Identifying indicators influencing emergency department performance during a medical surge: A consensus-based modified fuzzy Delphi approach

• Published in: PloS one 2022-04, Vol.17 (4), p.e0265101
• Main Authors: Etu, Egbe-Etu, Monplaisir, Leslie, Aguwa, Celestine, Arslanturk, Suzan, Masoud, Sara, Markevych, Ihor, Miller, Joseph
• Format: Article
• Language: English
• Subjects: Business metrics; Computer science; Consensus; COVID-19; Decision making; Delphi method; Delphi Technique; Departments; Emergency medical care; Emergency medical services; Emergency service; Emergency Service, Hospital; Health care; Health care policy; Hospitals; Humans; Indicators; Medical care; Medical diagnosis; Medical personnel; Medicine and Health Sciences; Patients; People and Places; Performance enhancement; Performance evaluation; Performance management; Polls & surveys; Public health; Quality Indicators, Health Care; Quality management; Quality of service; Questionnaires; Social Sciences; Stability; Surveys and Questionnaires; Systems engineering
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0265101

During a medical surge, resource scarcity and other factors influence the performance of the healthcare systems. To enhance their performance, hospitals need to identify the critical indicators that affect their operations for better decision-making. This study aims to model a pertinent set of indicators for improving emergency departments' (ED) performance during a medical surge. The framework comprises a three-stage process to survey, evaluate, and rank such indicators in a systematic approach. The first stage consists of a survey based on the literature and interviews to extract quality indicators that impact the EDs' performance. The second stage consists of forming a panel of medical professionals to complete the survey questionnaire and applying our proposed consensus-based modified fuzzy Delphi method, which integrates text mining to address the fuzziness and obtain the sentiment scores in expert responses. The final stage ranks the indicators based on their stability and convergence. Here, twenty-nine potential indicators are extracted in the first stage, categorized into five healthcare performance factors, are reduced to twenty consentaneous indicators monitoring ED's efficacy. The Mann-Whitney test confirmed the stability of the group opinions (p < 0.05). The agreement percentage indicates that ED beds (77.8%), nurse staffing per patient seen (77.3%), and length of stay (75.0%) are among the most significant indicators affecting the ED's performance when responding to a surge. This research proposes a framework that helps hospital administrators determine essential indicators to monitor, manage, and improve the performance of EDs systematically during a surge event.