AMIA Annu Symp Proc - Knowledge crystallization and clinical priorities: evaluating how physicians collect and synthesize patient-related data.

Tópicos

{ care(1570) inform(1187) nurs(1089) }
{ use(2086) technolog(871) perceiv(783) }
{ clinic(1479) use(1117) guidelin(835) }
{ control(1307) perform(991) simul(935) }
{ data(3963) clinic(1234) research(1004) }
{ patient(2837) hospit(1953) medic(668) }
{ system(1050) medic(1026) inform(1018) }
{ process(1125) use(805) approach(778) }
{ studi(1410) differ(1259) use(1210) }
{ survey(1388) particip(1329) question(1065) }
{ decis(3086) make(1611) patient(1517) }
{ can(774) often(719) complex(702) }
{ record(1888) medic(1808) patient(1693) }
{ state(1844) use(1261) util(961) }
{ data(2317) use(1299) case(1017) }
{ time(1939) patient(1703) rate(768) }
{ health(1844) social(1437) communiti(874) }
{ imag(1947) propos(1133) code(1026) }
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{ method(1219) similar(1157) match(930) }
{ motion(1329) object(1292) video(1091) }
{ surgeri(1148) surgic(1085) robot(1054) }
{ concept(1167) ontolog(924) domain(897) }
{ featur(1941) imag(1645) propos(1176) }
{ howev(809) still(633) remain(590) }
{ risk(3053) factor(974) diseas(938) }
{ research(1085) discuss(1038) issu(1018) }
{ visual(1396) interact(850) tool(830) }
{ blood(1257) pressur(1144) flow(957) }
{ health(3367) inform(1360) care(1135) }
{ ehr(2073) health(1662) electron(1139) }
{ research(1218) medic(880) student(794) }
{ gene(2352) biolog(1181) express(1162) }
{ intervent(3218) particip(2042) group(1664) }
{ high(1669) rate(1365) level(1280) }
{ cancer(2502) breast(956) screen(824) }
{ estim(2440) model(1874) function(577) }
{ model(3404) distribut(989) bayesian(671) }
{ data(1737) use(1416) pattern(1282) }
{ measur(2081) correl(1212) valu(896) }
{ imag(1057) registr(996) error(939) }
{ bind(1733) structur(1185) ligand(1036) }
{ sequenc(1873) structur(1644) protein(1328) }
{ featur(3375) classif(2383) classifi(1994) }
{ imag(2830) propos(1344) filter(1198) }
{ network(2748) neural(1063) input(814) }
{ imag(2675) segment(2577) method(1081) }
{ patient(2315) diseas(1263) diabet(1191) }
{ take(945) account(800) differ(722) }
{ studi(2440) review(1878) systemat(933) }
{ assess(1506) score(1403) qualiti(1306) }
{ treatment(1704) effect(941) patient(846) }
{ framework(1458) process(801) describ(734) }
{ problem(2511) optim(1539) algorithm(950) }
{ error(1145) method(1030) estim(1020) }
{ chang(1828) time(1643) increas(1301) }
{ learn(2355) train(1041) set(1003) }
{ algorithm(1844) comput(1787) effici(935) }
{ extract(1171) text(1153) clinic(932) }
{ method(1557) propos(1049) approach(1037) }
{ data(1714) softwar(1251) tool(1186) }
{ design(1359) user(1324) use(1319) }
{ model(2220) cell(1177) simul(1124) }
{ general(901) number(790) one(736) }
{ method(984) reconstruct(947) comput(926) }
{ search(2224) databas(1162) retriev(909) }
{ case(1353) use(1143) diagnosi(1136) }
{ perform(999) metric(946) measur(919) }
{ import(1318) role(1303) understand(862) }
{ model(2341) predict(2261) use(1141) }
{ compound(1573) activ(1297) structur(1058) }
{ perform(1367) use(1326) method(1137) }
{ studi(1119) effect(1106) posit(819) }
{ spatial(1525) area(1432) region(1030) }
{ model(3480) simul(1196) paramet(876) }
{ monitor(1329) mobil(1314) devic(1160) }
{ model(2656) set(1616) predict(1553) }
{ age(1611) year(1155) adult(843) }
{ medic(1828) order(1363) alert(1069) }
{ signal(2180) analysi(812) frequenc(800) }
{ cost(1906) reduc(1198) effect(832) }
{ group(2977) signific(1463) compar(1072) }
{ sampl(1606) size(1419) use(1276) }
{ data(3008) multipl(1320) sourc(1022) }
{ first(2504) two(1366) second(1323) }
{ activ(1138) subject(705) human(624) }
{ patient(1821) servic(1111) care(1106) }
{ can(981) present(881) function(850) }
{ analysi(2126) use(1163) compon(1037) }
{ structur(1116) can(940) graph(676) }
{ use(976) code(926) identifi(902) }
{ use(1733) differ(960) four(931) }
{ drug(1928) target(777) effect(648) }
{ result(1111) use(1088) new(759) }
{ implement(1333) system(1263) develop(1122) }
{ 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

Information seeking and synthesis are time consuming processes for physicians. Although systems have the potential to simplify these tasks, future improvements must be based on an understanding of how physicians perform these tasks during clinical prioritization. We enrolled 23 physicians in semi-structured focus groups discussing simulated inpatient populations. Participants documented and discussed their data gathering and prioritization processes. Transcripts were coded to identify themes and generalized process flows. Results indicate that data are collected to categorize and prioritize patients according to expected clinical course. When data do not support these expectations, or when categorization indicates potential for morbidity, physicians increase efforts to act or recategorize patients. Unexpected clinical changes have a significant impact on the decision-making and prioritization by clinicians. A modified version of the Knowledge Crystallization Framework helps to frame this work laying a foundation to advance information displays and facilitate information processing by physicians in clinical care environments.

Resumo Limpo

inform seek synthesi time consum process physician although system potenti simplifi task futur improv must base understand physician perform task clinic priorit enrol physician semistructur focus group discuss simul inpati popul particip document discuss data gather priorit process transcript code identifi theme general process flow result indic data collect categor priorit patient accord expect clinic cours data support expect categor indic potenti morbid physician increas effort act recategor patient unexpect clinic chang signific impact decisionmak priorit clinician modifi version knowledg crystal framework help frame work lay foundat advanc inform display facilit inform process physician clinic care environ

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