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Updated: Jun 3, 2026

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Published on: May 15, 2020

Patient safety analyses using Lombardy administrative archives.

Pietro Giorgio Lovaglio¹

¹CRISP and Department of Quantitative Methods, University of Bicocca-Milan, Milan, Italy. piergiorgio.lovaglio@unimib.it

International Journal of Health Care Quality Assurance

|April 5, 2011

Summary

Benchmarking patient safety in the Lombardy region is enhanced using hierarchical models with administrative data. This approach improves risk adjustment for indicators like failure to rescue, leading to more equitable comparisons of healthcare structures.

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Last Updated: Jun 3, 2026

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Published on: May 15, 2020

Area of Science:

Healthcare Management
Public Health
Health Services Research

Background:

Administrative databases offer a valuable, economical source for patient safety research.
Previous methods for benchmarking health structures have limitations.

Purpose of the Study:

To discuss strategies for benchmarking patient safety using Lombardy region administrative archives.
To present patient safety indicators and statistical methods for risk adjustment.
To benchmark regional health structures using failure to rescue and death in low mortality diagnostic related group indicators.

Main Methods:

Data from the Italian Agency of Regional Health Services (2002) were used.
Agency for Healthcare Research and Quality indicators and methods were applied.
Hierarchical models were proposed for equitable benchmark analyses.

Main Results:

Hierarchical approaches, comparing homogenous specialties, moderate variability in failure to rescue between hospitals.
This moderation is particularly evident in oncology, intensive care, and general medicine.

Conclusions:

Hierarchical models offer a superior method for benchmarking health structures compared to logistic regression.
Accurate coding, supported by software and administrative databases, is crucial for patient safety research.
Strategies for consistent benchmark analyses based on patient safety outcomes are proposed, applicable across various healthcare settings.