J Biomed Inform - Making sense: sensor-based investigation of clinician activities in complex critical care environments.

Tópicos

{ care(1570) inform(1187) nurs(1089) }
{ activ(1138) subject(705) human(624) }
{ research(1085) discuss(1038) issu(1018) }
{ monitor(1329) mobil(1314) devic(1160) }
{ decis(3086) make(1611) patient(1517) }
{ visual(1396) interact(850) tool(830) }
{ can(774) often(719) complex(702) }
{ data(3963) clinic(1234) research(1004) }
{ state(1844) use(1261) util(961) }
{ process(1125) use(805) approach(778) }
{ bind(1733) structur(1185) ligand(1036) }
{ motion(1329) object(1292) video(1091) }
{ import(1318) role(1303) understand(862) }
{ data(1737) use(1416) pattern(1282) }
{ design(1359) user(1324) use(1319) }
{ studi(1410) differ(1259) use(1210) }
{ model(2656) set(1616) predict(1553) }
{ medic(1828) order(1363) alert(1069) }
{ high(1669) rate(1365) level(1280) }
{ take(945) account(800) differ(722) }
{ problem(2511) optim(1539) algorithm(950) }
{ extract(1171) text(1153) clinic(932) }
{ control(1307) perform(991) simul(935) }
{ search(2224) databas(1162) retriev(909) }
{ research(1218) medic(880) student(794) }
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{ imag(2830) propos(1344) filter(1198) }
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{ featur(1941) imag(1645) propos(1176) }
{ case(1353) use(1143) diagnosi(1136) }
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{ risk(3053) factor(974) diseas(938) }
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{ health(3367) inform(1360) care(1135) }
{ model(3480) simul(1196) paramet(876) }
{ ehr(2073) health(1662) electron(1139) }
{ patient(2837) hospit(1953) medic(668) }
{ data(2317) use(1299) case(1017) }
{ age(1611) year(1155) adult(843) }
{ cost(1906) reduc(1198) effect(832) }
{ group(2977) signific(1463) compar(1072) }
{ gene(2352) biolog(1181) express(1162) }
{ data(3008) multipl(1320) sourc(1022) }
{ intervent(3218) particip(2042) group(1664) }
{ time(1939) patient(1703) rate(768) }
{ patient(1821) servic(1111) care(1106) }
{ use(2086) technolog(871) perceiv(783) }
{ can(981) present(881) function(850) }
{ health(1844) social(1437) communiti(874) }
{ structur(1116) can(940) graph(676) }
{ cancer(2502) breast(956) screen(824) }
{ use(976) code(926) identifi(902) }
{ use(1733) differ(960) four(931) }
{ drug(1928) target(777) effect(648) }
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{ survey(1388) particip(1329) question(1065) }
{ estim(2440) model(1874) function(577) }
{ 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

In many respects, the critical care workplace resembles a paradigmatic complex system: on account of the dynamic and interactive nature of collaborative clinical work, these settings are characterized by non-linear, inter-dependent and emergent activities. Developing a comprehensive understanding of the work activities in critical care settings enables the development of streamlined work practices, better clinician workflow and most importantly, helps in the avoidance of and recovery from potential errors. Sensor-based technology provides a flexible and viable way to complement human observations by providing a mechanism to capture the nuances of certain activities with greater precision and timing. In this paper, we use sensor-based technology to capture the movement and interactions of clinicians in the Trauma Center of an Emergency Department (ED). Remarkable consistency was found between sensor data and human observations in terms of clinician locations and interactions. With this validation and greater precision with sensors, ED environment was characterized in terms of (a) the degree of randomness or entropy in the environment, (b) the movement patterns of clinicians, (c) interactions with other clinicians and finally, (d) patterns of collaborative organization with team aggregation and dispersion. Based on our results, we propose three opportunities for the use of sensor technologies in critical care settings: as a mechanism for real-time monitoring and analysis for ED activities, education and training of clinicians, and perhaps most importantly, investigating the root-causes, origins and progression of errors in the ED. Lessons learned and the challenges encountered in designing and implementing the sensor technology sensor data are discussed.

Resumo Limpo

mani respect critic care workplac resembl paradigmat complex system account dynam interact natur collabor clinic work set character nonlinear interdepend emerg activ develop comprehens understand work activ critic care set enabl develop streamlin work practic better clinician workflow import help avoid recoveri potenti error sensorbas technolog provid flexibl viabl way complement human observ provid mechan captur nuanc certain activ greater precis time paper use sensorbas technolog captur movement interact clinician trauma center emerg depart ed remark consist found sensor data human observ term clinician locat interact valid greater precis sensor ed environ character term degre random entropi environ b movement pattern clinician c interact clinician final d pattern collabor organ team aggreg dispers base result propos three opportun use sensor technolog critic care set mechan realtim monitor analysi ed activ educ train clinician perhap import investig rootcaus origin progress error ed lesson learn challeng encount design implement sensor technolog sensor data discuss

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