Multiple case study of processes used by hospitals to select performance indicators: do they align with best practices?
Bibliographic record
Abstract
Several health policy institutes recommend reducing the number of indicators monitored by hospitals to better focus on indicators most relevant to local contexts. To determine which indicators are the most appropriate to eliminate, one must understand how indicator selection processes are undertaken. This study classifies hospital indicator selection processes and analyzes how they align with practices outlined in the 5-P Indicator Selection Process Framework. This qualitative, multiple case study examined indicator selection processes used by four large acute care hospitals in Ontario, Canada. Data were collected through 13 semistructured interviews and document analysis. A thematic analysis compared processes to the 5-P Indicator Selection Process Framework. Two types of hospital indicator selection processes were identified. Hospitals deployed most elements found within the 5-P Indicator Selection Process Framework including setting clear aims, having governance structures, considering indicators required by health agencies, and categorizing indicators into strategic themes. Framework elements largely absent included: adopting evidence-based selection criteria; incorporating finance and human resources indicators; considering if indicators measure structures, processes, or outcomes; and engaging a broader set of end users in the selection process. Hospitals have difficulty in balancing how to monitor government-mandated indicators with indicators more relevant to local operations. Hospitals often do not involve frontline managers in indicator selection processes. Not engaging frontline managers in selecting indicators may risk hospitals only choosing government-mandated indicators that are not reflective of frontline operations or valued by those managers accountable for improving unit-level performance.
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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 | 0.030 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".