Skip to main content

Advertisement

SpringerLink
Log in

Behavioral medicine perspectives on the design of health information technology to improve decision-making, guideline adherence, and care coordination in chronic pain management

  • Published:
Translational Behavioral Medicine

ABSTRACT

Development of clinical decision support systems (CDSs) has tended to focus on facilitating medication management. An understanding of behavioral medicine perspectives on the usefulness of a CDS for patient care can expand CDSs to improve management of chronic disease. The purpose of this study is to explore feedback from behavioral medicine providers regarding the potential for CDSs to improve decision-making, care coordination, and guideline adherence in pain management. Qualitative methods were used to analyze semi-structured interview responses from behavioral medicine stakeholders following demonstration of an existing CDS for opioid prescribing, ATHENA-OT. Participants suggested that a CDS could assist with decision-making by educating providers, providing recommendations about behavioral therapy, facilitating risk assessment, and improving referral decisions. They suggested that a CDS could improve care coordination by facilitating division of workload, improving patient education, and increasing consideration and knowledge of options in other disciplines. Clinical decision support systems are promising tools for improving behavioral medicine care for chronic pain.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig 1

Similar content being viewed by others

REFERENCES

  1. Garg, A. X., Adhikari, N. K. J., McDonald, H., et al. (2005). Effects of computerized clinical decision support systems on practitioner performance and patient outcomes: a systematic review. Journal of the American Medical Association, 293, 1223–1238.

    Article  PubMed  CAS  Google Scholar 

  2. Clark, J. D. (2002). Chronic pain prevalence and analgesic prescribing in a general medical population. Journal of Pain and Symptom Management, 23, 131–137.

    Article  PubMed  Google Scholar 

  3. Turk, D.C., Wilson, H., & Swanson, K.S. (eds.) (2011). The biopsychosocial model of pain and pain management. New York: Cambridge University Press (in press).

  4. Brown, K. S., & Folen, R. A. (2005). Psychologists as leaders of multidisciplinary chronic pain management teams: a model for health care delivery. Professional Psychology, Research and Practice, 36, 587–594.

    Article  Google Scholar 

  5. Turk, D. C., & Okifuji, A. (1998). Treatment of chronic pain patients: clinical outcome, cost-effectiveness, and cost-benefits. Critical Reviews in Physical and Rehabilitation Medicine, 10, 181–208.

    Google Scholar 

  6. Gatchel, R. J., & Okifuji, A. (2006). Evidence-based scientific data documenting the treatment and cost-effectiveness of comprehensive pain programs or chronic nonmalignant pain. The Journal of Pain, 7, 779–793.

    Article  PubMed  Google Scholar 

  7. Flor, H., Fydrich, T., & Turk, D. C. (1992). Efficacy of multidisciplinary pain treatment centers: a meta-analytic review. Pain, 49, 221–230.

    Article  PubMed  CAS  Google Scholar 

  8. Von Korff, M., Balderson, B. H. K., Saunders, K., et al. (2005). A trial of an activating intervention for chronic back pain in primary care and physical therapy settings. Pain, 113, 323–330.

    Article  Google Scholar 

  9. Turk, D. C., & Okifuji, A. (2002). Psychological factors in chronic pain: evolution and revolution. Journal of Consulting and Clinical Psychology, 70, 678–690.

    Article  PubMed  Google Scholar 

  10. Hoffman, B. M., Papas, R. K., Chatkoff, D. K., & Kerns, R. D. (2007). Meta-analysis of psychological interventions for chronic low back pain. Health Psychology, 26, 1–9.

    Article  PubMed  Google Scholar 

  11. Morley, S., Eccleston, C., & Williams, A. (1999). Systematic review and meta-analysis of randomized controlled trials of cognitive behaviour therapy and behaviour therapy for chronic pain in adults, excluding headache. Pain, 80, 1–13.

    Article  PubMed  CAS  Google Scholar 

  12. VA/DoD opioid therapy for chronic pain working group: the management of opioid therapy for chronic pain. Washington: The Office of Quality and Performance, VA and Quality Management Directorate, US Army MEDCOM, 2003.

  13. Pletcher, M., Kertesz, S., Kohn, M., & Gonzales, R. (2008). Trends in opioid prescribing by race/ethnicity for patients seeking care in US emergency departments. Journal of the American Medical Association, 299, 70–78.

    Article  PubMed  CAS  Google Scholar 

  14. Manchikanti, L., & Singh, A. (2008). Therapeutic opioids: a ten-year perspective on the complexities and complications of the escalating use, abuse, and nonmedical use of opioids. Pain Physician, 11, S63–S88.

    PubMed  Google Scholar 

  15. Reid, M., Engles-Horton, L., Weber, M., et al. (2002). Use of opioid medications for chronic noncancer pain syndromes in primary care. Journal of General Internal Medicine, 17, 173–179.

    Article  PubMed  Google Scholar 

  16. Dobscha, S. K., Corson, K., Flores, J. A., Tansill, E. C., & Gerrity, M. S. (2008). Veterans affairs primary care clinicians' attitudes toward chronic pain and correlates of opioid prescribing rates. Pain Medicine, 9, 564–571.

    Article  PubMed  Google Scholar 

  17. Goldstein MK, Hoffmann BB, Coleman RW, et al. (2000). Implementing clinical practice guidelines while taking account of changing evidence: ATHENA DSS, an easily modifiable decision-support system for managing hypertension in primary care. Proceedings—AMIA Symposium, pp. 300–304.

  18. Goldstein, M. K., Coleman, R. W., Tu, S. W., et al. (2004). Translating research into practice: organizational issues in implementing automated decision support for hypertension in three medical centers. Application of Information Technology, 11, 368–376.

    Google Scholar 

  19. Trafton, J. A., Martins, S. B., Michel, M. C., et al. (2010). Designing an automated clinical decision support system to match clinical practice guidelines for opioid therapy for chronic pain. Implementation Science, 5, 11.

    Article  Google Scholar 

  20. Trafton, J., Martins, S., Michel, M., et al. (2010). Evaluation of the acceptability and usability of a decision support system to encourage safe and effective use of opioid therapy for chronic, noncancer pain by primary care providers. Pain Medicine, 11, 575–585.

    Article  PubMed  Google Scholar 

  21. Lyons, S. S., Tripp-Reimer, T., Sorofman, B. A., et al. (2005). VA QUERI informatics paper: information technology for guideline implementation: perceptions of multidisciplinary stakeholders. Journal of the American Medical Informatics Association, 12, 64–71.

    Article  PubMed  Google Scholar 

  22. Glajchen, M. (2001). Chronic pain: treatment barriers and strategies for clinical practice. Medical Practice, 14, 211–218.

    CAS  Google Scholar 

  23. Keefe, F. J., Studts, J. L., Buffington, A. L. H., & Rumble, M. E. (2002). Behavioral medicine: 2002 and beyond. Journal of Consulting and Clinical Psychology, 70, 852–856.

    Article  PubMed  Google Scholar 

  24. Jamison, R. N., Gintner, L., Rogers, J. F., & Fairchild, D. G. (2002). Disease management for chronic pain: barriers of program implementation with primary care physicians. Pain Medicine, 3, 92–101.

    Article  PubMed  Google Scholar 

  25. Turk, D. C., & Burwinkle, T. M. (2005). Clinical outcomes, cost-effectiveness, and the role of psychology in treatments for chronic pain sufferers. Professional Psychology, Research and Practice, 36, 602–610.

    Article  Google Scholar 

  26. Martins SB, Lai S, Tu SW, et al. (2006). Offline testing of the ATHENA hypertension decision support system knowledge base to improve the accuracy of recommendations. AMIA Annual Symposium Proceedings, pp. 539–543.

Download references

Acknowledgments

The project was funded by VA Health Services Research and Development (HSR&D) Quality Enhancement Research Initiative (QUERI) Project # RRP 09-144 (Cronkite, PI), entitled “Implementation of ATHENA-OT Decision Support: Facilitators and Barriers”, VA HSR&D Project #TRX 04-402 (Trafton, PI), entitled “Decision Support for the Management of Opioid Therapy in Chronic Pain”, VA HSR&D Research Enhancement Award Program (REA 08-266), Geriatrics Research Education and Clinical Center (GRECC—VA Palo Alto Health Care System), and VA Office of Academic Affiliations Advanced Fellowship in Medical Informatics (Midboe, postdoctoral fellow, VA Palo Alto Health Care System). We thank Susana Martins, PhD, Martha Michel, PhD, and Dan Wang, PhD, for their assistance in design of the interview protocol and methodology, and Sharfun Ghaus, MD, for her assistance in transcription and coding of interview responses.

Disclaimer

The views expressed in this article are those of the authors and do not necessarily reflect the position or policy of the Department of Veterans Affairs or the United States government.

Author information

Authors and Affiliations

  1. Center for Health Care Evaluation, VA Palo Alto HealthCare System, 795 Willow Road (152-MPD), Menlo Park, CA, 94025, USA

    Amanda M Midboe PhD, Eleanor T Lewis, Ruth C Cronkite, Dallas Chambers, Mary K Goldstein & Jodie A Trafton

  2. Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, 401 Quarry Road, Stanford, CA, 94305, USA

    Amanda M Midboe PhD

  3. Center for Health Policy/Center for Primary Care and Outcomes Research (PCOR), 117 Encina Commons, Stanford University, Stanford, CA, 94305, USA

    Ruth C Cronkite, Mary K Goldstein & Jodie A Trafton

  4. Department of Sociology, 450 Serra Mall, Bldg. 120, Rm 160 (MC: 2047), Stanford University, Stanford, CA, 94305, USA

    Ruth C Cronkite

  5. Geriatrics Research Education and Clinical Center (GRECC), VA Palo Alto Health Care System, 3801 Miranda Ave (182B), Palo Alto, CA, 94304, USA

    Dallas Chambers & Mary K Goldstein

  6. Center for Biomedical Informatics Research, Stanford University Medical School, Medical School Office Building, Room X-215, 251 Campus Drive, Stanford, CA, 94305, USA

    Mary K Goldstein

  7. Pain Research, Informatics, Medical Comorbidities, and Education Center, VA Connecticut Health Care System, 950 Campbell Ave, West Haven, CT, 06516, USA

    Robert D Kerns

  8. Departments of Psychiatry, Neurology, and Psychology, Yale University, New Haven, CT, 06520, USA

    Robert D Kerns

Authors
  1. Amanda M Midboe PhD
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Eleanor T Lewis
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ruth C Cronkite
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Dallas Chambers
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Mary K Goldstein
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Robert D Kerns
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Jodie A Trafton
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Amanda M Midboe PhD.

Additional information

Implications

Practice: Behavioral medicine providers indicated that a clinical decision support system could be used across disciplines to promote effective decision-making, care coordination, and adherence to clinical practice guidelines.

Policy: Computerized clinical decision support systems provide a well-accepted and promising platform for interventions to improve patient-tailored use of evidence-based therapies and should be considered in quality improvement planning.

Research: The next phase of research and development should focus on adaptation of the clinical decision support system for use by behavioral medicine professionals as well as modification of factors critical to successful implementation in clinical settings.

About this article

Cite this article

Midboe, A.M., Lewis, E.T., Cronkite, R.C. et al. Behavioral medicine perspectives on the design of health information technology to improve decision-making, guideline adherence, and care coordination in chronic pain management. Behav. Med. Pract. Policy Res. 1, 35–44 (2011). https://doi.org/10.1007/s13142-011-0022-6

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13142-011-0022-6

KEYWORDS

Search

Navigation