ learn(2355) train(1041) set(1003) }
{ featur(1941) imag(1645) propos(1176) }
{ import(1318) role(1303) understand(862) }
{ patient(1821) servic(1111) care(1106) }
{ use(2086) technolog(871) perceiv(783) }
{ analysi(2126) use(1163) compon(1037) }
{ method(1219) similar(1157) match(930) }
{ general(901) number(790) one(736) }
{ method(984) reconstruct(947) comput(926) }
{ howev(809) still(633) remain(590) }
{ health(1844) social(1437) communiti(874) }
{ high(1669) rate(1365) level(1280) }
{ result(1111) use(1088) new(759) }
{ model(3404) distribut(989) bayesian(671) }
{ can(774) often(719) complex(702) }
{ motion(1329) object(1292) video(1091) }
{ treatment(1704) effect(941) patient(846) }
{ chang(1828) time(1643) increas(1301) }
{ method(1557) propos(1049) approach(1037) }
{ studi(1410) differ(1259) use(1210) }
{ visual(1396) interact(850) tool(830) }
{ medic(1828) order(1363) alert(1069) }
{ data(3008) multipl(1320) sourc(1022) }
{ implement(1333) system(1263) develop(1122) }
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{ imag(2830) propos(1344) filter(1198) }
{ concept(1167) ontolog(924) domain(897) }
{ extract(1171) text(1153) clinic(932) }
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{ framework(1458) process(801) describ(734) }
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{ compound(1573) activ(1297) structur(1058) }
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{ health(3367) inform(1360) care(1135) }
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{ state(1844) use(1261) util(961) }
{ patient(2837) hospit(1953) medic(668) }
{ model(2656) set(1616) predict(1553) }
{ data(2317) use(1299) case(1017) }
{ age(1611) year(1155) adult(843) }
{ signal(2180) analysi(812) frequenc(800) }
{ cost(1906) reduc(1198) effect(832) }
{ group(2977) signific(1463) compar(1072) }
{ gene(2352) biolog(1181) express(1162) }
{ first(2504) two(1366) second(1323) }
{ intervent(3218) particip(2042) group(1664) }
{ activ(1138) subject(705) human(624) }
{ structur(1116) can(940) graph(676) }
{ cancer(2502) breast(956) screen(824) }
{ estim(2440) model(1874) function(577) }
{ activ(1452) weight(1219) physic(1104) }
{ method(1969) cluster(1462) data(1082) }
{ method(2212) result(1239) propos(1039) }
{ detect(2391) sensit(1101) algorithm(908) }

Resumo

CKGROUND: Greater use of computerized decision support (DS) systems could address continuing safety and quality problems in healthcare, but the healthcare field has struggled to implement DS technology. This study surveys DS experience across multiple non-healthcare disciplines for new insights that are generalizable to healthcare provider decisions. In particular, it sought design principles and lessons learned from the other disciplines that could inform efforts to accelerate the adoption of clinical decision support (CDS).METHODS: Our systematic review drew broadly from non-healthcare databases in the basic sciences, social sciences, humanities, engineering, business, and defense: PsychINFO, BusinessSource Premier, Social Sciences Abstracts, Web of Science, and Defense Technical Information Center. Because our interest was in DS that could apply to clinical decisions, we selected articles that (1) provided a review, overview, discussion of lessons learned, or an evaluation of design or implementation aspects of DS within a non-healthcare discipline and (2) involved an element of human judgment at the individual level, as opposed to decisions that can be fully automated or that are made at the organizational level.RESULTS: Clinical decisions share some similarities with decisions made by military commanders, business managers, and other leaders: they involve assessing new situations and choosing courses of action with major consequences, under time pressure, and with incomplete information. We identified seven high-level DS system design features from the non-healthcare literature that could be applied to CDS: providing broad, system-level perspectives; customizing interfaces to specific users and roles; making the DS reasoning transparent; presenting data effectively; generating multiple scenarios covering disparate outcomes (e.g., effective; effective with side effects; ineffective); allowing for contingent adaptations; and facilitating collaboration. The article provides examples of each feature. The DS literature also emphasizes the importance of organizational culture and training in implementation success. The literature contrasts "rational-analytic" vs. "naturalistic-intuitive" decision-making styles, but the best approach is often a balanced approach that combines both styles. It is also important for DS systems to enable exploration of multiple assumptions, and incorporation of new information in response to changing circumstances.CONCLUSIONS: Complex, high-level decision-making has common features across disciplines as seemingly disparate as defense, business, and healthcare. National efforts to advance the health information technology agenda through broader CDS adoption could benefit by applying the DS principles identified in this review.

Resumo Limpo

ckground greater use computer decis support ds system address continu safeti qualiti problem healthcar healthcar field struggl implement ds technolog studi survey ds experi across multipl nonhealthcar disciplin new insight generaliz healthcar provid decis particular sought design principl lesson learn disciplin inform effort acceler adopt clinic decis support cdsmethod systemat review drew broad nonhealthcar databas basic scienc social scienc human engin busi defens psychinfo businesssourc premier social scienc abstract web scienc defens technic inform center interest ds appli clinic decis select articl provid review overview discuss lesson learn evalu design implement aspect ds within nonhealthcar disciplin involv element human judgment individu level oppos decis can fulli autom made organiz levelresult clinic decis share similar decis made militari command busi manag leader involv assess new situat choos cours action major consequ time pressur incomplet inform identifi seven highlevel ds system design featur nonhealthcar literatur appli cds provid broad systemlevel perspect custom interfac specif user role make ds reason transpar present data effect generat multipl scenario cover dispar outcom eg effect effect side effect ineffect allow conting adapt facilit collabor articl provid exampl featur ds literatur also emphas import organiz cultur train implement success literatur contrast rationalanalyt vs naturalisticintuit decisionmak style best approach often balanc approach combin style also import ds system enabl explor multipl assumpt incorpor new inform respons chang circumstancesconclus complex highlevel decisionmak common featur across disciplin seem dispar defens busi healthcar nation effort advanc health inform technolog agenda broader cds adopt benefit appli ds principl identifi review

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