How patients define success for integrated care: How the University Health Network identifies iterative improvement priorities
Bibliographic record
Abstract
Background: While many standardized surveys exist to measure patient experience, they are not designed to assess unique features of integrated care (IC) programs including transitions from hospital to community, satisfaction with homecare, or resources and supports provided post-discharge. The aim was to have patients lead efforts to develop a set of quality indicators for routine monitoring of patient experience in the University Health Network (UHN) integrated models of care in Toronto, Ontario, Canada. The output would be used to evaluate efforts and prioritize ongoing improvement to continue to meet patient needs. Population: We invited 29 stakeholders to participate in this study between April-July 2022. Twenty-three (79%) were female, 14% identified as a person with a disability, and 28% identified as a person of colour. Nearly half of all participants were patients or caregivers (42%), followed by administration/clinicians (58%). Engagement: This work was carried out in partnership with patients and caregivers. We worked collaboratively with the UHN Patient Experience Team to ensure materials and processes were effectively designed for engagement. Methods: We used the RAND-modified Delphi method, an approach combining scientific literature and expertise of stakeholders, to reach consensus on an agreed-upon set of indicators to measure patient experience. The study consisted of two surveys and a virtual meeting. In survey #1, participants answered demographic questions and rated 45 patient-reported experience measures (PREMs) on whether they felt the indicator was reliable, necessary, and actionable, and also provided written feedback on each. We averaged responses and feedback to rank indicators; those with above-average ratings were included in survey #2 where respondents identified if each indicator should be included as a key performance metric. All results were shared and discussed at the virtual meeting. Results: Among the 29 stakeholders surveyed, 27(93%), 25(86%) participated in survey #1 and #2, respectively. The average rating for indicators in survey #1 was 79%, resulting in 23 indicators proceeding to survey #2. In 71.1% of indicators from survey #1 there was full agreement, meaning that both patients/caregivers and administration/clinicians rated above or below 79% (i.e., there was agreement to keep or remove the indicator). The remaining 29% of indicators had half agreement between patients/caregivers and administration/clinicians. Divergence of opinion mostly occurred for indicators that assessed the patient’s emotional well-being (e.g., addressing anxieties, fears, worries). The final set of indicators covers pre-hospital admission through the post-discharge period and assesses domains such as informational continuity, person-centered care, timeliness of care, effectiveness of resources and supports, and efficacy of knowledge translation. Conclusions: After 2 survey rounds and a virtual meeting participants reached consensus on 13 indicators to measure patient experience. This is the first set of PREMs co-designed by patients and caregivers with a focus on IC applications in Canada. Lessons Learned: Patients and caregivers rated indicators that measure domains of emotional support and reassurance higher than administration/clinicians, highlighting the importance of sub-group analysis in designing patient experience measures. Next Steps: Following a successful pilot in the Division of Orthopedics, these indicators will be implemented in all UHN IC programs.
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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 | 0.161 | 0.212 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.019 | 0.020 |
| Scholarly communication | 0.035 | 0.039 |
| Open science | 0.007 | 0.041 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.007 | 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".