Patient Relations Measurement and Reporting to Improve Quality and Safety: Lessons from a Pilot Project
Bibliographic record
Abstract
BACKGROUND: Effective patient relations can improve the patient experience and the safe delivery of care. Recent Ontario policy and legislative changes have increased accountabilities for healthcare organizations and expanded Health Quality Ontario's mandate to measure and report on patient relations. The purpose of this pilot project was to support improved care by validating standardized measures, data submission processes and prototype reporting of patient relations indicators for the hospital, home and community care and long-term care sectors across Ontario. METHODS: Health Quality Ontario identified potential indicators and best practices by performing a comprehensive environmental scan and consulting with experts, including patients and caregivers. It shortlisted indicators based on alignment to best practices and Ontario legislative requirements. A provincial advisory group then used a modified Delphi process to prioritize and recommend five patient relations indicators for province-wide measurement and comparative public reporting. Through the pilot project, these indicators were validated using facility-level data for fiscal year (FY) 2015-2016 from 29 hospitals, home and community care organizations and long-term care homes across Ontario. RESULTS: In June 2016, Health Quality Ontario recruited 34 organizations for the pilot project. Twenty-nine sites successfully submitted summary-level data on patient relations indicators. More than 90% of the required data were retrieved from existing papers or electronic systems. All sites mapped facility-level "complaint" and "action taken" categories to the provincial standardized categories. Across the three health sectors, "care and treatment" was the top complaint category in FY 2015-2016. CONCLUSIONS: This pilot project reinforced the value of measuring patient relations and reporting feedback to support facility- and system-level improvement. The pilot sites and provincial advisory group recommended phased implementation. This would permit healthcare organizations to standardize data collection and align with provincial indicators and categories. The next step would be voluntary data submission to Health Quality Ontario in advance of any reporting. To facilitate voluntary implementation, Health Quality Ontario included one indicator, "complaints acknowledged," in the annual Quality Improvement Plans beginning in FY 2018-2019. This will allow organizations to monitor and report on the percentage of complaints acknowledged within 2, 5 and 10 days. Implementation will evolve based on input from patients, health sector organizations, Local Health Integration Networks and the Patient Ombudsman.
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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 | 0.182 | 0.140 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".