J Med Syst - An improvement in the appointment scheduling in primary health care centers using data mining.

Tópicos

{ patient(2837) hospit(1953) medic(668) }
{ medic(1828) order(1363) alert(1069) }
{ chang(1828) time(1643) increas(1301) }
{ inform(2794) health(2639) internet(1427) }
{ cost(1906) reduc(1198) effect(832) }
{ error(1145) method(1030) estim(1020) }
{ research(1085) discuss(1038) issu(1018) }
{ treatment(1704) effect(941) patient(846) }
{ design(1359) user(1324) use(1319) }
{ howev(809) still(633) remain(590) }
{ data(3963) clinic(1234) research(1004) }
{ system(1050) medic(1026) inform(1018) }
{ model(3480) simul(1196) paramet(876) }
{ patient(1821) servic(1111) care(1106) }
{ can(774) often(719) complex(702) }
{ system(1976) rule(880) can(841) }
{ imag(1057) registr(996) error(939) }
{ take(945) account(800) differ(722) }
{ extract(1171) text(1153) clinic(932) }
{ control(1307) perform(991) simul(935) }
{ care(1570) inform(1187) nurs(1089) }
{ case(1353) use(1143) diagnosi(1136) }
{ monitor(1329) mobil(1314) devic(1160) }
{ state(1844) use(1261) util(961) }
{ model(2656) set(1616) predict(1553) }
{ data(2317) use(1299) case(1017) }
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{ first(2504) two(1366) second(1323) }
{ time(1939) patient(1703) rate(768) }
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{ bind(1733) structur(1185) ligand(1036) }
{ sequenc(1873) structur(1644) protein(1328) }
{ method(1219) similar(1157) match(930) }
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{ imag(2675) segment(2577) method(1081) }
{ patient(2315) diseas(1263) diabet(1191) }
{ studi(2440) review(1878) systemat(933) }
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{ assess(1506) score(1403) qualiti(1306) }
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{ concept(1167) ontolog(924) domain(897) }
{ clinic(1479) use(1117) guidelin(835) }
{ algorithm(1844) comput(1787) effici(935) }
{ method(1557) propos(1049) approach(1037) }
{ data(1714) softwar(1251) tool(1186) }
{ 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) }
{ studi(1410) differ(1259) use(1210) }
{ risk(3053) factor(974) diseas(938) }
{ perform(999) metric(946) measur(919) }
{ import(1318) role(1303) understand(862) }
{ model(2341) predict(2261) use(1141) }
{ visual(1396) interact(850) tool(830) }
{ compound(1573) activ(1297) structur(1058) }
{ perform(1367) use(1326) method(1137) }
{ studi(1119) effect(1106) posit(819) }
{ blood(1257) pressur(1144) flow(957) }
{ spatial(1525) area(1432) region(1030) }
{ record(1888) medic(1808) patient(1693) }
{ health(3367) inform(1360) care(1135) }
{ ehr(2073) health(1662) electron(1139) }
{ research(1218) medic(880) student(794) }
{ age(1611) year(1155) adult(843) }
{ group(2977) signific(1463) compar(1072) }
{ gene(2352) biolog(1181) express(1162) }
{ data(3008) multipl(1320) sourc(1022) }
{ intervent(3218) particip(2042) group(1664) }
{ activ(1138) subject(705) human(624) }
{ 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) }
{ drug(1928) target(777) effect(648) }
{ result(1111) use(1088) new(759) }
{ implement(1333) system(1263) develop(1122) }
{ survey(1388) particip(1329) question(1065) }
{ estim(2440) model(1874) function(577) }
{ decis(3086) make(1611) patient(1517) }
{ 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

An optimal resource management in health care centers implies the use of an appropriate timetabling scheme to schedule appointments. Timetables of health centers are usually divided into time slots whose duration is equal to time required for clinical attendance. However doctors perform a series of tasks that are not always clinical in nature: issuing prescriptions or prescribing sick leave certificates. In this sense the time spent in attending a clinical or an administrative matter is different. This last required less time to attend the patient. This study is focused in the administrative task. A predictive model is generated to provide daily information on how many patients will go to the health center for an administrative issue. The accuracy of the model is less than 4,6 % absolute error and the improvement in scheduling appointments is a time saving of 21,73 %.

Resumo Limpo

optim resourc manag health care center impli use appropri timet scheme schedul appoint timet health center usual divid time slot whose durat equal time requir clinic attend howev doctor perform seri task alway clinic natur issu prescript prescrib sick leav certif sens time spent attend clinic administr matter differ last requir less time attend patient studi focus administr task predict model generat provid daili inform mani patient will go health center administr issu accuraci model less absolut error improv schedul appoint time save

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