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

View Article: PubMed Central - HTML - PubMed

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.

No MeSH data available.


Sample report produced by SAMI-L.
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figure2: Sample report produced by SAMI-L.

Mentions: SAMI-L also generated a printed report for clinicians to use during the clinical visit (Figure 2). This two-page report included a summary of patient-reported data along with patient-specific recommendations based on the symptom management algorithms. This information was presented in lists, tables, charts with color coding, and trend graphs to make it easily consumable by clinicians. The symptom management guidance was based on the severity of a patient’s symptoms. Guidance included specific suggestions for use of medications (including recommendations to initiate medications or explicit adjustments for medication doses), laboratory tests, supportive care referrals (ie, social work, palliative care, psychiatry), and use of a self-care symptom management toolkit for patients that provided behavioral self-care suggestions [32,33].


Increasing Complexity in Rule-Based Clinical Decision Support: The Symptom Assessment and Management Intervention
Sample report produced by SAMI-L.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC5120240&req=5

figure2: Sample report produced by SAMI-L.
Mentions: SAMI-L also generated a printed report for clinicians to use during the clinical visit (Figure 2). This two-page report included a summary of patient-reported data along with patient-specific recommendations based on the symptom management algorithms. This information was presented in lists, tables, charts with color coding, and trend graphs to make it easily consumable by clinicians. The symptom management guidance was based on the severity of a patient’s symptoms. Guidance included specific suggestions for use of medications (including recommendations to initiate medications or explicit adjustments for medication doses), laboratory tests, supportive care referrals (ie, social work, palliative care, psychiatry), and use of a self-care symptom management toolkit for patients that provided behavioral self-care suggestions [32,33].

View Article: PubMed Central - HTML - PubMed

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.

No MeSH data available.