Using optimization to provide decision support for strategic emergency medical service planning - Three case studies

Comprehensive Risk Analysis of Emergency Medical Response Systems in Serbian Healthcare: Assessing Systemic Vulnerabilities in Disaster Preparedness and Response

BACKGROUND/OBJECTIVES

Emergency Medical Response Systems (EMRSs) play a vital role in delivering medical aid during natural and man-made disasters. This quantitative research delves into the analysis of risk and effectiveness within Serbia's Emergency Medical Services (EMS), with a special emphasis on how work organization, resource distribution, and preparedness for mass casualty events contribute to overall disaster preparedness.

METHODS

The study was conducted using a questionnaire consisting of 7 sections and a total of 88 variables, distributed to and collected from 172 healthcare institutions (Public Health Centers and Hospitals). Statistical methods, including Pearson's correlation, multivariate regression analysis, and chi-square tests, were rigorously applied to analyze and interpret the data.

RESULTS

The results from the multivariate regression analysis revealed that the organization of working hours (β = 0.035) and shift work (β = 0.042) were significant predictors of EMS organization, explaining 1.9% of the variance (R2 = 0.019). Furthermore, shift work (β = -0.045) and working hours (β = -0.037) accounted for 2.0% of the variance in the number of EMS points performed (R2 = 0.020). Also, the availability of ambulance vehicles (β = 0.075) and financial resources (β = 0.033) explained 4.1% of the variance in mass casualty preparedness (R2 = 0.041). When it comes to service area coverage, the regression results suggest that none of the predictors were statistically significant. Based on Pearson's correlation results, there is a statistically significant correlation between the EMS organization and several key variables such as the number of EMS doctors (p = 0.000), emergency medicine specialists (p = 0.000), etc. Moreover, the Chi-square test results reveal statistically significant correlations between EMS organization and how EMS activities are conducted (p = 0.001), the number of activity locations (p = 0.005), and the structure of working hours (p = 0.001).

CONCLUSIONS

Additionally, the results underscore the necessity for increased financial support, standardized protocols, and enhanced intersectoral collaboration to strengthen Serbia's EMRS and improve overall disaster response effectiveness. Based on these findings, a clear roadmap is provided for policymakers, healthcare administrators, and EMS personnel to prioritize strategic interventions and build a robust emergency medical response system.

Bracing for the next wave: A critical incident study of frontline decision-making, adaptation and learning in ambulance care during COVID-19

AIM

To explore frontline decision-making, adaptation, and learning in ambulance care during the evolving COVID-19 pandemic.

DESIGN

Descriptive and interpretative qualitative study.

METHODS

Twenty-eight registered nurses from the Swedish ambulance services described 56 critical incidents during the COVID-19 pandemic through free-text questionnaires. The material was analysed using the Critical Incident Technique and Interpretive Description through the lens of potential for resilient performance.

RESULTS

The findings were synthesized into four themes: 'Navigating uncharted waters under never-ending pressure', 'Balancing on the brink of an abyss', 'Sacrificing the few to save the many' and 'Bracing for the next wave'. Frontline decision-making during a pandemic contribute to ethical dilemmas while necessitating difficult prioritizations to adapt and respond to limited resources. Learning was manifested through effective information sharing and the identification of successful adaptations as compared to maladaptations.

CONCLUSIONS

During pandemics or under other extreme conditions, decisions must be made promptly, even amidst emerging chaos, potentially necessitating the use of untested methods and ad-hoc solutions due to initial lack of knowledge and guidelines. Within ambulance care, dynamic leadership becomes imperative, combining autonomous frontline decision-making with support from management. Strengthening ethical competence and fostering ethical discourse may enhance confidence in decision-making, particularly under ethically challenging circumstances.

IMPACT

Performance under extreme conditions can elevate the risk of suboptimal decision-making and adverse outcomes, with older adults being especially vulnerable. Thus, requiring targeted decision support and interventions. Enhancing patient safety in ambulance care during such conditions demands active participation and governance from management, along with decision support and guidelines. Vertical communication and collaboration between management and frontline professionals are essential to ensure that critical information, guidelines, and resources are effectively disseminated and implemented. Further research is needed into management and leadership in ambulance care, alongside the ethical challenges in frontline decision-making under extreme conditions.

REPORTING METHOD

Findings are reported per consolidated criteria for reporting qualitative research (COREQ).

PATIENT OR PUBLIC CONTRIBUTION

No Patient or Public Contribution.

Resilient and social health service network design to reduce the effect of COVID-19 outbreak

With the severe outbreak of the novel coronavirus (COVID-19), researchers are motivated to develop efficient methods to face related issues. The present study aims to design a resilient health system to offer medical services to COVID-19 patients and prevent further disease outbreaks by social distancing, resiliency, cost, and commuting distance as decisive factors. It incorporated three novel resiliency measures (i.e., health facility criticality, patient dissatisfaction level, and dispersion of suspicious people) to promote the designed health network against potential infectious disease threats. Also, it introduced a novel hybrid uncertainty programming to resolve a mixed degree of the inherent uncertainty in the multi-objective problem, and it adopted an interactive fuzzy approach to address it. The actual data obtained from a case study in Tehran province in Iran proved the strong performance of the presented model. The findings show that the optimum use of medical centers' potential and the corresponding decisions result in a more resilient health system and cost reduction. A further outbreak of the COVID-19 pandemic is also prevented by shortening the commuting distance for patients and avoiding the increasing congestion in the medical centers. Also, the managerial insights show that establishing and evenly distributing camps and quarantine stations within the community and designing an efficient network for patients with different symptoms result in the optimum use of the potential capacity of medical centers and a decrease in the rate of bed shortage in the hospitals. Another insight drawn is that an efficient allocation of the suspect and definite cases to the nearest screening and care centers makes it possible to prevent the disease carriers from commuting within the community and increase the coronavirus transmission rate.

Emergency order allocation of e-medical supplies due to the disruptive events of the healthcare crisis

The availability of electronic (e-medical) homecare essentials, such as thermometers, oximeters, and oxygen concentrators during the peaks of the pandemic coronavirus disease (COVID-19), has been witnessed as critical in saving the lives of people across the world. This paper presents a supply order allocation strategy of e-medical homecare essentials (HCEs) in a multi-supplier environment by a distributor while ensuring sufficient and timely availability for emergency consumption during pandemic peaks. The results, based on the actual demand data of HCEs obtained from a regional HCE distributor during the pandemic peak of the second wave in India, i.e. April-May 2021, suggest that a minimum (maximum) average of 94% (98%) availability of e-medical HCEs respectively at pharmacies could be achieved during the peak demand period using the proposed emergency order allocation algorithm in this study. Conclusively, the analysis of this study could generate insightful implications for emergency operations decisions in the HCEs supply-distribution channel.

Reorganization of an Emergency Medical System in a Mixed Urban-Rural Area

The reorganization of an emergency medical system means that we look for new locations of ambulance stations with the aim of improving the accessibility of the service. We applied two tools that are well known in the operations research community, namely mathematical programming, and computer simulation. Using the hierarchical pq-median model, we proposed optimal locations of the stations throughout the country and within large towns. Several solutions have been calculated that differ in the number of stations that are supposed to be relocated to new positions. The locations proposed by the mathematical programming model were evaluated via computer simulation. The approach was demonstrated under the conditions of the Slovak Republic using real historical data on ambulance dispatches. We have concluded that (i) the distribution of the stations proposed by the hierarchical pq-median model overcomes the current distribution; the performance of the system has significantly improved even if only 10% of the stations are relocated to new municipalities; (ii) the variant that relocates 40% of the stations is a reasonable compromise between the benefits and induced costs; (iii) optimizing station locations in big towns can significantly improve the local as well as the nationwide performance indicators; the response times in two regional capitals has reduced by more than 4 min.

[Inequality in pre-hospital care in Brazil: Analysis of the efficiency and sufficiency of SAMU 192 coverage]

The scope of this article is to analyze the evolution and coverage of pre-hospital care in Brazil between 2015 and 2019 based on efficiency and sufficiency in all municipalities, according to macro-regions, population covered, situation and coverage strategy, geographic area covered, available modalities and mobile resources distributed. It is an observational, quantitative, descriptive, and exploratory census, conducted with data provided by the Ministry of Health, complemented with data from the Brazilian Institute of Geography and Statistics. Efficiency in pre-hospital coverage was analyzed according to 17 indicators linked to the population covered, distribution of mobile resources and configuration of teams by modality. Sufficiency was analyzed according to variations in these indicators between municipalities in different macro-regions of the country. In 5 years, coverage rose by 5.4%, attaining 85% of citizens and 3750 municipalities, with a predominance of the Basic Life Support modality. The meager expansion of pre-hospital care and the persistence of uncovered municipalities denote the inefficiency of coverage which, associated with the inequality in the distribution of resources and modalities, corrupts the guidelines of integrality and equality in access to health (sufficiency).

Ambulances Deployment Problems: Categorization, Evolution and Dynamic Problems Review

In this paper, an analytic review of the recent methodologies tackling the problem of dynamic allocation of ambulances was carried out. Considering that state-of-the-art is moving to deal with more extensive and dynamic problems to address in a better way real-life instances, this research looks to identify the evolution and recent applications of this kind of problem once the basic models are explored. This extensive review allowed us to identify the most recent developments in this problem and the most critical gaps to be addressed. In this sense, it is essential to point out that the dynamic location of emergency medical services (EMS) is nowadays a relevant topic considering its impact on the healthcare system outcomes. Issues related to forecasting, simulation, heterogeneous fleets, robustness, and solution speed for real-life problems, stand out in the identified gaps. Applications of machine learning the deployment challenges during epidemic outbreaks such as SARS and COVID-19 were also explored. At the same time, a proposed notation tries to tackle the fact that the word problem in this kind of work refers to a model on many occasions. The proposed notation eases the comparison between the different model proposals found in the literature.

Exploration of the police response time to motor-vehicle crashes in Pennsylvania, USA

INTRODUCTION

After roadway crashes occur, the quick emergency response is essential for minimizing tolls and economic losses. Many studies have analyzed the post-crash emergency medical services response time, but few ones have explored the post-crash police response time. However, the presence of police is the precondition for other agencies to perform their duties safely and smoothly.

METHOD

With Pennsylvania crash data from 2008 to 2017, this study gives an assessment of the post-crash police response time in Pennsylvania.

RESULTS

First, we demonstrate that police response time is highly correlated to crash consequences: the longer police response time is positively associated with more fatalities at both individual level and county level. Then, for fatal crashes, a negative binomial model with the police agency-level random effects is built to identify the significant factors influencing the police response time. The results indicate that day of week, illumination, weather, area, roadway type, and roadway location could significantly affect the police response time. Police respond much slower to fatal crashes occurring in rural areas, mid-blocks, turnpikes, adverse weather, on weekends, and at nighttime without streetlights. Police response time shows a decreasing trend since 2016 and varies a lot by police agencies. It is found that many police agencies affiliated to the Pennsylvania State Police, which oversees statewide law enforcement on all interstate and state highways, respond slower than other police agencies. Practical Applications: These findings are expected to provide some new insights for Pennsylvania police agencies to improve their response mechanisms to roadway crashes.

Priority setting in the Brazilian emergency medical service: a multi-criteria decision analysis (MCDA)

BACKGROUND

Despite the proven value of multicriteria decision analysis in the health field, there is a lack of studies focused on prioritising victims in the Emergency Medical Service, EMS. With this, and knowing that the decision maker needs a direction on which choice may be the most appropriate, based on different and often conflicting criteria. The current work developed a new model for prioritizing victims of SAMU/192, based on the multicriteria decision methodology, taking into account the scarcity of resources.

METHODS

An expert panel and a discussion group were formed, which defined the limits of the problem, and identified the evaluation criteria for choosing a victim, amongst four alternatives illustrated from hypothetical scenarios of emergency situations-clinical and traumatic diseases of absolute priority. For prioritization, an additive mathematical method was used that aggregates criteria in a flexible and interactive version, FITradeoff.

RESULTS

The structuring of the problem led the researchers to identify twenty-five evaluation criteria, amongst which ten were essential to guide decisions. As a result, in the simulation of prioritization of four requesting victims in view of the availability of only one ambulance, the proposed model supported the decision by suggesting the prioritization of one of the victims.

CONCLUSIONS

This work contributed to the prioritization of victims using multicriteria decision support methodology. Selecting and weighing the criteria in this study indicated that the protocols that guide regulatory physicians do not consider all the criteria for prioritizing victims in an environment of scarcity of resources. Finally, the proposed model can support crucial decision based on a rational and transparent decision-making process that can be applied in other EMS.

Optimization for Locating Emergency Medical Service Facilities: A Case Study for Health Planning from China

Purpose

Rational location of emergency medical service (EMS) facilities could improve access to EMS, and thus assist in saving patients’ lives and improving their health outcomes. A considerable amount of spatial optimization research has been devoted to the development of models to support location planning in the context of EMS, with extensive applications in policy making around the world. However, in China, studies on the location of EMS facilities have not been paid enough attention to, let alone their practical applications. This paper conducted location optimization for EMS facilities in Chengdu, one of the biggest cities in southwest China with more than 16.5 million population, aiming to optimize the EMS system by adding (upgrading) a minimum number of EMS facilities to achieve a given population coverage.

Methods

Location optimization was conducted according to regional health policy goal for the EMS system in Chengdu, China, 2017. The nearest-neighbor approach was used to calculate the shortest travel time based on geographical information system (GIS). The location set covering model was used to formulate the optimization problem under China’s context, and genetic algorithm (GA) was employed to determine the optimized locations.

Results

The results showed that a minimum number of 55 new facilities were required to upgrade to EMS facilities to achieve the policy goal of 90% population coverage of EMS within 15 minutes. Access to EMS also improved substantially in terms of shortest travel time after facility upgrading. The weighted median shortest travel time to EMS facilities in Chengdu decreased by 14.57%, from 6.45 minutes to 5.51 minutes.

Conclusion

Our study showed that the solution could effectively achieve the policy goal of population coverage with a minimum number of new EMS facilities. Our findings would support evidence-based decision-making in future EMS planning in China.