In the urban area of Milan, the Niguarda Trauma centre has a key role in the management and treatment of major trauma. Niguarda Trauma Centre receives a high number of patients not afflicted by a major trauma. This number is translate into a percentage, called over-triage. The reduction of this percentage would lead to a more effective utilization of this resource. However, the patient sorting within the hospital present on the territory is performed by AREU, i.e. regional company for emergency assistance. This company manages all the sanitary emergency of the territory, assigning them to the correct hospital typology. The process is composed by three phases, conducted respectively by three different teams: SOP1, collection of the first information about the patient through a telephone interview, SOP2, management of the vehicles available on the territory, and SOP3, collection of medical information, evaluation and selection of destination hospital. The analysis of patient’s evaluating process was performed with ALBA, i.e. Artificial Logic Bayesian Algorithm, a program able to represent all the universe of possible alternatives of a complex process, both considering stochastic and logical constrains present in the system. Another important tool was the Niguarda Trauma Centre database, containing data about circa three -thousand cases collected in the last six years. The analysis of the system and of the available data allows to identify the critical components of the system and the typology of events more involved in the generation of the over-triage. The analysis of some resolution approach lead to the theorization of another evaluation medical filter, able to support the SOP3 personnel in its decisional process, including also those contextual parameters linked especially to road accident traumas. The analysis of those parameters has also been useful for a modification of the SOP1 interview process. The insertion and the modification of some contextual parameters can potentially reduce the percentage of red codes of the twenty percent in the first phase. Globally, the proposed resolution approaches can effectively reduce the over-triage up to 44%.
Il Trauma Centre dell’ospedale di Milano è una struttura di riferimento nel trattamento dei traumi maggiori che si verificano nell’area urbana che circonda Milano. Il Trauma Centre riceve però un alto numero di pazienti non afflitti da traumi maggiori: questa percentuale viene detta over-triage. La riduzione di questa percentuale permetterebbe un migliore sfruttamento di questa struttura. Lo smistamento dei pazienti è però a carico di AREU, l’azienda regionale che si occupa della gestione delle emergenze sanitarie, che si occupa della valutazione dei singoli casi e li assegna alle varie strutture presenti sul territorio. Il processo di valutazione passa da tre fasi, gestite da tre differenti team: SOP1, raccolta delle prime informazioni attraverso un’intervista, SOP2, gestione dei veicoli presenti sul territorio, e SOP3, raccolta d’informazioni mediche, valutazione del paziente e scelta dell’ospedale di destinazione. L’analisi del processo di valutazione del paziente è stata condotta con ALBA, Artificial Logic Bayesian Algorithm, un programma in grado di rappresentare l’universo di possibili alternative presenti in un processo complesso, considerando i vincoli logici e stocastici presenti nella realtà. Un altro importante mezzo di supporto è stato il database creato dal Trauma Centre, contenente le informazioni riguardanti circa tremila casi registrati negli ultimi sei anni. L’analisi del sistema e dei dati disponibili ha permesso di individuare le componenti critiche del processo e le tipologie di eventi più connessi alla generazione di over-triage. L’analisi di alcuni approcci risolutivi ha portato alla teorizzazione di un nuovo filtro valutativo, capace di supportare la SOP3 nel processo decisionale. Il filtro si baserebbe su aspetti medici, ma anche su alcuni parametri contestuali, soprattutto per quanto riguarda gli incidenti stradali. L’analisi di questi parametri è stata anche utile per modificare l’esistente fase di intervista di SOP1, portando ad una riduzione finale del venti percento dei codici rossi generati in questa fase. Complessivamente, le modifiche proposte garantirebbero una riduzione dell’over-triage del sistema fino al 44%.
Risk-based reduction of the over-triage of greater Milan's emergency sanitary system : the Niguarda major trauma centre use case
BIELLA, ARIANNA
2016/2017
Abstract
In the urban area of Milan, the Niguarda Trauma centre has a key role in the management and treatment of major trauma. Niguarda Trauma Centre receives a high number of patients not afflicted by a major trauma. This number is translate into a percentage, called over-triage. The reduction of this percentage would lead to a more effective utilization of this resource. However, the patient sorting within the hospital present on the territory is performed by AREU, i.e. regional company for emergency assistance. This company manages all the sanitary emergency of the territory, assigning them to the correct hospital typology. The process is composed by three phases, conducted respectively by three different teams: SOP1, collection of the first information about the patient through a telephone interview, SOP2, management of the vehicles available on the territory, and SOP3, collection of medical information, evaluation and selection of destination hospital. The analysis of patient’s evaluating process was performed with ALBA, i.e. Artificial Logic Bayesian Algorithm, a program able to represent all the universe of possible alternatives of a complex process, both considering stochastic and logical constrains present in the system. Another important tool was the Niguarda Trauma Centre database, containing data about circa three -thousand cases collected in the last six years. The analysis of the system and of the available data allows to identify the critical components of the system and the typology of events more involved in the generation of the over-triage. The analysis of some resolution approach lead to the theorization of another evaluation medical filter, able to support the SOP3 personnel in its decisional process, including also those contextual parameters linked especially to road accident traumas. The analysis of those parameters has also been useful for a modification of the SOP1 interview process. The insertion and the modification of some contextual parameters can potentially reduce the percentage of red codes of the twenty percent in the first phase. Globally, the proposed resolution approaches can effectively reduce the over-triage up to 44%.File | Dimensione | Formato | |
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https://hdl.handle.net/10589/133106