Dimensions of performance and related key performance indicators addressed in healthcare organisations: A literature review
Bibliographic record
Abstract
INTRODUCTION: Performance measurement systems have become essential managerial tools for healthcare organisations in the last few decades. They allow hospital managers to pilot their institution and assess the development of the organisation in helping managers in decision-making and viewing the different impacts of these decisions. However, there is a need to investigate further the dimensions of performance those performance measurement systems address. METHODS: A literature review was primarily conduced about performance measures in healthcare organisations. A comparative study was secondly made to identify the different performance dimensions that are present in the literature during the last decade. Forty-nine studies were considered and sixteen proposal frameworks were used to make the comparative analyses. RESULTS: We classified dimensions depending on the frequency of mobilisation of their components in four categories: the stars, the first runners-up, the opportunists and the forgotten ones. For each of the dimensions presented in this classification, the main types of KPIs proposed in the theoretical frameworks are presented. A discussion on relevance and possible blind spots is then conducted. CONCLUSION: Although they were a lot of proposal frameworks of KPI proposed in the last decades to assess healthcare organisations, some dimensions remain underrepresented. There is still a need to develop structure KPI and describe their links. To go further, the development of dashboards asks the question of the definition of KPI, the description of their interconnections and their temporality of driving, because static performance reporting systems are not able to completely satisfy healthcare manager's decision support needs.
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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 | 0.010 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.023 | 0.033 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.002 |
| 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".