Google Health’s Post

The mention of "insurance companies" in this context raises important questions. Why do these companies require large data sets to help individuals take preventative health measures? Granting insurance companies access to such data could have unintended consequences, including the potential denial of necessary care. Integrating large data sets in healthcare can yield positive outcomes, provided that implementation prioritizes patient well being. While AI holds significant promise in healthcare, its accuracy and fairness depend on the use of extensive and diverse datasets to mitigate bias across demographic groups. However, the high costs associated with AI integration raise concerns that healthcare systems in under served, low income, and rural communities may lack access to these critical data resources. Another critical consideration is patient access to their own health data. If individuals lack the financial resources to implement recommended lifestyle changes, how effective is this data in improving health outcomes? AI driven analytics should go beyond tracking habits; they must be accompanied by education, financial support, and accessible tools to empower meaningful change.

After so many years of FHIR, LOINC, SNOMED, and other initiatives, we’re finally facing the reality that it’s not so easy to get doctors to create high-quality directories for the interoperability of medical data)) AI will undoubtedly generate such datasets from various sources, but who will ensure their validity? The main risk is that simple cases will still be verified by the same participants, while complex cases could introduce significant errors into real clinical practice.

In my opinion the future of healthcare data capture, exchange and analysis depends strongly on the adoption of HL7 FHIR and SNOMED CT by more and more software providers and all sorts of specialists in the medical field who really take their time to create unambiguous high-quality medical data. Great standards need people who start using them. The potential for various forms of ai in healthcare is huge - once that data is captured and stored in a more structured way. Just throwing any new ai tool at unstructured medical text sounds tempting, but most of the time results in mediocre (or really bad) results.

A huge factor determining the success of this endeavor will be finding a path that’s actually best for the patients/consumers. Data efficiencies wielded in ways that benefit corporations over humans wouldn’t be ideal. We don’t need insurance companies denying coverage or inflating premiums because an enhanced but imperfect AI attempts to make specific individual predictions that are based on models trained with aggregate population data with limited applicability.

Well stated and totally agree. Exactly what we're unleashing at VISOLYR. And now with #NVIDIAInception we will accelerate our journey even faster. For more information: https://visolyr.io/ Google Health NVIDIA #ViVEEvent #ViVE2025 #Innovation #HealthTech #DigitalHealth #AI #ML

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AI in healthcare is evolving rapidly, but one of the biggest opportunities is tackling fragmented data. In General Practice, when a new patient with a complex history joins, we often spend valuable time piecing together hospital letters, GP consultations, and specialist reports. What if AI could instantly summarise these records, providing key clinical insights at a glance? This could transform how we deliver care—faster decisions, fewer delays, and better outcomes. 🚀 As an ANP, I’d love to see AI making this a reality. What other AI-driven solutions would make a difference in primary care? #AIinHealthcare #PrimaryCare #DigitalHealth

Abimbola Olaniran MD, EMBA, M.MedSci, PhD, P.M.P, CSSBB

AI could be a great tool to assist physicians in patient selection for medical (device) procedures, by using available patient data and running outcome predictions (based on clinical evidence and procedural statistics), with the goal to provide the best possible treatment outcomes for patients.

Parsing and normalizing all HL7 v2.x trigger events into a widely accepted format like JSON is essential for eliminating redundant data points, facilitating seamless data integration, and enhancing the usability of this information by AI models. By converting healthcare data into a structured, easily accessible format, we can ensure that data is consistent, standardized, and ready for analysis, thereby enabling more accurate and efficient AI-driven insights.