Incorporating Contextual Factors into a Comprehensive Analysis of Operational Efficiency and Service Quality in Healthcare Sector
Service quality is believed to influence the productive efficiency of firms, particularly in a service focused industry such as healthcare. However, there is mixed evidence in the literature of both positive and negative correlation (e.g., the cost drivers of care providers vis-à-vis capacity expans...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Ram Arti Publishers
2025-04-01
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| Series: | International Journal of Mathematical, Engineering and Management Sciences |
| Subjects: | |
| Online Access: | https://www.ijmems.in/cms/storage/app/public/uploads/volumes/17-IJMEMS-24-0599-10-2-300-349-2025.pdf |
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| Summary: | Service quality is believed to influence the productive efficiency of firms, particularly in a service focused industry such as healthcare. However, there is mixed evidence in the literature of both positive and negative correlation (e.g., the cost drivers of care providers vis-à-vis capacity expansion for better quality of service) between quality and efficiency. To address this challenge, a two-phase data-driven analysis is undertaken. In the first stage, an output-oriented Data Envelopment Analysis is employed to model the interdependency between operational efficiency and service quality by assessing the allocation of the input resources for achieving these two objectives. While accounting for the external influences and avoiding the ‘best practice trap’ in the healthcare sector, a set of classification algorithms are used to quantify the impact of external factors on efficiency levels. The proposed model is empirically tested using healthcare data of 31 provinces of China for a period from 2013 to 2018. The results show that the efficiency scores in operational productivity and quality of service are 67% and 64%, respectively. The major source of inefficiency is the number of cases in observation rooms (almost 47%) followed by health examination (22%). The provinces are categorized into three classes (optimally chosen number of clusters) using K-means clustering. The second phase of the analysis starts with selecting a subset of relevant features from 33 explanatory variables using information gain and correlation analysis. The proposed two-phased integrated technique enhances the performance of healthcare services and provides a roadmap for improvement for inefficient regions. |
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| ISSN: | 2455-7749 |