Dr. Suhina Singh, CEO of Jonda Health, shares how her company is reimaging how patients can take control of their own healthcare with greater access to data.
Data Integration Key to Achieving Needed Breakthroughs in Healthcare When peers from other industries ask why data hasn’t driven comparable breakthroughs in healthcare, the answer is usually hiding in plain sight: clinical and administrative data still live in different systems and “speak” different digital languages. Integrating them is a prerequisite for the next wave of progress—better care, better patient experience, and better operational performance at the same time. Fortunately, there are now multiple mandates spurring this integration, from Centers for Medicare & Medicaid Services requiring Fast Healthcare Interoperability Resources APIs, to National Committee for Quality Assurance embracing digital quality measures, to enforcement of prohibitions against information blocking. Health plans and providers, however, must guard against swapping these two longstanding data siloes for several new ones.
FHIR is the REST API standard transforming healthcare data exchange. Learn how the VA and FDA use FHIR APIs for automated adverse event reporting, and what it means for your health system.
Overview An eye surgery center was losing patients. Not to competitors, but to no-shows. Eighteen percent of scheduled appointments went unfilled every week. Four full-time staff members spent their days calling roughly 500 patients, trying to confirm appointments. When that didn't work, the clinic overbooked randomly, hoping the math would balance out. Some days every patient showed up and the waiting room was chaos. Other days slots sat empty.
Conversational AI in healthcare is reducing patient portal overload with urgency tagging, AI-assisted responses, and intelligent message routing. Learn what's deployed and what the evidence shows.
RAG retrieves knowledge from your documents. MCP gives AI a standardized way to take actions. Learn when to use each, when to use both, and how they work together in production systems.
Agentic AI architecture assigns specialized AI agents to distinct tasks so the combined result outperforms any single model. Learn core components, patterns, and decision frameworks.