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Improving Mental Health Treatments with Synthetic Patient Data

To more securely explore artificial intelligence applications to advance patient care, the Virginia Department of Behavioural Health and Developmental Services (DBHDS) will be creating an anonymised digital twin of patient data to more securely explore artificial intelligence applications to advance patient care.

BHDS will first deploy GEMINAI synthetic data engine, which creates a duplicate dataset of patient information. That digital twin will have the same statistical properties, nuances and characteristics of a population of interest, but it will contain no personal information associated with patients that might reveal their identity.

DBHDS can generate “synthetic patients” with specific medical conditions that fit certain demographic profiles, all without the personal health information of the original dataset, and with no one-to-one relationship back to the production data or any way to reverse-engineer the data to tie it back to a real person.

According to the Department of Health and Human Services, a synthetic health data engine employs an open-source development model. Synthea uses publicly available data to generate synthetic health records and can export information in multiple standardised formats. Synthea generates realistic patients, simulates their entire lives, and outputs electronic health record data.

Synthetic health data sets are compatible with a variety of technologies, such as the Health Level Seven International. This type of synthetic health data engine can support the greater Patient-Centered Outcomes Research (PCOR) data infrastructure by providing researchers and health IT developers with a low-risk, readily available synthetic health data source to provide access to data until real clinical data are available.

Clinical data are critical for the conduct of PCOR, which focuses on the effectiveness of prevention and treatment options. However, realistic patient data are often difficult to access because of cost, patient privacy concerns, or other legal restrictions. Synthetic health data help address these issues and speed the initiation, refinement, and testing of innovative health and research approaches.

The department said the data it had been using in its test and development environment did not meet security baselines for the protection of patient data. For those less secure applications, DBHDS needed synthetic, or properly de-identified and HIPAA compliant data. In addition to providing the synthetic data, the department said it wanted capabilities for machine learning prediction, data characterisation, decision reasoning, transparency and auditability.

As reported by OpenGov Asia, People with diseases or conditions that affect the base of the skull, such as otologic abnormalities, cancerous tumours and birth defects, might need to undergo skull base surgery at some point in their life. Surgeons must skillfully operate on and within a person’s skull, accessing specific regions using drills to successfully conduct these challenging procedures.

Researchers at Johns Hopkins University (JHU) have recently developed a new system that could be used to train surgeons to complete skull base surgeries, as well as potentially other complex surgical procedures. This system, presented in a paper published in Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualisation, is based on the use of a virtual reality (VR) simulator.

So far, the system provides an immersive simulation environment where the surgeons can interact with a virtual skull which is generated from a patient CT (Computer Tomography) scan. A virtual drill that is controlled via a haptic device (or a keyboard) is used to drill through the virtual skull. The interaction between the drill and the skull is used to generate force feedback which is provided via the haptic device for realistic tactility. Finally, for visual realism and depth perception, stereoscopic video is displayed on a VR (Virtual Reality) headset.

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