Abstract P1-17-17: Real-word implementation of <i>p</i>atient <i>r</i>eport <i>o</i>utcomes: Sustainability constraints and impact on patients health outcomes
Bibliographic record
Abstract
Abstract Background: St Mary’s Hospital is an affiliated McGill Teaching University Hospital in Montreal, receiving ~700 new cancer patients yearly. In 2008, the oncology department implemented Patient Reported Outcomes (PROs) on paper. In 2014-17, electronic PROs were implemented through the Improving Patient Experience and Health in Outcomes Collaborative (iPEHOC) Implementation Project, as part of a PRO Canadian Initiative to improve the Patient Experience across the cancer journey through standardized measurement that accelerates optimal care and measures impact (health-related outcomes for patients) across Canada. Methodology: The PROs measures included the Edmonton Symptom Assessment Scale (ESASr), four additional secondary PROMs for Fatigue, Anxiety, Depression, and Pain, the Social Difficulties Inventory-21 and a single item Quality of Life. PRO data was scored in real-time; nurses and patients received a printed summary report. Nurses were provided with an algorithm (Global Response to Distress) and follow-up instructions. Monthly Distress Screening Dashboard ware generated with number of screenings, number of patients screened, symptom prevalence, secondary PROMs trigger rates, Social Difficulties triggered and clinically significant symptoms changes on the four secondary PROMs. In 2016, all patients receiving chemotherapy were assessed at each cycle of treatment (screening). After project completion, due to nursing staff shortage, the frequency of screenings was reduced and offered at pre-established time-points (assessment). Yearly Dashboard results were used to promote a discussion with clinicians and address sustainability constraints of PROs in real-word oncology practice. Results: In 2016, a total of 366 patients were screened, resulting in 1366 annual screens. In 2018, 325 patients were screened resulting in 753 annual screens. This change in practice, that culminated organically, allows for an observation analysis between screening versus assessment. Noteworthy comparison between 2018 and 2016 showed that the decrease in frequency of screenings resulted in significant increase in symptom severity in all health-related outcomes. Breast cancer represented 35% of all screens completed. We propose to present a three year retrospectively cross analyzed patient data to examine symptom change over time, frequencies of screening, the impact on patients’ health outcomes, the overlapping sustainability constraints encountered and mitigation plans. Discussion: Embedding the use of PRO into existing hospital structures requires constant review involving administrative, clinician and patient engagement. PRO data can be used to better identify the needs of a specific cancer type population and create tailored care paths. Citation Format: Ashley Kushneryk, Rosana Faria. Real-word implementation of patient report outcomes: Sustainability constraints and impact on patients health outcomes [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr P1-17-17.
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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 | 0.009 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.150 | 0.042 |
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".