Construction of an index system for assessing emergency department overcrowding in Chinese tertiary hospitals: a Delphi study.

BACKGROUND

Emergency department crowding (EDC) is a global public health crisis associated with adverse patient- and physician-related events. Currently, a reliable EDC assessment tool is lacking in China, as existing international models cannot be directly adapted owing to differences in healthcare systems, data accessibility constraints, and national policy contexts. The issue is multifactorial, data are difficult to obtain, and EDC varies regionally and temporally. Therefore, we developed a crowding assessment index system for tertiary hospital emergency departments in China, aiming to support the development of quantitative models suited to local conditions and formulation of related policies.

METHODS

This study used two rounds of Delphi surveys involving a multidisciplinary expert panel from China, with expertise in emergency care crowding research and management. Experts rated 96 presumptive assessment indicators. The index system’s reliability was assessed by evaluating the experts’ enthusiasm, degree of authority, and degree of consistency and coordination in their opinions. The core EDC indicators were screened and optimised based on the boundary value method, with decision rules including coefficient of variation < 0.25 and full-score ratio ≥ 50%, referenced from prior Delphi studies. The final assessment system was established after modifying the indicators per the experts’ opinions. Data were summarised using descriptive statistics.

RESULTS

All 16 invited and eligible panellists participated (response rate, 100% in both rounds); the authority coefficient was 0.85. Most were aged > 40 years (14/16 [88%]), and the sex distribution was equal (eight men, eight women). Panellists achieved consensus on 3 primary, 8 secondary, and 56 tertiary indicators for EDC assessment. The three primary indicators included the emergency department ‘input-process-output’ phases. The input indicators included patient (e.g., age) and temporal (e.g., day or night) characteristics. The process indicators covered resource requirements (e.g., intravenous infusions), resource supply (e.g., doctor–patient ratio), and process efficiency (e.g., waiting time). The output indicators included patient survival outcomes, hospitalisation outcomes, and hospitalisation supply (e.g., bed occupancy rate).

CONCLUSIONS

In this study, Chinese experts reached consensus on an EDC assessment index system. These criteria provide a basis for developing quantitative EDC prediction tools and inform future research and policy development.