An Empirical Investigation of Organizational Readiness towards Hospital Autonomy
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Abstract
Background: We aimed to investigate Tehran’s University of Medical Sciences (TUMS) affiliated hospitals organizational readiness toward implementing the ‘Autonomous Hospitals’ program as a change initiative from a managerial perspective in 2020.
Methods: A census covering all eligible managers working in TUMS affiliated hospitals, Tehran, Iran (350 individuals) was carried out. Overall, 281 questionnaires were returned (a 30% non-responsiveness rate). A standard construct was adopted for data collection which was validated through a process of translation- back translation, face validity, and content validity (CVI=0.86, CVR=0.76). The reliability was acquired using Cronbach’s alpha coefficient (0.87 and over 0.7). Both descriptive and inferential statistics were employed to draw conclusions .SPSS 26 was used for data analysis.
Result: Total organizational readiness for change (TORC) in hospitals was 60.75%±10.11 showing a state of medium to upper-medium readiness status. Also, the 'Clear mandate and centralized leadership' theme scored the lowest mean (53.02%±15.78) for ORC. 'Hospital accreditation level' (r=-0.14, P≤0.05), ‘bed occupancy rate’ (r=-0.19, P ≤0.05), and 'leadership status' (r=0.26, P≤0.001), also showed significant association with TORC. In addition, 'standardized bed occuPancy rate' (P≤0.05, B=-2.41), a 'male' leader (P ≤0.05, B=3.42), and 'academic affiliation' (P≤0. 1, B=-9.52), were good Predictors of TORC based on ‘Backward Multiple Linear Regression’ analysis.
Conclusion: Full support from hospital and headquarters executives, delegation of sufficient decision-making authority to hospital managers, and implementation of comprehensive performance evaluation criteria were prerequisites for robust hospital autonomy in TUMS-affiliated hospitals.
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| Issue | Vol 53 No 9 (2024) | |
| Section | Original Article(s) | |
| DOI | https://doi.org/10.18502/ijph.v53i9.16466 | |
| Keywords | ||
| Organizational Change Decentralization Administrators Public Hospitals Data-driven analysis | ||
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