Implementation of electronic prospective surveillance models in cancer care: a scoping review
Bibliographic record
Abstract
BACKGROUND: Electronic prospective surveillance models (ePSMs) for cancer rehabilitation include routine monitoring of the development of treatment toxicities and impairments via electronic patient-reported outcomes. Implementing ePSMs to address the knowledge-to-practice gap between the high incidence of impairments and low uptake of rehabilitation services is a top priority in cancer care. METHODS: We conducted a scoping review to understand the state of the evidence concerning the implementation of ePSMs in oncology. Seven electronic databases were searched from inception to February 2021. All articles were screened and extracted by two independent reviewers. Data regarding the implementation strategies, outcomes, and determinants were extracted. The Expert Recommendations for Implementing Change taxonomy and the implementation outcomes taxonomy guided the synthesis of the implementation strategies and outcomes, respectively. The Consolidated Framework for Implementation Research guided the synthesis of determinants based on five domains (intervention characteristics, individual characteristics, inner setting, outer setting, and process). RESULTS: Of the 5122 records identified, 46 interventions met inclusion criteria. The common implementation strategies employed were "conduct educational meetings," "distribute educational materials," "change record systems," and "intervene with patients to enhance uptake and adherence." Feasibility and acceptability were the prominent outcomes used to assess implementation. The complexity, relative advantage, design quality, and packaging were major implementation determinants at the intervention level. Knowledge was key at the individual level. At the inner setting level, major determinants were the implementation climate and readiness for implementation. At the outer setting level, meeting the needs of patients was the primary determinant. Engaging various stakeholders was key at the process level. CONCLUSIONS: This review provides a comprehensive summary of what is known concerning the implementation of ePSMs. The results can inform future implementation and evaluation of ePSMs, including planning for key determinants, selecting implementation strategies, and considering outcomes alongside local contextual factors to guide the implementation process.
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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 | 0.087 | 0.284 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.008 |
| Bibliometrics | 0.024 | 0.030 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.005 | 0.003 |
| 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".