A study of medical emergency workflow

Improved workflow modelling using role activity diagram-based modelling with application to a radiology service case study

The modelling of complex workflows is an important problem-solving technique within healthcare settings. However, currently most of the workflow models use a simplified flow chart of patient flow obtained using on-site observations, group-based debates and brainstorming sessions, together with historic patient data. This paper presents a systematic and semi-automatic methodology for knowledge acquisition with detailed process representation using sequential interviews of people in the key roles involved in the service delivery process. The proposed methodology allows the modelling of roles, interactions, actions, and decisions involved in the service delivery process. This approach is based on protocol generation and analysis techniques such as: (i) initial protocol generation based on qualitative interviews of radiology staff, (ii) extraction of key features of the service delivery process, (iii) discovering the relationships among the key features extracted, and, (iv) a graphical representation of the final structured model of the service delivery process. The methodology is demonstrated through a case study of a magnetic resonance (MR) scanning service-delivery process in the radiology department of a large hospital. A set of guidelines is also presented in this paper to visually analyze the resulting process model for identifying process vulnerabilities. A comparative analysis of different workflow models is also conducted.

Review of modeling approaches for emergency department patient flow and crowding research

Emergency department (ED) crowding is an international phenomenon that continues to challenge operational efficiency. Many statistical modeling approaches have been offered to describe, and at times predict, ED patient load and crowding. A number of formula-based equations, regression models, time-series analyses, queuing theory-based models, and discrete-event (or process) simulation (DES) models have been proposed. In this review, we compare and contrast these modeling methodologies, describe the fundamental assumptions each makes, and outline the potential applications and limitations for each with regard to usability in ED operations and in ED operations and crowding research.

Computer-simulated assessment of methods of transporting severely injured individuals in disaster--case study of an airport accident

A system utilizing computer simulations was developed in order to compare (1) situations wherein critically wounded individuals are, at first, ranked and classified into subgroups based on criticalness of the wounds, and then be evaluated and re-ordered within the subgroups before transported to hospitals, with (2) those situations in which the conventional method of transport was employed. The objective of the study is to infer whether there is a further possibility for enhancing the possibility of the wounded individual's survival rate at the time of arrival at the hospital. The variables that are required to be entered include the number of patient subgroups, the number of patients in each of the subgroups, the total number of patients to be transported, the time required to assess the severity of each patient, the speed of the ambulance, the interval between the ambulances' arrivals and departures, and the distance between the site at which the transportation begins and the destination or the hospital. Utilizing the same system, a virtual simulation of a large-scale disaster at airport was used as sample data. As a result, it was confirmed that the survival rate would improve under certain conditions, if the values for some of the variables, such as geographic conditions, were altered and then calculated.

Modeling an emergency medical services system using computer simulation

STUDY OBJECTIVES

In the emergency medical services (EMS) system, appropriate prehospital care can substantially decrease casualty mortality and morbidity. This study designed a simulation model, evaluated the existing EMS system, and suggested improvements.

METHODS

The study focused on 23 networked EMS hospitals affiliated with 36 emergency response units (subgroups) to perform two-tier rescues (advanced life support [ALS] in addition to basic life support [BLS] services) in Taipei, Taiwan. Using the existing EMS model as a base, this research constructed a computer simulation model and explored several model alternatives to achieve the study's objectives. The virtual models varied with staffing level, number of assigned emergency network hospitals, and various two-tier rescue probabilities.

RESULTS

Increasing the staffing to two teams for Hospital 22 lessened the call waiting probability (delay between rescue call and ambulance dispatch) by 50%, even if the dispatch rate of the two-tier rescue increased from the empirical 2% to a simulated 10 and 20%. Changing the two-tier rescue pattern so each EMS subgroup cooperated with two specific, preassigned network hospitals lowered the probability of patients having to wait for rescue dispatch to under 1%.

CONCLUSION

The following alternatives provided the greatest combination of effectiveness, quality patient care, and cost-efficiency: (1) because of its unique location, increase Hospital 22's staffing level to two ALS teams. (2) Establish a specific rescue protocol for the two-tier system that preassigns two network hospitals to each of the 36 EMS subgroups along with a prearranged calling sequence. If implemented, this will improve EMS performance, streamline the system, reduce randomness, and enhance efficiency.

Resource reallocation in an emergency medical service system using computer simulation

Emergency medical service (EMS) policy makers must seek to achieve maximum effectiveness with finite resources. This research establishes an EMS computer simulation model using eM-Plant software. The simulation model is based on Taipei city's EMS system with input data from prehospital care records from December 2000; it manipulates resource allocation levels and rates of idle errands. Presently, EMS ambulance utilization is about 8.78%. On average, 20.89 minutes are required to transport a patient to the hospital. Computer simulations showed that reducing the number of ambulances to one at each of the 36 response units increases the utilization rate to 15.47% but does not compromise the current service quality level. Thus, ambulance utilization improves, times of patients waiting for pre-hospital care and arrival at hospitals are only slightly affected, and considerable cost savings result. This study provides a research methodology and suggests specific policy directions for resource allocation in EMS. Limiting the number of ambulances to one per response unit reduces costs, increases efficiency, and yet maintains the same operational pattern of medical service.