Compiling a Set of Actionable Quality Indicators for Medical Practitioners in Dutch Nursing Homes: A Delphi Study
Bibliographic record
Abstract
Most quality indicators (QIs) currently used in nursing homes reflect the care delivered by the entire multidisciplinary team and are not specific for medical practitioners. International experts have proposed a set of QIs that specifically reflect the quality of medical care in nursing homes. The objective of the Delphi study described here was to compile a set of actionable QIs tailored for medical practitioners working within Dutch nursing homes. This was achieved through the evaluation of 15 existing national QIs and 35 international QIs by a panel of medical practitioners, comprising medical specialists, nurse practitioners, and physician assistants, who are working in Dutch nursing homes. Panelists rated each QI on (1) level of direct control by medical practitioners and (2) its relevance to the quality of medical care. QIs progressing to subsequent rounds required panel agreement on both direct control (≥70% ≥3 points on a 4-point scale) and relevance (≥70% ≥8 on a 10-point scale). In the last round, each panelist selected the 5 most relevant QIs and arranged them in order of importance. These top 5 rankings were converted into points for an overall final ranking. There was consensus on 42 QIs being under the control of medical practitioners, and 21 of these QIs were considered relevant for quality of care. Most of the 21 QIs originated from the international QI set. This finding supports the transferability of the internationally developed QIs to the Dutch nursing home context and provides opportunities to compare the quality of medical care in nursing homes across countries. In the final ranking, the QI related to new medication prescriptions received the highest rating, followed by 3 QIs related to advance care planning. Future research should focus on evaluating the feasibility of measuring the selected QIs and assessing their measurement properties before implementing them in professional learning and quality improvement initiatives for medical practitioners in nursing homes.
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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 | 0.024 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".