Exploring Factors Affecting the Emergency Specialists' Decision-Making in Case of Emergencies in Patients

PHarmacist Avoidance or Reductions in Medical Costs in Patients Presenting the EMergency Department: PHARM-EM Study

To comprehensively classify interventions performed by emergency medicine clinical pharmacists and quantify cost avoidance generated through their accepted interventions.

DESIGN

A multicenter, prospective, observational study was performed between August 2018 and January 2019.

SETTING

Community and academic hospitals in the United States.

PARTICIPANTS

Emergency medicine clinical pharmacists.

INTERVENTIONS

Recommendations classified into one of 38 intervention categories associated with cost avoidance.

MEASUREMENTS AND MAIN RESULTS

Eighty-eight emergency medicine pharmacists at 49 centers performed 13,984 interventions during 917 shifts that were accepted on 8,602 patients and generated $7,531,862 of cost avoidance. The quantity of accepted interventions and cost avoidance generated in six established categories were as follows: adverse drug event prevention (1,631 interventions; $2,225,049 cost avoidance), resource utilization (628; $310,582), individualization of patient care (6,122; $1,787,170), prophylaxis (24; $22,804), hands-on care (3,533; $2,836,811), and administrative/supportive tasks (2,046; $342,881). Mean cost avoidance was $538.61 per intervention, $875.60 per patient, and $8,213.59 per emergency medicine pharmacist shift. The annualized cost avoidance from an emergency medicine pharmacist was $1,971,262. The monetary cost avoidance to pharmacist salary ratio was between $1.4:1 and $10.6:1.

CONCLUSIONS

Pharmacist involvement in the care of patients presenting to the emergency department results in significant avoidance of healthcare costs, particularly in the areas of hands-on care and adverse drug event prevention. The potential monetary benefit-to-cost ratio for emergency medicine pharmacists is between $1.4:1 and $10.6:1.

Novel Case-Based Reasoning System for Public Health Emergencies

Purpose

Several threatening infectious diseases, including influenza, Ebola, SARS, and COVID-19, have affected human society over the past decades. These disease outbreaks naturally inspire a demand for sustained and advanced safety and suppression measures. To protect public health and safety, further research developments on emergency analysis methods and approaches for effective emergency treatment generation are urgently needed to mitigate the severity of the pandemic and save lives.

Methods

To address these issues, a novel case-based reasoning (CBR) system is proposed using three phases. In the first phase, the similarity between the current case and the historical cases is calculated under a variety of heterogeneous information. In the second phase, a filter approach based on grey clustering analysis is created to retrieve relevant cases. In the third phase, the cases retrieved are taken as initial host nests in a cuckoo search (CS) algorithm, and our system searches an optimal solution through iteration of this algorithm.

Results

The proposed model is compared with a CBR method improved by particle swarm optimization (PSO) and a CBR method improved by a differential evolution algorithm (DE), to confirm the efficiency of our CS algorithm in adapting solutions for public health emergencies. The results show that the proposed model is better than the existing algorithms.

Conclusion

The proposed model improves the speed of case retrieval using grey clustering and increases solution accuracy with CS algorithms. The present research can contribute to government, CDC, and infectious disease emergency management fields with regard to the implementation of fast and accurate public biohazard prevention and control measures based on a variety of heterogeneous information.

Emergency decision-making model of environmental emergencies based on case-based reasoning method

Environmental emergencies are characterized by high uncertainty, complex evolution, and potential for serious damage, thus posing enormous pressure and difficulties to the emergency responses of enterprises and governments. Improving the efficiency and quality of emergency decision-making constitutes the primary focus of today's research in this field. This study systematically analyzes the scenario evolution mechanism of environmental emergencies with a multi-dimensional scenario space method, and key scenario factors are identified from disaster-inducing factors, disaster-bearing factors, disaster-pregnant environments, and emergency actions. Based on these, an emergency decision-making model for environmental emergencies (EEEDM) is constructed based on case-based reasoning (CBR). First, different matching algorithms are designed for accurate numerical data, fuzzy semantic data, and symbolic data. The similarity between the target scenario and the historical scenario is calculated, and the historical scenario similarity set is built according to the given threshold value. Finally, the emergency action plan of the scenario is modified with its utility value evaluated. A solution that applies to the target scenario is then obtained. Additionally, the decision-making model proposed in this paper is validated by an example of environmental emergencies. The results show that this model is scientific and reasonable, and it can better realize the multi-dimensional expression and fast matching of the scenarios and meet the decision requirements of "scenario-response". In practice, the model is capable of providing support for relevant departments' emergency decision-making.