An Analytic Hierarchy Framework for Evaluating Balanced Scorecards of Healthcare Organizations
Bibliographic record
Abstract
Abstract Healthcare organizations have been operating in a turbulent environment for years. Pressures from the government and competition as well as escalating costs have driven administrators to search for effective management tools. Balanced scorecard (BSC), a strategic management system, has been implemented in business organizations with success and is gaining acceptance in the not-for-profit and healthcare sectors. Despite potential benefits, there are challenges for implementers of BSC such as judgment biases, information overload, and the synthesis of information. This paper proposes to apply the analytic hierarchy process (AHP) to hospital scorecards in performance assessment. Although AHP could be a time-consuming exercise, it allows participative input in determining a comprehensive measure for comparing performance of healthcare organizations. Résumé Depuis des années, les organisations de soins de santé évoluent dans un environnement difficile. Les pressions gouvernementales, la concurrence et l'envolée des coûts poussent les administrateurs à rechercher des outils de gestion plus efficaces. C'est dans ce cadre que le Tableau de bord équilibré (BSC) a été mis en æuvre. Malgré ses avantages potentiels, le BSC bute sur certains problèmes dont la partialité des jugements, l'excès, et la synthèse des informations. Cette étude applique la méthode de la hiérarchie multicritère aux tableaux de bords des hôpitaux dans la gestion de la performance. Même si l'application de cette méthode peut s'avérer chronophage, elle permet de déterminer une mesure d'ensemble pour la comparaison de la performance des organisations de soins de santé.
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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 | 0.027 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".