A person-centered e-proms multi-faceted intervention to improve patient experience and health outcomes: A multi-site implementation study in diverse ambulatory oncology practices.
Bibliographic record
Abstract
182 Background: The Improving Patient Experience and Health Outcomes Collaborative (iPEHOC) aims to improve health outcomes through uptake of electronic patient reported outcome measures (e-PROMs) in oncology practices in Ontario and Quebec. Building on screening with the Edmonton Symptom Assessment System (ESAS), e-PROMs were triggered based on cut scores to focus multidimensional assessment and management of pain (BPI), fatigue (CFS), anxiety (GAD-7) and depression (PHQ-9). Methods: Multifaceted implementation strategies and practice change coaching facilitated the use of e-PROMs to improve symptom outcomes. A mixed-method, pre-post quasi-experimental design assessed process and impact of the intervention on symptom screening rates, symptom burden, patient experience and activation, clinician satisfaction, team collaboration and health care use. Mann-Whitney U statistics examined significance of change from baseline to the 8-month post comparison. Qualitative data explored uptake of e-PROMs in practice. Results: Over the implementation period 10,248 ESAS screens were completed in iPEHOC clinics; 17.5% triggered an additional e-PROM. A slight improvement was noted in person-centeredness of communication (mean change of 1.43 to 1.37; four-point scale of 1 = very satisfied, 4 = very dissatisfied) and in team collaboration. A significant increase in patient activation levels (p = 0.045) was related to decreased emergency department visits (2% pre/post change, p = 0.81) and hospitalization within 30 days of an e-PROMs completion (2.2% change, p = 0.034) in Ontario. Patients and clinicians perceived e-PROMs as valuable to focus communication in the clinic visit and for shared treatment planning. Focus group data suggests that patients use e-PROMs as a ‘self-check-in’, to communicate their symptoms and normalize disclosure of depression in clinical care. Conclusions: Uptake of e-PROMs in diverse settings is complex and demanding. Improving symptom management quality requires PROM data to be fed-back for ‘real-time’ use in the clinical encounter and practice change facilitation for meaningful use in routine care.
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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 | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".