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Supporting implementation of evidence-based behavioral interventions: the role of data liquidity in facilitating translational behavioral medicine.

ABSTRACT: The advancement of translational behavioral medicine will require that we discover new methods of managing large volumes of data from disparate sources such as disease surveillance systems, public health systems, and health information systems containing patient-centered data informed by behavioral and social sciences. The term "liquidity," when applied to data, refers to its availability and free flow throughout human/computer interactions. In seeking to achieve liquidity, the focus is not on creating a single, comprehensive database or set of coordinated datasets, nor is it solely on developing the electronic health record as the "one-stop shopping" source of health-related data. Rather, attention is on ensuring the availability of secure data through the various methods of collecting and storing data currently existent or under development-so that these components of the health information infrastructure together support a liquid data system. The value of accessible, interoperable, high-volume, reliable, secure, and contextually appropriate data is becoming apparent in many areas of the healthcare system, and health information liquidity is currently viewed as an important component of a patient-centered healthcare system. The translation from research interventions to behavioral and psychosocial indicators challenges the designers of healthcare systems to include this new set of data in the correct context. With the intention of advancing translational behavioral medicine at the local level, "on the ground" in the clinical office and research institution, this commentary discusses data liquidity from the patient's and clinician's perspective, requirements for a liquid healthcare data system, and the ways in which data liquidity can support translational behavioral medicine.

SUBMITTER: Abernethy AP

PROVIDER: S-EPMC3717701 | biostudies-other | 2011 Mar

REPOSITORIES: biostudies-other

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Project description:BackgroundBehavioral intention (which captures one's level of motivation to perform a behavior) is considered a causal and proximal mechanism influencing the use of evidence-based practice (EBP). Implementation studies have measured intention differently, and it is unclear which is most predictive. Some use items referring to "evidence-based practice" in general, whereas others refer to a specific EBP. There are also unresolved debates about whether item stems should be worded "I intend to," "I will," or "How likely are you to" and if a single-item measure can suffice. Using each stem to refer to either a specific EBP or to "evidence-based practice," this study compares the ability of these commonly used measures to predict future EBP implementation. The predictive validity is important for causal model testing and the development of effective implementation strategies.MethodsA longitudinal study enrolled 70 teachers to track their use of two EBPs and compare the predictive validity of six different items measuring teachers' intention. The measures differ by whether an item refers to a specific EBP, or to "evidence-based practices" in general, and whether the stem is worded in one of the three ways: "I intend to," "I will," or "How likely are you to." For each item, linear regressions estimated the variance in future behavior explained. We also compared the predictive validity of a single item versus an aggregate of items by inter-correlating the items using different stems and estimating the explained variance in EBP implementation.ResultsDepending on the EBP and how intention was measured, the explained variance in implementation ranged from 3.5 to 29.0%. Measures that referred to a specific EBP, rather than "evidence-based practices" in general, accounted for more variance in implementation (e.g., 29.0% vs. 8.6%, and 11.3% vs. 3.5%). The predictive validity varied depending on whether stems were worded "I intend to," "I will," or "How likely are you to."ConclusionsThe observed strength of the association between intentions and EBP use will depend on how intention is measured. The association was much stronger if an item referred to a specific EBP, rather than EBP in general. To predict implementation, the results support using an aggregate of two or three intention items that refer to the specific EBP. An even more pragmatic measure of intention consisting of a single item can also predict implementation. As discussed, the relationship will also vary depending on the EBP, which has direct implications for causal model testing and the design of implementation strategies.

Project description:BackgroundFacilitation is a complex, relational implementation strategy that guides change processes. Facilitators engage in multiple activities and tailor efforts to local contexts. How this work is coordinated and shared among multiple, external actors and the contextual factors that prompt and moderate facilitators to tailor activities have not been well-described.MethodsWe conducted a mixed methods evaluation of a trial to improve the quality of transient ischemic attack care. Six sites in the Veterans Health Administration received external facilitation (EF) before and during a 1-year active implementation period. We examined how EF was employed and activated. Data analysis included prospective logs of facilitator correspondence with sites (160 site-directed episodes), stakeholder interviews (a total of 78 interviews, involving 42 unique individuals), and collaborative call debriefs (n=22) spanning implementation stages. Logs were descriptively analyzed across facilitators, sites, time periods, and activity types. Interview transcripts were coded for content related to EF and themes were identified. Debriefs were reviewed to identify instances of and utilization of EF during site critical junctures.ResultsMulti-tiered EF was supported by two groups (site-facing quality improvement [QI] facilitators and the implementation support team) that were connected by feedback loops. Each site received an average of 24 episodes of site-directed EF; most of the EF was delivered by the QI nurse. For each site, site-directed EF frequently involved networking (45%), preparation and planning (44%), process monitoring (44%), and/or education (36%). EF less commonly involved audit and feedback (20%), brainstorming solutions (16%), and/or stakeholder engagement (5%). However, site-directed EF varied widely across sites and time periods in terms of these facilitation types. Site participants recognized the responsiveness of the QI nurse and valued her problem-solving, feedback, and accountability support. External facilitators used monitoring and dialogue to intervene by facilitating redirection during challenging periods of uncertainty about project direction and feasibility for sites. External facilitators, in collaboration with the implementation support team, successfully used strategies tailored to diverse local contexts, including networking, providing data, and brainstorming solutions.ConclusionsMulti-tiered facilitation capitalizing on emergent feedback loops allowed for tailored, site-directed facilitation. Critical juncture cases illustrate the complexity of EF and the need to often try multiple strategies in combination to facilitate implementation progress.Trial registrationThe Protocol-guided Rapid Evaluation of Veterans Experiencing New Transient Neurological Symptoms (PREVENT) is a registered trial ( NCT02769338 ), May 11, 2016-prospectively registered.

Project description:Model organism databases (MODs) have been collecting and integrating biomedical research data for 30 years and were designed to meet specific needs of each model organism research community. The contributions of model organism research to understanding biological systems would be hard to overstate. Modern molecular biology methods and cost reductions in nucleotide sequencing have opened avenues for direct application of model organism research to elucidating mechanisms of human diseases. Thus, the mandate for model organism research and databases has now grown to include facilitating use of these data in translational applications. Challenges in meeting this opportunity include the distribution of research data across many databases and websites, a lack of data format standards for some data types, and sustainability of scale and cost for genomic database resources like MODs. The issues of widely distributed data and application of data standards are some of the challenges addressed by FAIR (Findable, Accessible, Interoperable, and Re-usable) data principles. The Alliance of Genome Resources is now moving to address these challenges by bringing together expertly curated research data from fly, mouse, rat, worm, yeast, zebrafish, and the Gene Ontology consortium. Centralized multi-species data access, integration, and format standardization will lower the data utilization barrier in comparative genomics and translational applications and will provide a framework in which sustainable scale and cost can be addressed. This article presents a brief historical perspective on how the Alliance model organisms are complementary and how they have already contributed to understanding the etiology of human diseases. In addition, we discuss four challenges for using data from MODs in translational applications and how the Alliance is working to address them, in part by applying FAIR data principles. Ultimately, combined data from these animal models are more powerful than the sum of the parts.

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Supporting implementation of evidence-based behavioral interventions: the role of data liquidity in facilitating translational behavioral medicine.

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