Telemed J E Health - NHS connecting for health: healthcare professionals, mobile technology, and infection control.

Tópicos

{ monitor(1329) mobil(1314) devic(1160) }
{ patient(1821) servic(1111) care(1106) }
{ ehr(2073) health(1662) electron(1139) }
{ studi(2440) review(1878) systemat(933) }
{ cost(1906) reduc(1198) effect(832) }
{ system(1976) rule(880) can(841) }
{ sampl(1606) size(1419) use(1276) }
{ error(1145) method(1030) estim(1020) }
{ algorithm(1844) comput(1787) effici(935) }
{ control(1307) perform(991) simul(935) }
{ problem(2511) optim(1539) algorithm(950) }
{ howev(809) still(633) remain(590) }
{ risk(3053) factor(974) diseas(938) }
{ system(1050) medic(1026) inform(1018) }
{ import(1318) role(1303) understand(862) }
{ model(3404) distribut(989) bayesian(671) }
{ can(774) often(719) complex(702) }
{ inform(2794) health(2639) internet(1427) }
{ measur(2081) correl(1212) valu(896) }
{ patient(2315) diseas(1263) diabet(1191) }
{ surgeri(1148) surgic(1085) robot(1054) }
{ method(1557) propos(1049) approach(1037) }
{ method(984) reconstruct(947) comput(926) }
{ studi(1410) differ(1259) use(1210) }
{ visual(1396) interact(850) tool(830) }
{ studi(1119) effect(1106) posit(819) }
{ spatial(1525) area(1432) region(1030) }
{ health(3367) inform(1360) care(1135) }
{ patient(2837) hospit(1953) medic(668) }
{ age(1611) year(1155) adult(843) }
{ group(2977) signific(1463) compar(1072) }
{ gene(2352) biolog(1181) express(1162) }
{ activ(1138) subject(705) human(624) }
{ analysi(2126) use(1163) compon(1037) }
{ drug(1928) target(777) effect(648) }
{ imag(1947) propos(1133) code(1026) }
{ data(1737) use(1416) pattern(1282) }
{ imag(1057) registr(996) error(939) }
{ bind(1733) structur(1185) ligand(1036) }
{ sequenc(1873) structur(1644) protein(1328) }
{ method(1219) similar(1157) match(930) }
{ featur(3375) classif(2383) classifi(1994) }
{ imag(2830) propos(1344) filter(1198) }
{ network(2748) neural(1063) input(814) }
{ imag(2675) segment(2577) method(1081) }
{ take(945) account(800) differ(722) }
{ motion(1329) object(1292) video(1091) }
{ assess(1506) score(1403) qualiti(1306) }
{ treatment(1704) effect(941) patient(846) }
{ framework(1458) process(801) describ(734) }
{ chang(1828) time(1643) increas(1301) }
{ learn(2355) train(1041) set(1003) }
{ concept(1167) ontolog(924) domain(897) }
{ clinic(1479) use(1117) guidelin(835) }
{ extract(1171) text(1153) clinic(932) }
{ data(1714) softwar(1251) tool(1186) }
{ design(1359) user(1324) use(1319) }
{ model(2220) cell(1177) simul(1124) }
{ care(1570) inform(1187) nurs(1089) }
{ general(901) number(790) one(736) }
{ search(2224) databas(1162) retriev(909) }
{ featur(1941) imag(1645) propos(1176) }
{ case(1353) use(1143) diagnosi(1136) }
{ data(3963) clinic(1234) research(1004) }
{ perform(999) metric(946) measur(919) }
{ research(1085) discuss(1038) issu(1018) }
{ model(2341) predict(2261) use(1141) }
{ compound(1573) activ(1297) structur(1058) }
{ perform(1367) use(1326) method(1137) }
{ blood(1257) pressur(1144) flow(957) }
{ record(1888) medic(1808) patient(1693) }
{ model(3480) simul(1196) paramet(876) }
{ state(1844) use(1261) util(961) }
{ research(1218) medic(880) student(794) }
{ model(2656) set(1616) predict(1553) }
{ data(2317) use(1299) case(1017) }
{ medic(1828) order(1363) alert(1069) }
{ signal(2180) analysi(812) frequenc(800) }
{ data(3008) multipl(1320) sourc(1022) }
{ first(2504) two(1366) second(1323) }
{ intervent(3218) particip(2042) group(1664) }
{ time(1939) patient(1703) rate(768) }
{ use(2086) technolog(871) perceiv(783) }
{ can(981) present(881) function(850) }
{ 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) }
{ 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

CKGROUND: Mobile phones improve the efficiency of clinical communication and are increasingly involved in all areas of healthcare delivery. Despite this, healthcare workers' mobile phones provide a known reservoir of pathogenic bacteria, with the potential to undermine infection control efforts aimed at the reducing bacterial cross-contamination in hospitals. This potential could be amplified further when employers require doctors to carry additional electronic devices for communication, without concurrently providing appropriate guidance on decontamination or use.METHODS: Eighty-seven on-call doctors' mobile phones were sampled for bacterial growth prior to, and 12 h after, a cleaning intervention involving 70% isopropyl alcohol.RESULTS: Seventy-eight percent of doctors were aware that mobile phones could carry pathogenic bacteria, but only 8% cleaned their phones regularly. The cleaning intervention reduced the number of phones that grew bacteria by 79% (55% [48 of 87] before versus 16% [14 of 87] after cleaning). Eight percent of the phones grew Staphyloccus aureus, and 44.8% of phones grew Gram-positive cocci. All S. aureus isolates were methicillin-sensitive. Bacterial contamination was not associated with gender, specialty, or seniority of the phone user (p>0.05).CONCLUSIONS: Simple cleaning interventions can reduce the surface bioburden of hospital-provided doctors' mobile phones and therefore the potential for cross-contamination. This cleaning intervention is inexpensive, easily instituted, and effective. Healthcare workers should carry the minimum number of electronic devices on their person, maintain good hand hygiene, and clean their device appropriately in order to minimize the potential for cross-contamination in the work place.

Resumo Limpo

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