Development of an Integrated Data-Driven Budget Allocation Approach for Maintenance Management in Healthcare Facilities
Notice bibliographique
Résumé
Healthcare facilities are fundamental infrastructure assets as their number and quality are common measures of a country’s prosperity and quality of life. Despite Canada being one of the highest countries all over the globe in the health spending, the Canadian hospitals were described in multiple reports as facilities with a crumbling status with a poor condition rating. This was determined according to their high value of deferred maintenance as opposed to their current replacement values. As a result, this study was initiated with the objective of developing a comprehensive asset maintenance and renewals management framework replacing the current approaches in place. The developed framework was thus expected to enhance the performance of hospital buildings assets and efficiently utilize the funds assigned for healthcare facilities on an informed and objective basis. The objective of this study was achieved through four different phases tackling various levels of the healthcare decision-making hierarchy, namely: Asset-Level, Facility-Level and Network-Level. The first pillar introduces an automated priority setting methodology for assets in hospital facilities utilizing multi-criteria decision-making techniques as well as Python-programmed supervised learning algorithms. The following model is concerned with forecasting the possible deterioration in the hospital assets on an integrated mechanism combining between a Matlab-based fuzzy inference system, Markovian models, and metaheuristics. Moving on to a higher level in the decision-making process, a facility renewal scheduling model is advanced to incorporate healthcare-tailored objectives into the planning process. This tri-objective model aims at the reduction of associated wait times and cost while maximizing the performance enhancement gained from the renewal interventions application. Furthermore, unsupervised learning clustering algorithms were used as part of this model to generate a further reduction in the wait times related to renewal intervention applications by grouping relevant interventions together according to the resources available, their location and their priority levels. Finally, a network-level budget allocation model was established relying on the outputs of previous models as inputs for an informed and objective distribution of available budget across hospitals located within one network. The first three models were applied on case study hospitals from Canada and Egypt, and they demonstrated a significant improvement in the current status of assets and facilities. While the final model was applied on a network of hospitals in the province of Alberta and all previous models were re-applied on the current assets and facilities to enable the application of the network-level model. The model results were compared to the actually selected and implemented interventions as well as the allocated budgets, and the model proved an improvement in the prioritization of the assets of 25.97%, a reduction in the deterioration prediction error of 39.29% as compared to the currently implemented mechanism on assets. The facility-level scheduling and clustering models demonstrated a criticality weighted performance enhancement of 28.01%, while maintaining a reduction in the associated downtime of 17.40%. Lastly, applying the network-level budget allocation model resulted in an 33.64% higher network performance for the same budget allocated as per collected records. This proves the capabilities of the developed models in producing more sound and informed decisions relating to healthcare asset management and accordingly, reducing the associated wait times to renewal interventions, refining the overall healthcare performance levels while maintaining minimal budget expenditure possible.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,003 | 0,001 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».