Int J Med Inform - Conceptualising and creating a global learning health system.

Tópicos

{ health(3367) inform(1360) care(1135) }
{ howev(809) still(633) remain(590) }
{ ehr(2073) health(1662) electron(1139) }
{ learn(2355) train(1041) set(1003) }
{ model(2656) set(1616) predict(1553) }
{ import(1318) role(1303) understand(862) }
{ imag(1057) registr(996) error(939) }
{ research(1218) medic(880) student(794) }
{ decis(3086) make(1611) patient(1517) }
{ motion(1329) object(1292) video(1091) }
{ spatial(1525) area(1432) region(1030) }
{ record(1888) medic(1808) patient(1693) }
{ search(2224) databas(1162) retriev(909) }
{ drug(1928) target(777) effect(648) }
{ network(2748) neural(1063) input(814) }
{ take(945) account(800) differ(722) }
{ problem(2511) optim(1539) algorithm(950) }
{ extract(1171) text(1153) clinic(932) }
{ data(1714) softwar(1251) tool(1186) }
{ studi(1410) differ(1259) use(1210) }
{ model(2341) predict(2261) use(1141) }
{ state(1844) use(1261) util(961) }
{ activ(1138) subject(705) human(624) }
{ inform(2794) health(2639) internet(1427) }
{ bind(1733) structur(1185) ligand(1036) }
{ imag(2830) propos(1344) filter(1198) }
{ studi(2440) review(1878) systemat(933) }
{ treatment(1704) effect(941) patient(846) }
{ framework(1458) process(801) describ(734) }
{ error(1145) method(1030) estim(1020) }
{ chang(1828) time(1643) increas(1301) }
{ care(1570) inform(1187) nurs(1089) }
{ general(901) number(790) one(736) }
{ data(3963) clinic(1234) research(1004) }
{ risk(3053) factor(974) diseas(938) }
{ visual(1396) interact(850) tool(830) }
{ compound(1573) activ(1297) structur(1058) }
{ blood(1257) pressur(1144) flow(957) }
{ model(3480) simul(1196) paramet(876) }
{ data(2317) use(1299) case(1017) }
{ age(1611) year(1155) adult(843) }
{ first(2504) two(1366) second(1323) }
{ intervent(3218) particip(2042) group(1664) }
{ time(1939) patient(1703) rate(768) }
{ use(2086) technolog(871) perceiv(783) }
{ result(1111) use(1088) new(759) }
{ process(1125) use(805) approach(778) }
{ activ(1452) weight(1219) physic(1104) }
{ model(3404) distribut(989) bayesian(671) }
{ can(774) often(719) complex(702) }
{ imag(1947) propos(1133) code(1026) }
{ data(1737) use(1416) pattern(1282) }
{ system(1976) rule(880) can(841) }
{ measur(2081) correl(1212) valu(896) }
{ sequenc(1873) structur(1644) protein(1328) }
{ method(1219) similar(1157) match(930) }
{ featur(3375) classif(2383) classifi(1994) }
{ imag(2675) segment(2577) method(1081) }
{ patient(2315) diseas(1263) diabet(1191) }
{ assess(1506) score(1403) qualiti(1306) }
{ surgeri(1148) surgic(1085) robot(1054) }
{ 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) }
{ method(984) reconstruct(947) comput(926) }
{ featur(1941) imag(1645) propos(1176) }
{ case(1353) use(1143) diagnosi(1136) }
{ perform(999) metric(946) measur(919) }
{ research(1085) discuss(1038) issu(1018) }
{ system(1050) medic(1026) inform(1018) }
{ perform(1367) use(1326) method(1137) }
{ studi(1119) effect(1106) posit(819) }
{ monitor(1329) mobil(1314) devic(1160) }
{ patient(2837) hospit(1953) medic(668) }
{ 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) }
{ gene(2352) biolog(1181) express(1162) }
{ data(3008) multipl(1320) sourc(1022) }
{ patient(1821) servic(1111) care(1106) }
{ 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) }
{ use(1733) differ(960) four(931) }
{ implement(1333) system(1263) develop(1122) }
{ survey(1388) particip(1329) question(1065) }
{ estim(2440) model(1874) function(577) }
{ method(1969) cluster(1462) data(1082) }
{ method(2212) result(1239) propos(1039) }
{ detect(2391) sensit(1101) algorithm(908) }

Resumo

In any country the health sector is important in terms of human wellbeing and large in terms of economics. The health sector might therefore be expected to be a finely tuned enterprise, utilising corporate knowledge in a constant process of critically reviewing and improving its activities and processes. However, this is seldom the case. Health systems and practice are highly variable and lag behind research discovery. This contrasts strongly with commercial bodies, and particularly service industries, where the concept of the learning organisation is strongly seen as the key to optimisation. A learning organisation accesses for analytic purposes operational data, which though captured and recorded for day-to-day transactions at the customer level, become also the basis of understanding changes in both demand and delivery process. In health care, the concept of the learning organisation is well grounded ethically. Anything which can improve health, including understanding of optimal care delivery processes and how to improve longer term outcomes, should be seized upon to drive service improvement - but currently this occurs haphazardly. The limitations of paper-based systems, priority given to digitalization of financial transactions, concerns about electronic data insecurity, and other factors have inhibited progress towards organisational learning at a national scale. But in recent years, new means of capturing, managing, and exchanging data have created new opportunities, while ever increasing pressures on health systems have produced strengthened incentive. In the United States, the current policy and investment impetus to electronic health records and concomitantly their 'meaningful use' create opportunities to build the foundations for data re-use for corporate learning - and thus for societal gain. In Europe and other settings there are islands of innovation, but not yet a coherent culture or impetus to build foundations for a learning health system. This paper considers how to move forward, in the light of the urgent need for smarter health systems where experience becomes the fuel for rapid improvement, and best practices are routinely identified and applied.

Resumo Limpo

countri health sector import term human wellb larg term econom health sector might therefor expect fine tune enterpris utilis corpor knowledg constant process critic review improv activ process howev seldom case health system practic high variabl lag behind research discoveri contrast strong commerci bodi particular servic industri concept learn organis strong seen key optimis learn organis access analyt purpos oper data though captur record daytoday transact custom level becom also basi understand chang demand deliveri process health care concept learn organis well ground ethic anyth can improv health includ understand optim care deliveri process improv longer term outcom seiz upon drive servic improv current occur haphazard limit paperbas system prioriti given digit financi transact concern electron data insecur factor inhibit progress toward organis learn nation scale recent year new mean captur manag exchang data creat new opportun ever increas pressur health system produc strengthen incent unit state current polici invest impetus electron health record concomit meaning use creat opportun build foundat data reus corpor learn thus societ gain europ set island innov yet coher cultur impetus build foundat learn health system paper consid move forward light urgent need smarter health system experi becom fuel rapid improv best practic routin identifi appli

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