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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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Generating Connected Synthetic Electronic Health Records and Social Media Data for Modeling and Simulation
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.
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- Award ID(s):
- 1915780
- PAR ID:
- 10291773
- Date Published:
- Journal Name:
- Interservice/Industry Training, Simulation and Education Conference (I/ITSEC)
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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