J Med Syst - EHR in emergency rooms: exploring the effect of key information components on main complaints.

Tópicos

{ inform(2794) health(2639) internet(1427) }
{ ehr(2073) health(1662) electron(1139) }
{ patient(2837) hospit(1953) medic(668) }
{ studi(1119) effect(1106) posit(819) }
{ decis(3086) make(1611) patient(1517) }
{ record(1888) medic(1808) patient(1693) }
{ data(2317) use(1299) case(1017) }
{ analysi(2126) use(1163) compon(1037) }
{ use(976) code(926) identifi(902) }
{ data(1737) use(1416) pattern(1282) }
{ surgeri(1148) surgic(1085) robot(1054) }
{ case(1353) use(1143) diagnosi(1136) }
{ model(3480) simul(1196) paramet(876) }
{ medic(1828) order(1363) alert(1069) }
{ cost(1906) reduc(1198) effect(832) }
{ assess(1506) score(1403) qualiti(1306) }
{ framework(1458) process(801) describ(734) }
{ error(1145) method(1030) estim(1020) }
{ import(1318) role(1303) understand(862) }
{ perform(1367) use(1326) method(1137) }
{ blood(1257) pressur(1144) flow(957) }
{ use(2086) technolog(871) perceiv(783) }
{ sequenc(1873) structur(1644) protein(1328) }
{ network(2748) neural(1063) input(814) }
{ imag(2675) segment(2577) method(1081) }
{ patient(2315) diseas(1263) diabet(1191) }
{ studi(2440) review(1878) systemat(933) }
{ problem(2511) optim(1539) algorithm(950) }
{ extract(1171) text(1153) clinic(932) }
{ data(1714) softwar(1251) tool(1186) }
{ general(901) number(790) one(736) }
{ system(1050) medic(1026) inform(1018) }
{ monitor(1329) mobil(1314) devic(1160) }
{ research(1218) medic(880) student(794) }
{ model(2656) set(1616) predict(1553) }
{ time(1939) patient(1703) rate(768) }
{ can(981) present(881) function(850) }
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{ detect(2391) sensit(1101) algorithm(908) }
{ model(3404) distribut(989) bayesian(671) }
{ can(774) often(719) complex(702) }
{ imag(1947) propos(1133) code(1026) }
{ system(1976) rule(880) can(841) }
{ measur(2081) correl(1212) valu(896) }
{ imag(1057) registr(996) error(939) }
{ bind(1733) structur(1185) ligand(1036) }
{ method(1219) similar(1157) match(930) }
{ featur(3375) classif(2383) classifi(1994) }
{ imag(2830) propos(1344) filter(1198) }
{ take(945) account(800) differ(722) }
{ motion(1329) object(1292) video(1091) }
{ treatment(1704) effect(941) patient(846) }
{ chang(1828) time(1643) increas(1301) }
{ learn(2355) train(1041) set(1003) }
{ concept(1167) ontolog(924) domain(897) }
{ clinic(1479) use(1117) guidelin(835) }
{ algorithm(1844) comput(1787) effici(935) }
{ method(1557) propos(1049) approach(1037) }
{ design(1359) user(1324) use(1319) }
{ control(1307) perform(991) simul(935) }
{ model(2220) cell(1177) simul(1124) }
{ care(1570) inform(1187) nurs(1089) }
{ method(984) reconstruct(947) comput(926) }
{ search(2224) databas(1162) retriev(909) }
{ featur(1941) imag(1645) propos(1176) }
{ howev(809) still(633) remain(590) }
{ data(3963) clinic(1234) research(1004) }
{ studi(1410) differ(1259) use(1210) }
{ risk(3053) factor(974) diseas(938) }
{ perform(999) metric(946) measur(919) }
{ research(1085) discuss(1038) issu(1018) }
{ model(2341) predict(2261) use(1141) }
{ visual(1396) interact(850) tool(830) }
{ compound(1573) activ(1297) structur(1058) }
{ spatial(1525) area(1432) region(1030) }
{ health(3367) inform(1360) care(1135) }
{ state(1844) use(1261) util(961) }
{ age(1611) year(1155) adult(843) }
{ signal(2180) analysi(812) frequenc(800) }
{ group(2977) signific(1463) compar(1072) }
{ sampl(1606) size(1419) use(1276) }
{ gene(2352) biolog(1181) express(1162) }
{ data(3008) multipl(1320) sourc(1022) }
{ first(2504) two(1366) second(1323) }
{ intervent(3218) particip(2042) group(1664) }
{ activ(1138) subject(705) human(624) }
{ patient(1821) servic(1111) care(1106) }
{ health(1844) social(1437) communiti(874) }
{ structur(1116) can(940) graph(676) }
{ high(1669) rate(1365) level(1280) }
{ cancer(2502) breast(956) screen(824) }
{ drug(1928) target(777) effect(648) }
{ result(1111) use(1088) new(759) }
{ implement(1333) system(1263) develop(1122) }
{ survey(1388) particip(1329) question(1065) }
{ estim(2440) model(1874) function(577) }
{ process(1125) use(805) approach(778) }
{ activ(1452) weight(1219) physic(1104) }
{ method(1969) cluster(1462) data(1082) }
{ method(2212) result(1239) propos(1039) }

Resumo

This study characterizes the information components associated with improved medical decision-making in the emergency room (ER). We looked at doctors' decisions to use or not to use information available to them on an electronic health record (EHR) and a Health Information Exchange (HIE) network, and tested for associations between their decision and parameters related to healthcare outcomes and processes. Using information components from the EHR and HIE was significantly related to improved quality of healthcare processes. Specifically, it was associated with both a reduction in potentially avoidable admissions as well as a reduction in rapid readmissions. Overall, the three information components; namely, previous encounters, imaging, and lab results emerged as having the strongest relationship with physicians' decisions to admit or discharge. Certain information components, however, presented an association between the diagnosis and the admission decisions (blood pressure was the most strongly associated parameter in cases of chest pain complaints and a previous surgical record for abdominal pain). These findings show that the ability to access patients' medical history and their long term health conditions (via the EHR), including information about medications, diagnoses, recent procedures and laboratory tests is critical to forming an appropriate plan of care and eventually making more accurate admission decisions.

Resumo Limpo

studi character inform compon associ improv medic decisionmak emerg room er look doctor decis use use inform avail electron health record ehr health inform exchang hie network test associ decis paramet relat healthcar outcom process use inform compon ehr hie signific relat improv qualiti healthcar process specif associ reduct potenti avoid admiss well reduct rapid readmiss overal three inform compon name previous encount imag lab result emerg strongest relationship physician decis admit discharg certain inform compon howev present associ diagnosi admiss decis blood pressur strong associ paramet case chest pain complaint previous surgic record abdomin pain find show abil access patient medic histori long term health condit via ehr includ inform medic diagnos recent procedur laboratori test critic form appropri plan care eventu make accur admiss decis

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