In recent years, the field of medical informatics has seen significant advancements with the introduction of medical large language models (LLMs). These models, powered by artificial intelligence, have demonstrated remarkable capabilities in understanding and generating medical text, providing valuable assistance in clinical decision - making, diagnostics, and patient care. Prom inent examples include models such as Meditron [1], BioMistral [2] and OpenBioLLM [3], which have shown considerable promise in various medical applications. However, despite these advancements, the inherent limitations of medical LLMs highlight the need for more robust solutions.

The rapid advancement of digital technologies and recent global pandemic scenarios have led to a growing focus on how these technologies can enhance healthcare service delivery and workflow to address crises. Action plans that consolidate existing digital transformation programs are being reviewed to establish core infrastructure and foundations for sustainable healthcare solutions. Reforming health and social care to personalize home care, for example, can help avoid treatment in overcrowded acute hospital settings and improve the experiences and outcomes for both healthcare professionals and service users. In this information-intensive domain, addressing the interoperability challenge through standards-based roadmaps is crucial for enabling effective connections between health and social care services. This approach facilitates safe and trustworthy data workflows between different healthcare system providers. In this paper, we present a methodology for extracting, transforming, and loading data through a semi-automated process using a Common Semantic Standardized Data Model (CSSDM) to create personalized healthcare knowledge graph (KG). The CSSDM is grounded in the formal ontology of ISO 13940 ContSys and incorporates FHIR-based specifications to support structural attributes for generating KGs. We propose that the CSSDM facilitates data harmonization and linking, offering an alternative approach to interoperability. This approach promotes a novel form of collaboration between companies developing health information systems and cloud-enabled health services. Consequently, it provides multiple stakeholders with access to high-quality data and information sharing.

Each year, the lack of efficient data standardization and interoperability in cancer care contributes to the severe lack of timely and effective diagnosis, while constantly adding to the burden of cost, with cancer costs nationally reaching over $208 billion in 2023 alone. Traditional methods regarding clinical trial enrollment and clinical care in oncology are often manual, time-consuming, and lack a data-driven approach. This paper presents a novel framework to streamline standardization, interoperability, and exchange of cancer domains and enhance the integration of oncology-based EHRs across disparate healthcare systems. This paper utilizes advanced LLMs and Computer Engineering to streamline cancer clinical trials and discovery. By utilizing FHIR's resource-based approach and LLM-generated mCODE profiles, we ensure timely, accurate, and efficient sharing of patient information across disparate healthcare systems. Our methodology involves transforming unstructured patient treatment data, PDFs, free-text information, and progress notes into enriched mCODE profiles, facilitating seamless integration with our novel AI and ML-based clinical trial matching engine. The results of this study show a significant improvement in data standardization, with accuracy rates of our trained LLM peaking at over 92% with datasets consisting of thousands of patient data. Additionally, our LLM demonstrated an accuracy rate of 87% for SNOMED-CT, 90% for LOINC, and 84% for RxNorm codes. This trumps the current status quo, with LLMs such as GPT-4 and Claude's 3.5 peaking at an average of 77%. This paper successfully underscores the potential of our standardization and interoperability framework, paving the way for more efficient and personalized cancer treatment.

Background: Health 3.0 allows decision making to be based on longitudinal data from multiple institutions, from across the patient's healthcare journey. In such a distributed setting, blockchain smart contracts can act as neutral intermediaries to implement trustworthy decision making. Objective: In a distributed setting, transmitted data will be structured using standards (such as HL7 FHIR) for semantic interoperability. In turn, the smart contract will require interoperability with this standard, implement a complex communication setup (e.g., using oracles), and be developed using blockchain languages (e.g., Solidity). We propose the encoding of smart contract logic using a high-level semantic Knowledge Graph, using concepts from the domain standard. We then deploy this semantic KG on blockchain. Methods: Off-chain, a code generation pipeline compiles the KG into a concrete smart contract, which is then deployed on-chain. Our pipeline targets an intermediary bridge representation, which can be transpiled into a specific blockchain language. Our choice avoids on-chain rule engines, with unpredictable and likely higher computational cost; it is thus in line with the economic rules of blockchain. Results: We applied our code generation approach to generate smart contracts for 3 health insurance cases from Medicare. We discuss the suitability of our approach - the need for a neutral intermediary - for a number of healthcare use cases. Our evaluation finds that the generated contracts perform well in terms of correctness and execution cost ("gas") on blockchain. Conclusions: We showed that it is feasible to automatically generate smart contract code based on a semantic KG, in a way that respects the economic rules of blockchain. Future work includes studying the use of Large Language Models (LLM) in our approach, and evaluations on other blockchains.

Objective: To enhance health literacy and accessibility of health information for a diverse patient population by developing a patient-centered artificial intelligence (AI) solution using large language models (LLMs) and Fast Healthcare Interoperability Resources (FHIR) application programming interfaces (APIs). Materials and Methods: The research involved developing LLM on FHIR, an open-source mobile application allowing users to interact with their health records using LLMs. The app is built on Stanford's Spezi ecosystem and uses OpenAI's GPT-4. A pilot study was conducted with the SyntheticMass patient dataset and evaluated by medical experts to assess the app's effectiveness in increasing health literacy. The evaluation focused on the accuracy, relevance, and understandability of the LLM's responses to common patient questions. Results: LLM on FHIR demonstrated varying but generally high degrees of accuracy and relevance in providing understandable health information to patients. The app effectively translated medical data into patient-friendly language and was able to adapt its responses to different patient profiles. However, challenges included variability in LLM responses and the need for precise filtering of health data. Discussion and Conclusion: LLMs offer significant potential in improving health literacy and making health records more accessible. LLM on FHIR, as a pioneering application in this field, demonstrates the feasibility and challenges of integrating LLMs into patient care. While promising, the implementation and pilot also highlight risks such as inconsistent responses and the importance of replicable output. Future directions include better resource identification mechanisms and executing LLMs on-device to enhance privacy and reduce costs.

Artificial intelligence (AI) has been the promise of healthcare for nearly a decade, but the industry has yet to adopt it widely. Applications of AI in arguably more difficult domains, such as search, language and image recognition, have seen massive success over the past decade. While neural net algorithms and compute power have improved dramatically, AI in healthcare is still lagging behind. The big reason these domains, and not healthcare, have been able to utilize AI tech is due to the internet's ability to make massive amounts of data available. Now data access via internet technologies is finally happening in healthcare through secure channels.

Hospitals in Victoria's South West, including public health agencies under the South West Alliance of Rural Health and Barwon Health in Geelong, are set to roll out a data platform capable of real-time analysis using AI, machine learning, as well as business and clinical intelligence. The health organisations will be deploying the IRIS for Health platform by global tech provider InterSystems. The data platform, according to InterSystems's website, is specifically engineered to extract value from healthcare data. It is a standards-based platform that is able to read and write Health Level 7's Fast Healthcare Interoperability Resources (HL7 FHIR) for developing healthcare applications. It is also capable of ingesting, processing and storing transaction data "at high rates" while simultaneously processing high volume analytic workloads involving historical and real-time data. While the health providers have interconnected systems, including clinical and patient administration systems, specialist healthcare applications and data analytics solutions, they don't have a single data repository supporting real-time data analysis.

Amazon HealthLake is a new, HIPAA-eligible service designed to store, transform, query, and analyze health data at scale. You can bring your healthcare data into Amazon HealthLake using Fast Healthcare Interoperability Resources (FHIR) R4 APIs. If you don't have your data in FHIR R4, Amazon has collaborated with industry experts to build Amazon HealthLake connectors to help you with custom file and HL7 to FHIR R4 mappings. This post highlights one of those partners, Redox, and their Amazon HealthLake Connector. Developers at over 300 companies use the Redox platform to exchange data with more than 1,700 healthcare provider organizations.

As healthcare providers have faced unprecedented workloads (individually and institutionally) around the world, the pandemic response continues to cause seismic shifts in how, where, and when care is provided. Longer-term, it has revealed the need for fundamental shifts across the care continuum. As a physician, I have seen first-hand the challenges of not having the right data, at the right time, in the right format to make informed shared decisions with my patients. These challenges amplify the urgency for trusted partners and solutions to help solve emergent health challenges. Today we're taking a big step forward to address these challenges with the general availability of Microsoft Cloud for Healthcare.

HL7's FHIR (Fast Healthcare Interoperability Resources) is largely seen as an enabler of health data exchange, which of course it is, but executives at IBM, Google and Microsoft said it will also lay the foundation for artificial intelligence and machine learning. "Interoperability is the cornerstone of our healthcare strategy -- teaching cloud to speak the language of healthcare: HL7, FHIR, DICOM," said Aashima Gupta, global head of healthcare and life sciences at Google Cloud, during a panel discussion here at HIMSS19 on Thursday. Google, in addition to IBM, Microsoft, Oracle and Salesforce, signed a pledge to remove interoperability barriers back in August 2018 during the Blue Button 2.0 hackathon at the White House. And while the companies have yet to provide specific details they said it will involve cloud computing, FHIR and open APIs. "We're competitors in many ways but also very much aligned because without interoperability we can't really make a change and make a difference," said Mark Dudman, health of global product and AI development at IBM. "Right now, FHIR is taking systems that don't interact to talk quickly. We've hit that first real target of getting systems to talk, but now we have to talk in volume."