The Gap Analysis and Compliance Strategies for Computer System Validation in Healthcare
Keywords:
Computer System Validation (CSV), Regulatory Compliance, Healthcare and Life Sciences, Operational Efficiency, Digital TransformationAbstract
Computer System Validation (CSV) is a critical requirement in healthcare and life sciences to ensure data integrity, regulatory compliance, and patient safety amid increasing digitalization. This study examines regulatory and operational gaps in existing CSV practices and evaluates how technology adoption and best practices influence compliance and operational efficiency. Using a quantitative, explanatory research design, primary data were collected through a structured questionnaire from 480 CSV professionals across healthcare and life sciences organizations. Statistical analyses, including descriptive statistics, t-tests, regression analysis, and factor analysis, were conducted using SPSS. The findings reveal moderate levels of regulatory awareness and compliance, alongside significant operational inefficiencies such as workflow delays, redundant validation steps, and inadequate performance measurement. One-sample t-test results confirm that current CSV processes fall significantly below optimal standards. Factor analysis highlights that emerging technologies, automation, employee training, and AI-driven tools play a substantial role in improving validation efficiency while maintaining compliance. The study underscores the need for continual validation, structured compliance strategies, and strategic technology integration to bridge existing gaps and enhance CSV effectiveness.
Downloads
References
1. Gudavalli, S., & Tangudu, A. (2025). AI-driven Customer Insight Models in Healthcare. SSRN Electronic Journal, 7(2), 839–860. https://doi.org/10.2139/ssrn.5068359
2. Jeyaraman, N., Jeyaraman, M., Yadav, S., Ramasubramanian, S., & Balaji, S. (2024). Revolutionizing Healthcare: The Emerging Role of Quantum Computing in Enhancing Medical Technology and Treatment. Cureus, 16(8), 1–11. https://doi.org/10.7759/cureus.67486
3. Joshua Idowu Akerele, Abel Uzoka, Pascal Ugochukwu Ojukwu, & Olugbenga Jeremiah Olamijuwon. (2024). Improving healthcare application scalability through microservices architecture in the cloud. International Journal of Scientific Research Updates, 8(2), 100–109. https://doi.org/10.53430/ijsru.2024.8.2.0064
4. Mockute, R., Desai, S., Perera, S., Assuncao, B., Danysz, K., Tetarenko, N., Gaddam, D., Abatemarco, D., Widdowson, M., Beauchamp, S., Cicirello, S., & Mingle, E. (2019). Artificial Intelligence Within Pharmacovigilance: A Means to Identify Cognitive Services and the Framework for Their Validation. Pharmaceutical Medicine, 33(2), 109–120. https://doi.org/10.1007/s40290-019-00269-0
5. Rahman Akorede Shittu, Anita Jumai Ehidiamen, Opeyemi Olaoluawa Ojo, Stephane Jean Christophe Zouo, Jeremiah Olamijuwon, Bamidele Michael Omowole, & Amarachi Queen Olufemi-Phillips. (2024). The role of business intelligence tools in improving healthcare patient outcomes and operations. World Journal of Advanced Research and Reviews, 24(2), 1039–1060. https://doi.org/10.30574/wjarr.2024.24.2.3414
6. Seth, D., Najana, M., & Ranjan, P. (2024). Compliance and Regulatory Challenges in Cloud Computing: A Sector-Wise Analysis. International Journal of Global Innovations and Solutions (IJGIS), June. https://doi.org/10.21428/e90189c8.68b5dea5
7. Syed, F. M., & Kousar, F. (2024). AI in Securing Pharma Manufacturing Systems Under GxP Compliance. International Journal of Machine Learning Research in Cybersecurity and Artificial Intelligence, 15(1).
8. Torab-Miandoab, A., Samad-Soltani, T., Jodati, A., & Rezaei-Hachesu, P. (2023). Interoperability of heterogeneous health information systems: a systematic literature review. BMC Medical Informatics and Decision Making, 23(1), 1–13. https://doi.org/10.1186/s12911-023-02115-5
