Outsourcing Non-Core Services in Healthcare: A Cost-Benefit Analysis
Downloads
Outsourcing non-core services in healthcare, such as housekeeping, IT support, catering, and security, has emerged as a popular strategy for healthcare organizations looking to reduce costs and focus on patient care. This paper presents a comprehensive cost-benefit analysis of outsourcing these services, considering both financial and operational implications. Through a combination of quantitative analysis and qualitative insights from healthcare administrators, the study explores how outsourcing affects cost savings, operational efficiency, and overall service quality.
The findings indicate that outsourcing non-core services can lead to significant cost reductions—ranging from 7% to 28% depending on the service—while also enhancing operational efficiency. In particular, services such as housekeeping and IT support benefit from outsourcing due to the specialized expertise of third-party providers. Outsourced services not only reduce internal operational burdens but also contribute to a more streamlined allocation of resources toward core healthcare functions, including patient care.
However, the analysis also highlights several risks, such as loss of control over service quality and concerns related to data security, particularly when IT services are outsourced. These challenges can potentially affect the reliability of healthcare operations if not properly managed. The study suggests that healthcare organizations must implement strict service-level agreements (SLAs) and performance monitoring mechanisms to mitigate these risks and ensure continuity of high-quality service delivery.
Downloads
1. Karakolias, S., Kastanioti, C., Theodorou, M., & Polyzos, N. (2017). Primary care doctors’ assessment of and preferences on their remuneration: Evidence from Greek public sector. INQUIRY: The Journal of Health Care Organization, Provision, and Financing, 54, 0046958017692274.
2. Romero-Domínguez, L., Martín-Santana, J. D., Sánchez-Medina, A. J., & Beerli-Palacio, A. (2021). Lines of scientific research in the study of blood donor behavior from a social marketing perspective. Journal of Nonprofit & Public Sector Marketing, 33(3), 307-358
3. Stephanou, A. T., & Moreira, M. C. (2019). Blood donors’ perception of incentive campaigns. Paidéia (Ribeirão Preto), 29, e2927
4. Dobbin, S. A. M. U. E. L. (2016). Social Marketing On Regular Voluntary Blood Donation In Ghana (Doctoral dissertation, University of Ghana).
5. Polonsky, M., Francis, K., & Renzaho, A. (2015). Is removing blood donation barriers a donation facilitator? Australian African migrants’ view. Journal of Social Marketing, 5(3), 190-205.
6. Lauri, M. A. (2008). Changing public opinion towards organ donation. A social psychological approach to social marketing. Public opinion research focus, 9-36.
7. Martín-Santana, J. D., Reinares-Lara, E., & Reinares-Lara, P. (2018). Using radio advertising to promote blood donation. Journal of Nonprofit & Public Sector Marketing, 30(1), 52-73.
8. Karakolias, S. E., & Polyzos, N. M. (2014). The newly established unified healthcare fund (EOPYY): current situation and proposed structural changes, towards an upgraded model of primary health care, in Greece. Health, 2014.
9. Polyzos, N., Karakolias, S., Dikeos, C., Theodorou, M., Kastanioti, C., Mama, K., ... & Thireos, E. (2014). The introduction of Greek Central Health Fund: Has the reform met its goal in the sector of Primary Health Care or is there a new model needed?. BMC health services research, 14, 1-11.
10. Polyzos, N., Kastanioti, C., Zilidis, C., Mavridoglou, G., Karakolias, S., Litsa, P., ... & Kani, C. (2016). Greek national e-prescribing system: Preliminary results of a tool for rationalizing pharmaceutical use and cost. Glob J Health Sci, 8(10), 55711.
11. Karakolias, S., & Polyzos, N. (2015). Application and assessment of a financial distress projection model in private general clinics. Archives of Hellenic Medicine/Arheia Ellenikes Iatrikes, 32(4).
12. Karakolias, S., & Kastanioti, C. (2018). Application of an organizational assessment tool of primary health care. Arch Hell Med, 35, 497-505.
13. Vozikis, A., Panagiotou, A., & Karakolias, S. (2021). Α Tool for Litigation Risk Analysis for Medical Liability Cases. HAPSc Policy Briefs Series, 2(2), 268-277.
14. Polyzos, N., Kastanioti, C., Theodorou, M., Karakolias, S., Mama, K., Thireos, E., ... & Dikaios, C. (2013). Study on reimbursement system of public and private primary health care units contracted with EOPYY. Democritus University of Thrace, Komotini.
15. Karakolias, S., Batzokas, D., & Polyzos, N. (2021). Primary health care: the Greek case, in the perspective of reform. Arch Hell Med, 38, 524-30.
16. Dalakaki, Ε., Karakolias, S., Kastanioti, C., & Polyzos, N. (2018). Analysis of out-of-hospital pharmaceutical prescribing and Health Insurance System expenditure. ARCHIVES OF HELLENIC MEDICINE, 35(6), 791-801.
17. Kastanioti, C., Karakolias, S., Karanikas, H., Zilidis, C., & Polyzos, N. (2016). Economic evaluation based on KEN-DRGs in a NHS hospital.
18. Zilides, C., Polyzos, N., & Karakolias, S. (2016). Comparative evaluation of efficiency in the university and National Health Service departments of a regional university hospital. Archives of Hellenic Medicine, 33(2), 217-223.
19. Karakolias, S., Georgi, C., & Georgis, V. (2024). Patient Satisfaction With Public Pharmacy Services: Structural and Policy Implications From Greece. Cureus, 16(4).
20. Psarras, A., & Karakolias, S. (2024). A Groundbreaking Insight Into Primary Care Physiotherapists’ Remuneration. Cureus, 16(2).
21. Georgi, C., Georgis, V., & Karakolias, S. (2023). HSD79 Assessment of Patient Satisfaction with Public Pharmacies Dispensing High-Cost Drugs in Greece. Value in Health, 26(12), S308-S309.
22. Khokha, S., & Reddy, K. R. (2016). Low Power-Area Design of Full Adder Using Self Resetting Logic With GDI Technique. International Journal of VLSI design & Communication Systems (VLSICS) Vol, 7.
23. Zabihi, A., Sadeghkhani, I., & Fani, B. (2021). A partial shading detection algorithm for photovoltaic generation systems. Journal of Solar Energy Research, 6(1), 678-687.
24. Zabihi, A., Parhamfar, M., Duvvuri, S. S., & Abtahi, M. (2024). Increase power output and radiation in photovoltaic systems by installing mirrors. Measurement: Sensors, 31, 100946.
25. Peng, L., Zabihi, A., Azimian, M., Shirvani, H., & Shahnia, F. (2022). Developing a robust expansion planning approach for transmission networks and privately-owned renewable sources. IEEE access, 11, 76046-76058.
26. Zabihi, A. (2024). Assessment of Faults in the Performance of Hydropower Plants within Power Systems. Energy, 7(2).
27. Ramey, K., Dunphy, M., Schamberger, B., Shoraka, Z. B., Mabadeje, Y., & Tu, L. (2024). Teaching in the Wild: Dilemmas Experienced by K-12 Teachers Learning to Facilitate Outdoor Education. In Proceedings of the 18th International Conference of the Learning Sciences-ICLS 2024, pp. 1195-1198. International Society of the Learning Sciences.
28. Raghuwanshi, P. (2024). AI-Powered Neural Network Verification: System Verilog Methodologies for Machine Learning in Hardware. Journal of Artificial Intelligence General science (JAIGS) ISSN: 3006-4023, 6(1), 39-45.
29. Raghuwanshi, P. (2016). Verification of Verilog model of neural networks using System Verilog.
30. Raghuwanshi, P. (2024). Integrating Generative AI into IoT-Based Cloud Computing: Opportunities and Challenges in the United States. Journal of Artificial Intelligence General science (JAIGS) ISSN: 3006-4023, 5(1), 451-460.
31. Raghuweanshi, P. (2024). REVOLUTIONIZING SEMICONDUCTOR DESIGN AND MANUFACTURING WITH AI. Journal of Knowledge Learning and Science Technology ISSN: 2959-6386 (online), 3(3), 272-277.
32. Atri, P. (2024). Enhancing Big Data Security through Comprehensive Data Protection Measures: A Focus on Securing Data at Rest and In-Transit. International Journal of Computing and Engineering, 5(4), 44-55.
33. Atri, P. (2023). Mitigating Downstream Disruptions: A Future-Oriented Approach to Data Pipeline Dependency Management with the GCS File Dependency Monitor. J Artif Intell Mach Learn & Data Sci, 1(4), 635-637.
34. Atri, P. (2023). Cloud Storage Optimization Through Data Compression: Analyzing the Compress-CSV-Files-GCS-Bucket Library. J Artif Intell Mach Learn & Data Sci, 1(3), 498-500.
35. Atri, P. (2023). Empowering AI with Efficient Data Pipelines: A Python Library for Seamless Elasticsearch to BigQuery Integration. International Journal of Science and Research (IJSR), 12(5), 2664-2666.
36. Atri, P. (2022). Advancing Financial Inclusion through Data Engineering: Strategies for Equitable Banking. International Journal of Science and Research (IJSR), 11(8), 1504-1506.
37. Atri, P. (2022). Enabling AI Work flows: A Python Library for Seamless Data Transfer between Elasticsearch and Google Cloud Storage. J Artif Intell Mach Learn & Data Sci, 1(1), 489-491.
38. Atri, P. (2020). Enhancing Data Engineering and AI Development with the'Consolidatecsv-files-from-gcs' Python Library. International Journal of Science and Research (IJSR), 9(5), 1863-1865.
39. Atri, P. (2019). Unlocking Data Potential: The GCS XML CSV Transformer for Enhanced Accessibility in Google Cloud. International Journal of Science and Research (IJSR), 8(10), 1870-1871.
40. Atri, P. (2019). Enhancing Big Data Interoperability: Automating Schema Expansion from Parquet to BigQuery. International Journal of Science and Research (IJSR), 8(4), 2000-2002.
41. Atri, P. (2018). Optimizing Financial Services Through Advanced Data Engineering: A Framework for Enhanced Efficiency and Customer Satisfaction. International Journal of Science and Research (IJSR), 7(12), 1593-1596.
42. Atri, P. (2018). Design and Implementation of High-Throughput Data Streams using Apache Kafka for Real-Time Data Pipelines. International Journal of Science and Research (IJSR), 7(11), 1988-1991.
43. Alsemaid, O. M., Atri, P., Kande, S. K., & Lembhe, P. (2024). Cutting-Edge Innovations in Technology and Security. Cari Journals USA LLC.
44. Julian, A., Mary, G. I., Selvi, S., Rele, M., & Vaithianathan, M. (2024). Blockchain based solutions for privacy-preserving authentication and authorization in networks. Journal of Discrete Mathematical Sciences and Cryptography, 27(2-B), 797-808.
45. Vaithianathan, M., Patil, M., Ng, S. F., & Udkar, S. (2023). Comparative Study of FPGA and GPU for High-Performance Computing and AI. ESP International Journal of Advancements in Computational Technology (ESP-IJACT), 1(1), 37-46.
46. Vaithianathan, M., Patil, M., Ng, S. F., & Udkar, S. (2024). Low-Power FPGA Design Techniques for Next-Generation Mobile Devices. ESP International Journal of Advancements in Computational Technology (ESP-IJACT), 2(2), 82-93.
47. Vaithianathan, M. (2024). Real-Time Object Detection and Recognition in FPGA-Based Autonomous Driving Systems. International Journal of Computer Trends and Technology, 72(4), 145-152.
48. Vaithianathan, M., Patil, M., Ng, S. F., & Udkar, S. (2024). Energy-Efficient FPGA Design for Wearable and Implantable Devices. ESP International Journal of Advancements in Science & Technology (ESP-IJAST), 2(2), 37-51.
49. Vaithianathan, M., Patil, M., Ng, S. F., & Udkar, S. (2024). Integrating AI and Machine Learning with UVM in Semiconductor Design. ESP International Journal of Advancements in Computational Technology (ESP-IJACT) Volume, 2, 37-51.
50. Vaithianathan, M., Patil, M., Ng, S. F., & Udkar, S. Verification of Low-Power Semiconductor Designs Using UVM.
51. Al-Zahrani, Saleh. Computer network system for university hospitals in Saudi Arabia. Diss. Loughborough University, 2001.
52. Malaysia, A. A. U. T. M. U., Indonesia, A. B. U., Baharum, A., Algeria, N. B. E., Conjeevaram, A., Daniati, E., ... & Abood, L. H. Vlad Apopei Bournemouth University
53. Kausar, M., Ishtiaq, M., & Hussain, S. (2021). Distributed agile patterns-using agile practices to solve offshore development issues. IEEE Access, 10, 8840-8854
54. Xiao, G., Lin, Y., Lin, H., Dai, M., Chen, L., Jiang, X., ... & Zhang, W. (2022). Bioinspired self-assembled Fe/Cu-phenolic building blocks of hierarchical porous biomass-derived carbon aerogels for enhanced electrocatalytic oxygen reduction. Colloids and Surfaces A: Physicochemical and Engineering Aspects, 648, 128932
55. Kausar, M., & Al-Yasiri, A. (2015, July). Distributed agile patterns for offshore software development. In 12th International Joint Conference on Computer Science and Software Engineering (JCSSE), IEEE (p. 17).
56. Kausar, M., & Al-Yasiri, A. (2017). Using distributed agile patterns for supporting the requirements engineering process. Requirements Engineering for Service and Cloud Computing, 291-316.
57. Kausar, M., Muhammad, A. W., Jabbar, R., & Ishtiaq, M. (2022). Key challenges of requirement change management in the context of global software development: systematic literature review. Pakistan Journal of Engineering and Applied Sciences.
58. Xiao, G., Lin, H., Lin, Y., Chen, L., Jiang, X., Cao, X., ... & Zhang, W. (2022). Self-assembled hierarchical metal–polyphenol-coordinated hybrid 2D Co–C TA@ gC 3 N 4 heterostructured nanosheets for efficient electrocatalytic oxygen reduction. Catalysis Science & Technology, 12(14), 4653-4661.
59. Kausar, M., Mazhar, N., Ishtiaq, M., & Alabrah, A. (2023). Decision Making of Agile Patterns in Offshore Software Development Outsourcing: A Fuzzy Logic-Based Analysis. Axioms, 12(3), 307.
60. Kausar, M. (2018). Distributed agile patterns: an approach to facilitate agile adoption in offshore software development. University of Salford (United Kingdom).
61. Mazhar, N., & Kausar, M. (2023). Rational Coordination in Cognitive Agents: A Decision-Theoretic Approach Using ERMM. IEEE Access.
62. Kassim, M. E., Kausar, M., Al-Shammari, S., Khan, N. A., Alsahlani, A., Mohammed, R., ... & Nassrullah, Z. F. A. (2016). Proceedings of the CSE 2016 Annual PGR Symposium (CSE-PGSym 16).
63. Shehzad, N., & Kausar, M. Organizational Factors Impacting Agile Software Development-A Systematic Literature.
64. Kausar, M. Using Distributed Agile Patterns for Developing Offshore Projects.
65. Raman, P. K. (2022). Omnichannel Commerce in the Grocery Sector: A Comparative Study of India, UK, and US with Technological Insights on APIs and Headless Commerce. Journal of Science & Technology, 3(3), 136-200.
66. Raman, P. K. (2021). Comprehensive Analysis of eCommerce and Marketplaces: Global Perspectives with Emphasis on the Indian Context. Asian Journal of Multidisciplinary Research & Review, 2(1), 1-52.
67. JOSHI, D., SAYED, F., BERI, J., & PAL, R. (2021). An efficient supervised machine learning model approach for forecasting of renewable energy to tackle climate change. Int J Comp Sci Eng Inform Technol Res, 11, 25-32.
68. Joshi, D., Sayed, F., Saraf, A., Sutaria, A., & Karamchandani, S. (2021). Elements of Nature Optimized into Smart Energy Grids using Machine Learning. Design Engineering, 1886-1892.
69. Joshi, D., Parikh, A., Mangla, R., Sayed, F., & Karamchandani, S. H. (2021). AI Based Nose for Trace of Churn in Assessment of Captive Customers. Turkish Online Journal of Qualitative Inquiry, 12(6).
70. Khambaty, A., Joshi, D., Sayed, F., Pinto, K., & Karamchandani, S. (2022, January). Delve into the Realms with 3D Forms: Visualization System Aid Design in an IOT-Driven World. In Proceedings of International Conference on Wireless Communication: ICWiCom 2021 (pp. 335-343). Singapore: Springer Nature Singapore.
71. Khambati, A. (2021). Innovative Smart Water Management System Using Artificial Intelligence. Turkish Journal of Computer and Mathematics Education (TURCOMAT), 12(3), 4726-4734.
72. Chen, X. (2024). Research on the Application of Machine Learning Technology in Enterprise Intelligent Finance. International Journal of Computer Science and Information Technology, 3(3), 199-205.
73. Chen, X. (2023). Efficient Algorithms for Real-Time Semantic Segmantation in Augmented reality. Journal of Innovative Technologies, 6(1).
74. Chen, X. (2023). Optimization Strategies for Reducing Energy Consumption in AI Model Training. Advances in Computer Sciences, 6(1).
75. Chen, X. (2023). Real-Time Detection of Adversarial Attacks in Deep Learning Models. MZ Computing Journal, 4(2).
76. Chen, X., & Olson, E. (2022). AI in Transportation: Current Developments and Future Directions. Innovative Computer Sciences Journal, 8(1).
77. Chen, X. (2024). AI in Healthcare: Revolutionizing Diagnosis and Treatment through Machine Learning. MZ Journal of Artificial Intelligence, 1(2).
78. Chen, X., Ryan, T., & Wang, H. (2022). Exploring AI in Education: Personalized Learning, Automated Grading, and Classroom Management. MZ Computing Journal, 3(1).
79. Chen, X. (2024). AI for Social Good: Leveraging Machine Learning for Addressing Global Challenges. Innovative Computer Sciences Journal, 10(1)
Copyright (c) 2024 Dr. Stefanos Karakolias
This work is licensed under a Creative Commons Attribution 4.0 International License.
