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Increasing Complexity In Rule-Based Clinical Decision Support: The Symptom Assessment And Management Intervention

D. Lobach, Ellis B Johns, Barbara Halpenny, T. Saunders, J. Brzozowski, Guilherme Del Fiol, D. Berry, I. Braun, Kathleen Finn, J. Wolfe, J. Abrahm, M. Cooley
Published 2016 · Medicine

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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.
This paper references
10.1016/0885-3924(95)00056-5
Symptom distress in newly diagnosed ambulatory cancer patients and as a predictor of survival in lung cancer.
L. Degner (1995)
10.1016/j.jbi.2012.09.002
Interface design principles for usable decision support: A targeted review of best practices for clinical prescribing interventions
J. Horsky (2012)
10.1097/NCC.0000000000000128
Sleeping With the Enemy: Sleep and Quality of Life in Patients With Lung Cancer
G. Dean (2015)
10.2174/1874431101004010235
Standards for Scalable Clinical Decision Support: Need, Current and Emerging Standards, Gaps, and Proposal for Progress
K. Kawamoto (2010)
10.1016/j.jpainsymman.2015.06.010
Pilot Study of a Brief Behavioral Intervention for Dyspnea in Patients With Advanced Lung Cancer.
J. Greer (2015)
10.1089/jpm.2012.0400
A pilot study to investigate adherence to long-acting opioids among patients with advanced lung cancer.
J. Yoong (2013)
10.1200/JCO.2006.07.1126
Longitudinal study of the relationship between chemoradiation therapy for non-small-cell lung cancer and patient symptoms.
X. Wang (2006)
10.1258/jtt.2009.001008
Use of web services for computerized medical decision support, including infection control and antibiotic management, in the intensive care unit
K. Steurbaut (2010)
10.1186/1472-6947-14-31
Measuring agreement between decision support reminders: the cloud vs. the local expert
B. Dixon (2014)
10.1200/JCO.2013.54.8149
Developing a service model that integrates palliative care throughout cancer care: the time is now.
A. Partridge (2014)
10.1186/1748-5908-5-26
Designing an automated clinical decision support system to match clinical practice guidelines for opioid therapy for chronic pain
J. Trafton (2010)
10.1097/SPC.0b013e32835c7d88
Update on interventions focused on symptom clusters: what has been tried and what have we learned?
A. Berger (2013)
Design, Implementation, Use, and Preliminary Evaluation of SEBASTIAN, a Standards-Based Web Service for Clinical Decision Support
K. Kawamoto (2005)
10.1200/JCO.2006.08.7874
Symptom prevalence, intensity, and distress in patients with inoperable lung cancer in relation to time of death.
C. Tishelman (2007)
10.1037/T03589-000
The hospital anxiety and depression scale.
A. Zigmond (1983)
10.1016/j.cllc.2013.06.008
Interdisciplinary palliative care intervention in metastatic non-small-cell lung cancer.
M. Koczywas (2013)
10.1002/PON.694
Symptom prevalence, distress, and change over time in adults receiving treatment for lung cancer
M. Cooley (2003)
10.1136/amiajnl-2013-001613
Formative evaluation of the accuracy of a clinical decision support system for cervical cancer screening
K. Wagholikar (2013)
10.4088/PCC.08m00687
A computerized decision support system for depression in primary care.
B. Kurian (2009)
10.1097/00002820-197810000-00003
Development of a symptom distress scale
R. McCorkle (1978)
10.1016/j.jpainsymman.2013.01.016
Creating computable algorithms for symptom management in an outpatient thoracic oncology setting.
M. Cooley (2013)
10.1186/s12911-015-0145-7
A scoping review of cloud computing in healthcare
L. Griebel (2015)
10.1098/rsif.2014.0534
Lung Cancer Assistant: a hybrid clinical decision support application for lung cancer care
M. K. Sesen (2014)
10.2196/medinform.5728
Increasing Complexity in Rule-Based Clinical Decision Support: The Symptom Assessment and Management Intervention
David F. Lobach (2016)
Feb 08. IBM's Watson gets its first piece of business in healthcare URL: http://www.forbes.com/ sites/bruceupbin/2013/02/08/ibms-watson-gets-its-first-piece-of-business-in-healthcare/#eaa2c4f44b16
Upbin B. Forbes (2013)
10.1016/S0885-3924(00)00120-2
Symptomatology and loss of physical functioning among geriatric patients with lung cancer.
M. Kurtz (2000)
10.1186/1477-7525-12-73
Quality of life after the initial treatments of non-small cell lung cancer: a persistent predictor for patients’ survival
I. Lemonnier (2014)
10.1016/j.jpainsymman.2014.05.003
Feasibility of using algorithm-based clinical decision support for symptom assessment and management in lung cancer.
M. Cooley (2015)
10.3233/978-1-60750-588-4-816
Implementation of a Clinical Decision Support System using a Service Model: Results of a Feasibility Study
D. Borbolla (2010)
10.1016/j.apnr.2009.04.003
Validation and testing of the Acceptability E-scale for web-based patient-reported outcomes in cancer care.
J. Tariman (2011)
The PHQ-9: validity of a brief depression severity measure.
K. Kroenke (2001)
10.4158/EP13271.OR
A Computer-Interpretable Version of the AACE, AME, ETA Medical Guidelines for Clinical Practice for the Diagnosis and Management of Thyroid Nodules.
M. Peleg (2014)
10.1145/57167.57203
Development of an instrument measuring user satisfaction of the human-computer interface
J. P. Chin (1988)
10.1200/JCO.2011.38.5161
American Society of Clinical Oncology provisional clinical opinion: the integration of palliative care into standard oncology care.
T. Smith (2012)
10.3322/caac.21249
Patient‐centered, evidence‐based, and cost‐conscious cancer care across the continuum: Translating the Institute of Medicine report into clinical practice
L. Nekhlyudov (2014)
10.15265/IY-2014-0002
IBM's Health Analytics and Clinical Decision Support.
M. S. Kohn (2014)
10.1109/EMBC.2013.6609748
RespDoc: A new Clinical Decision Support System for Childhood Asthma Management based on Fraction of Exhaled Nitric Oxide (FeNO) measurements
Aikaterini V. Rigopoulou (2013)
10.2307/248851
The Measurement of End-User Computing Satisfaction
W. Doll (1988)
10.1093/ANNONC/MDI191
Does an oral analgesic protocol improve pain control for patients with cancer? An intergroup study coordinated by the Eastern Cooperative Oncology Group.
C. Cleeland (2005)
10.1093/jnci/dju244
Development of the National Cancer Institute's patient-reported outcomes version of the common terminology criteria for adverse events (PRO-CTCAE).
E. Basch (2014)
10.1002/PON.545
Predictors of depressive symptomatology of geriatric patients with lung cancer—a longitudinal analysis
M. Kurtz (2002)
10.1056/NEJMSA020847
Improving safety with information technology.
D. Bates (2003)
10.1200/JCO.2000.18.4.893
Depression in patients with lung cancer: prevalence and risk factors derived from quality-of-life data.
Penny Hopwood (2000)
10.1200/JCO.2004.01.241
Effect of a cognitive behavioral intervention on reducing symptom severity during chemotherapy.
C. Given (2004)
10.1016/S0885-3924(02)00493-1
Use of a depression screening tool and a fluoxetine-based algorithm to improve the recognition and treatment of depression in cancer patients. A demonstration project.
S. Passik (2002)
10.1200/JCO.2012.44.7540
Prevalence and predictors of the short-term trajectory of anxiety and depression in the first year after a cancer diagnosis: a population-based longitudinal study.
Allison W Boyes (2013)



This paper is referenced by
10.2196/17695
Identifying Lung Cancer Risk Factors in the Elderly Using Deep Neural Networks: Quantitative Analysis of Web-Based Survey Data
Songjing Chen (2020)
10.2196/MEDINFORM.5728
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D. Lobach (2016)
10.1093/jamia/ocz109
Clinical trial cohort selection based on multi-level rule-based natural language processing system
Long Chen (2019)
10.1093/jamia/ocz128
Medical knowledge infused convolutional neural networks for cohort selection in clinical trials
Chi-Jen Chen (2019)
A rule-based system proposal to aid in the evaluation and decision-making in external beam radiation treatment planning
R. C. Fernandes (2018)
10.9781/ijimai.2017.03.003
Development of a Predictive Model for Induction Success of Labour
Cristina Pruenza (2018)
10.2196/medinform.9679
Agile Acceptance Test–Driven Development of Clinical Decision Support Advisories: Feasibility of Using Open Source Software
M. Basit (2018)
10.1002/hed.25922
Changing functional status within 6 months posttreatment is prognostic of overall survival in patients with head and neck cancer: NRG Oncology Study
R. C. Eldridge (2019)
10.1097/NNR.0000000000000405
Design and Evaluation of the Electronic Patient-Generated Index
S. S. Tavernier (2019)
10.1111/acer.13327
Identifying Future Drinkers: Behavioral Analysis of Monkeys Initiating Drinking to Intoxication is Predictive of Future Drinking Classification
E. Baker (2017)
10.1186/s12877-020-01809-z
Self-reported activities of daily living, health and quality of life among older adults in South Africa and Uganda: a cross sectional study
S. Yaya (2020)
10.1186/s12911-018-0608-8
Algorithm-based decision support for symptom self-management among adults with Cancer: results of usability testing
M. Cooley (2018)
10.1188/18.ONF.619-630
Nurse-Delivered Symptom Assessment for Individuals With Advanced Lung Cancer.
M. Flannery (2018)
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