The migration to electronic health records (EHR) in the healthcare industry has raised issues with respect to security and privacy. One issue that has become a concern for healthcare providers, insurance companies, and pharmacies is patient health information (PHI) leaks because PHI leaks can lead to violation of privacy laws, which protect the privacy of individuals’ identifiable health information, potentially resulting in a healthcare crisis. This study explores the issue of PHI leaks from an access control viewpoint. We utilize access control policies and PHI leak scenarios derived from semi structured interviews with four healthcare practitioners and use the lens of activity theory to articulate the design of an access control model for detecting and mitigating PHI leaks. Subsequently, we follow up with a prototype as a proof of concept.
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EHR Security and Privacy Aspects: A Systematic Review
Electronic Health Records (EHRs) have become increasingly popular in recent years, providing a convenient way to store, manage and share relevant information among healthcare providers. However, as EHRs contain sensitive personal information, ensuring their security and privacy is most important. This paper reviews the key aspects of EHR security and privacy, including authentication, access control, data encryption, auditing, and risk management. Additionally, the paper dis- cusses the legal and ethical issues surrounding EHRs, such as patient consent, data ownership, and breaches of confidentiality. Effective implementation of security and privacy measures in EHR systems requires a multi-disciplinary approach involving healthcare providers, IT specialists, and regulatory bodies. Ultimately, the goal is to come upon a balance between protecting patient privacy and ensuring timely access to critical medical information for feature healthcare delivery.
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- Award ID(s):
- 2219741
- PAR ID:
- 10535332
- Publisher / Repository:
- Springer Nature Switzerland
- Date Published:
- ISBN:
- 978-3-031-45877-4
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract Objective Electronic health records (EHRs) are rich sources of patient-level data, offering valuable resources for medical data analysis. However, privacy concerns often restrict access to EHRs, hindering downstream analysis. Current EHR deidentification methods are flawed and can lead to potential privacy leakage. Additionally, existing publicly available EHR databases are limited, preventing the advancement of medical research using EHR. This study aims to overcome these challenges by generating realistic and privacy-preserving synthetic EHRs time series efficiently.
Materials and Methods We introduce a new method for generating diverse and realistic synthetic EHR time series data using denoizing diffusion probabilistic models. We conducted experiments on 6 databases: Medical Information Mart for Intensive Care III and IV, the eICU Collaborative Research Database (eICU), and non-EHR datasets on Stocks and Energy. We compared our proposed method with 8 existing methods.
Results Our results demonstrate that our approach significantly outperforms all existing methods in terms of data fidelity while requiring less training effort. Additionally, data generated by our method yield a lower discriminative accuracy compared to other baseline methods, indicating the proposed method can generate data with less privacy risk.
Discussion The proposed model utilizes a mixed diffusion process to generate realistic synthetic EHR samples that protect patient privacy. This method could be useful in tackling data availability issues in the field of healthcare by reducing barrier to EHR access and supporting research in machine learning for health.
Conclusion The proposed diffusion model-based method can reliably and efficiently generate synthetic EHR time series, which facilitates the downstream medical data analysis. Our numerical results show the superiority of the proposed method over all other existing methods.
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Patient health records(PHRs) are crucial and sensitive as they contain essential information and are frequently shared among healthcare entities. This information must remain correct, up to date, private and accessible only to the authorized entities. Moreover, access must also be assured during health emergency crises such as the recent outbreak, which represents the greatest test of the flexibility and the efficiency of PHR sharing among healthcare providers, which ended up an immense interruption to the healthcare industry. Moreover, the right to privacy is the most fundamental right for a patient. Hence, the patient health records in the healthcare sector have faced issues with privacy breaches, insider outside attacks, and unauthorized access to crucial patients’ records. As a result, it pushes more patients to demand more control, security, and a smoother experience when they want to access their health records. Furthermore, the lack of interoperability among the healthcare system and providers and the added weight of cyber-attacks on an already overwhelmed system have called for an immediate solution. In this work, we developed a secured blockchain framework that safeguards patients’ full control over their health data which can be stored in their private IPFS and later shared with an authorized provider. Furthermore, the system ensures privacy and security while handling patient data, which can only be shared with the patients. The proposed Security and privacy analysis show promising results in providing time savings, enhanced confidentiality, and less disruption in patient-provider interactions.more » « less
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The healthcare sector is constantly improving patient health record systems. However, these systems face a significant challenge when confronted with patient health record (PHR) data due to its sensitivity. In addition, patient’s data is stored and spread generally across various healthcare facilities and among providers. This arrangement of distributed data becomes problematic whenever patients want to access their health records and then share them with their care provider, which yields a lack of interoperability among various healthcare systems. Moreover, most patient health record systems adopt a centralized management structure and deploy PHRs to the cloud, which raises privacy concerns when sharing patient information over a network. Therefore, it is vital to design a framework that considers patient privacy and data security when sharing sensitive information with healthcare facilities and providers. This paper proposes a blockchain framework for secured patient health records sharing that allows patients to have full access and control over their health records. With this novel approach, our framework applies the Ethereum blockchain smart contracts, the Inter-Planetary File System (IPFS) as an off-chain storage system, and the NuCypher protocol, which functions as key management and blockchain-based proxy re-encryption to create a secured on-demand patient health records sharing system effectively. Results show that the proposed framework is more secure than other schemes, and the PHRs will not be accessible to unauthorized providers or users. In addition, all encrypted data will only be accessible to and readable by verified entities set by the patient.more » « less
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null (Ed.)Research and experimentation using big data sets, specifically large sets of electronic health records (EHR) and social media data, is demonstrating the potential to understand the spread of diseases and a variety of other issues. Applications of advanced algorithms, machine learning, and artificial intelligence indicate a potential for rapidly advancing improvements in public health. For example, several reports indicate that social media data can be used to predict disease outbreak and spread (Brown, 2015). Since real-world EHR data has complicated security and privacy issues preventing it from being widely used by researchers, there is a real need to synthetically generate EHR data that is realistic and representative. Current EHR generators, such as Syntheaä (Walonoski et al., 2018) only simulate and generate pure medical-related data. However, adding patients’ social media data with their simulated EHR data would make combined data more comprehensive and realistic for healthcare research. This paper presents a patients’ social media data generator that extends an EHR data generator. By adding coherent social media data to EHR data, a variety of issues can be examined for emerging interests, such as where a contagious patient may have been and others with whom they may have been in contact. Social media data, specifically Twitter data, is generated with phrases indicating the onset of symptoms corresponding to the synthetically generated EHR reports of simulated patients. This enables creation of an open data set that is scalable up to a big-data size, and is not subject to the security, privacy concerns, and restrictions of real healthcare data sets. This capability is important to the modeling and simulation community, such as scientists and epidemiologists who are developing algorithms to analyze the spread of diseases. It enables testing a variety of analytics without revealing real-world private patient information.more » « less
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