AMIA Annu Symp Proc - Toward a Virtual Lifetime Electronic Record: the Department of Veterans Affairs experience with the Nationwide Health Information Network.

Tópicos

{ health(3367) inform(1360) care(1135) }
{ care(1570) inform(1187) nurs(1089) }
{ research(1218) medic(880) student(794) }
{ system(1050) medic(1026) inform(1018) }
{ record(1888) medic(1808) patient(1693) }
{ general(901) number(790) one(736) }
{ research(1085) discuss(1038) issu(1018) }
{ patient(1821) servic(1111) care(1106) }
{ implement(1333) system(1263) develop(1122) }
{ can(774) often(719) complex(702) }
{ featur(1941) imag(1645) propos(1176) }
{ ehr(2073) health(1662) electron(1139) }
{ model(2656) set(1616) predict(1553) }
{ use(1733) differ(960) four(931) }
{ measur(2081) correl(1212) valu(896) }
{ imag(1057) registr(996) error(939) }
{ sequenc(1873) structur(1644) protein(1328) }
{ network(2748) neural(1063) input(814) }
{ studi(2440) review(1878) systemat(933) }
{ clinic(1479) use(1117) guidelin(835) }
{ algorithm(1844) comput(1787) effici(935) }
{ data(1714) softwar(1251) tool(1186) }
{ patient(2837) hospit(1953) medic(668) }
{ medic(1828) order(1363) alert(1069) }
{ intervent(3218) particip(2042) group(1664) }
{ activ(1138) subject(705) human(624) }
{ result(1111) use(1088) new(759) }
{ process(1125) use(805) approach(778) }
{ model(3404) distribut(989) bayesian(671) }
{ imag(1947) propos(1133) code(1026) }
{ data(1737) use(1416) pattern(1282) }
{ inform(2794) health(2639) internet(1427) }
{ system(1976) rule(880) can(841) }
{ bind(1733) structur(1185) ligand(1036) }
{ method(1219) similar(1157) match(930) }
{ featur(3375) classif(2383) classifi(1994) }
{ imag(2830) propos(1344) filter(1198) }
{ imag(2675) segment(2577) method(1081) }
{ patient(2315) diseas(1263) diabet(1191) }
{ take(945) account(800) differ(722) }
{ motion(1329) object(1292) video(1091) }
{ assess(1506) score(1403) qualiti(1306) }
{ treatment(1704) effect(941) patient(846) }
{ surgeri(1148) surgic(1085) robot(1054) }
{ 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) }
{ concept(1167) ontolog(924) domain(897) }
{ extract(1171) text(1153) clinic(932) }
{ method(1557) propos(1049) approach(1037) }
{ design(1359) user(1324) use(1319) }
{ control(1307) perform(991) simul(935) }
{ model(2220) cell(1177) simul(1124) }
{ method(984) reconstruct(947) comput(926) }
{ search(2224) databas(1162) retriev(909) }
{ case(1353) use(1143) diagnosi(1136) }
{ 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) }
{ import(1318) role(1303) understand(862) }
{ model(2341) predict(2261) use(1141) }
{ visual(1396) interact(850) tool(830) }
{ compound(1573) activ(1297) structur(1058) }
{ perform(1367) use(1326) method(1137) }
{ studi(1119) effect(1106) posit(819) }
{ blood(1257) pressur(1144) flow(957) }
{ spatial(1525) area(1432) region(1030) }
{ model(3480) simul(1196) paramet(876) }
{ monitor(1329) mobil(1314) devic(1160) }
{ state(1844) use(1261) util(961) }
{ 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) }
{ sampl(1606) size(1419) use(1276) }
{ gene(2352) biolog(1181) express(1162) }
{ data(3008) multipl(1320) sourc(1022) }
{ first(2504) two(1366) second(1323) }
{ time(1939) patient(1703) rate(768) }
{ use(2086) technolog(871) perceiv(783) }
{ can(981) present(881) function(850) }
{ analysi(2126) use(1163) compon(1037) }
{ health(1844) social(1437) communiti(874) }
{ structur(1116) can(940) graph(676) }
{ high(1669) rate(1365) level(1280) }
{ cancer(2502) breast(956) screen(824) }
{ use(976) code(926) identifi(902) }
{ drug(1928) target(777) effect(648) }
{ survey(1388) particip(1329) question(1065) }
{ estim(2440) model(1874) function(577) }
{ decis(3086) make(1611) patient(1517) }
{ 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

Health information exchange is expected of all electronic health records (EHRs) in order to ensure safe, quality care coordination. The U.S. Department of Veterans Affairs (VA) has a long history of information exchange across VA facilities and with the U.S. Department of Defense (DoD). However, since a majority of VA and DoD patients receive a portion of their health care from the private sector, it is essential that both agencies enable health information exchange with private sector providers. This has been made possible by the use of the specifications and trust agreement developed by the Nationwide Health Information Network (NwHIN) initiative. Currently, VA has 12 medical centers exchanging information with the private sector and is evaluating the value of the exchange. The authors report on the success of these pilots as well as on the challenges, which include stricter technical specifications and a more efficient approach to patient identification (ID) matching and consent management.

Resumo Limpo

health inform exchang expect electron health record ehr order ensur safe qualiti care coordin us depart veteran affair va long histori inform exchang across va facil us depart defens dod howev sinc major va dod patient receiv portion health care privat sector essenti agenc enabl health inform exchang privat sector provid made possibl use specif trust agreement develop nationwid health inform network nwhin initi current va medic center exchang inform privat sector evalu valu exchang author report success pilot well challeng includ stricter technic specif effici approach patient identif id match consent manag

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