Quality improvement project to transition psychosocial oncology clinical care to a telehealth workflow during the COVID-19 pandemic: a quasi-experimental study
Bibliographic record
Abstract
INTRODUCTION: The COVID-19 pandemic created an urgent need for an innovative method of care delivery for psychosocial oncology. The psychosocial oncology services at the University Health Network in Toronto, Canada transitioned expeditiously to digital technologies that were readily available and accessible for patients and clinicians, facilitating care provision while reducing the transmission of COVID-19. This study aims to provide a validated framework for transitioning to digital delivery methods of care. METHODS: A quality improvement team was established and tasked with successfully transitioning services from primarily in-person to digital delivery methods of care quickly and seamlessly. This included analyzing the psychosocial oncology workflow, planning and implementing a digital transition, and collecting data and feedback on the impact of this digital workflow through the use of surveys. RESULTS: The average response rate of the surveys was 68.0%. Feedback and data collection demonstrated that more than 90% of psychosocial oncology processes were completed with digital tools following the transition with limited impact on clinical delivery. The clinicians reported feeling confident and satisfied providing care using digital workflow tools. CONCLUSION: The psychosocial oncology quality improvement team at the University Health Network provides a validated framework for transitioning to new methods of delivering care. As technology continues to develop, guidance on transitioning clinics and departments to new digital tools will be crucial for healthcare institutions. The framework provided in this study can be utilized to ensure the successful implementation of new technologies.
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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 | 0.033 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 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".