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IIR 19-365 – HSR Study

IIR 19-365
Enhancing the Efficiency of Data Collection for Surgical Quality Improvement
Nader N Massarweh, MD
Atlanta VA Medical and Rehab Center, Decatur, GA
Decatur, GA
Funding Period: May 2021 - April 2025


Electronic health record (EHR) and clinical registry data are commonly used for the evaluation of hospital surgical quality and safety. Although the majority of national quality initiatives utilize EHR or administrative data, their ability to discriminate performance on nationally endorsed quality indicators has been brought into question and it is unclear how well certain outcomes, such as postoperative complications, are ascertained. By comparison, while clinical registry data are widely considered robust for performance evaluation and quality assessment, the time and personnel required for data collection are substantial. The VA Surgical Quality Improvement Program (VASQIP) is one of the most successful and longest-standing national clinical registries used for surgical quality improvement (QI). VASQIP data are obtained through rigorous chart abstraction by trained local Surgical Quality Nurses (SQNs) for a systematic sample of surgical cases performed at all VA hospitals and used to characterize the quality and safety of surgical care at each hospital. There are two important potential limitations associated with current VASQIP data collection practices. First, because perioperative morbidity and mortality rates have significantly decreased across VA over the past two decades, it is unclear that systematic case sampling is adequately powered to ensure underperforming hospitals are correctly identified (i.e.: adequate negative predictive value)—in 2017, VASQIP only captured data for ~25% of all surgical cases performed across VA. Since VASQIP’s inception, no study has evaluated this specific issue. Second, the time required for VASQIP data collection detracts from SQNs’ effort that could be directed at their other important job function, such as proactive engagement in local QI activities. Given that SQNs spend substantial time working with local VASQIP data, this may represent an important missed opportunity to inform the surgical care line at the time when a quality problem is evolving rather than when it has already occurred. As such, alternative approaches that can provide reliable data while simultaneously decreasing the burden of data collection could enhance current QI efforts and have tangible benefits for VHA. The overall goal of this proposal is to address two important, but as yet unanswered, questions regarding VASQIP and its current approach to data collection. First, given low perioperative morbidity and mortality rates across VA, is VASQIP’s systematic sampling approach used over the past 3 decades still robust enough to inform surgical QI? Second, are hybrid data (i.e.: EHR combined with clinical registry variables) a potentially reliable alternative for measuring VA hospital surgical performance? This mixed-methods proposal will involve hospital-level, observational studies using VASQIP and Corporate Data Warehouse data from patients who underwent surgery at a VA hospital between 2016 and 2018 as well as qualitative interviews with SQNs. With comparative effectiveness in mind, these data will be used to explore what would be observed if data from all surgical cases were included in VASQIP and using other existing VA data sources might enhance the efficiency of VASQIP data collection and improve local QI efforts. The specific aims are to: (1) evaluate whether analyzing all VASQIP-eligible surgical cases, relative to current systematic case sampling, improves negative predictive value (i.e.: decreases false negative rates) for identifying VA hospitals with outlier performance; (2) explore the use of hybrid EHR and clinical registry data, relative to clinical registry alone, for evaluating risk- adjusted surgical performance at VA hospitals; [(3) explore how more efficient VASQIP data collection could enhance local QI efforts through in-depth, key informant interviews with SQNs]. This project is important and novel because it will provide real-world, generalizable data that can only be obtained within VA’s data platform. [The findings would have significant value for VA hospitals’ local QI efforts and could be used to inform national surgical and non-surgical QI initiatives within VA and the private sector.]

External Links for this Project

NIH Reporter

Grant Number: I01HX003127-01A1

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Journal Articles

  1. Chen VW, Rosen T, Dong Y, Richardson PA, Kramer JR, Petersen LA, Massarweh NN. Case Sampling for Evaluating Hospital Postoperative Morbidity in US Surgical Quality Improvement Programs. JAMA surgery. 2024 Mar 1; 159(3):315-322. [view]
  2. Chen VW, Chidi AP, Rosen T, Dong Y, Richardson PA, Kramer J, Axelrod DA, Petersen LA, Massarweh NN. Case Sampling vs Universal Review for Evaluating Hospital Postoperative Mortality in US Surgical Quality Improvement Programs. JAMA surgery. 2023 Dec 1; 158(12):1312-1319. [view]
  3. Kaul B, Petersen LA, Rosas IO, Lee JS, Martinez FJ, Bandi VD, Helmer DA, Wolters PJ, Collard HR, Whooley MA. Interstitial Lung Disease in Veterans: Leveraging Big Data to Bridge Evidence and Practice Gaps. Annals of the American Thoracic Society. 2023 Apr 1; 20(4):504-507. [view]
  4. Chen VW, Chidi AP, Dong Y, Richardson PA, Axelrod DA, Petersen LA, Massarweh NN. Risk-Adjusted Cumulative Sum for Early Detection of Hospitals With Excess Perioperative Mortality. JAMA surgery. 2023 Nov 1; 158(11):1176-1183. [view]

DRA: Health Systems
DRE: TRL - Applied/Translational
Keywords: Best Practices, Provider Performance Measures, Quality Indicators
MeSH Terms: None at this time.

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