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

Abstract

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.

Acute care pathway assessed through performance indicators during the COVID-19 pandemic in OECD countries (2020-2021): a scoping review

“…Previous studies have investigated the impact of the COVID-19 pandemic in the acute care setting on speci c countries (23,203,199,201), phases of the pathway (136, 201), or diseases (197,202,204). This scoping review provides a comprehensive overview of the changes reported on the quality of acute care during the rst year and a half of the COVID-19 pandemic, covering OECD countries, multiple diseases, and following a care pathway approach.…”

“…Quality indicators can be de ned as "quantitative measures that provide information about the effectiveness, safety and/or people-centeredness of care" (21). Previous authors pursued efforts to identify indicators that could be used to monitor ED's performance, from the literature and/or with consensus-based techniques (22)(23)(24)(25). Nonetheless, indicators that are t for purpose and use (26), reported transparently, timely, internationally accepted, and grounded on robust health information systems lacked during the COVID-19 pandemic and are urgently needed to guarantee the continuous monitoring of the quality of acute care provided and to tailor policy responses (27, 28).…”

Prediksi Lama Rawat Pasien Covid-19 Berbasis Machine Learning

“…sebagian besar Para peneliti mengembangkan suatu model yang dapat memprediksi LOS yang berbasis pada data-data klinis pasien. kami menyajikan studi terkait LOS dan Covid-19 dalam beberapa paragraf berikutnya [5] Pada tahun 2022, [6] mengembangkan suatu model yang dapat memprediksi lama perawatan di IGD untuk pasien Covid-19 dengan target adalah ≥4 Jam dan < 4 Jam perawatan di IGD, Data yang dikumpulkan sebanyak 3.301 data dan 16 variabel Klinis dimana data tersebut berasal dari pasien IGD RS Henry Ford Hospital (HFH) yang didiagnosis covid-19 mulai dari tanggal 16 Maret-29 Desember 2020. terletak di Detroit, Wayne County, Michigan. Beberapa model di latih diantaranya logistic regression (LR), gradient boosting (GB), decision tree(DT), dan random forest (RF) dimana algoritma GB unggul dengan akurasi 85% dan skor F1 0.88.…”

“…Smpel penelitian terdiri dari 1000 (50.4%) pasien laki-laki dan 986(49.6%) pasien Perempuan. Pada kategori Umur Balita (0-5), Kanak-kanak (6)(7)(8)(9)(10)(11), Remaja (12-25), Dewasa (26-45), Lansia (46-65), Manula (65 keatas) masing-masing adalah 1.36%, 0.7%, 6.4%, 30.5%, 42.9%, 18.2%. Kategori umur lansia menjadi kategori umur yang mayoritas di rawat di RSUD Pasar Minggu.…”

An integrated framework to evaluate and improve the performance of emergency departments during the COVID-19 pandemic: A mathematical programing approach

“…Thus, it is important to examine KPIs such as waiting time until visit, ED beds, and nurse staffing per patient seen that exert a significant impact on making a precise evaluation of emergency centers and improving patients’ satisfaction. 14 By comparing the mentioned indicators (i.e. key performance indicators, COVID-19, health, and safety), we compare and improve the performance of EDs in the context of critical COVID-19 pandemic.…”