Int J Med Inform - Comparing New Zealand's 'Middle Out' health information technology strategy with other OECD nations.

Tópicos

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{ result(1111) use(1088) new(759) }
{ care(1570) inform(1187) nurs(1089) }
{ howev(809) still(633) remain(590) }
{ research(1218) medic(880) student(794) }
{ can(774) often(719) complex(702) }
{ data(1737) use(1416) pattern(1282) }
{ system(1976) rule(880) can(841) }
{ patient(2315) diseas(1263) diabet(1191) }
{ algorithm(1844) comput(1787) effici(935) }
{ perform(999) metric(946) measur(919) }
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{ record(1888) medic(1808) patient(1693) }
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{ method(2212) result(1239) propos(1039) }
{ take(945) account(800) differ(722) }
{ treatment(1704) effect(941) patient(846) }
{ control(1307) perform(991) simul(935) }
{ general(901) number(790) one(736) }
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{ detect(2391) sensit(1101) algorithm(908) }
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{ framework(1458) process(801) describ(734) }
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{ error(1145) method(1030) estim(1020) }
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{ design(1359) user(1324) use(1319) }
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{ surgeri(1148) surgic(1085) robot(1054) }
{ learn(2355) train(1041) set(1003) }
{ clinic(1479) use(1117) guidelin(835) }
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{ model(2220) cell(1177) simul(1124) }
{ method(984) reconstruct(947) comput(926) }
{ search(2224) databas(1162) retriev(909) }
{ featur(1941) imag(1645) propos(1176) }
{ case(1353) use(1143) diagnosi(1136) }
{ data(3963) clinic(1234) research(1004) }
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{ patient(2837) hospit(1953) medic(668) }
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{ data(2317) use(1299) case(1017) }
{ age(1611) year(1155) adult(843) }
{ medic(1828) order(1363) alert(1069) }
{ signal(2180) analysi(812) frequenc(800) }
{ group(2977) signific(1463) compar(1072) }
{ 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) }
{ can(981) present(881) function(850) }
{ analysi(2126) use(1163) compon(1037) }
{ 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) }
{ 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) }

Resumo

Implementation of efficient, universally applied, computer to computer communications is a high priority for many national health systems. As a consequence, much effort has been channelled into finding ways in which a patient's previous medical history can be made accessible when needed. A number of countries have attempted to share patients' records, with varying degrees of success. While most efforts to create record-sharing architectures have relied upon government-provided strategy and funding, New Zealand has taken a different approach. Like most British Commonwealth nations, New Zealand has a 'hybrid' publicly/privately funded health system. However its information technology infrastructure and automation has largely been developed by the private sector, working closely with regional and central government agencies. Currently the sector is focused on finding ways in which patient records can be shared amongst providers across three different regions. New Zealand's healthcare IT model combines government contributed funding, core infrastructure, facilitation and leadership with private sector investment and skills and is being delivered via a set of controlled experiments. The net result is a 'Middle Out' approach to healthcare automation. 'Middle Out' relies upon having a clear, well-articulated health-reform strategy and a determination by both public and private sector organisations to implement useful healthcare IT solutions by working closely together.

Resumo Limpo

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