49 Enhancing engagement with and accuracy of patient centred outcome measures; a multi-cycle quality improvement project
Bibliographic record
Abstract
Background Patient centred outcome measures (PCOMs) support proactive symptom assessment and patient-centred care. This quality improvement project (QIP) aimed to address engagement with, and accuracy of inpatient palliative care PCOM data in a specialist cancer centre. Methods Routine recording of PCOM data for inpatients was introduced in 2017 including IPOS (Integrated Palliative Outcome Scale), Palliative Phase of Illness, AKPS and ECOG performance status. PCOMs are recorded at the start (initial) and end (follow-up) of an inpatient’s episode of care. 4 QIP cycles were undertaken 2017 - 2023. Engagement was assessed by the proportion of eligible patients with completed PCOM data. The proportion of incomplete data was used as a measure of data accuracy. Quality improvement interventions included delivering and attending teaching and training, establishing PCOM champions, and engaging with communities of practice. Recording moved from using pen and paper to a semi-integrated digital PCOM system in April 2021. Results In Cycle 1 (2017–19) there were 1592 patient care episodes. 1141 (72%) had initial data and 722 (45%) had follow up data. In cycle 2 (2019–21) there were 1345/1784 (75%) initial data and 642/1784 (36%) follow up data recorded. Cycle 3 and 4 (April 2021 – Jan 2023) had 1122/1481 (76%) initial and 389/1418 (26%) follow up data recorded. The proportion of incomplete data decreased over time and with the switch to digital PCOM records, from 16% in cycle 1 to 6.5% in cycles 3 and 4. Conclusions ‘Initial’ PCOM data is consistently recorded in over 70% of patients, but engagement with ‘follow up’ end of episode PCOMs has been more challenging to implement. The development of a fully-integrated digital platform for recording and visualising PCOMs within the electronic patient record in March 2023 aims to further improve both engagement and accuracy.
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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 | 0.211 | 0.161 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.005 | 0.017 |
| Research integrity | 0.002 | 0.004 |
| 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".