A Survey on Current Electronic Health Record (EHR) Systems and the Role of AI in Enhancing Healthcare Efficiency

Abstract

Electronic Health Record (EHR) systems have be- come the foundation of modern healthcare infrastructure, en- abling the digitalization and efficient management of patient data. However, despite continuous advancements, current EHR systems still face challenges related to interoperability, scalability, data privacy, and integration with artificial intelligence (AI). This paper presents a comprehensive survey of fifteen recent research studies (2022–2025) focusing on EHR technologies, AI- driven healthcare solutions, and related innovations. The survey highlights the advantages and disadvantages of current systems, identifies research gaps, and proposes how our project, CURA- ID, aims to overcome these limitations through AI integration, blockchain-based security, and patient-centric features.

Introduction

The healthcare industry has transitioned from paper-based systems to Electronic Health Records (EHRs), improving clinical efficiency and patient outcomes. However, the full potential of EHRs remains unrealized due to major issues such as poor interoperability, inconsistent data formats, centralized storage vulnerabilities, and limited integration of artificial intelligence. These shortcomings create fragmented patient histories, redundant tests, diagnostic errors, and increased cybersecurity risks—especially as telemedicine and cloud platforms continue to expand.

Traditional EHR systems also struggle to support AI-driven analytics, predictive models, and real-time monitoring because they lack structured, complete datasets and the infrastructure required for advanced processing. Centralized databases further expose sensitive information to breaches and unauthorized access.

Emerging solutions like blockchain and federated learning address these issues by enabling decentralized, secure, and privacy-preserving data management. Blockchain offers immutable audit trails, while federated learning allows AI models to train on distributed data without compromising privacy. These technologies form the foundation for next-generation intelligent healthcare systems.

A literature survey of 15 studies (2022–2025) reveals key trends: cloud-based EHRs improve accessibility but suffer from security risks; AI enhances diagnostics but faces data bias; blockchain boosts data integrity yet has high computational demands; federated learning protects privacy but increases training complexity; and hybrid AI–IoT systems support real-time monitoring but require costly infrastructure. Many approaches show promise but lack holistic integration.

Legacy EHR systems exhibit numerous limitations, including centralized and vulnerable storage, lack of interoperability, weak encryption, poor usability, absence of AI capabilities, limited IoT integration, scalability challenges, and inadequate support for modern APIs and data standards. These constraints highlight the pressing need for a modern, modular, and intelligent EHR framework.

To address these gaps, the proposed model CURA-ID aims to unify healthcare data into a secure, interoperable, AI-enhanced EHR ecosystem. It emphasizes patient empowerment, predictive analytics, blockchain-backed integrity, and scalable cloud architecture.

Future work focuses on strengthening interoperability using standardized APIs, deploying AI-based prediction tools, developing real-time dashboards, enhancing security through decentralized storage and federated learning, supporting personalized care, and implementing microservices to improve scalability and reliability.

Conclusion

This survey highlights that while existing Electronic Health Record (EHR) systems have brought considerable advance- ments to healthcare digitization, they continue to face persis- tent challenges in interoperability, data privacy, scalability, and the integration of intelligent decision-support systems. Many legacy systems operate within isolated environments, limiting efficient data exchange between healthcare institutions and creating bottlenecks in clinical workflows. Additionally, the lack of deep AI integration restricts predictive analytics and diagnostic assistance, resulting in reactive rather than proactive healthcare management. Our proposed solution, CURA-ID, addresses these lim- itations by introducing a comprehensive, AI-empowered EHR platform that integrates modern technologies such as blockchain for secure data management, federated learning for privacy-preserving analytics, and advanced visualization tools for real-time insights. By leveraging a modular archi- tecture, CURA-ID ensures flexibility, scalability, and cross- institutional interoperability through standardized APIs and decentralized governance models. The inclusion of patient- centric features such as chatbot assistance, personalized lifestyle recommendations, and recovery prediction models fosters active engagement and shared decision-making be- tween patients and healthcare providers. Furthermore, the system emphasizes ethical AI use and compliance with evolving healthcare data standards, ensur- ing that data remains both accessible and trustworthy. The integration of predictive heatmaps and intelligent dashboards aids in identifying disease trends, optimizing hospital resource allocation, and supporting data-driven clinical decisions. In essence, CURA-ID lays the foundation for a next- generation EHR ecosystem—one that is secure, intelligent, interoperable, and patient-driven. This work provides a roadmap for future research and implementation in healthcare informatics, encouraging collaboration between technologists, clinicians, and policymakers to build a transparent, efficient, and human-centered digital health infrastructure.

References

[1] S. Rao and P. Kaur, “Cloud-based frameworks for efficient electronic health record management,” Journal of Medical Systems, vol. 46, no. 5, pp. 1–12, 2022. [Online]. Available: https://doi.org/10.1007/s10916-022-01876-2 [2] D. Kim and J. Park, “Ai-driven clinical decision support using ehr data,” IEEE Access, vol. 10, pp. 89 034–89 046, 2022. [Online]. Available: https://doi.org/10.1109/ACCESS.2022.3190457 [3] R. Gupta and A. Mehta, “Blockchain-based electronic health record systems: A security perspective,” Computers in Biology and Medicine, vol. 155, p. 106620, 2023. [Online]. Available: https://doi.org/10.1016/j.compbiomed.2023.106620 [4] J. Smith and T. Khan, “Overcoming interoperability challenges in healthcare systems,” Health Informatics Journal, vol. 29, no. 2, pp. 350– 366, 2023. [5] Y. Zhang and X. Li, “Privacy-preserving federated learning for medical data sharing,” IEEE Journal of Biomedical and Health Informatics, vol. 27, no. 3, pp. 1256–1268, 2023. [6] W. Liu and Z. Tan, “Predictive analytics in ehr data for disease detection,” BMC Medical Informatics and Decision Making, vol. 23, no. 4, pp. 65–79, 2023. [7] L. Pe´rez and M. Gonza´lez, “Enhancing ehr security through encryption and access control,” Journal of Healthcare Engineering, vol. 2023, pp. 1–10, 2023. [8] M. Ali and H. Singh, “Ai and iot integration in healthcare monitoring systems,” Sensors, vol. 23, no. 6, p. 3121, 2023. [9] R. Johnson and L. Carter, “Analyzing usability and user experience in ehr systems,” International Journal of Human-Computer Interaction, vol. 40, no. 1, pp. 110–126, 2024. [10] S. Patel and A. Deshmukh, “Hybrid cloud architectures for healthcare data storage,” Future Generation Computer Systems, vol. 147, pp. 512– 524, 2024. [11] J. Ferna´ndez and C. Ortiz, “Ai-based health risk prediction using multi- modal data,” Artificial Intelligence in Medicine, vol. 146, p. 102624, 2024. [12] K. Miller and H. Zhao, “Api-based interoperability layers in healthcare it,” Journal of Medical Internet Research, vol. 26, p. e45782, 2024. [13] P. Singh and R. Ahmed, “Patient-centered ehr systems for improved transparency,” Health Information Science and Systems, vol. 12, no. 2, pp. 1–14, 2024. [14] T. Brown and N. Davis, “Machine learning models for diagnosis prediction from ehr data,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 32, no. 7, pp. 945–956, 2024. [15] E. Williams and L. Chen, “Future trends in ai and blockchain-based ehr systems,” Frontiers in Digital Health, vol. 7, pp. 112–129, 2025.

Copyright

Copyright © 2025 Dr. P. B. Wakhare, Arin Verma, Vaibhav Agade, Darshan Thube. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.