10:30 AM - 11:00 AM
Designing Offload Zones to reduce Offload Delay
Offload delay occurs when the transfer of a patient from an ambulance service to an emergency department is prolonged. Offload delay negatively impacts patient care (e.g. poor pain control, delayed time to antibiotics, etc.) and ambulance coverage by delaying the return of an ambulance to service. In Nova Scotia the 90th percentile of offload delay has increased by a factor of 4 since 2002. The Halifax Infirmary and Dartmouth General Hospital have implemented Offload Zones as a solution to the offload delay problem. The Offload Zone is an area where patients can wait with a paramedic and a nurse allowing the ambulance to return to service immediately.
In this talk I will present two studies related to evaluating and (re)designing Offload Zones. The first is a retrospective evaluation of the Offload Zone at the Halifax Infirmary following the Healthcare Failure Modes and Effect Analysis framework. From this study we found that when the Offload Zone was implemented staff behavior changed. In particular, patients arriving by ambulance went from “high priority to admit” to “low priority to admit”, since Offload Zone patients wait with a nurse and paramedic. Patients from the waiting room, who are waiting without a health care provider, are admitted instead. This change in priority causes the Offload Zone to “fill-up”, leaving nowhere for arriving ambulances to transfer their patients and, hence, the continuation of offload delay.
The second study uses a continuous time Markov chain to study this effect. Specifically we identify priority thresholds which 1) ensures the Offload Zone, at a minimum, performs as well as when there is not an Offload Zone and 2) identifies when additional priority reduces offload delay by a negligible amount. Both studies are supporting policy makers in determining the future feasibility of Offload Zones at the Capital District Health Authority.
11:00 AM - 11:30 AM
Multicriteria Patient Transportation Planning
The German Emergency Medical Service (EMS) system is responsible for executing patient transports when a patient needs to be transported in an ambulance to, from or between hospitals while the attendance of an emergency medical technician is necessary. For many EMS regions, a high percentage of transports is known in advance, but short-term requests often need to be handled immediately throughout the day. In this research we want to schedule patient transportation requests and assign them to transportation ambulances. First, we present a corresponding formulation for the patient transportation problem and extend it to a multicriteria formulation. In addition, the possibility of modeling some of the objectives as additional constraints is investigated. In order to solve larger instances in reasonable time, we propose a column-wise neighborhood search. A starting solution is determined by a best insertion heuristic. The solution is expressed as a set of columns with one column basically representing the route of one ambulance. Then, for each column several neighbors are determined by repeatedly adding/removing tasks. Finally, a small ILP is solved for choosing the columns. As sometimes only very few patient transportation requests are known in advance, an online approach is studied that assigns requests to ambulances whenever they become idle. The approach is very close to the current method used in German EMS practice. It considers the current location of the ambulance, the pick-up location as well as a possible look-ahead on future requests when assigning a patient transport to an ambulance. We test our formulations and approaches using a set of randomly generated instances that are based on real data from an EMS region in the southwest of Germany and compare the solutions for the different approaches.
11:30 AM - 12:00 PM
A Taxonomy for Health Care Emergency Management: Interlinking Emergency Interventions, Responders, and Equipment/Materials
For a successful emergency management (EM) it is crucial that all stakeholders, especially health care emergency responders, use the same terminology. For this reason, the team of the University of Vienna, UNIVIE, developed the S-HELP UNIVIE wiki (cf. Figure 1). This is a collaborative platform that provides main glossary terms, definitions, and standards for strategic disaster management. It was implemented for the FP7-EU S-HELP (Securing Health.Emergency. Learning.Planning) project coordinated by Dr. Karen Neville, University College Cork, Ireland which develops a Decision Support (DS) tool for EM (http://www.fp7-shelp.eu/ ).
As a next step, we established a skills taxonomy template to interlink emergency interventions/tasks and emergency responders/skills (cf. Figure 2). Furthermore, we provided an overview which emergency interventions/tasks can be covered by EU Civil Protection Modules by incorporating availability, start of operation, self-sufficiency, and operation time. Next, the resource taxonomy template contained the linkage of emergency interventions/tasks and emergency responders/skills to emergency equipment/materials needed. The skills and resource taxonomy templates considered the complex and multi-disciplinary nature of health services in emergency preparedness, response, and recovery.
These taxonomies are currently implemented and integrated into the S-HELP Decision Support Tool for emergency responders by University College Cork, Ireland. They are also used for health care responder training. A future improvement step of our taxonomies is the integration of special emergency equipment/materials, responders/skills, and interventions/tasks used in the disaster scenarios (flooding, chemical spill, epidemic). For the interoperability, we will investigate in detail specific main core emergency responders of selected European countries.