Browsing by Subject "Learning health system"

Now showing 1 - 4 of 4
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    A pragmatic, stepped-wedge, hybrid type II trial of interoperable clinical decision support to improve venous thromboembolism prophylaxis for patients with traumatic brain injury
    (Springer Nature, 2024-08-05) Tignanelli, Christopher J.; Shah, Surbhi; Vock, David; Siegel, Lianne; Serrano, Carlos; Haut, Elliott; Switzer, Sean; Martin, Christie L.; Rizvi, Rubina; Peta, Vincent; Jenkins, Peter C.; Lemke, Nicholas; Thyvalikakath, Thankam; Osheroff, Jerome A.; Torres, Denise; Vawdrey, David; Callcut, Rachael A.; Butler, Mary; Melton, Genevieve B.; Surgery, School of Medicine
    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.
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    Alliances to disseminate addiction prevention and treatment (ADAPT): A statewide learning health system to reduce substance use among justice-involved youth in rural communities
    (Elsevier, 2021) Aalsma, Matthew C.; Aarons, Gregory A.; Adams, Zachary W.; Alton, Madison D.; Boustani, Malaz; Dir, Allyson L.; Embi, Peter J.; Grannis, Shaun; Hulvershorn, Leslie A.; Huntsinger, Douglas; Lewis, Cara C.; Monahan, Patrick; Saldana, Lisa; Schwartz, Katherine; Simon, Kosali I.; Terry, Nicolas; Wiehe, Sarah E.; Zapolski, Tamika C. B.; Pediatrics, School of Medicine
    Background: Youth in the justice system (YJS) are more likely than youth who have never been arrested to have mental health and substance use problems. However, a low percentage of YJS receive SUD services during their justice system involvement. The SUD care cascade can identify potential missed opportunities for treatment for YJS. Steps along the continuum of the cascade include identification of treatment need, referral to services, and treatment engagement. To address gaps in care for YJS, we will (1) implement a learning health system (LHS) to develop, or improve upon, alliances between juvenile justice (JJ) agencies and community mental health centers (CMHC) and (2) present local cascade data during continuous quality improvement cycles within the LHS alliances. Methods/design: ADAPT is a hybrid Type II effectiveness implementation trial. We will collaborate with JJ and CMHCs in eight Indiana counties. Application of the EPIS (exploration, preparation, implementation, and sustainment) framework will guide the implementation of the LHS alliances. The study team will review local cascade data quarterly with the alliances to identify gaps along the continuum. The study will collect self-report survey measures longitudinally at each site regarding readiness for change, implementation climate, organizational leadership, and program sustainability. The study will use the Stages of Implementation Completion (SIC) tool to assess the process of implementation across interventions. Additionally, the study team will conduct focus groups and qualitative interviews with JJ and CMHC personnel across the intervention period to assess for impact. Discussion: Findings have the potential to increase SUD need identification, referral to services, and treatment for YJS.
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    Hypothesis Generation Using Network Structures on Community Health Center Cancer-Screening Performance
    (Elsevier, 2015-10) Carney, Timothy Jay; Morgan, Geoffrey P.; Jones, Josette; McDaniel, Anna M.; Weaver, Michael; Weiner, Bryan; Haggstrom, David A.; BioHealth Informatics, School of Informatics and Computing
    RESEARCH OBJECTIVES: Nationally sponsored cancer-care quality-improvement efforts have been deployed in community health centers to increase breast, cervical, and colorectal cancer-screening rates among vulnerable populations. Despite several immediate and short-term gains, screening rates remain below national benchmark objectives. Overall improvement has been both difficult to sustain over time in some organizational settings and/or challenging to diffuse to other settings as repeatable best practices. Reasons for this include facility-level changes, which typically occur in dynamic organizational environments that are complex, adaptive, and unpredictable. This study seeks to understand the factors that shape community health center facility-level cancer-screening performance over time. This study applies a computational-modeling approach, combining principles of health-services research, health informatics, network theory, and systems science. METHODS: To investigate the roles of knowledge acquisition, retention, and sharing within the setting of the community health center and to examine their effects on the relationship between clinical decision support capabilities and improvement in cancer-screening rate improvement, we employed Construct-TM to create simulated community health centers using previously collected point-in-time survey data. Construct-TM is a multi-agent model of network evolution. Because social, knowledge, and belief networks co-evolve, groups and organizations are treated as complex systems to capture the variability of human and organizational factors. In Construct-TM, individuals and groups interact by communicating, learning, and making decisions in a continuous cycle. Data from the survey was used to differentiate high-performing simulated community health centers from low-performing ones based on computer-based decision support usage and self-reported cancer-screening improvement. RESULTS: This virtual experiment revealed that patterns of overall network symmetry, agent cohesion, and connectedness varied by community health center performance level. Visual assessment of both the agent-to-agent knowledge sharing network and agent-to-resource knowledge use network diagrams demonstrated that community health centers labeled as high performers typically showed higher levels of collaboration and cohesiveness among agent classes, faster knowledge-absorption rates, and fewer agents that were unconnected to key knowledge resources. Conclusions and research implications: Using the point-in-time survey data outlining community health center cancer-screening practices, our computational model successfully distinguished between high and low performers. Results indicated that high-performance environments displayed distinctive network characteristics in patterns of interaction among agents, as well as in the access and utilization of key knowledge resources. Our study demonstrated how non-network-specific data obtained from a point-in-time survey can be employed to forecast community health center performance over time, thereby enhancing the sustainability of long-term strategic-improvement efforts. Our results revealed a strategic profile for community health center cancer-screening improvement via simulation over a projected 10-year period. The use of computational modeling allows additional inferential knowledge to be drawn from existing data when examining organizational performance in increasingly complex environments.
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    Stroke Learning Health Systems: A Topical Narrative Review With Case Examples
    (American Heart Association, 2023) Cadilhac, Dominique A.; Bravata, Dawn M.; Bettger, Janet Prvu; Mikulik, Robert; Norrving, Bo; Uvere, Ezinne O.; Owolabi, Mayowa; Ranta, Annemarei; Kilkenny, Monique F.; Medicine, School of Medicine
    To our knowledge, the adoption of Learning Health System (LHS) concepts or approaches for improving stroke care, patient outcomes, and value have not previously been summarized. This topical review provides a summary of the published evidence about LHSs applied to stroke, and case examples applied to different aspects of stroke care from high and low-to-middle income countries. Our attempt to systematically identify the relevant literature and obtain real-world examples demonstrated the dissemination gaps, the lack of learning and action for many of the related LHS concepts across the continuum of care but also elucidated the opportunity for continued dialogue on how to study and scale LHS advances. In the field of stroke, we found only a few published examples of LHSs and health systems globally implementing some selected LHS concepts, but the term is not common. A major barrier to identifying relevant LHS examples in stroke may be the lack of an agreed taxonomy or terminology for classification. We acknowledge that health service delivery settings that leverage many of the LHS concepts do so operationally and the lessons learned are not shared in peer-reviewed literature. It is likely that this topical review will further stimulate the stroke community to disseminate related activities and use keywords such as learning health system so that the evidence base can be more readily identified.