Critical Success Factors and Acceptance of the Casemix System Implementation Within the Total Hospital Information System: Exploratory Factor Analysis of a Pilot Study
Notice bibliographique
Résumé
BACKGROUND: The health care landscape is evolving rapidly due to rising costs, an aging population, and the increasing prevalence of diseases. To address these challenges, the Ministry of Health of Malaysia implemented transformation strategies such as the Casemix system and hospital information system to enhance health care quality, resource allocation, and cost-effectiveness. However, successful implementation relies not just on the technology itself but on the acceptance and engagement of the users involved. OBJECTIVE: This study aims to develop and refine items of a quantitative instrument measuring the critical success factors influencing acceptance of Casemix system implementation within the Ministry of Health's Total Hospital Information System (THIS). METHODS: A cross-sectional pilot study collected data from medical doctors at a hospital equipped with the THIS in the federal territory of Putrajaya, Malaysia. This pilot study's minimum sample size was 125, achieved through proportionate stratified random sampling. Data were collected using a web-based questionnaire adapted from the human, organization, and technology-fit evaluation framework and the technology acceptance model. The pilot data were analyzed using exploratory factor analysis (EFA), and the Cronbach α assessed internal reliability. Both analyses were conducted in SPSS (version 25.0; IBM Corp). RESULTS: This study obtained 106 valid responses, equivalent to an 84.8% (106/125) response rate. The Kaiser-Meyer-Olkin measure of sampling adequacy was 0.859, and the Bartlett test of sphericity yielded statistically significant results (P<.001). Principal component analysis identified 9 components explaining 84.07% of the total variance, surpassing the minimum requirement of 60%. In total, 9 unique slopes indicated the identification of 9 components through EFA. While no new components emerged from the other 7 constructs, only the organizational factors construct was divided into 2 components, later named organizational structure and organizational environment. In total, 98% (41/42) of the items had factor loadings of >0.6, leading to the removal of 1 item for the final instrument for the field study. EFA ultimately identified 8 main constructs influencing Casemix implementation within the THIS: system quality, information quality, service quality, organizational characteristics, perceived ease of use, perceived usefulness, intention to use, and acceptance. Internal reliability measured using the Cronbach α ranged from 0.914 to 0.969, demonstrating high reliability. CONCLUSIONS: This study provides insights into the complexities of EFA and the distinct dimensions underlying the constructs that influence Casemix system acceptance in the THIS. While the findings align with extensive technology acceptance literature, the results accentuate the necessity for further research to develop a consensus regarding the most critical factors for successful Casemix adoption. The developed instrument is a substantial step toward better understanding the multidimensional challenges of health care system transformations in Malaysia, postulating an underpinning for future fieldwork and broader application across other hospitals.
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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 | 0,015 | 0,034 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 ».