J Am Med Inform Assoc - From web search to healthcare utilization: privacy-sensitive studies from mobile data.

Tópicos

{ monitor(1329) mobil(1314) devic(1160) }
{ health(3367) inform(1360) care(1135) }
{ studi(1410) differ(1259) use(1210) }
{ data(3008) multipl(1320) sourc(1022) }
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{ data(2317) use(1299) case(1017) }
{ model(3404) distribut(989) bayesian(671) }
{ data(1737) use(1416) pattern(1282) }
{ state(1844) use(1261) util(961) }
{ clinic(1479) use(1117) guidelin(835) }
{ treatment(1704) effect(941) patient(846) }
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{ structur(1116) can(940) graph(676) }
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{ featur(3375) classif(2383) classifi(1994) }
{ control(1307) perform(991) simul(935) }
{ perform(999) metric(946) measur(919) }
{ system(1050) medic(1026) inform(1018) }
{ model(2341) predict(2261) use(1141) }
{ estim(2440) model(1874) function(577) }
{ sequenc(1873) structur(1644) protein(1328) }
{ take(945) account(800) differ(722) }
{ chang(1828) time(1643) increas(1301) }
{ case(1353) use(1143) diagnosi(1136) }
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{ spatial(1525) area(1432) region(1030) }
{ ehr(2073) health(1662) electron(1139) }
{ medic(1828) order(1363) alert(1069) }
{ gene(2352) biolog(1181) express(1162) }
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{ model(2220) cell(1177) simul(1124) }
{ care(1570) inform(1187) nurs(1089) }
{ general(901) number(790) one(736) }
{ method(984) reconstruct(947) comput(926) }
{ featur(1941) imag(1645) propos(1176) }
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{ blood(1257) pressur(1144) flow(957) }
{ record(1888) medic(1808) patient(1693) }
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{ research(1218) medic(880) student(794) }
{ patient(2837) hospit(1953) medic(668) }
{ model(2656) set(1616) predict(1553) }
{ 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) }
{ first(2504) two(1366) second(1323) }
{ intervent(3218) particip(2042) group(1664) }
{ activ(1138) subject(705) human(624) }
{ patient(1821) servic(1111) care(1106) }
{ use(2086) technolog(871) perceiv(783) }
{ can(981) present(881) function(850) }
{ analysi(2126) use(1163) compon(1037) }
{ health(1844) social(1437) communiti(874) }
{ high(1669) rate(1365) level(1280) }
{ cancer(2502) breast(956) screen(824) }
{ use(1733) differ(960) four(931) }
{ implement(1333) system(1263) develop(1122) }
{ survey(1388) particip(1329) question(1065) }
{ 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

JECTIVE: We explore relationships between health information seeking activities and engagement with healthcare professionals via a privacy-sensitive analysis of geo-tagged data from mobile devices.MATERIALS AND METHODS: We analyze logs of mobile interaction data stripped of individually identifiable information and location data. The data analyzed consist of time-stamped search queries and distances to medical care centers. We examine search activity that precedes the observation of salient evidence of healthcare utilization (EHU) (ie, data suggesting that the searcher is using healthcare resources), in our case taken as queries occurring at or near medical facilities.RESULTS: We show that the time between symptom searches and observation of salient evidence of seeking healthcare utilization depends on the acuity of symptoms. We construct statistical models that make predictions of forthcoming EHU based on observations about the current search session, prior medical search activities, and prior EHU. The predictive accuracy of the models varies (65%-90%) depending on the features used and the timeframe of the analysis, which we explore via a sensitivity analysis.DISCUSSION: We provide a privacy-sensitive analysis that can be used to generate insights about the pursuit of health information and healthcare. The findings demonstrate how large-scale studies of mobile devices can provide insights on how concerns about symptomatology lead to the pursuit of professional care.CONCLUSION: We present new methods for the analysis of mobile logs and describe a study that provides evidence about how people transition from mobile searches on symptoms and diseases to the pursuit of healthcare in the world.

Resumo Limpo

jectiv explor relationship health inform seek activ engag healthcar profession via privacysensit analysi geotag data mobil devicesmateri method analyz log mobil interact data strip individu identifi inform locat data data analyz consist timestamp search queri distanc medic care center examin search activ preced observ salient evid healthcar util ehu ie data suggest searcher use healthcar resourc case taken queri occur near medic facilitiesresult show time symptom search observ salient evid seek healthcar util depend acuiti symptom construct statist model make predict forthcom ehu base observ current search session prior medic search activ prior ehu predict accuraci model vari depend featur use timefram analysi explor via sensit analysisdiscuss provid privacysensit analysi can use generat insight pursuit health inform healthcar find demonstr largescal studi mobil devic can provid insight concern symptomatolog lead pursuit profession careconclus present new method analysi mobil log describ studi provid evid peopl transit mobil search symptom diseas pursuit healthcar world

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