MediKarma Scales Provider-Supervised Care Through Longitudinal Data AI

The company’s Jack™ engine aggregates data from hospitals, clinics, pharmacies, and wearable devices into a unified longitudinal history. According to MediKarma CEO Kris Narayan, the goal is not to replace existing digital health models—whether they involve chatbots or care teams—but to provide a structured clinical foundation that prevents the risks associated with incomplete patient charts. Dr. Miguel Venereo, the company’s Chief Medical Officer, emphasizes that an incomplete record often hides in plain sight, potentially leading to flawed clinical judgments as automated systems take on more diagnostic responsibilities.

MediKarma recently validated its approach through a cohort study of 8 million patients, identifying a 90.6% correlation between its proprietary health scores and actual insurance claims costs. Additionally, a 120-day pilot program with an endocrinology practice resulted in half of the participants either lowering or maintaining their A1C levels. These results have earned the company a spot in the Centers for Medicare and Medicaid Services (CMS) Health Technology Ecosystem initiative, specifically within the Diabetes and Obesity track.

Beyond data aggregation, the company utilizes its Jill™ technology to support summarization and provider-reviewed workflows. This dual-engine architecture allows pharmaceutical firms, device makers, and telehealth platforms to integrate clinical context into their offerings without the burden of manually reconciling fragmented records. By ensuring that patient engagement remains tethered to a comprehensive medical history, MediKarma provides a pathway for external companies to improve care efficiency while keeping licensed providers firmly at the center of the decision-making process.