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Tignanelli CJ, Shah S, Vock D, Siegel L, Serrano C, Haut E, Switzer S, Martin CL, Rizvi R, Peta V, Jenkins PC, Lemke N, Thyvalikakath T, Osheroff JA, Torres D, Vawdrey D, Callcut RA, Butler M, Melton GB. A pragmatic, stepped-wedge, hybrid type II trial of interoperable clinical decision support to improve venous thromboembolism prophylaxis for patients with traumatic brain injury. Implement Sci 2024; 19:57. [PMID: 39103955 PMCID: PMC11299263 DOI: 10.1186/s13012-024-01386-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2024] [Accepted: 07/14/2024] [Indexed: 08/07/2024] Open
Abstract
BACKGROUND Venous thromboembolism (VTE) is a preventable medical condition which has substantial impact on patient morbidity, mortality, and disability. Unfortunately, adherence to the published best practices for VTE prevention, based on patient centered outcomes research (PCOR), is highly variable across U.S. hospitals, which represents a gap between current evidence and clinical practice leading to adverse patient outcomes. This gap is especially large in the case of traumatic brain injury (TBI), where reluctance to initiate VTE prevention due to concerns for potentially increasing the rates of intracranial bleeding drives poor rates of VTE prophylaxis. This is despite research which has shown early initiation of VTE prophylaxis to be safe in TBI without increased risk of delayed neurosurgical intervention or death. Clinical decision support (CDS) is an indispensable solution to close this practice gap; however, design and implementation barriers hinder CDS adoption and successful scaling across health systems. Clinical practice guidelines (CPGs) informed by PCOR evidence can be deployed using CDS systems to improve the evidence to practice gap. In the Scaling AcceptabLE cDs (SCALED) study, we will implement a VTE prevention CPG within an interoperable CDS system and evaluate both CPG effectiveness (improved clinical outcomes) and CDS implementation. METHODS The SCALED trial is a hybrid type 2 randomized stepped wedge effectiveness-implementation trial to scale the CDS across 4 heterogeneous healthcare systems. Trial outcomes will be assessed using the RE2-AIM planning and evaluation framework. Efforts will be made to ensure implementation consistency. Nonetheless, it is expected that CDS adoption will vary across each site. To assess these differences, we will evaluate implementation processes across trial sites using the Exploration, Preparation, Implementation, and Sustainment (EPIS) implementation framework (a determinant framework) using mixed-methods. Finally, it is critical that PCOR CPGs are maintained as evidence evolves. To date, an accepted process for evidence maintenance does not exist. We will pilot a "Living Guideline" process model for the VTE prevention CDS system. DISCUSSION The stepped wedge hybrid type 2 trial will provide evidence regarding the effectiveness of CDS based on the Berne-Norwood criteria for VTE prevention in patients with TBI. Additionally, it will provide evidence regarding a successful strategy to scale interoperable CDS systems across U.S. healthcare systems, advancing both the fields of implementation science and health informatics. TRIAL REGISTRATION Clinicaltrials.gov - NCT05628207. Prospectively registered 11/28/2022, https://classic. CLINICALTRIALS gov/ct2/show/NCT05628207 .
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Affiliation(s)
- Christopher J Tignanelli
- Department of Surgery, University of Minnesota, 420 Delaware St SE, MMC 195, Minneapolis, MN, 55455, USA.
- Institute for Health Informatics, University of Minnesota, Minneapolis, MN, USA.
- Center for Learning Health Systems Science, University of Minnesota, Minneapolis, MN, USA.
- Center for Quality Outcomes, Discovery and Evaluation, University of Minnesota, Minneapolis, MN, USA.
| | - Surbhi Shah
- Department of Medicine, Mayo Clinic, Scottsdale, AZ, USA
| | - David Vock
- Division of Biostatistics and Health Data Science, University of Minnesota, Minneapolis, MN, USA
| | - Lianne Siegel
- Division of Biostatistics and Health Data Science, University of Minnesota, Minneapolis, MN, USA
| | - Carlos Serrano
- Division of Biostatistics and Health Data Science, University of Minnesota, Minneapolis, MN, USA
| | - Elliott Haut
- Department of Surgery, Johns Hopkins University, Baltimore, MD, USA
| | | | | | - Rubina Rizvi
- Institute for Health Informatics, University of Minnesota, Minneapolis, MN, USA
- Center for Learning Health Systems Science, University of Minnesota, Minneapolis, MN, USA
| | - Vincent Peta
- Department of Surgery, University of Minnesota, 420 Delaware St SE, MMC 195, Minneapolis, MN, 55455, USA
| | - Peter C Jenkins
- Department of Surgery, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Nicholas Lemke
- Department of Surgery, University of Minnesota, 420 Delaware St SE, MMC 195, Minneapolis, MN, 55455, USA
| | - Thankam Thyvalikakath
- Center for Biomedical Informatics, Regenstrief Institute, Indianapolis, IN, USA
- Indiana University School of Dentistry, Indianapolis, IN, USA
| | | | - Denise Torres
- Department of Surgery, Geisinger Health, Danville, PA, USA
| | - David Vawdrey
- Department of Biomedical Informatics, Geisinger Health, Danville, PA, USA
| | - Rachael A Callcut
- Department of Surgery, UC Davis School of Medicine, Sacramento, CA, USA
| | - Mary Butler
- Center for Learning Health Systems Science, University of Minnesota, Minneapolis, MN, USA
- School of Publish Health, University of Minnesota, Minneapolis, MN, USA
| | - Genevieve B Melton
- Department of Surgery, University of Minnesota, 420 Delaware St SE, MMC 195, Minneapolis, MN, 55455, USA
- Institute for Health Informatics, University of Minnesota, Minneapolis, MN, USA
- Center for Learning Health Systems Science, University of Minnesota, Minneapolis, MN, USA
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