Using exogenous organizational and regional hospital attributes to explain differences in case‐mix adjusted hospital costs

Abstract

Diagnosis‐related group (DRG) hospital reimbursement systems differentiate cases into cost‐homogenous groups based on patient characteristics. However, exogenous organizational and regional factors can influence hospital costs beyond case‐mix differences. Therefore, most countries using DRG systems incorporate adjustments for such factors into their reimbursement structure. This study investigates structural hospital attributes that explain differences in average case‐mix adjusted hospital costs in Switzerland. Using rich patient and hospital‐level data containing 4 million cases from 120 hospitals across 3 years, we show that a regression model using only five variables (number of discharges, ratio of emergency/ambulance admissions, rate of DRGs to patients, expected loss potential based on DRG mix, and location in large agglomeration) can explain more than half of the variance in average case‐mix adjusted hospital costs, capture all cost variations across commonly differentiated hospital types (e.g., academic teaching hospitals, children's hospitals, birth centers, etc.), and is robust in cross‐validations across several years (despite differing hospital samples). Based on our findings, we propose a simple practical approach to differentiate legitimate from inefficiency‐related or unexplainable cost differences across hospitals and discuss the potential of such an approach as a transparent way to incorporate structural hospital differences into cost benchmarking and payment schemes.

Efficiency and Resource Allocation in Government Hospitals in Saudi Arabi: A Casemix Index Approach

“…In Finland and Norway, the most efficient hospitals were observed to be small- and medium-sized local hospitals with a below-average DRG case mix. This suggests that efficiency is influenced by factors beyond hospital size and case mix, and further research is needed to understand the specific factors contributing to efficiency variations in different healthcare contexts [ 34 , 35 ]. Overall, CMI highlights the diversity in resource utilization and service intensity across hospitals.…”