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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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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.

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Acknowledgments

Preparation of this manuscript was supported by NIH Grants P01 AR050245, R01 CA122704, R01 CA131148, R01 CA100771, R01 AR054626, R01 NR010777, and R01 NS053759 and a Within Our Reach grant from the American College of Rheumatology. Additionally, this project has been funded in whole or in part with federal funds from the National Cancer Institute, National Institutes of Health, under contract no. HHSN261200800001E. The content of this publication does not necessarily reflect the views or policies of the Department of Health and Human Services nor does mention of trade names, commercial products, or organizations imply endorsement by the U.S. Government.

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Authors and Affiliations

  1. Department of Medicine, Division of Medical Oncology, Duke University Medical Center, P.O. Box 3436, Durham, NC, USA

    Amy P Abernethy MD (Associate Professor of Medicine) & Jane L Wheeler MSPH (Medical Instructor)

  2. Department of Palliative and Supportive Services, Division of Medicine, Flinders University, Bedford Park, South Australia, Australia

    Amy P Abernethy MD (Associate Professor of Medicine)

  3. Clinical Monitoring Research Program, Office of the Director, Division of Cancer Control and Population Science, SAIC-Frederick, Inc., National Cancer Institute, Rockville, MD, USA

    Paul K Courtney MS (Biomedical Informaticist)

  4. Department of Psychiatry, Division of Behavioral Medicine, Duke University Medical Center, Durham, NC, USA

    Francis J Keefe PhD (Professor)

Authors
  1. Amy P Abernethy MD
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  2. Jane L Wheeler MSPH
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  3. Paul K Courtney MS
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  4. Francis J Keefe PhD
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Correspondence to Amy P Abernethy MD.

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Implications

Practice: In a liquid data system, behavioral medicine clinicians stand to benefit from the greater availability of data that can help guide their clinical decisions and assist them in monitoring patients’ status and outcomes.

Policy: Large, population-based datasets could provide a strong foundation for healthcare policy decision-making and could be especially useful in areas such as behavioral medicine that warrant advocacy based on sound, generalizable evidence.

Research: A liquid data environment would make data more readily available for researchers to examine and analyze, aggregate, share and compare, and generally utilize for multiple research purposes such as identification of clinically important research questions and conduct of diverse research studies.

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Abernethy, A.P., Wheeler, J.L., Courtney, P.K. et al. Supporting implementation of evidence-based behavioral interventions: the role of data liquidity in facilitating translational behavioral medicine. Behav. Med. Pract. Policy Res. 1, 45–52 (2011). https://doi.org/10.1007/s13142-011-0024-4

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  • DOI: https://doi.org/10.1007/s13142-011-0024-4

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