Objective: Timely access to data is needed to improve care for substance-exposed birthing persons and their infants, a significant public health problem in the United States. We examined the current state of birthing person and infant/child (dyad) data-sharing capabilities supported by health information exchange (HIE) standards and HIE network capabilities for data exchange to inform point-of-care needs assessment for the substance-exposed dyad.
Material And Methods: A cross-map analysis was performed using a set of dyadic data elements focused on pediatric development and longitudinal supportive care for substance-exposed dyads (70 birthing person and 110 infant/child elements). Cross-mapping was conducted to identify definitional alignment to standardized data fields within national healthcare data exchange standards, the United States Core Data for Interoperability (USCDI) version 4 (v4) and Fast Healthcare Interoperability Resources (FHIR) release 4 (R4), and applicable structured vocabulary standards or terminology associated with USCDI. Subsequent survey analysis examined representative HIE network sharing capabilities, focusing on USCDI and FHIR usage.
Results: 91.11% of dyadic data elements cross-mapped to at least 1 USCDI v4 standardized data field (87.80% of those structured) and 88.89% to FHIR R4. 75% of the surveyed HIE networks reported supporting USCDI versions 1 or 2 and the capability to use FHIR, though demand is limited.
Discussion: HIE of clinical and supportive care data for substance-exposed dyads is supported by current national standards, though limitations exist.
Conclusion: These findings offer a dyadic-focused framework for electronic health record-centered data exchange to inform bedside care longitudinally across clinical touchpoints and population-level health.
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http://dx.doi.org/10.1093/jamia/ocae315 | DOI Listing |
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Department of Biomedical Engineering, Duke University, Durham, NC, USA.
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Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, USA.
Adaptive deep brain stimulation (DBS) provides individualized therapy for people with Parkinson's disease (PWP) by adjusting the stimulation in real-time using neural signals that reflect their motor state. Current algorithms, however, utilize condensed and manually selected neural features which may result in a less robust and biased therapy. In this study, we propose Neural-to-Gait Neural network (N2GNet), a novel deep learning-based regression model capable of tracking real-time gait performance from subthalamic nucleus local field potentials (STN LFPs).
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