Patient-generated health data (PGHD), created and captured from patients via wearable devices and mobile apps, are proliferating outside of clinical settings. Examples include sleep tracking, fitness trackers, continuous glucose monitors, and RFID-enabled implants, with many additional biometric or health surveillance applications in development or envisioned. These data are included in growing stockpiles of personal health data being mined for insight via big data analytics and artificial intelligence/deep learning technologies. Governing these data resources to facilitate patient care and health research while preserving individual privacy and autonomy will be challenging, as PGHD are the least regulated domains of digitalized personal health data (U.S. Department of Health and Human Services, 2018). When patients themselves collect digitalized PGHD using “apps” provided by technology firms, these data fall outside of conventional health data regulation, such as HIPAA. Instead, PGHD are maintained primarily on the information technology infrastructure of vendors, and data are governed under the IT firm’s own privacy policies and within the firm’s intellectual property rights. Dominant narratives position these highly personal data as valuable resources to transform healthcare, stimulate innovation in medical research, and engage individuals in their health and healthcare. However, ensuring privacy, security, and equity of benefits from PGHD will be challenging. PGHD can be aggregated and, despite putative “deidentification,” be linked with other health, economic, and social data for predictive analytics. As large tech companies enter the healthcare sector (e.g., Google Health is partnering with Ascension Health to analyze the PHI of millions of people across 21 U.S. states), the lack of harmonization between regulatory regimes may render existing safeguards to preserve patient privacy and control over their PHI ineffective. While healthcare providers are bound to adhere to health privacy laws, Big Tech comes under more relaxed regulatory regimes that will facilitate monetizing PGHD. We explore three existing data protection regimes relevant to PGHD in the United States that are currently in tension with one another: federal and state health-sector laws, data use and reuse for research and innovation, and industry self-regulation by large tech companies We then identify three types of structures (organizational, regulatory, technological/algorithmic), which synergistically could help enact needed regulatory oversight while limiting the friction and economic costs of regulation. This analysis provides a starting point for further discussions and negotiations among stakeholders and regulators to do so.
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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
- NSF-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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Patient-generated health data (PGHD), created and captured from patients via wearable devices and mobile apps, are proliferating outside of clinical settings. Examples include sleep tracking, fitness trackers, continuous glucose monitors, and RFID-enabled implants, with many additional biometric or health surveillance applications in development or envisioned. These data are included in growing stockpiles of personal health data being mined for insight via big data analytics and artificial intelligence/deep learning technologies. Governing these data resources to facilitate patient care and health research while preserving individual privacy and autonomy will be challenging, as PGHD are the least regulated domains of digitalized personal health data (U.S. Department of Health and Human Services, 2018). When patients themselves collect digitalized PGHD using “apps” provided by technology firms, these data fall outside of conventional health data regulation, such as HIPAA. Instead, PGHD are maintained primarily on the information technology infrastructure of vendors, and data are governed under the IT firm’s own privacy policies and within the firm’s intellectual property rights. Dominant narratives position these highly personal data as valuable resources to transform healthcare, stimulate innovation in medical research, and engage individuals in their health and healthcare. However, ensuring privacy, security, and equity of benefits from PGHD will be challenging. PGHD can be aggregated and, despite putative “deidentification,” be linked with other health, economic, and social data for predictive analytics. As large tech companies enter the healthcare sector (e.g., Google Health is partnering with Ascension Health to analyze the PHI of millions of people across 21 U.S. states), the lack of harmonization between regulatory regimes may render existing safeguards to preserve patient privacy and control over their PHI ineffective. While healthcare providers are bound to adhere to health privacy laws, Big Tech comes under more relaxed regulatory regimes that will facilitate monetizing PGHD. We explore three existing data protection regimes relevant to PGHD in the United States that are currently in tension with one another: federal and state health-sector laws, data use and reuse for research and innovation, and industry self-regulation by large tech companies We then identify three types of structures (organizational, regulatory, technological/algorithmic), which synergistically could help enact needed regulatory oversight while limiting the friction and economic costs of regulation. This analysis provides a starting point for further discussions and negotiations among stakeholders and regulators to do so.more » « less
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Accepted Manuscript1.0
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