Application of the case-mix index and length of stay for hospital waste management comparison: introduction of a new adjusted metric

TL;DR

This paper introduces a new metric called CAWI to better compare hospital waste by accounting for patient complexity and treatment intensity.

Contribution

The study introduces and validates the Complexity-Adjusted Waste Index (CAWI), a novel benchmarking metric for hospital waste.

Findings

Higher Case-Mix Index correlates with increased hazardous healthcare waste (r = 0.49, p < 0.001).

Shorter Length of Stay is associated with higher daily waste intensity (r = −0.67, p < 0.001).

CAWI shows reduced statistical dispersion and stronger correlations with institutional variables compared to traditional metrics.

Abstract

Hazardous healthcare waste (HHCW) presents escalating environmental and operational challenges, yet traditional indicators such as waste generation rate (kg/bed/day) fail to account for patient complexity or care intensity, leading to biased institutional comparisons. Despite various previous normalization attempts, no validated framework has yet integrated clinical and operational heterogeneity into a single benchmarking metric. This study introduces and validates the Complexity-Adjusted Waste Index (CAWI), a novel metric that integrates the Case-Mix Index (CMI) and Length of Stay (LOS) to normalize waste generation across hospitals with heterogeneous clinical profiles.

Using national data from 94 inpatient institutions in Hungary (2017–2021), CAWI was calculated and compared with conventional HHCW generation rates through Spearman correlation, Fisher’s Z-tests, and robust regression models.

Results show that higher CMI correlates with increased HHCW (r = 0.49, p < 0.001), while shorter LOS is associated with higher daily waste intensity (r = −0.67, p < 0.001). CAWI demonstrated reduced statistical dispersion (SD = 0.15 vs. 0.27) and stronger correlations with key institutional variables, including number of ICU-patients (r = 0.78 vs. 0.67) and number of inpatients (r = 0.71 vs. 0.54), with significantly lower model error terms.

By explicitly combining patient complexity and treatment intensity into a transferable normalization framework, CAWI advances current benchmarking approaches both theoretically and methodologically. The CAWI framework offers a statistically robust and scalable solution for complexity-sensitive benchmarking, enabling more accurate cross-institutional comparisons and supporting targeted waste reduction strategies aligned with circular economy principles.

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