The VA Surgical Quality Improvement Program (VASQIP) is a long-standing source of robust clinical data used by the VA National Surgery Office (NSO) to inform quality improvement (QI) efforts at each VA hospital. There are two important limitations associated with current approaches to quality improvement (QI). The first is the retrospective and episodic (i.e.: quarterly) approach to feedback which creates a time lag between when performance is declining and when the hospital is made aware. The second is the possibility that short runs, or small clusters, of clinically meaningful poor performance may go of undetected. Furthermore, most healthcare entities are willing to accept average performance, but will respond if performance starts to fall below an acceptable threshold. As such, alternative approaches that could shorten this time lag, improve the detection of hospitals with potentially suboptimal perioperative care processes, or inspire hospitals to be more proactive in engaging in programmatic improvement activities could enhance QI efforts and have tangible benefits for VHA.
The goal of the proposed study is to develop comparative effectiveness data for alternative approaches for monitoring VA hospital surgical performance relative to the current standard of episodic data analysis and feedback. Specifically, the objectives are to: (a) compare the CUSUM (an industrial statistical process control technique for monitoring the quality of production processes in real-time) to the episodic observed-to-expected (O-E) approach for evaluating surgical performance at VA hospitals; (b) evaluate a composite outcome of 30-day mortality, major morbidity, and reoperation as a more sensitive and earlier indicator of declining institutional performance compared to 30-day mortality or morbidity alone.
This study will be a hospital-level, observational study using VASQIP data for patients who underwent inpatient, non-cardiac operations between 2010 and 2016. For the first aim, using a risk-adjusted, time-to-event CUSUM, the detection of outlier hospitals as well as the timing in which outliers are identified will be compared to the standard approach of episodic evaluation of risk-adjusted O-E ratios. For the second aim, hierarchical modeling will be used to explore the potential value of a composite outcome of 30-day mortality, major morbidity, and reoperation as an indicator of declining institutional performance relative to the standard outcomes of 30-day mortality or morbidity alone. Using standard VASQIP methodology, data will be stratified into quarterly and rolling 1-year time periods for the purpose of analyses.
We anticipate the CUSUM will detect poor performing hospital outliers at an earlier time point compared to standard of episodic evaluation. We also expect the proposed composite outcome provide a more reliable signal of declining hospital performance relative to 30-day mortality or morbidity alone.
This work could have a number of tangible benefits to Veterans' health including: (a) prevention of harm in future surgical patients; (b) new and innovative approaches to data analysis and reporting from NSO to each VA hospital allowing for earlier recognition of declining performance and implementation of local corrective measures in response; (c) in cases where a hospital is unaware patients' post-operative outcomes are being affected by suboptimal perioperative care processes, the costs associated with correcting errant care processes and treating potentially preventable morbidity could be reduced by addressing such issues earlier. In summary, this proposal is novel and potentially impactful because it can change the paradigm regarding surgical QI from reactive in response to episodic data to proactive as data are evaluated and provided in a more timely fashion.
External Links for this Project
Grant Number: I01HX002447-01A1
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Other Conditions, Brain and Spinal Cord Injuries and Disorders, Cardiovascular Disease
Treatment - Observational, Treatment - Comparative Effectiveness, TRL - Applied/Translational
Quality Improvement, Quality Indicators