# Secondary Use of Electronic Health Record: Opportunities and Challenges

arXiv: https://arxiv.org/abs/2001.09479 · HTML: https://mlsanity.com/paper/2001.09479/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Jan 2020.

## Built on by (most cited first, 14 of 14)

- [medigan: a Python library of pretrained generative models for medical image synthesis](https://mlsanity.com/paper/2209.14472/) (2022)
- [Machine learning for modeling the progression of Alzheimer disease dementia using clinical data: a systematic literature review](https://mlsanity.com/paper/2108.04174/) (2021)
- [Ethical Framework for Harnessing the Power of AI in Healthcare and Beyond](https://mlsanity.com/paper/2309.00064/) (2023)
- [Breast Cancer Classification using Deep Learned Features Boosted with Handcrafted Features](https://mlsanity.com/paper/2206.12815/) (2022)
- [Artificial Intelligence For Breast Cancer Detection: Trends & Directions](https://mlsanity.com/paper/2110.00942/) (2021)
- [MedPromptX: Grounded Multimodal Prompting for Chest X-ray Diagnosis](https://mlsanity.com/paper/2403.15585/) (2024)
- [Enhancing Breast Cancer Diagnosis in Mammography: Evaluation and Integration of Convolutional Neural Networks and Explainable AI](https://mlsanity.com/paper/2404.03892/) (2024)
- [Uncovering Promises and Challenges of Federated Learning to Detect Cardiovascular Diseases: A Scoping Literature Review](https://mlsanity.com/paper/2308.13714/) (2023)
- [Predicting Visit Cost of Obstructive Sleep Apnea using Electronic Healthcare Records with Transformer](https://mlsanity.com/paper/2301.12289/) (2023)
- [A Robust Framework for Secure Cardiovascular Risk Prediction: An Architectural Case Study of Differentially Private Federated Learning](https://mlsanity.com/paper/2603.13293/) (2026)
- [Machine Learning for Administrative Health Records: A Systematic Review of Techniques and Applications](https://mlsanity.com/paper/2308.14216/) (2023)
- [A Global Cybersecurity Standardization Framework for Healthcare Informatics](https://mlsanity.com/paper/2410.05333/) (2024)
- [Trustworthy Blockchain-based Federated Learning for Electronic Health Records: Securing Participant Identity with Decentralized Identifiers and Verifiable Credentials](https://mlsanity.com/paper/2602.02629/) (2026)
- [Recovering Clinical Utility Under Differential Privacy: Empirical Validation of Adaptive Federated Aggregation on Heterogeneous Cardiovascular Datasets](https://mlsanity.com/paper/2607.19403/) (2026)

## Abstract (arXiv)

In present technological era, healthcare providers generate huge amount of clinical data on daily basis. Generated clinical data is stored digitally in the form of Electronic Health Records (EHR) as a central data repository of hospitals. Data contained in EHR is not only used for the patients' primary care but also for various secondary purposes such as clinical research, automated disease surveillance and clinical audits for quality enhancement. Using EHR data for secondary purposes without consent or in some cases even with consent creates privacy issues for individuals. Secondly, EHR data is also made accessible to various stake holders including different government agencies at various geographical sites through wired or wireless networks. Sharing of EHR across multiples agencies makes it vulnerable to cyber attacks and also makes it difficult to implement strict privacy laws as in some cases data is shared with organization that is governed by specific regional law. Privacy of an individual could be severely affected when their sensitive private information contained in EHR is leaked or exposed to public. Data leak can cause financial losses or an individuals may encounter social boycott if their medical condition is exposed in public. To protect patients personal data from such threats, there exists different privacy regulations such as GDPR, HIPAA and MHR. However, continually evolving state-of-the-art techniques in machine learning, data analytics and hacking are making it even more difficult to completely protect individual's / patient's privacy. In this article, we have systematically examined various secondary uses of EHR with the aim to highlight how these secondary uses effect patients' privacy. Secondly, we have critically analyzed GDPR and highlighted possible areas of improvement, considering escalating use of technology and different secondary uses of EHR.