Advanced Health Data Analytics Integration

This paper presents an innovative method for embedding AI-derived measurements into radiology reports using common data elements (CDEs) to enhance clinical workflow, interoperability, and research capabilities. 1️⃣ AI was used to segment the liver and spleen in 3,920 non-contrast CT scans, measuring organ volume and attenuation, with 8.7% of cases showing hepatic steatosis. 2️⃣ CDEs were applied as standardized data units, ensuring AI-generated findings were precise, interoperable, and easily integrated into structured radiology reports. 3️⃣ AI results were incorporated into reports using the DICOM-SR framework, minimizing transcription errors and improving reporting efficiency. 4️⃣ The integration followed interoperability standards such as AI Workflow for Imaging (AIW-I) and AI Results (AIR) profiles, facilitating seamless data exchange across healthcare systems. 5️⃣ Automating CDE-based AI reporting streamlines clinical workflows, reduces radiologists' cognitive burden, and enhances decision support systems. 6️⃣ The study highlights the potential for expanding CDE-based AI reporting beyond radiology to oncology, radiation oncology, and other medical specialties. 7️⃣ Standardized AI-derived data using CDEs enables large-scale observational research through networks like the Observational Health Data Sciences and Informatics (OHDSI) initiative. ✍🏻 Garv Mehdiratta, Jeffrey Duda, Ameena Elahi - MPA, RT(R), CIIP, Arijitt Borthakur, Neal Chatterjee, James Gee, Hersh Sagreiya, Walter Witschey, Charles Kahn. Automated Integration of AI Results into Radiology Reports Using Common Data Elements. Journal of Imaging Informatics in Medicine. 2025. DOI: 10.1007/s10278-025-01414-9

Research from Harvard & MIT used AI to unlock molecular insights in cancer pathology. Foundation models are revolutionizing computational pathology. But, most struggle to analyze entire whole-slide images (WSIs) and incorporate molecular data. 𝗧𝗛𝗥𝗘𝗔𝗗𝗦 𝗶𝗻𝘁𝗿𝗼𝗱𝘂𝗰𝗲𝘀 𝗮 𝗺𝘂𝗹𝘁𝗶𝗺𝗼𝗱𝗮𝗹 𝗳𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻 𝗺𝗼𝗱𝗲𝗹 𝘁𝗵𝗮𝘁 𝗹𝗲𝗮𝗿𝗻𝘀 𝗳𝗿𝗼𝗺 𝗯𝗼𝘁𝗵 𝗵𝗶𝘀𝘁𝗼𝗽𝗮𝘁𝗵𝗼𝗹𝗼𝗴𝘆 𝘀𝗹𝗶𝗱𝗲𝘀 𝗮𝗻𝗱 𝗺𝗼𝗹𝗲𝗰𝘂𝗹𝗮𝗿 𝗽𝗿𝗼𝗳𝗶𝗹𝗲𝘀. • 𝗣𝗿𝗲𝘁𝗿𝗮𝗶𝗻𝗲𝗱 𝗼𝗻 𝟰𝟳,𝟭𝟳𝟭 𝗛&𝗘-𝘀𝘁𝗮𝗶𝗻𝗲𝗱 𝗪𝗦𝗜𝘀 𝘄𝗶𝘁𝗵 𝗴𝗲𝗻𝗼𝗺𝗶𝗰 𝗮𝗻𝗱 𝘁𝗿𝗮𝗻𝘀𝗰𝗿𝗶𝗽𝘁𝗼𝗺𝗶𝗰 𝗽𝗿𝗼𝗳𝗶𝗹𝗲𝘀, the largest dataset of its kind. • Enabled state-of-the-art survival prediction, identifying high-risk patients with up to 8.9% higher accuracy than previous models. • 𝗘𝘅𝗰𝗲𝗹𝗹𝗲𝗱 𝗶𝗻 𝗹𝗼𝘄-𝗱𝗮𝘁𝗮 𝘀𝗰𝗲𝗻𝗮𝗿𝗶𝗼𝘀, achieving near-clinical accuracy with just 4 training samples per class. • Introduced “molecular prompting”, allowing AI to classify cancer types and mutations without task-specific training. I like that the architecture of THREADS is notably modular. It begins with an ROI encoder based on CONCHV1.5 (a ViT-L model fine-tuned with vision–language data) to extract patch features. The patch features are then aggregated into a slide-level embedding via an attention-based multiple instance learning (ABMIL) slide encoder. In parallel, distinct encoders for transcriptomic data (a modified scGPT) and genomic data (a multi-layer perceptron) create molecular embeddings. This design not only enables integration of heterogeneous data types but also achieves remarkable parameter efficiency. For instance, THREADS is reported to be 4× smaller than PRISM and 7.5× smaller than GIGAPATH, yet outperforms them on 54 oncology tasks. Here's the awesome work: https://lnkd.in/g5y5HFuV Congrats to Faisal Mahmood, Anurag Vaidya, Andrew Zhang, Guillaume Jaume, and co! I post my takes on the latest developments in health AI – 𝗰𝗼𝗻𝗻𝗲𝗰𝘁 𝘄𝗶𝘁𝗵 𝗺𝗲 𝘁𝗼 𝘀𝘁𝗮𝘆 𝘂𝗽𝗱𝗮𝘁𝗲𝗱! Also, check out my health AI blog here: https://lnkd.in/g3nrQFxW

The healthcare industry stands at the cusp of a transformative opportunity driven by the integration of comprehensive healthcare data assets. This analysis explores the profound implications of a unified dataset that encompasses the entire United States healthcare system's transactional and operational data. At its core, this dataset represents the first-ever complete integration of medical and pharmacy claims, remittance data, eligibility verification transactions, and extensive provider information, all while maintaining patient privacy through sophisticated tokenization. The foundation of this dataset begins with complete coverage of medical and pharmacy remittance data across the United States, providing unprecedented visibility into payment flows throughout the healthcare system. This is complemented by comprehensive medical and pharmacy claims data, offering a complete picture of healthcare service delivery and medication utilization. The integration of all 270/271 eligibility verification transactions adds another crucial dimension, illuminating the pre-service verification processes that shape healthcare delivery. Privacy and continuity of patient care tracking are maintained through sophisticated deidentification and tokenization systems that allow longitudinal patient tracking while protecting sensitive information. This technical framework is enhanced by integrated connections to social determinants of health data, consumer credit information, and other consumer datasets, providing crucial context for healthcare utilization patterns and outcomes. The dataset's provider information represents the industry's most accurate and comprehensive master national provider file. This includes detailed credentialing information, current and historical plan network participation data, and complex mappings between TINs & NPIs. The provider profiles contain rich detail about credentials, specialties, and practice locations, while also mapping complex referral patterns and organizational affiliations. The provider data extends beyond individual practitioners to encompass detailed information about healthcare facilities. Hospitals, clinics, and group practices are profiled with facility type classifications, comprehensive service offerings, actual patient volumes, detailed payer mix information, and various performance metrics. This facility-level data creates a complete picture of healthcare delivery infrastructure and capabilities. A unique aspect of this dataset is its complete indexing of payer machine readable files, including historical versions that track changes over time. These files are matched and integrated with the master claims and remittance database, creating a comprehensive view of pricing and payment policies. This integration is supported by standardized reference tables that include detailed code mappings for diagnoses, procedures, and medications, ensuring consistency in analysis and interpretation. Continued..

Wearable tech and patient-generated health data (PGHD) are poised to revolutionize electronic health records (#EHRs), offering clinicians unprecedented real-time insights into patient health. By integrating data from wearables, such as heart rate variability and oxygen levels, EHRs can help identify early warning signs, track trends, and personalize care in ways traditional visits cannot. However, to maximize impact, we must address challenges like data overload, privacy concerns, and the need for smarter, action-oriented EHR dashboards. As healthcare executives and physician leaders, 𝘯𝘰𝘸 𝘪𝘴 𝘵𝘩𝘦 𝘵𝘪𝘮𝘦 𝘵𝘰 𝘱𝘳𝘦𝘱𝘢𝘳𝘦 𝘧𝘰𝘳 𝘵𝘩𝘪𝘴 𝘸𝘢𝘷𝘦 𝘰𝘧 𝘪𝘯𝘯𝘰𝘷𝘢𝘵𝘪𝘰𝘯. Proactive planning will ensure your organization stays ahead of the curve. 𝗔𝗰𝘁𝗶𝗼𝗻 𝗜𝘁𝗲𝗺𝘀 𝗳𝗼𝗿 𝗛𝗲𝗮𝗹𝘁𝗵𝗰𝗮𝗿𝗲 𝗘𝘅𝗲𝗰𝘂𝘁𝗶𝘃𝗲𝘀: 🩺 𝗖𝗵𝗮𝗺𝗽𝗶𝗼𝗻 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻: Prioritize EHR upgrades that can accommodate wearable data and provide clinicians with user-friendly dashboards. 🛡️ 𝗦𝘁𝗿𝗲𝗻𝗴𝘁𝗵𝗲𝗻 𝗦𝗲𝗰𝘂𝗿𝗶𝘁𝘆: Invest in robust data privacy measures to build and maintain patient trust. 🤖 𝗘𝗺𝗯𝗿𝗮𝗰𝗲 𝗔𝗜: Leverage advanced algorithms to filter and highlight actionable insights, minimizing clinician burnout. 🤝 𝗖𝗼𝗹𝗹𝗮𝗯𝗼𝗿𝗮𝘁𝗲 𝘄𝗶𝘁𝗵 𝗣𝗮𝘁𝗶𝗲𝗻𝘁𝘀: Develop policies that empower patients to control what data is shared, fostering trust and engagement.

While we now have powerful tools to study the #gutmicrobiome through multiple lenses (#genomics #metabolomics #proteomics ), the real challenge lies in integrating this complex data effectively. A comprehensive review paper "Advances in multi-omics integrated analysis methods based on the gut microbiome and their applications" https://lnkd.in/eYtvzQtR summarized the multi-omics research analysis methods currently used to study the interaction between the microbiome and the host. The field is evolving rapidly, and here's why it matters: Key insights 1. Technical Integration Challenges: -Current microbiome analysis faces accuracy limitations in species classification -Data is compositional, requiring specialized statistical approaches -High dimensionality and sparsity of data demand novel computational methods -Integration of multiple data types requires sophisticated statistical frameworks 2. Emerging Analysis Methods: -Moving beyond simple correlation to advanced network-based approaches -New tools like MOFA (Multi-omics Factor Analysis) for dimensionality reduction -Advanced clustering algorithms for complex multi-omics datasets -Integration of knowledge-driven and data-driven approaches 3. Practical Applications: -Microbiome-wide association studies (mGWAS) revealing host-microbe interactions -Metabolomic integration uncovering functional pathways -Proteomic analysis showing real-time functional activities -Clinical applications in diseases like IBD, colorectal cancer, and obesity Future Directions: -Need for standardization in multi-omics analysis -Development of more robust statistical methods -Integration with precision medicine approaches -Focus on mechanistic understanding rather than just correlations The most fascinating aspect? The shift from studying individual omics layers to understanding the complex interplay between host genetics, microbial communities, and metabolic functions. 🧬🔍 Most significant challenge ahead: Developing computational methods that can handle the immense complexity while producing biologically meaningful insights. #Microbiome #Bioinformatics #PrecisionMedicine #Research #Genomics #DataScience What computational challenges have you encountered in #multiomics integration? How are you addressing them?

The Right Use of LLMs in Healthcare: Technical Insights Integrating large language models (LLMs) like MedPalm, Clmbr, Gemini, etc., marks a pivotal advancement. LLMs can process vast amounts of unstructured clinical data, enabling applications such as intelligent diagnostic support, automated medical coding, and advanced patient data analytics. LLMs and integrating technical frameworks -> Data Integration & Interoperability: Seamless integration with EHRs, HIE, LIS, RIS, and RPM devices using HL7 FHIR standards ensures real-time data availability. -> Compliance and Security: Utilizing encryption protocols and access controls to maintain HIPAA & HITECH compliance and safeguard PII, and PHI. -> Scalable Infrastructure: Deploying on scalable cloud platforms like GCP, AWS, or Azure to handle large datasets and computational needs. To make sure your organization is ready start from a small and valuable use case. By leveraging the right technologies, we can enhance diagnostic accuracy, reduce clinician workload, and improve patient outcomes. The key is a strategic approach that aligns advanced AI capabilities with stringent healthcare regulations. #ArihantHealthTech #healthcareit #llms #aiinhealthcare #hipaacompliance #datasecurity #healthtech #patientcare #ehrintegration #healthcareinnovation #artificialintelligence #digitalhealth #healthit #interoperability #healthcaretechnology #datascience

Implementing Salesforce AI in healthcare is a complex task. But it's worth it. Here's how to do it right. ✅ Start with data quality and integration Use Salesforce's Data Cloud for Health and MuleSoft Direct for Health Cloud. Integrate data from EHRs, claims systems, and health apps. Implement robust data governance policies to maintain data integrity and security. ✅ Develop and validate AI models Collaborate with healthcare professionals to define use cases and outcomes. Use diverse datasets to reduce bias. ✅ Leverage Salesforce's Einstein Copilot Health Actions include automating clinical summaries, enhancing workflows for appointment scheduling, and generating interaction summaries for care coordinators. ✅ Compliance and ethics are crucial Ensure HIPAA compliance for all AI implementations. Implement strong data privacy and security measures. ✅ Design with the user in mind Create intuitive interfaces that integrate AI insights seamlessly into clinical workflows. Provide clear explanations of AI-generated recommendations. Establish feedback loops with clinicians. ✅ Build transparency and patient trust Be transparent with patients about the use of AI in their care. Provide options for patients to opt-out of AI-assisted processes. By following these best practices, healthcare organizations can leverage Einstein AI within Salesforce Health Cloud to enhance clinical decision-making while maintaining high standards of patient care, data security, and regulatory compliance. #Salesforce #Healthcare #AI #Data #DataCloud #EinsteinAI #HealthCloud #MuleSoft #AIInHealthcare

🚨 New Publication Alert! 🚨 Excited to share our latest work published in Nature Portfolio journal npj Precision Oncology: "Computationally integrating radiology and pathology image features for predicting treatment benefit and outcome in lung cancer" In this study, we tackled one of the biggest challenges in lung cancer care—the lack of robust biomarkers for guiding treatment decisions. By combining radiomics from CT scans and pathomics from H&E slides, we developed integrated AI models that significantly improved risk stratification and prediction of treatment benefit in: 🔹 Early-stage NSCLC – where our integrated model predicted disease recurrence with a hazard ratio of 8.35 (C-index: 0.71) 🔹 Advanced-stage NSCLC – achieving improved immunotherapy response prediction (AUC: 0.75) 🔹 Small Cell Lung Cancer (SCLC) – outperforming individual models in predicting chemotherapy response (AUC: 0.78) This work underscores the power of computationally fusing imaging data across scales—from radiology to pathology—to advance precision oncology. Grateful to have collaborated with a fantastic multidisciplinary team: Pranjal Vaidya, Mohammadhadi Khorrami, Kaustav Bera, Pingfu Fu, Lukas Delasos, Amit Gupta, Cristian Barrera, Nathan Pennell MD, PhD, FASCO, Vamsi Velcheti MD MBA FASCO Read the full paper here: https://rdcu.be/eppuU Our commercial partner Picture Health is working to translate these multimodal #AI tools to the clinic. #AIinHealthcare #LungCancer #PrecisionOncology #Radiomics #Pathomics #AnantMadabhushi #EmoryAI4Health #npjPrecisionOncology #ComputationalPathology #MultiscaleImaging #CancerResearch