Feasibility of Privacy-Preserving Entity Resolution on Confidential Healthcare Datasets Using Homomorphic Encryption

Patient datasets contain confidential information which is protected by laws and regulations such as HIPAA and GDPR. Ensuring comprehensive patient information necessitates privacy-preserving entity resolution (PPER), which identifies identical patient entities across multiple databases from different healthcare organizations while maintaining data privacy. Existing methods often lack cryptographic security or are computationally impractical for real-world datasets. We introduce a PPER pipeline based on AMPPERE, a secure abstract computation model utilizing cryptographic tools like homomorphic encryption. Our tailored approach incorporates extensive parallelization techniques and optimal parameters specifically for patient datasets. Experimental results demonstrate the proposed method's effectiveness in terms of accuracy and efficiency compared to various baselines

Development of a social and environmental determinants of health informatics maturity model

INTRODUCTION: Integrating social and environmental determinants of health (SEDoH) into enterprise-wide clinical workflows and decision-making is one of the most important and challenging aspects of improving health equity. We engaged domain experts to develop a SEDoH informatics maturity model (SIMM) to help guide organizations to address technical, operational, and policy gaps. METHODS: We established a core expert group consisting of developers, informaticists, and subject matter experts to identify different SIMM domains and define maturity levels. The candidate model (v0.9) was evaluated by 15 informaticists at a Center for Data to Health community meeting. After incorporating feedback, a second evaluation round for v1.0 collected feedback and self-assessments from 35 respondents from the National COVID Cohort Collaborative, the Center for Leading Innovation and Collaboration\u27s Informatics Enterprise Committee, and a publicly available online self-assessment tool. RESULTS: We developed a SIMM comprising seven maturity levels across five domains: data collection policies, data collection methods and technologies, technology platforms for analysis and visualization, analytics capacity, and operational and strategic impact. The evaluation demonstrated relatively high maturity in analytics and technological capacity, but more moderate maturity in operational and strategic impact among academic medical centers. Changes made to the tool in between rounds improved its ability to discriminate between intermediate maturity levels. CONCLUSION: The SIMM can help organizations identify current gaps and next steps in improving SEDoH informatics. Improving the collection and use of SEDoH data is one important component of addressing health inequities

Implementing a Common Data Model in Ophthalmology: Mapping Structured Electronic Health Record Ophthalmic Examination Data to Standard Vocabularies

ObjectiveTo identify and characterize concept coverage gaps of ophthalmology examination data elements within the Cerner Millennium electronic health record (EHR) implementations by the Observational Health Data Sciences and Informatics Observational Medical Outcomes Partnership (OMOP) common data model (CDM).DesignAnalysis of data elements in EHRs.SubjectsNot applicable.MethodsSource eye examination data elements from the default Cerner Model Experience EHR and a local implementation of the Cerner Millennium EHR were extracted, classified into one of 8 subject categories, and mapped to the semantically closest standard concept in the OMOP CDM. Mappings were categorized as exact, if the data element and OMOP concept represented equivalent information, wider, if the OMOP concept was missing conceptual granularity, narrower, if the OMOP concept introduced excess information, and unmatched, if no standard concept adequately represented the data element. Descriptive statistics and qualitative analysis were used to describe the concept coverage for each subject category.Main outcome measuresConcept coverage gaps in 8 ophthalmology subject categories of data elements by the OMOP CDM.ResultsThere were 409 and 947 ophthalmology data elements in the default and local Cerner modules, respectively. Of the 409 mappings in the default Cerner module, 25% (n = 102) were exact, 53% (n = 217) were wider, 3% (n = 11) were narrower, and 19% (n = 79) were unmatched. In the local Cerner module, 18% (n = 173) of mappings were exact, 54% (n = 514) were wider, 1% (n = 10) were narrower, and 26% (n = 250) were unmatched. The largest coverage gaps were seen in the local Cerner module under the visual acuity, sensorimotor testing, and refraction categories, with 95%, 95%, and 81% of data elements in each respective category having mappings that were not exact. Concept coverage gaps spanned all 8 categories in both EHR implementations.ConclusionsConsiderable coverage gaps by the OMOP CDM exist in all areas of the ophthalmology examination, which should be addressed to improve the OMOP CDM's effectiveness in ophthalmic research. We identify specific subject categories that may benefit from increased granularity in the OMOP CDM and provide suggestions for facilitating consistency of standard concepts, with the goal of improving data standards in ophthalmology.Financial disclosuresProprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article

Environment Scan of Generative AI Infrastructure for Clinical and Translational Science

This study reports a comprehensive environmental scan of the generative AI (GenAI) infrastructure in the national network for clinical and translational science across 36 institutions supported by the CTSA Program led by the National Center for Advancing Translational Sciences (NCATS) of the National Institutes of Health (NIH) at the United States. Key findings indicate a diverse range of institutional strategies, with most organizations in the experimental phase of GenAI deployment. The results underscore the need for a more coordinated approach to GenAI governance, emphasizing collaboration among senior leaders, clinicians, information technology staff, and researchers. Our analysis reveals that 53% of institutions identified data security as a primary concern, followed by lack of clinician trust (50%) and AI bias (44%), which must be addressed to ensure the ethical and effective implementation of GenAI technologies

324 An umbrella protocol that establishes an enterprise-wide framework for the operation of a Clinical Data Warehouse

OBJECTIVES/GOALS: To streamline the standards and procedures for operating a research-specific, clinical data warehouse, acheived by defining roles, introducing a common language, and categorizing dataset types to provide transparency regarding data security risks inherent in the use of patient data. METHODS/STUDY POPULATION: We established a Bioethics committee responsible for ensuring clinical data is securely procured, maintained, and extracted in a manner that adheres to all federal, state, and local laws. We created an operational framework in the form of an umbrella IRB protocol and shared it with the bioethics committee for feedback and approval. The protocol was approved first by the bioethics committee and subsequently by the IRB. It was then disseminated across the institution and published online for continuous reference and use by committee members, researchers, and the data warehouse service team. RESULTS/ANTICIPATED RESULTS: The resulting framework defined the roles of researchers, data warehouse service team members, and honest brokers; explains the procedures for accessing and securely delivering data; and lists six categories of datasets according to type and implicit risks: datasets that are preparatory for research/aggregate counts, anonymized datasets, coded datasets, limited datasets, identified datasets for recruitment purposes, and defined identified cohort datasets. The protocol is approved and in use enterprise-wide, has reduced the number of questions from stakeholders, and has given researchers, IRB members, and informatics staff confidence in the use of the clinical research data warehouse. DISCUSSION/SIGNIFICANCE: We offer our framework to CTSAs interested in streamlining their data warehouse operations. We believe the adoption of this framework will establish strong procedures for ensuring compliance with IRB requirements, data privacy, and data security while reducing barriers to clinical research

The general mode of translation inhibition by macrolide antibiotics.

Macrolides are clinically important antibiotics thought to inhibit bacterial growth by impeding the passage of newly synthesized polypeptides through the nascent peptide exit tunnel of the bacterial ribosome. Recent data challenged this view by showing that macrolide antibiotics can differentially affect synthesis of individual proteins. To understand the general mechanism of macrolide action, we used genome-wide ribosome profiling and analyzed the redistribution of ribosomes translating highly expressed genes in bacterial cells treated with high concentrations of macrolide antibiotics. The metagene analysis indicated that inhibition of early rounds of translation, which would be characteristic of the conventional view of macrolide action, occurs only at a limited number of genes. Translation of most genes proceeds past the 5'-proximal codons and can be arrested at more distal codons when the ribosome encounters specific short sequence motifs. The problematic sequence motifs are confined to the nascent peptide residues in the peptidyl transferase center but not to the peptide segment that contacts the antibiotic molecule in the exit tunnel. Therefore, it appears that the general mode of macrolide action involves selective inhibition of peptide bond formation between specific combinations of donor and acceptor substrates. Additional factors operating in the living cell but not functioning during in vitro protein synthesis may modulate site-specific action of macrolide antibiotics

(A-E) Selected signaling pathway schematics demonstrating increase (red bar) or decrease (blue) bar and relative amplitude (height of bar) of differentially expression in each gene and their roles in the signaling pathway depicted.

Green arrows indicate positive interactions or activation, red arrows indicate negative interactions or inhibition, while grey arrows indicate an unknown interaction. More details of symbol meanings can be found in the supplemental tables in the Metacore ® quick reference guide.</p

The IRF4 gene regulatory module functions as a read-write integrator to dynamically control T helper cell fate

Abstract Transcriptional regulation during CD4+ T cell fate decisions enables their differentiation into distinct states, guiding immune responses towards antibody production via Tfh cells or inflammation by Teff cells. Tfh–Teff fate commitment is regulated by mutual antagonism between the transcription factors Bcl6 and Blimp-1. Here we examined how T cell receptor (TCR) signals establish and arbitrate Bcl6–Blimp-1 counter-antagonism. We found that the TCR-signal induced transcription factor IRF4 is essential for the differentiation of Bcl6-expressing Tfh and Blimp-1-expressing Teff cells. Increased TCR signaling raised IRF4 amounts and promoted Teff fates at the expense of Tfh ones. Importantly, orthogonal induction of IRF4 expression redirected Tfh fate trajectories towards those of Teff and this occurred independently of IL-2 signals. Mechanistically, we linked greater IRF4 abundance with its recruitment towards low affinity binding sites within Teff cis-regulatory elements, including those of Prdm1. We propose that the Irf4 locus functions as the “reader” of TCR signal strength, in turn, concentration-dependent activity of IRF4 “writes” T helper fate choice.</jats:p

The IRF4 Gene Regulatory Module Functions as a Read-Write Integrator to Dynamically Coordinate T&nbsp;Helper Cell Fate

Transcriptional regulation during CD4+ T&nbsp;cell fate decisions enables their differentiation into distinct states, guiding immune responses toward antibody production via Tfh cells or inflammation by Teff cells. Tfh-Teff cell fate commitment is regulated by mutual antagonism between the transcription factors Bcl6 and Blimp-1. Here we examined how T&nbsp;cell receptor (TCR) signals establish and arbitrate Bcl6-Blimp-1 counter-antagonism. We found that the TCR-signal-induced transcription factor Irf4 is essential for the differentiation of Bcl6-expressing Tfh and Blimp-1-expressing Teff cells. Increased TCR signaling raised Irf4 amounts and promoted Teff cell fates at the expense of Tfh ones. Importantly, orthogonal induction of Irf4 expression redirected Tfh cell fate trajectories toward those of Teff. Mechanistically, we linked greater Irf4 abundance with its recruitment toward low-affinity binding sites within Teff cell cis-regulatory elements, including those of Prdm1. We propose that the Irf4 locus functions as the "reader" of TCR signal strength, and in turn, concentration-dependent activity of Irf4 "writes" T helper fate choice