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Schulz, Wade L.

0000-0002-2048-4028

Mayo Clinic

Yale New Haven Hospital

Citations 2

Capacity assessment for EHR-based medical device post-market surveillance for synthetic mid-urethral slings among women with stress urinary incontinence: a NEST consortium study
Matheny, Michael E. , Perkins, Amy M. , Rieger‐Christ, Kimberly , Ross, Joseph S. , Mao, Jialin , Sedrakyan, Art , Shah, Nilay D. , Mourtzinos, Arthur, Bartlett, Victoria , Solotke, Michael T. , Pandya, Sameer, Aryal, Suvekshya , Cho, Ahra, Frankenberger, Edward , Park, Daniel , Bostrom, Danielle, Robbins, Susan P. , Yustein, Aron, Trabuco, Emanuel C. , Frankenberger, Edward A , Chughtai, Bilal , Trabuco, Emanuel C , Matheny, Michael , Ross, Joseph S , Winter, Robert, Reynolds, William M. , Schulz, Wade L. , Robbins, Susan, Yustein, Aron S.

BMJ Surgery, Interventions, & Health Technologies | February 12, 2025 | Volume: 7 | Issue: 1 | Pages: e000193

10.1136/bmjsit-2023-000193 Get It

Objectives To evaluate the feasibility for use of electronic health record (EHR) data in conducting adverse event surveillance among women who received mid-urethral slings (MUS) to treat stress urinary incontinence (SUI) in five health systems. Design Retrospective observational study using EHR data from 2010 through 2021. Women with a history of MUS were identified using common data models; a common analytic code was executed at each site. A manual chart review was conducted in a per-site random patient subset to establish a reference standard. Automated text processing (Text Processed Integrated (TPI)) was developed and evaluated at each site to determine the surgical approach and synthetic mesh implantation. Patients were characterized and surgical outcomes were ascertained over 730 subsequent days. Setting Five large tertiary care academic medical centers. Participants Across five health systems, 9,906 eligible patients (mean age 57–60 per site) were identified. Main outcome measures Determination of surgical approach, synthetic mesh implantation, and assessment of the duration of surveillance for mortality and reoperation rates following MUS implantation. Results In the TPI cohort analysis, 3,331 patients were identified. Surgical approach per site was retropubic (42% to 77%), transobturator (6% to 44%), single incision (0% to 24%), and adjustable sling (0% to <4%). Concordance rates for TPI using chart review were 71%–90% at each site for the surgical approach and 28%–85% for synthetic mesh implantation. Patient follow-up observation rates for mortality and reoperation ranged from 22% to 36% at 90 days, 15% to 30% at 365 days, and 8% to 19% at 730 days. Conclusion Using EHR data alone, identification of medical devices and surgical approaches was feasible among women with MUS surgery for SUI, but long-term follow-up ascertainment rates were low. Medical device surveillance using EHR data should be evaluated in the context of the clinical use case, as applicability may vary.
Feasibility of capturing real-world data from health information technology systems at multiple centers to assess cardiac ablation device outcomes: A fit-for-purpose informatics analysis report
Jiang, Guoqian , Dhruva, Sanket S. , Chen, Jiajing , Doshi, Amit, Zhang, Shumin , Young, H. P. , Ervin, Keondae, Shah, Nilay D. , Patrick Young, H , Drozda, Joseph P. , Schulz, Wade L. , Noseworthy, Peter A. , Yu, Yue , Brandt, Eric, Ross, Joseph S. , COPLAN, PAUL M.

Journal of the American Medical Informatics Association | May 29, 2021 | Volume: 28 | Issue: 10 | Pages: 2241-2250

10.1093/jamia/ocab117 Get It

Abstract Objective The study sought to conduct an informatics analysis on the National Evaluation System for Health Technology Coordinating Center test case of cardiac ablation catheters and to demonstrate the role of informatics approaches in the feasibility assessment of capturing real-world data using unique device identifiers (UDIs) that are fit for purpose for label extensions for 2 cardiac ablation catheters from the electronic health records and other health information technology systems in a multicenter evaluation. Materials and Methods We focused on data capture and transformation and data quality maturity model specified in the National Evaluation System for Health Technology Coordinating Center data quality framework. The informatics analysis included 4 elements: the use of UDIs for identifying device exposure data, the use of standardized codes for defining computable phenotypes, the use of natural language processing for capturing unstructured data elements from clinical data systems, and the use of common data models for standardizing data collection and analyses. Results We found that, with the UDI implementation at 3 health systems, the target device exposure data could be effectively identified, particularly for brand-specific devices. Computable phenotypes for study outcomes could be defined using codes; however, ablation registries, natural language processing tools, and chart reviews were required for validating data quality of the phenotypes. The common data model implementation status varied across sites. The maturity level of the key informatics technologies was highly aligned with the data quality maturity model. Conclusions We demonstrated that the informatics approaches can be feasibly used to capture safety and effectiveness outcomes in real-world data for use in medical device studies supporting label extensions.