#populationhealth Tag

01 Jul

Posted at 13:18h in Author Interviews, Technology
Healthcare has a data problem — not a shortage of it, but an inability to act on it. The average large health system generates hundreds of millions of clinical events annually. Claims databases hold years of longitudinal patient history. EHRs log every medication, every vital sign, every lab result. And most of that data sits in silos, incompatible formats, and legacy systems that were never designed to talk to each other. Organizations that turn clinical, pharmaceutical and financial data into better decisions use purpose-built healthcare analytics platforms. In 2026, these platforms must support FHIR interoperability, near real-time population health analytics, value-based care, and AI-driven insights. But not all healthcare analytics solutions are the same. The market ranges from FHIR-native clinical intelligence platforms to general-purpose BI tools with healthcare connectors. Choosing the wrong solution can lead to costly implementations, limited clinical capabilities, and analytics that can't scale with your healthcare data. This guide profiles seven leading healthcare analytics solutions for 2026, evaluated on clinical depth, interoperability support, analytical sophistication, and fit for healthcare-specific workflows. They are not all the same — and that distinction matters.

07 Jun

Posted at 00:31h in Author Interviews, Diabetes, Technology
MedicalResearch.com Interview with: [caption id="attachment_74111" align="alignleft" width="92"]Luis A. Rodriguez, PhD, MPH, RDResearch Scientist, Kaiser Permanente Northern California Division of Research Assistant Professor, Department of Health System Sciences Kaiser Permanente Bernard J. Tyson School of Medicine Assistant Adjunct Professor, Department of Epidemiology & Biostatistics University of California, San Francisco Dr. Rodriguez[/caption] Luis A. Rodriguez, PhD, MPH, RD Research Scientist, Kaiser Permanente Northern California Division of Research Assistant Professor, Department of Health System Sciences Kaiser Permanente Bernard J. Tyson School of Medicine Assistant Adjunct Professor, Department of Epidemiology & Biostatistics University of California, San Francisco ADA 2026 Poster Presentation: Machine-Learning Modeling for T2DM Prediction in over 3 Million Adults American Diabetes Association 85th Scientific Sessions, June 2026
MedicalResearch.com: What is the background for this study? What are the risk factors used to develop the prediction model? Response: Type 2 diabetes develops gradually over many years, often without clear warning signs. As a result, it can be difficult for health systems to identify which adults are most likely to benefit from prevention efforts before the disease develops. In this study, we used electronic health record data from more than 3 million adults in Kaiser Permanente Northern California to develop a prediction model that estimates an individual's risk of developing type 2 diabetes over 1, 3, and 10 years. The model is based on information routinely collected during clinical care, including age, sex, race/ethnicity, body mass index, blood glucose levels, smoking, physical activity, medical and family history, and medication use. By combining these clinical, biological and behavioral factors, the model provides a more comprehensive assessment of diabetes risk than traditional screening approaches.