Int J Med Inform - A Rasch model analysis of technology usage in Minnesota hospitals.

Tópicos

{ health(3367) inform(1360) care(1135) }
{ patient(2837) hospit(1953) medic(668) }
{ implement(1333) system(1263) develop(1122) }
{ general(901) number(790) one(736) }
{ take(945) account(800) differ(722) }
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{ decis(3086) make(1611) patient(1517) }
{ model(3404) distribut(989) bayesian(671) }
{ model(2341) predict(2261) use(1141) }
{ studi(1119) effect(1106) posit(819) }
{ use(976) code(926) identifi(902) }
{ surgeri(1148) surgic(1085) robot(1054) }
{ search(2224) databas(1162) retriev(909) }
{ studi(1410) differ(1259) use(1210) }
{ risk(3053) factor(974) diseas(938) }
{ intervent(3218) particip(2042) group(1664) }
{ activ(1138) subject(705) human(624) }
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{ care(1570) inform(1187) nurs(1089) }
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{ perform(1367) use(1326) method(1137) }
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{ record(1888) medic(1808) patient(1693) }
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{ research(1218) medic(880) student(794) }
{ model(2656) set(1616) predict(1553) }
{ data(2317) use(1299) case(1017) }
{ age(1611) year(1155) adult(843) }
{ 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) }
{ first(2504) two(1366) second(1323) }
{ time(1939) patient(1703) rate(768) }
{ patient(1821) servic(1111) care(1106) }
{ can(981) present(881) function(850) }
{ health(1844) social(1437) communiti(874) }
{ cancer(2502) breast(956) screen(824) }
{ estim(2440) model(1874) function(577) }
{ process(1125) use(805) approach(778) }
{ method(1969) cluster(1462) data(1082) }
{ method(2212) result(1239) propos(1039) }
{ detect(2391) sensit(1101) algorithm(908) }

Resumo

RPOSE: To identify whether the level of difficulty varied among various healthcare technologies. In addition, to examine the whether the degree of healthcare technology adoption was related to patient safety.METHODS: The data on healthcare technology usage came from a survey of hospitals in Minnesota. There were responses from individuals within 104 hospitals for an effective response rate of 72.7%. The data on patient safety was taken from the Hospital Compare database. Rasch model analysis and regression analysis were used to analyze the data.RESULTS: Rasch model analysis revealed that the difficulty of implementation of healthcare information technologies varied by the particular technology. That is, some technologies were more difficult than other technologies. Further, it was found that the degree of healthcare information technology implementation within a hospital was significantly related to patient safety.CONCLUSION: This study identified design and policy implications for hospital decision makers. In particular, it was shown that the technological capability of a hospital is a key consideration in determining the level of resources that are necessary to implement specific healthcare technologies within a hospital.

Resumo Limpo

rpose identifi whether level difficulti vari among various healthcar technolog addit examin whether degre healthcar technolog adopt relat patient safetymethod data healthcar technolog usag came survey hospit minnesota respons individu within hospit effect respons rate data patient safeti taken hospit compar databas rasch model analysi regress analysi use analyz dataresult rasch model analysi reveal difficulti implement healthcar inform technolog vari particular technolog technolog difficult technolog found degre healthcar inform technolog implement within hospit signific relat patient safetyconclus studi identifi design polici implic hospit decis maker particular shown technolog capabl hospit key consider determin level resourc necessari implement specif healthcar technolog within hospit

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