Syfte: Syftet med denna studie var att utvärdera tillförlitligheten av informationen som ambulanspersonalen får från sjukvårdens larmcentral. Frågeställningen är i vilken utsträckning utalarmeringsinformationen stämmer överens med vad ambulans personalen gör för bedömningar väl framme på plats hos patienten. Metod: I denna studie användes en deskriptiv och korrelativ design och sambandet mellan de två variablerna utalarmeringsorsak och bedömt tillstånd. Studien inkluderade alla de larm som utalarmerats som prioritet 1A/B under insamlingsperioden, vecka 10 till vecka 18 2012, vilket resulterade i 62 larm. Resultat: Analysen av utalarmeringsorsak och bedömt tillstånd på plats visar en hög grad av överensstämmelse, kappa koefficienten 0.78. Det är enbart fem fall av sextiotvå, cirka 8 % som avviker utan någon rimlig förklaring, det kan därav antas att det medicinska beslutsstödet fungerar bra. Resultatet visar dock att det sker avvikanden när den intervjuande sjuksköterskan får ett larm med en medvetandepåverkad patient vid utalarmeringen. Slutsats: Studien visar att den utalarmeringsinformation som ambulansen får från sjukvårdens larmcentral anses fungera bra vid utalarmeringen av prioritering 1. Dock upptäcktes att det förekommer oförklarliga avvikande utalarmeringar, där det behövs mer forskning för att kunna utvärdera orsaken kring. / Objective: The objective of this study is to evaluate the reliability of the information that the ambulance crew receive from the emergency dispatch center. And in what extent the dispatch information is consistent with the ambulance crew’s evaluation of the patient’s condition. Method: In this study, a descriptive and correlative design and the relationship between two variables, emergency dispatch and assessed condition, and how well these were consistent with each other were evaluated. The study included all alarms that were dispatched as a priority 1A/B during the data collection period that lasted between week 10 to week 18 2012. This resulted in sixty-two alarms from the emergency dispatch center and the assessments made by the ambulance crew. Results: The analysis of the emergency dispatch and the assessed condition on the seen shows a high level of agreement, kappa coefficient 0.78. There are only five cases of sixty-two, about 8 % that’s without reasonable explanation. It can be assumed that the medical dispatch support tool works well. The result also shows that it sometimes occur mistakes when the interviewing nurse at the medical dispatch center had a patient whit impaired consciousness. Conclusion: The study shows that the dispatch information received from the emergency dispatch center should be considered to work well when dispatching priority 1 alarms. Furthermore, it was discovered that there are unexplained divergent dispatches, where more research is needed to evaluate the occurred mistakes.

<p>Smart and connected technology solutions have emerged as a promising way to enhance EMS services, particularly in areas where access to professional services is limited. However, a significant challenge for improving their implementation is determining which technologies to use and how they will change current logistic operations to enhance service efficiencies and expand access to care. In this context, this thesis explores opportunities for the smart and connected technology solutions.</p> <p>The first study explores the use of medically trained volunteers in the community, known as Citizen Responders (CRs). These individuals can be quickly notified of an EMS request upon its arrival via a mobile alert receiver, which allows them to provide timely and potentially life-saving assistance before an ambulance arrives. However, traditional EMS logistic decision platforms are not equipped to effectively leverage the sharing of the real-time CR information enabled by connected technologies, such as their location and availability. To improve coordination between CRs and ambulances, this study proposes two decision tools that incorporate real-time CR information: one for redeploying ambulances after they complete service and another for dispatching ambulances in response to calls. The redeployment procedure uses mixed-integer linear programming (MILP) to maximize patient survival, while the dispatch procedure enhances a locally optimal dispatch procedure by integrating real-time CR information for priority-differentiated emergencies.</p> <p>In the second study, a third decision tool was developed to take advantage of the increasing availability of feature information provided by connected technologies: an AI-enabled dispatch rule recommendation model that is more usable for dispatchers than black-box decision models. This is a model based on supervised learning that outputs a “promising” metric-based dispatch rule for the human decision-maker. The model maintains the usability of rules while enhancing the system’s performance and alleviating the cognitive burden of dispatchers. A set of experiments were performed on a self-developed simulator to assess the performance of all the decision tools. The findings suggest they have the potential to significantly enhance the EMS system performance. </p>