Real world implementation of patient report outcomes: Sustainability constraints and impact on patients health outcomes.
Bibliographic record
Abstract
291 Background: St Mary’s Hospital, an affiliated McGill Teaching University Hospital, receives ~700 new cancer patients yearly. In 2014-17, electronic PROs were implemented through the Improving Patient Experience and Health in Outcomes Collaborative in partnership with Cancer Care Ontario, granted by Canadian Partnership against Cancer and Rossy Cancer Network, as part of a PRO Canadian Initiative to improve the Patient Experience across Canada through standardized measurement of health-related outcomes for patients. Methods: The PROs measures included the Edmonton Symptom Assessment Scale (ESAS), additional measures for Fatigue, Anxiety, Depression, and Pain, the Social Difficulties Inventory-21 and Quality of Life. PRO-data was scored in real-time; nurses and patients received a printed summary report. Nurses were provided with an algorithm and instructions. Monthly Distress Screening Dashboard were generated with key indicators. In 2016, all patients receiving chemotherapy were assessed at each cycle of treatment. After project completion, due to nursing staff shortage, the frequency of screenings was reduced. Yearly Dashboard results were used to promote discussion with clinicians and address sustainability constraints of PROs in real-word oncology practice. Results: In 2016 1366 total screens completed ( 376 patients) and in 2018, 753 total screens (325 patients). This comparison indicates that a decrease in frequency of screenings resulted in an increase in symptom severity in all health-related outcomes. Physicians’ participation was proposed as mitigation plan to increase screening frequency. We propose to present a three year retrospectively cross analyzed patient data to examine symptom change over time, frequencies of screening, impact on patients’ health outcomes, overlapping sustainability constraints encountered and mitigation plans. Conclusions: Embedding the use of PRO into existing hospital structures requires constant review involving administrative, clinician and patient engagement.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.120 | 0.201 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".