Stay informed about AI innovation, quality measurement breakthroughs, data architecture best practices, healthcare interoperability developments, and Canonical Health's accelerator-based solutions
Microsoft's AI Diagnostic Orchestrator, paired with OpenAI's o3 model, correctly solved 85.5% of complex medical cases from the New England Journal of Medicine, dramatically outperforming 21 experienced physicians who averaged only 20% accuracy. The system demonstrates potential for reducing healthcare costs while improving diagnostic precision.
Read morePwC Strategy& projects AI's healthcare market potential will grow from 15% to over 30% of the global healthcare market by 2030, yielding $646 billion in cost savings and $222 billion in revenue gains. North America leads with $479 billion potential, followed by Europe at $203 billion and Asia Pacific at $186 billion.
Read moreNew SSRN research investigates generative AI applications using Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs) for healthcare analytics. The study demonstrates synthetic data generation capabilities for training ML models while addressing privacy concerns and data scarcity in clinical settings.
Read moreDeloitte's survey of 121 C-suite healthcare executives across six countries reveals that 90% expect accelerated digital technology adoption in 2025, with over 40% already seeing significant ROI from generative AI investments. Key priorities include operational efficiency, productivity gains, and enhanced patient engagement.
Read moreHealthTech Magazine identifies key AI adoption trends for 2025, highlighting ambient listening solutions that reduce clinical documentation burden, machine vision for patient monitoring, and retrieval-augmented generation (RAG) for improved accuracy. Healthcare organizations seek clear ROI through better clinician experience and reduced costs.
Read moreCanada's Drug Agency releases comprehensive horizon scan identifying top 5 AI healthcare technologies (notetaking, clinical training, disease detection, treatment optimization, remote monitoring) and critical implementation issues including privacy, liability, data quality, sovereignty, and environmental costs.
Read moreResearchers developed an innovative data quality evaluation tool using HL7 FHIR standards, Bayesian networks, and expert rules for obstetrics real-world data. The tool demonstrated strong performance with AUROC between 75-97% for identifying data quality issues in electronic health records across 9 Portuguese hospitals.
Read moreNCQA releases comprehensive technical guidance for implementing Clinical Quality Language (CQL) engines. The guide covers CQL engine architecture, FHIR integration, data flows, and strategic considerations for digital quality measurement implementation across healthcare organizations.
Read moreStudy identifies four critical levels for electronic clinical quality measure harmonization: measure concepts, logical clauses, data elements, and terminology. Researchers propose using Clinical Quality Language and FHIR standards to improve consistency and reduce implementation burden across healthcare systems.
Read moreNCQA provides comprehensive guidance on CQL-based digital quality measure execution. The guide details HEDIS digital measures implementation, CQL engine operations, data flows, and best practices for creating effective CQL statements and ensuring FHIR compatibility.
Read moreCMS reports on comprehensive stakeholder engagement project addressing eCQM implementation burdens. The initiative resulted in 117 recommendations across six themes: alignment, value, development processes, implementation, EHR certification, and communication to reduce reporting burden while maintaining quality measurement value.
Read moreThe Trusted Exchange Framework and Common Agreement continues making progress since going live in 2023, with Qualified Health Information Networks including Epic, CommonWell Health Alliance, and Health Gorilla now operational. While full interoperability remains a goal, TEFCA has successfully reduced friction between fragmented state exchanges and is moving toward FHIR standards adoption to enable broader technical consolidation.
Read MoreHL7 announces a global innovation competition inviting technologists, researchers, and clinicians worldwide to demonstrate how HL7 standards and AI can revolutionize healthcare. Winners will be recognized at HL7's 39th Annual Plenary in Pittsburgh, September 2025. Submissions due July 30, 2025.
Read MoreCMS releases updated electronic clinical quality measure (eCQM) specifications for 2025 performance reporting across Eligible Hospitals (EH), Critical Access Hospitals (CAH), Outpatient Quality Reporting (OQR), and Eligible Clinician (EC) programs. Updates align with current clinical guidelines and code systems, including telehealth guidance, value sets through VSAC, and program candidate measures for emerging quality reporting requirements.
Read UpdateLandmark CMS regulation putting patients first by requiring payers to implement FHIR-based APIs for patient data access and healthcare information exchange. Applies to Medicare Advantage, Medicaid, CHIP, and QHP issuers, driving interoperability through standardized Application Programming Interfaces that enable seamless data sharing between patients, providers, and payers while adopting HHS content and vocabulary standards.
Read RuleONC's comprehensive certification companion guide for standardized APIs supporting patient and population services. Covers FHIR R4, US Core implementation guides, SMART on FHIR authentication, bulk data access, and USCDI requirements. Essential reference for health IT developers implementing certified interoperability solutions with detailed technical requirements, authentication protocols, and compliance clarifications.
Read GuideComprehensive review of AI's transformative potential in healthcare, outlining a roadmap for building effective AI systems through human-centered design, stakeholder engagement, and rigorous evaluation. Covers current applications in precision diagnostics, future therapeutic possibilities, and implementation challenges for AI-augmented healthcare systems.
Read ReviewComprehensive guide for expressing clinical quality logic using CQL, a human-readable query language for healthcare. Covers library declarations, data retrieval, queries, clinical values, and operations for building shareable quality measures and clinical decision support artifacts. Essential reference for healthcare informatics professionals developing standards-based quality measurement and improvement solutions.
Read MoreHL7 FHIR (Fast Healthcare Interoperability Resources) R5 represents the latest evolution in healthcare data exchange standards. This comprehensive specification provides a modern framework for REST APIs, data types, resource definitions, and interoperability protocols, enabling seamless health information exchange across clinical, administrative, financial, and workflow domains while supporting advanced features like genomics, clinical reasoning, and medication definitions.
Read MoreHL7's Patient Information Quality Improvement (PIQI) Framework provides a comprehensive approach to enhancing patient-centered data quality and reducing clinician burden. Presented by Clinical Architecture's Charlie Harp, this framework addresses the critical need for patient-centric information quality standards that support both clinical decision-making and operational efficiency in healthcare systems.
Read MoreInternational standard defining a comprehensive data quality model for structured data within computer systems. Part of the SQuaRE series, it categorizes quality attributes into fifteen characteristics from inherent and system-dependent viewpoints, enabling standardized data quality requirements definition, measurement planning, and compliance evaluation across data production, acquisition, and integration processes.
Read MorePCORI-funded research establishes a comprehensive data quality terminology and framework for electronic health record data reuse. The study harmonizes existing DQ concepts into three core categories—Conformance, Completeness, and Plausibility—within Verification and Validation contexts, providing standardized methods for assessing data quality in clinical research networks and operational analytics.
Read MoreNCQA introduces the Bulk FHIR Quality Coalition to perform real-world testing of FHIR data exchange using digital HEDIS measurement. The coalition brings together health care payers, delivery organizations, and regulators to evaluate FHIR data quality across conformance, completeness, and plausibility, focusing on three key HEDIS measures to advance digital quality measurement strategies.
Read MoreLeavitt Partners introduces a comprehensive framework addressing systemic healthcare data quality issues across atomic data, data models/schemas, and analysis algorithms. This initiative, developed with public and private sector leaders, emphasizes practical implementation using existing EHR technology and standardized API pipelines to improve patient care, safety, and enable AI advancement in healthcare.
Read MoreThe eCQI Resource Center releases a comprehensive strategic roadmap for digital quality measures (dQMs), outlining the future of electronic clinical quality improvement. This roadmap addresses modernizing quality measurement infrastructure, enhancing interoperability, and leveraging FHIR standards to create more efficient and accurate quality reporting processes.
Read MoreNew legislation aims to advance Health IT standards and reduce patient misidentification errors. This type of standards-driven quality improvement demonstrates the need for AI-powered data quality solutions that can automatically detect and prevent identification errors at scale.
Read MoreStanford deploys conversational AI that allows clinicians to interact with EHR data using natural language. This innovation showcases how AI can bridge the gap between complex healthcare data and intuitive clinical workflows—a core principle of our accelerator-based approach.
Read MoreTry adjusting your filters or search terms to find relevant content.