Skip to main content
Key Specialties
Cardiology Diabetology Endocrinology Gastroenterology Geriatrics and Gerontology Gynecology and Obstetrics Hematology Infectious Disease Internal Medicine Nephrology Neurology Oncology Pediatrics Respiratory Medicine Rheumatology Surgery
Congress Coverage
2024 ACC Congress 2023 ESMO Congress 2023 EASD Congress 2023 ERS Congress 2023 ESC Congress
Clinical Collections
Advances in Alzheimer’s

Keynote webinar | Spotlight on medication adherence

LIVE: Thursday 27th June 2024, 18:00-19:30 (CEST)
Join our expert panel to discover why you need to understand the drivers of non-adherence in your patients, and how you can optimize medication adherence in your clinics to drastically improve patient outcomes.
ADVANCED SEARCH
Log in
Top
Translational Behavioral Medicine
Published in: Translational Behavioral Medicine 1/2011

01-03-2011

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

Authors: Amy P Abernethy, MD, Jane L Wheeler, MSPH, Paul K Courtney, MS, Francis J Keefe, PhD

Published in: Translational Behavioral Medicine | Issue 1/2011

Login to get access

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.
Literature
1.
Abernethy, A. P., & Wheeler, J. L. (2011). True translational research: Bridging the three phases of translation through data and behavior. Translational Behavioral Medicine. doi:10.​1007/​s13142-010-0013-z.
2.
Sung, N. S., Crowley, W. F., Jr., Genel, M., et al. (2003). Central challenges facing the national clinical research enterprise. JAMA, 289(10), 1278–1287.PubMedCrossRef
3.
Dougherty, D., & Conway, P. H. (2008). The “3T’s” road map to transform US health care: The “how” of high-quality care. Jama, 299(19), 2319–2321.PubMedCrossRef
4.
DeWalt, D. A., Davis, T. C., Wallace, A. S., et al. (2009). Goal setting in diabetes self-management: Taking the baby steps to success. Patient Education and Counseling, 77(2), 218–223.PubMedCrossRef
5.
Kim, M. T., Han, H. R., Song, H. J., et al. (2009). A community-based, culturally tailored behavioral intervention for Korean Americans with type 2 diabetes. The Diabetes Educator, 35(6), 986–994.PubMedCrossRef
6.
Curry, S. J., Keller, P. A., Orleans, C. T., & Fiore, M. C. (2008). The role of health care systems in increased tobacco cessation. Annual Review of Public Health, 29, 411–428.PubMedCrossRef
7.
Fiore, M. C., & Jaen, C. R. (2008). A clinical blueprint to accelerate the elimination of tobacco use. Jama, 299(17), 2083–2085.PubMedCrossRef
8.
Fiore, M. C., Keller, P. A., & Curry, S. J. (2007). Health system changes to facilitate the delivery of tobacco-dependence treatment. American Journal of Preventive Medicine, 33(6 Suppl), S349–S356.PubMedCrossRef
9.
Smith, S. S., McCarthy, D. E., Japuntich, S. J., et al. (2009). Comparative effectiveness of 5 smoking cessation pharmacotherapies in primary care clinics. Archives of Internal Medicine, 169(22), 2148–2155.PubMedCrossRef
10.
Slutsky, J. R. (2007). Moving closer to a rapid-learning health care system. Health Affairs, 26(2), w122–w124.PubMedCrossRef
11.
12.
Gray, J. A. (1998). Where’s the chief knowledge officer? To manage the most precious resource of all. BMJ, 317(7162), 26.
13.
Gray, J. A., & de Lusignan, S. (1999). Information in practice: national electronic library for health (NELH). BMJ, 319, 1476–1479.
14.
Abernethy, A. P., Coeytaux, R. R., Carson, K., et al. (2009). Technology assessment: Report on the evidence regarding off-label indications for targeted therapies used in cancer treatment. Rockville: Agency for Healthcare Quality and Research, U.S. Department of Health and Human Services.
15.
Tunis, S. R., Stryer, D. B., & Clancy, C. M. (2003). Practical clinical trials: Increasing the value of clinical research for decision making in clinical and health policy. Jama, 290(12), 1624–1632.PubMedCrossRef
16.
Shneiderman, B. (1993). Designing the user interface: Strategies for effective human–computer interaction. Reading: Addison-Wesley.
17.
Greitzer, F. L., Dauenhauer, P. M., Wierks, T. G. D., & Podmore, R. (2009). Human factors evaluation of advanced electric power grid visualization tools richland, WA: PNNL-18386, Pacific Northwest National Laboratory.
18.
Patel, V. L., & Currie, L. M. (2006). Clinical cognition and biomedical informatics: Issues of patient safety. Studies in Health Technology and Informatics, 122, 18–22.PubMed
19.
Thew, S., Sutcliffe, A., Procter, R., et al. (2009). Requirements engineering for E-science: Experiences in epidemiology. IEEE Software, 26(1), 80–87.CrossRef
20.
Holbrook, T. L., Galarneau, M. R., Dye, J. L., Quinn, K., & Dougherty, A. L. (2010). Morphine use after combat injury in Iraq and post-traumatic stress disorder. The New England Journal of Medicine, 362(2), 110–117.PubMedCrossRef
21.
Penfield, S. L., Anderson, K. M., Edmunds, M., & Belanger, M. (2009). Toward health information liquidity: realization of better, more efficient care from the free flow of health information: Booz Allen Hamilton, Inc.
22.
Pignone, M., DeWalt, D. A., Sheridan, S., Berkman, N., & Lohr, K. N. (2005). Interventions to improve health outcomes for patients with low literacy. A systematic review. Journal of General Internal Medicine, 20(2), 185–192.PubMedCrossRef
23.
NIMH Multisite HIV/STD Prevention Trial for African American Couples Group. (2008). Eban health promotion intervention: Conceptual basis and procedures. Journal of Acquired Immune Deficiency Syndromes, 49(1), 1.CrossRef
24.
25.
26.
27.
Johnson, J. D., Case, D. O., Andrews, J. E., & Allard, S. L. (2005). Genomics—the perfect information-seeking research problem. Journal of Health Communication, 10(4), 323–329.PubMedCrossRef
28.
Fisher, E. B., Brownson, C. A., O’Toole, M. L., Shetty, G., Anwuri, V. V., & Glasgow, R. E. (2005). Ecological approaches to self-management: The case of diabetes. American Journal of Public Health, 95(9), 1523–1535.PubMedCrossRef
29.
Green, C. J., Fortin, P., Maclure, M., Macgregor, A., & Robinson, S. (2006). Information system support as a critical success factor for chronic disease management: Necessary but not sufficient. International Journal of Medical Informatics, 75(12), 818–828.PubMedCrossRef
30.
31.
Moser, R. P. (2011). The grid-enabled measures (GEM) database: A science 2.0 tool to facilitate the use of standardized measures and sharing harmonized data. American Journal of Preventive Medicine (in press).
32.
33.
Forman, M. R., Greene, S. M., Avis, N. E., et al. (2010). Bioinformatics: Tools to accelerate population science and disease control research. American Journal of Preventive Medicine, 38(6), 646–651.PubMedCrossRef
34.
35.
Kazdin, A. E. (1983). Single-case research designs in clinical child psychiatry. Journal of the American Academy of Child Psychiatry, 22(5), 423–432.PubMedCrossRef
36.
Abernethy, A. P., Keefe, F. J., McCrory, D. C., Scipio, C. D., & Matchar, D. B. (2005). Technology assessment on the use of behavioral therapies for treatment of medical disorders: Part 2—Impact on management of patients with cancer pain. Rockville: Agency for Healthcare Quality and Research, U.S. Department of Health and Human Services.
37.
Uronis, H. E., Currow, D. C., McCrory, D. C., Samsa, G. P., & Abernethy, A. P. (2008). Oxygen for relief of dyspnoea in mildly- or non-hypoxaemic patients with cancer: A systematic review and meta-analysis. British Journal of Cancer, 98(2), 294–299.PubMedCrossRef
38.
Abernethy, A. P., Samsa, G. P., & Matchar, D. B. (2003). A clinical decision and economic analysis model of cancer pain management. The American Journal of Managed Care, 9(10), 651–664.PubMed
39.
Metadata
Title
Supporting implementation of evidence-based behavioral interventions: the role of data liquidity in facilitating translational behavioral medicine
Authors
Amy P Abernethy, MD
Jane L Wheeler, MSPH
Paul K Courtney, MS
Francis J Keefe, PhD
Publication date
01-03-2011
Publisher
Springer-Verlag
Published in
Translational Behavioral Medicine / Issue 1/2011
Print ISSN: 1869-6716
Electronic ISSN: 1613-9860
DOI
https://doi.org/10.1007/s13142-011-0024-4

Other articles of this Issue 1/2011

Translational Behavioral Medicine 1/2011 Go to the issue

OriginalPaper

Integrating eHealth with human services for breast cancer patients

EditorialNotes

Translational Behavioral Medicine: a pathway to better health

Policy Brief

Consumer health informatics: improving patient engagement

OriginalPaper

CALM technology-supported intervention: synopsis of evidence for an emerging class of practice tool

Case Study

CYberinfrastructure for COmparative effectiveness REsearch (CYCORE): improving data from cancer clinical trials

OriginalPaper

Stakeholder perspectives on implementing the National Cancer Institute’s patient-reported outcomes version of the Common Terminology Criteria for Adverse Events (PRO-CTCAE)