Display options
Cite
Lobach DF, Johns EB, Halpenny B, et al. Increasing Complexity in Rule-Based Clinical Decision Support: The Symptom Assessment and Management Intervention. JMIR Med Inform. 2016;4(4):e36doi: 10.2196/medinform.5728.
Lobach, D. F., Johns, E. B., Halpenny, B., Saunders, T. A., Brzozowski, J., Del Fiol, G., Berry, D. L., Braun, I. M., Finn, K., Wolfe, J., Abrahm, J. L., & Cooley, M. E. (2016). Increasing Complexity in Rule-Based Clinical Decision Support: The Symptom Assessment and Management Intervention. JMIR medical informatics, 4(4), e36. https://doi.org/10.2196/medinform.5728
Lobach, David F, et al. "Increasing Complexity in Rule-Based Clinical Decision Support: The Symptom Assessment and Management Intervention." JMIR medical informatics vol. 4,4 (2016): e36. doi: https://doi.org/10.2196/medinform.5728
Lobach DF, Johns EB, Halpenny B, Saunders TA, Brzozowski J, Del Fiol G, Berry DL, Braun IM, Finn K, Wolfe J, Abrahm JL, Cooley ME. Increasing Complexity in Rule-Based Clinical Decision Support: The Symptom Assessment and Management Intervention. JMIR Med Inform. 2016 Nov 08;4(4):e36. doi: 10.2196/medinform.5728. PMID: 27826132; PMCID: PMC5120240.
Copy Download .nbib Format: NLM
Share it on

JMIR Med Inform. 2016 Nov 08;4(4):e36. doi: 10.2196/medinform.5728.

Increasing Complexity in Rule-Based Clinical Decision Support: The Symptom Assessment and Management Intervention.

JMIR medical informatics

David F Lobach, Ellis B Johns, Barbara Halpenny, Toni-Ann Saunders, Jane Brzozowski, Guilherme Del Fiol, Donna L Berry, Ilana M Braun, Kathleen Finn, Joanne Wolfe, Janet L Abrahm, Mary E Cooley

Affiliations

  1. School of Medicine, Department of Community & Family Medicine, Duke University, Durham, NC, United States.
  2. Klesis Healthcare, Durham, NC, United States.
  3. Family Medicine of Albemarle, Charlottesville, VA, United States.
  4. Medengineers Informatics, Charlottesville, VA, United States.
  5. Dana-Farber Cancer Institute, The Phyllis F. Cantor Center, Boston, MA, United States.
  6. Independent Clinical Informatics Consultant, Boston, MA, United States.
  7. Department of Biomedical Informatics, University of Utah, Salt Lake City, UT, United States.
  8. Department of Psychosocial Oncology and Palliative Care, Dana-Farber Cancer Institute, Boston, MA, United States.
  9. City of Hope, Clinical Trials Office, Duarte, CA, United States.

PMID: 27826132 PMCID: PMC5120240 DOI: 10.2196/medinform.5728

Abstract

BACKGROUND: Management of uncontrolled symptoms is an important component of quality cancer care. Clinical guidelines are available for optimal symptom management, but are not often integrated into the front lines of care. The use of clinical decision support (CDS) at the point-of-care is an innovative way to incorporate guideline-based symptom management into routine cancer care.

OBJECTIVE: The objective of this study was to develop and evaluate a rule-based CDS system to enable management of multiple symptoms in lung cancer patients at the point-of-care.

METHODS: This study was conducted in three phases involving a formative evaluation, a system evaluation, and a contextual evaluation of clinical use. In Phase 1, we conducted iterative usability testing of user interface prototypes with patients and health care providers (HCPs) in two thoracic oncology clinics. In Phase 2, we programmed complex algorithms derived from clinical practice guidelines into a rules engine that used Web services to communicate with the end-user application. Unit testing of algorithms was conducted using a stack-traversal tree-spanning methodology to identify all possible permutations of pathways through each algorithm, to validate accuracy. In Phase 3, we evaluated clinical use of the system among patients and HCPs in the two clinics via observations, structured interviews, and questionnaires.

RESULTS: In Phase 1, 13 patients and 5 HCPs engaged in two rounds of formative testing, and suggested improvements leading to revisions until overall usability scores met a priori benchmarks. In Phase 2, symptom management algorithms contained between 29 and 1425 decision nodes, resulting in 19 to 3194 unique pathways per algorithm. Unit testing required 240 person-hours, and integration testing required 40 person-hours. In Phase 3, both patients and HCPs found the system usable and acceptable, and offered suggestions for improvements.

CONCLUSIONS: A rule-based CDS system for complex symptom management was systematically developed and tested. The complexity of the algorithms required extensive development and innovative testing. The Web service-based approach allowed remote access to CDS knowledge, and could enable scaling and sharing of this knowledge to accelerate availability, and reduce duplication of effort. Patients and HCPs found the system to be usable and useful.

©David F Lobach, Ellis B Johns, Barbara Halpenny, Toni-Ann Saunders, Jane Brzozowski, Guilherme Del Fiol, Donna L Berry, Ilana M Braun, Kathleen Finn, Joanne Wolfe, Janet L Abrahm, Mary E Cooley. Originally published in JMIR Medical Informatics (http://medinform.jmir.org), 08.11.2016.

Keywords: Web services; clinical algorithms; lung cancer; patient-reported outcomes; rule-based clinical decision support; software as a service; symptom management

Conflict of interest statement

Conflicts of Interest: None declared.

References

  1. J Gen Intern Med. 2001 Sep;16(9):606-13 - PubMed
  2. Curr Opin Support Palliat Care. 2013 Mar;7(1):60-6 - PubMed
  3. J Pain Symptom Manage. 2002 Sep;24(3):318-27 - PubMed
  4. Implement Sci. 2010 Apr 12;5:26 - PubMed
  5. J R Soc Interface. 2014 Sep 6;11(98):20140534 - PubMed
  6. Psychooncology. 2003 Oct-Nov;12(7):694-708 - PubMed
  7. BMC Med Inform Decis Mak. 2015 Mar 19;15:17 - PubMed
  8. J Clin Oncol. 2006 Sep 20;24(27):4485-91 - PubMed
  9. Appl Nurs Res. 2011 Feb;24(1):53-8 - PubMed
  10. J Clin Oncol. 2012 Mar 10;30(8):880-7 - PubMed
  11. J Pain Symptom Manage. 2015 Jan;49(1):13-26 - PubMed
  12. J Clin Oncol. 2007 Dec 1;25(34):5381-9 - PubMed
  13. Clin Lung Cancer. 2013 Nov;14(6):736-44 - PubMed
  14. J Pain Symptom Manage. 2013 Dec;46(6):911-924.e1 - PubMed
  15. J Telemed Telecare. 2010;16(1):25-9 - PubMed
  16. Stud Health Technol Inform. 2010;160(Pt 2):816-20 - PubMed
  17. J Pain Symptom Manage. 1995 Aug;10(6):423-31 - PubMed
  18. J Biomed Inform. 2012 Dec;45(6):1202-16 - PubMed
  19. Acta Psychiatr Scand. 1983 Jun;67(6):361-70 - PubMed
  20. J Clin Oncol. 2004 Feb 1;22(3):507-16 - PubMed
  21. Open Med Inform J. 2010;4:235-44 - PubMed
  22. Health Qual Life Outcomes. 2014 May 15;12:73 - PubMed
  23. J Clin Oncol. 2014 Oct 10;32(29):3330-6 - PubMed
  24. J Palliat Med. 2013 Apr;16(4):391-6 - PubMed
  25. J Clin Oncol. 2013 Jul 20;31(21):2724-9 - PubMed
  26. CA Cancer J Clin. 2014 Nov-Dec;64(6):408-21 - PubMed
  27. Conf Proc IEEE Eng Med Biol Soc. 2013;2013:1306-9 - PubMed
  28. Yearb Med Inform. 2014 Aug 15;9:154-62 - PubMed
  29. Psychooncology. 2002 Jan-Feb;11(1):12-22 - PubMed
  30. Cancer Nurs. 1978 Oct;1(5):373-8 - PubMed
  31. J Clin Oncol. 2000 Feb;18(4):893-903 - PubMed
  32. Cancer Nurs. 2015 Jan-Feb;38(1):60-70 - PubMed
  33. Endocr Pract. 2014 Apr;20(4):352-9 - PubMed
  34. J Pain Symptom Manage. 2000 Apr;19(4):249-56 - PubMed
  35. J Natl Cancer Inst. 2014 Sep 29;106(9):null - PubMed
  36. AMIA Annu Symp Proc. 2005;:380-4 - PubMed
  37. N Engl J Med. 2003 Jun 19;348(25):2526-34 - PubMed
  38. J Am Med Inform Assoc. 2013 Jul-Aug;20(4):749-57 - PubMed
  39. Prim Care Companion J Clin Psychiatry. 2009;11(4):140-6 - PubMed
  40. Ann Oncol. 2005 Jun;16(6):972-80 - PubMed
  41. BMC Med Inform Decis Mak. 2014 Apr 10;14:31 - PubMed
  42. J Pain Symptom Manage. 2015 Dec;50(6):854-60 - PubMed

Publication Types

Grant support