Improving the quality of person-centred healthcare from the patient perspective: development of person-centred quality indicators
Bibliographic record
Abstract
IMPORTANCE: International efforts are being made towards a person-centred care (PCC) model, but there are currently no standardised mechanisms to measure and monitor PCC at a healthcare system level. The use of metrics to measure PCC can help to drive the changes needed to improve the quality of healthcare that is person centred. OBJECTIVE: To develop and validate person-centred care quality indicators (PC-QIs) measuring PCC at a healthcare system level through a synthesis of the evidence and a person-centred consensus approach to ensure the PC-QIs reflect what matters most to people in their care. METHODS: Existing indicators were first identified through a scoping review of the literature and an international environmental scan. Focus group discussions with diverse patients and caregivers and interviews with clinicians and experts in quality improvement allowed us to identify gaps in current measurement of PCC and inform the development of new PC-QIs. A set of identified and newly developed PC-QIs were subsequently refined by Delphi consensus process using a modified RAND/UCLA Appropriateness Method. The international consensus panel consisted of patients, family members, community representatives, clinicians, researchers and healthcare quality experts. RESULTS: From an initial 39 unique evidence-based PC-QIs identified and developed, the consensus process yielded 26 final PC-QIs. These included 7 related to structure, 16 related to process, 2 related to outcome and 1 overall global PC-QI. CONCLUSIONS: The final 26 evidence-based and person-informed PC-QIs can be used to measure and evaluate quality incorporating patient perspectives, empowering jurisdictions to monitor healthcare system performance and evaluate policy and practice related to PCC.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".