Integrating care planning for cancer patients: A scoping review
Bibliographic record
Abstract
Introduction: Given the high cost and complexity of cancer patients throughout their disease trajectory (~55% reported to have multiple comorbidities) there is a strong emphasis on using care plans to deliver comprehensive care. Integrated care is intended to improve the quality of care and facilitate coordination and transitions, and as part of Ontario Cancer Plan IV, Cancer Care Ontario identified the delivery of standardized care plans across the care continuum as central to delivering patient-centered care. Yet, there remains limited consensus around the components of a standardized integrated care plan (ICP) and its role in managing cancer patients.Methods: A scoping review was conducted based on methodologies developed by Arksey & O'Malley (2005) and Levac (2008), to explore the key components and contextual facilitating features of ICPs for cancer patients, alongside outcomes used to assess their application and impact.Results: A total of 1061 articles (identified from March 1995-2015) underwent abstract review, 256 articles underwent full-text review, and 67 articles were included. Five types of ICPs are described in the literature, based on stages of care: surgical, systemic, survivorship, palliative and comprehensive (involved a transition between stages). Breast, esophageal and colorectal cancers were common disease sites. Findings from the thematic analysis were organized into the following categories:Design features: Iterative development, staff training, implementation point-of-contact, IT support, evaluationComponents: Multi-disciplinary teams, role clarity, patient needs assessment, transition planning (navigation), care documentation, information exchange, symptom/outcome monitoring, goals of careOutcome measures: Patient (HRQOL & satisfaction), provider (uptake, workflow and satisfaction), system-level outcomes (length of stay, readmissions, costs)Facilitators: Provider buy-in, dedicated oversight/resources, policy-based incentivesBarriers: Limited IT support, staff turnover, time and resource intensityDiscussion: Most ICPs focus on a single stage, but similarities in design features and components highlight potential for creating ICPs that span across stages. Moreover while ICPs are intended to be patient-facing, very few studies included patients in ICP development. System-level outcomes reported largely through surgical ICPs, suggest reductions in LOS, costs, and post-operative complications. Patient-level outcomes were mixed, with some studies reporting improvements in patient-reported satisfaction and anxiety, and others reporting no significant differences in cancer-related stress and HrQOL.Conclusions: Multi-disciplinary teams, iterative development, patient needs assessment, and transitional planning emerged as key features of ICPs for cancer patients. Provider training, buy-in, and IT support were important facilitators. Provider-level measurement was considerably less robust compared to patient and system-level indicators.Lessons learned: Similarities in design features, components and facilitators across ICP types indicates opportunities to leverage shared features to reorient the delivery of care, by shifting towards a management lens that spans the trajectory of the patient’s cancer journey.Limitations: Study quality wasn't accounted for, thus generalizations cannot be made about patient, provider or system-level outcomes. Most studies didn’t report on patient characteristics thereby the impact of patient complexity on ICP effectiveness is unknown.Suggestions for future research: Validating conceptual framework through broader consensus, assessing outcome variability across patient sub-groups and exploring more robust measurement of patient and provider outcomes are important next steps.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.022 | 0.077 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.024 | 0.033 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".