AMIA Annu Symp Proc - A multi-site content analysis of social history information in clinical notes.

Tópicos

{ health(1844) social(1437) communiti(874) }
{ record(1888) medic(1808) patient(1693) }
{ patient(2315) diseas(1263) diabet(1191) }
{ design(1359) user(1324) use(1319) }
{ state(1844) use(1261) util(961) }
{ ehr(2073) health(1662) electron(1139) }
{ use(976) code(926) identifi(902) }
{ use(1733) differ(960) four(931) }
{ import(1318) role(1303) understand(862) }
{ clinic(1479) use(1117) guidelin(835) }
{ data(1714) softwar(1251) tool(1186) }
{ data(3963) clinic(1234) research(1004) }
{ studi(1119) effect(1106) posit(819) }
{ analysi(2126) use(1163) compon(1037) }
{ model(3404) distribut(989) bayesian(671) }
{ method(1219) similar(1157) match(930) }
{ imag(2830) propos(1344) filter(1198) }
{ surgeri(1148) surgic(1085) robot(1054) }
{ learn(2355) train(1041) set(1003) }
{ extract(1171) text(1153) clinic(932) }
{ method(1557) propos(1049) approach(1037) }
{ method(984) reconstruct(947) comput(926) }
{ studi(1410) differ(1259) use(1210) }
{ spatial(1525) area(1432) region(1030) }
{ research(1218) medic(880) student(794) }
{ patient(2837) hospit(1953) medic(668) }
{ data(2317) use(1299) case(1017) }
{ medic(1828) order(1363) alert(1069) }
{ use(2086) technolog(871) perceiv(783) }
{ can(774) often(719) complex(702) }
{ imag(1947) propos(1133) code(1026) }
{ data(1737) use(1416) pattern(1282) }
{ inform(2794) health(2639) internet(1427) }
{ system(1976) rule(880) can(841) }
{ 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) }
{ network(2748) neural(1063) input(814) }
{ imag(2675) segment(2577) method(1081) }
{ take(945) account(800) differ(722) }
{ studi(2440) review(1878) systemat(933) }
{ motion(1329) object(1292) video(1091) }
{ 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) }
{ concept(1167) ontolog(924) domain(897) }
{ algorithm(1844) comput(1787) effici(935) }
{ control(1307) perform(991) simul(935) }
{ model(2220) cell(1177) simul(1124) }
{ care(1570) inform(1187) nurs(1089) }
{ general(901) number(790) one(736) }
{ search(2224) databas(1162) retriev(909) }
{ featur(1941) imag(1645) propos(1176) }
{ case(1353) use(1143) diagnosi(1136) }
{ howev(809) still(633) remain(590) }
{ risk(3053) factor(974) diseas(938) }
{ perform(999) metric(946) measur(919) }
{ research(1085) discuss(1038) issu(1018) }
{ system(1050) medic(1026) inform(1018) }
{ model(2341) predict(2261) use(1141) }
{ visual(1396) interact(850) tool(830) }
{ compound(1573) activ(1297) structur(1058) }
{ perform(1367) use(1326) method(1137) }
{ blood(1257) pressur(1144) flow(957) }
{ health(3367) inform(1360) care(1135) }
{ model(3480) simul(1196) paramet(876) }
{ monitor(1329) mobil(1314) devic(1160) }
{ model(2656) set(1616) predict(1553) }
{ age(1611) year(1155) adult(843) }
{ signal(2180) analysi(812) frequenc(800) }
{ cost(1906) reduc(1198) effect(832) }
{ 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) }
{ time(1939) patient(1703) rate(768) }
{ patient(1821) servic(1111) care(1106) }
{ can(981) present(881) function(850) }
{ 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) }
{ decis(3086) make(1611) patient(1517) }
{ process(1125) use(805) approach(778) }
{ 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

Within Electronic Health Records (EHRs), the social history section contains information relevant to social, behavioral, and environmental determinants of health. While social history is playing an increasingly important role in patient care, biomedical research, and public health, little analysis has been done to describe content in the EHR or the adequacy of existing standards for representing this information. In this study, social history sections from 260 clinical notes containing 989 sentences and 1,439 statements were analyzed from three sources. In total, 35 statement types were identified along with categories of information within statements for each type. For the 8 most common types, HL7 CDA and openEHR were found to provide different representations capable of capturing the breadth and granularity of information to some extent. The results of this study provide valuable insights for guiding efforts in the enhanced collection, standardization, and use of social history information in the EHR.

Resumo Limpo

within electron health record ehr social histori section contain inform relev social behavior environment determin health social histori play increas import role patient care biomed research public health littl analysi done describ content ehr adequaci exist standard repres inform studi social histori section clinic note contain sentenc statement analyz three sourc total statement type identifi along categori inform within statement type common type hl cda openehr found provid differ represent capabl captur breadth granular inform extent result studi provid valuabl insight guid effort enhanc collect standard use social histori inform ehr

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