AMIA Annu Symp Proc - OpenMRS, a global medical records system collaborative: factors influencing successful implementation.

Tópicos

{ ehr(2073) health(1662) electron(1139) }
{ data(1714) softwar(1251) tool(1186) }
{ data(3008) multipl(1320) sourc(1022) }
{ health(3367) inform(1360) care(1135) }
{ data(2317) use(1299) case(1017) }
{ inform(2794) health(2639) internet(1427) }
{ research(1085) discuss(1038) issu(1018) }
{ result(1111) use(1088) new(759) }
{ care(1570) inform(1187) nurs(1089) }
{ studi(1119) effect(1106) posit(819) }
{ cancer(2502) breast(956) screen(824) }
{ imag(1947) propos(1133) code(1026) }
{ perform(1367) use(1326) method(1137) }
{ medic(1828) order(1363) alert(1069) }
{ cost(1906) reduc(1198) effect(832) }
{ gene(2352) biolog(1181) express(1162) }
{ data(1737) use(1416) pattern(1282) }
{ bind(1733) structur(1185) ligand(1036) }
{ imag(2675) segment(2577) method(1081) }
{ framework(1458) process(801) describ(734) }
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{ chang(1828) time(1643) increas(1301) }
{ learn(2355) train(1041) set(1003) }
{ algorithm(1844) comput(1787) effici(935) }
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{ import(1318) role(1303) understand(862) }
{ model(2341) predict(2261) use(1141) }
{ blood(1257) pressur(1144) flow(957) }
{ spatial(1525) area(1432) region(1030) }
{ research(1218) medic(880) student(794) }
{ group(2977) signific(1463) compar(1072) }
{ activ(1138) subject(705) human(624) }
{ patient(1821) servic(1111) care(1106) }
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{ health(1844) social(1437) communiti(874) }
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{ implement(1333) system(1263) develop(1122) }
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{ can(774) often(719) complex(702) }
{ system(1976) rule(880) can(841) }
{ measur(2081) correl(1212) valu(896) }
{ imag(1057) registr(996) error(939) }
{ sequenc(1873) structur(1644) protein(1328) }
{ method(1219) similar(1157) match(930) }
{ featur(3375) classif(2383) classifi(1994) }
{ imag(2830) propos(1344) filter(1198) }
{ network(2748) neural(1063) input(814) }
{ patient(2315) diseas(1263) diabet(1191) }
{ 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) }
{ surgeri(1148) surgic(1085) robot(1054) }
{ error(1145) method(1030) estim(1020) }
{ concept(1167) ontolog(924) domain(897) }
{ clinic(1479) use(1117) guidelin(835) }
{ extract(1171) text(1153) clinic(932) }
{ method(1557) propos(1049) approach(1037) }
{ 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) }
{ featur(1941) imag(1645) propos(1176) }
{ 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) }
{ visual(1396) interact(850) tool(830) }
{ compound(1573) activ(1297) structur(1058) }
{ record(1888) medic(1808) patient(1693) }
{ model(3480) simul(1196) paramet(876) }
{ monitor(1329) mobil(1314) devic(1160) }
{ state(1844) use(1261) util(961) }
{ patient(2837) hospit(1953) medic(668) }
{ model(2656) set(1616) predict(1553) }
{ age(1611) year(1155) adult(843) }
{ signal(2180) analysi(812) frequenc(800) }
{ sampl(1606) size(1419) use(1276) }
{ first(2504) two(1366) second(1323) }
{ intervent(3218) particip(2042) group(1664) }
{ time(1939) patient(1703) rate(768) }
{ can(981) present(881) function(850) }
{ analysi(2126) use(1163) compon(1037) }
{ high(1669) rate(1365) level(1280) }
{ use(976) code(926) identifi(902) }
{ use(1733) differ(960) four(931) }
{ drug(1928) target(777) effect(648) }
{ 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) }
{ detect(2391) sensit(1101) algorithm(908) }

Resumo

OpenMRS is an open-source, robust electronic health record (EHR) platform that is supported by a large global network and used in over forty countries. We explored what factors lead to successful implementation of OpenMRS in resource constrained settings. Data sources included in-person and telephone key informant interviews, focus groups and responses to an electronic survey from 10 sites in 7 countries. Qualitative data was coded through independent coding, discussion and consensus. The most common perceived benefits of implementation were for providing clinical care, reporting to funders, managing operations and research. Successful implementation factors include securing adequate infrastructure, and sociotechnical system factors, particularly adequate staffing, computers, and ability to use software. Strategic and tactical planning were successful strategies, including understanding and addressing the infrastructure and human costs involved, training or hiring personnel technically capable of modifying the software and integrating it into the daily work flow to meet clinicians' needs.

Resumo Limpo

openmr opensourc robust electron health record ehr platform support larg global network use forti countri explor factor lead success implement openmr resourc constrain set data sourc includ inperson telephon key inform interview focus group respons electron survey site countri qualit data code independ code discuss consensus common perceiv benefit implement provid clinic care report funder manag oper research success implement factor includ secur adequ infrastructur sociotechn system factor particular adequ staf comput abil use softwar strateg tactic plan success strategi includ understand address infrastructur human cost involv train hire personnel technic capabl modifi softwar integr daili work flow meet clinician need

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