Increasing Complexity in Rule-Based Clinical Decision Support: The Symptom Assessment and Management Intervention.
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.
School of Medicine, Department of Community & Family Medicine, Duke University, Durham, NC, United States.|Klesis Healthcare, Durham, NC, United States.
David F
http://orcid.org/0000-0001-6126-6441
DF
Lobach
Family Medicine of Albemarle, Charlottesville, VA, United States.|Medengineers Informatics, Charlottesville, VA, United States.
Ellis B
http://orcid.org/0000-0002-3471-295X
EB
Johns
Dana-Farber Cancer Institute, The Phyllis F. Cantor Center, Boston, MA, United States.
Barbara
http://orcid.org/0000-0001-6273-8674
B
Halpenny
Dana-Farber Cancer Institute, The Phyllis F. Cantor Center, Boston, MA, United States.
Toni-Ann
http://orcid.org/0000-0002-3434-9636
TA
Saunders
Independent Clinical Informatics Consultant, Boston, MA, United States.
Jane
http://orcid.org/0000-0002-8833-5782
J
Brzozowski
Department of Biomedical Informatics, University of Utah, Salt Lake City, UT, United States.
Guilherme
http://orcid.org/0000-0001-9954-6799
G
Del Fiol
Dana-Farber Cancer Institute, The Phyllis F. Cantor Center, Boston, MA, United States.
Donna L
http://orcid.org/0000-0001-7935-2807
DL
Berry
Department of Psychosocial Oncology and Palliative Care, Dana-Farber Cancer Institute, Boston, MA, United States.
Ilana M
http://orcid.org/0000-0003-3532-7057
IM
Braun
City of Hope, Clinical Trials Office, Duarte, CA, United States.
Kathleen
http://orcid.org/0000-0003-4066-6416
K
Finn
Department of Psychosocial Oncology and Palliative Care, Dana-Farber Cancer Institute, Boston, MA, United States.
Joanne
http://orcid.org/0000-0002-4406-7413
J
Wolfe
Department of Psychosocial Oncology and Palliative Care, Dana-Farber Cancer Institute, Boston, MA, United States.
Janet L
http://orcid.org/0000-0002-7737-1323
JL
Abrahm
Dana-Farber Cancer Institute, The Phyllis F. Cantor Center, Boston, MA, United States.
Mary E
http://orcid.org/0000-0001-5353-9542
ME
Cooley
Web services; clinical algorithms; lung cancer; patient-reported outcomes; rule-based clinical decision support; software as a service; symptom management