Int J Health Geogr - Mass casualty modelling: a spatial tool to support triage decision making.

Tópicos

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{ patient(2837) hospit(1953) medic(668) }
{ network(2748) neural(1063) input(814) }
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{ model(2220) cell(1177) simul(1124) }
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{ implement(1333) system(1263) develop(1122) }
{ algorithm(1844) comput(1787) effici(935) }
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{ imag(1057) registr(996) error(939) }
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{ treatment(1704) effect(941) patient(846) }
{ studi(1410) differ(1259) use(1210) }
{ system(1050) medic(1026) inform(1018) }
{ medic(1828) order(1363) alert(1069) }
{ use(976) code(926) identifi(902) }
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{ can(774) often(719) complex(702) }
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{ activ(1138) subject(705) human(624) }
{ time(1939) patient(1703) rate(768) }
{ patient(1821) servic(1111) care(1106) }
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{ can(981) present(881) function(850) }
{ analysi(2126) use(1163) compon(1037) }
{ structur(1116) can(940) graph(676) }
{ high(1669) rate(1365) level(1280) }
{ survey(1388) particip(1329) question(1065) }
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Resumo

CKGROUND: During a mass casualty incident, evacuation of patients to the appropriate health care facility is critical to survival. Despite this, no existing system provides the evidence required to make informed evacuation decisions from the scene of the incident. To mitigate this absence and enable more informed decision making, a web based spatial decision support system (SDSS) was developed. This system supports decision making by providing data regarding hospital proximity, capacity, and treatment specializations to decision makers at the scene of the incident.METHODS: This web-based SDSS utilizes pre-calculated driving times to estimate the actual driving time to each hospital within the inclusive trauma system of the large metropolitan region within which it is situated. In calculating and displaying its results, the model incorporates both road network and hospital data (e.g. capacity, treatment specialties, etc.), and produces results in a matter of seconds, as is required in a MCI situation. In addition, its application interface allows the user to map the incident location and assists in the execution of triage decisions.RESULTS: Upon running the model, driving time from the MCI location to the surrounding hospitals is quickly displayed alongside information regarding hospital capacity and capability, thereby assisting the user in the decision-making process.CONCLUSIONS: The use of SDSS in the prioritization of MCI evacuation decision making is potentially valuable in cases of mass casualty. The key to this model is the utilization of pre-calculated driving times from each hospital in the region to each point on the road network. The incorporation of real-time traffic and hospital capacity data would further improve this model.

Resumo Limpo

ckground mass casualti incid evacu patient appropri health care facil critic surviv despit exist system provid evid requir make inform evacu decis scene incid mitig absenc enabl inform decis make web base spatial decis support system sdss develop system support decis make provid data regard hospit proxim capac treatment special decis maker scene incidentmethod webbas sdss util precalcul drive time estim actual drive time hospit within inclus trauma system larg metropolitan region within situat calcul display result model incorpor road network hospit data eg capac treatment specialti etc produc result matter second requir mci situat addit applic interfac allow user map incid locat assist execut triag decisionsresult upon run model drive time mci locat surround hospit quick display alongsid inform regard hospit capac capabl therebi assist user decisionmak processconclus use sdss priorit mci evacu decis make potenti valuabl case mass casualti key model util precalcul drive time hospit region point road network incorpor realtim traffic hospit capac data improv model

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