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Incorporating Contextual Factors into a Comprehensive Analysis of Operational Efficiency and Service Quality in Healthcare Sector

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Název: Incorporating Contextual Factors into a Comprehensive Analysis of Operational Efficiency and Service Quality in Healthcare Sector
Autoři: Utsav Pandey, Sanjeet Singh
Zdroj: International Journal of Mathematical, Engineering and Management Sciences, Vol 10, Iss 2, Pp 300-349 (2025)
Informace o vydavateli: Ram Arti Publishers, 2025.
Rok vydání: 2025
Sbírka: LCC:Technology
LCC:Mathematics
Témata: healthcare efficiency, quality, dea, healthcare analytics, Technology, Mathematics, QA1-939
Popis: 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.
Druh dokumentu: article
Popis souboru: electronic resource
Jazyk: English
ISSN: 2455-7749
Relation: https://www.ijmems.in/cms/storage/app/public/uploads/volumes/17-IJMEMS-24-0599-10-2-300-349-2025.pdf; https://doaj.org/toc/2455-7749
DOI: 10.33889/IJMEMS.2025.10.2.017
Přístupová URL adresa: https://doaj.org/article/f623149f504e42729e4bc8998dd3a2c1
Přístupové číslo: edsdoj.f623149f504e42729e4bc8998dd3a2c1
Databáze: Directory of Open Access Journals
Popis
Abstrakt: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.
ISSN:24557749
DOI:10.33889/IJMEMS.2025.10.2.017