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Temporal Patterns in Hospital Performance Indicators in the Chilean Public Hospital System: A Longitudinal Analysis

José Rodríguez1,2, Manuel Vargas3, Pilar Cos2

  • 1Facultad de Ciencias Económicas y Administrativas, Universidad Católica de la Santísima Concepción, Concepción 4090541, Chile.

Healthcare (Basel, Switzerland)
|July 28, 2026
PubMed

Insights

Chilean public hospitals demonstrated stable performance indicator trends from 2019-2023 using dimensionless coefficients. This framework enables longitudinal comparisons across diverse institutions, highlighting consistent efficiency and mortality patterns.

Area of Science:

  • Health Services Research
  • Hospital Performance Analysis
  • Public Health Systems

Background:

  • Comparing performance across heterogeneous public hospitals is challenging due to variations in size and case-mix.
  • Existing methods often lack the ability to standardize indicators for effective longitudinal analysis.
  • The Chilean public health system sought a method to evaluate temporal performance trends.

Purpose of the Study:

  • To analyze temporal patterns in hospital performance indicators within the Chilean public system (2019-2023).
  • To utilize dimensionless coefficients for comparing hospitals of varying sizes and case-mix.
  • To assess the stability and trends of key performance metrics over time.

Main Methods:

  • Analysis of 3,803,275 discharges from 68 Chilean public hospitals.
  • Construction of three dimensionless indicators: relative efficiency, severity, and mortality, using DRG FONASA data.
  • Application of descriptive time trends, coefficient of variation (CV), outlier identification, and Random Forest (RF) predictive modeling with time-series cross-validation.

Main Results:

  • 95.6% of hospitals exhibited low within-hospital variability (CV < 0.5).
  • The mean efficiency proxy increased from 0.3051 in 2019 to 0.3134 in 2023.
  • Lagged relative efficiency was the strongest predictor (85.1% variable importance), showing strong correlation (0.7857) with a Data Envelopment Analysis (DEA) model.

Conclusions:

  • The proposed dimensionless indicators revealed stable temporal performance patterns in most Chilean public hospitals.
  • This framework supports descriptive longitudinal comparisons across heterogeneous healthcare institutions.
  • External validation is recommended to further confirm the robustness of the findings.

Keywords:
Chilean healthcare systemdimensionless coefficientshealthcare managementhospital performance indicatorsmachine learningtemporal stability

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