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


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          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.


          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.


          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.


          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.


          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.

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          Most cited references38

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          The Measurement of End-User Computing Satisfaction

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            Development of an instrument measuring user satisfaction of the human-computer interface

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              Depression in patients with lung cancer: prevalence and risk factors derived from quality-of-life data.

              To evaluate self-reported depression rates in patients with inoperable lung cancer and to explore demographic, clinical, and quality-of-life (QOL) factors associated with depression and thus identify patients at risk. Nine hundred eighty-seven patients from three palliative treatment trials conducted by the Medical Research Council Lung Cancer Working Party formed the study sample. 526 patients (53%) had poor prognosis small-cell lung cancer (SCLC) and 461 patients (47%) had good prognosis non-small-cell lung cancer (NSCLC). Hospital Anxiety and Depression Scale data and QOL items from the Rotterdam Symptom Checklist were analyzed, together with relevant demographic and clinical factors. Depression was self-rated in 322 patients (33%) before treatment and persisted in more than 50% of patients. SCLC patients had a three-fold greater prevalence of case depression than those with NSCLC (25% v 9%; P <.0001). An increased rate for women was found for good performance status (PS) patients (PS of 0 or 1) but the sex difference reduced for poor PS patients (PS of 3 or 4) because of increased depression rates for men (chi(2) for trend, P <.0001). Multivariate analysis showed that functional impairment was the most important risk factor; depression increased by 41% for each increment on the impairment scale. Pretreatment physical symptom burden, fatigue, and clinician-rated PS were also independent predictors, but cell type was not. Depression is common and persistent in lung cancer patients, especially those with more severe symptoms or functional limitations. Psychologic screening and appropriate intervention is an essential part of palliative care.

                Author and article information

                JMIR Med Inform
                JMIR Med Inform
                JMIR Medical Informatics
                JMIR Publications (Toronto, Canada )
                Oct-Dec 2016
                08 November 2016
                : 4
                : 4
                [1] 1School of Medicine Department of Community & Family Medicine Duke University Durham, NCUnited States
                [2] 2Klesis Healthcare Durham, NCUnited States
                [3] 3Family Medicine of Albemarle Charlottesville, VAUnited States
                [4] 4Medengineers Informatics Charlottesville, VAUnited States
                [5] 5Dana-Farber Cancer Institute The Phyllis F. Cantor Center Boston, MAUnited States
                [6] 6Independent Clinical Informatics Consultant Boston, MAUnited States
                [7] 7Department of Biomedical Informatics University of Utah Salt Lake City, UTUnited States
                [8] 8Department of Psychosocial Oncology and Palliative Care Dana-Farber Cancer Institute Boston, MAUnited States
                [9] 9City of Hope Clinical Trials Office Duarte, CAUnited States
                Author notes
                Corresponding Author: David F Lobach David.Lobach@ 123456klesishealthcare.com
                ©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.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Medical Informatics, is properly cited. The complete bibliographic information, a link to the original publication on http://medinform.jmir.org/, as well as this copyright and license information must be included.

                Original Paper
                Original Paper

                rule-based clinical decision support,clinical algorithms,web services,software as a service,symptom management,patient-reported outcomes,lung cancer


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