# Optimizing the Risk-Adjusted CUSUM for Monitoring Hospital Non-cardiac Perioperative Outcomes

> **NIH AHRQ F32** · BAYLOR COLLEGE OF MEDICINE · 2022 · $85,802

## Abstract

National surgical quality improvement (QI) programs, such as the Veterans Affairs (VA) Surgical Quality
Improvement Program (VASQIP) and the American College of Surgeons National Surgical Quality
Improvement Program (ACS NSQIP), collect, analyze, and provide hospitals with institution-level, risk-adjusted
data on perioperative morbidity and mortality in an episodic (e.g., quarterly) fashion. Hospitals in turn use
these data to inform their local QI efforts. While this episodic approach to feedback is a convention in nearly all
national surgical QI programs, it is associated with two important limitations: 1) a time lag between when
hospital performance is declining and when notification is provided at the end of the quarterly data
ascertainment period; 2) a limited ability to detect potentially meaningful clusters of adverse events that may go
unrecognized with episodic evaluation strategies. One potential alternative analytic strategy that could
supplement and enhance current approaches to surgical QI is the cumulative sum (CUSUM). The CUSUM is
a statistical process control method that has been utilized in the industrial setting to provide real-time
monitoring of the quality of production processes. In health care, the CUSUM has been used to ascertain
when outcome variation is approaching an unacceptable level and is well-suited for detecting relatively small,
yet persistent, changes in a given outcome over time. It is currently used by the United Network for Organ
Sharing Membership and Profession Standard Committee, a regulatory body for transplant surgery in the
United States, for monitoring hospital-level post-transplant patient and graft long-term survival in real-time. In
addition, our group’s ongoing work has shown the CUSUM consistently provides early, meaningful,
performance-based institutional feedback regarding perioperative outcomes. The overall goal of this proposal
is to build upon our prior work by exploring how to optimize the CUSUM for use within national surgical QI
programs to enable early and accurate detection of hospitals with poor performance. Using national VASQIP
data, our goals are to: 1) evaluate whether multiple CUSUM signals in a given quarter decreases false
positive hospital detection; 2) explore whether a continuously running CUSUM (over multiple quarters)
decreases false positive hospital detection; 3) describe hospital factors associated with false positive and false
negative CUSUM signaling. This work will help to inform a future prospective evaluation of the CUSUM and
eventual implementation withing VASQIP. Leveraging VASQIP’s existing infrastructure to provide institutions
with more real-time, actionable data could ultimately result in the detection of previously unappreciated
suboptimal care processes and save patients from preventable morbidity and/or mortality.

## Key facts

- **NIH application ID:** 10448264
- **Project number:** 5F32HS028560-02
- **Recipient organization:** BAYLOR COLLEGE OF MEDICINE
- **Principal Investigator:** Vivi Chen
- **Activity code:** F32 (R01, R21, SBIR, etc.)
- **Funding institute:** AHRQ
- **Fiscal year:** 2022
- **Award amount:** $85,802
- **Award type:** 5
- **Project period:** 2021-07-01 → 2023-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10448264

## Citation

> US National Institutes of Health, RePORTER application 10448264, Optimizing the Risk-Adjusted CUSUM for Monitoring Hospital Non-cardiac Perioperative Outcomes (5F32HS028560-02). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10448264. Licensed CC0.

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