Sanjeev Kumar’s Path to Enhancing Healthcare Research with CareFrame

Sanjeev Kumar’s Path to Enhancing Healthcare Research with CareFrame

Sanjeev Kumar’s Path to Enhancing Healthcare Research with CareFrame

Healthcare research often faces challenges such as fragmented data, isolated evidence, and outdated information. These issues can hinder collaboration and slow down innovation. Sanjeev Kumar, founder of CareFrame, is addressing these obstacles by developing technology that automates and improves quantitative research in healthcare, aiming to bridge gaps and promote collaboration across the industry. 

Early Career and Education 

Sanjeev Kumar began his career in healthcare in India, earning his dental degree at the age of 20. Seeking broader perspectives and solutions to the challenges he observed, he pursued opportunities abroad. In 2011, Kumar undertook a non-clinical externship at the University of California, Los Angeles (UCLA), where he gained broader exposure to care delivery and various specialties. 

Driven by a desire to make an impact, Kumar transitioned from clinical practice to healthcare administration. He enrolled in the Master of Health Services Administration program at the University of Michigan, Ann Arbor, from 2012 to 2014. During his studies, he focused on bioinformatics, statistics, and operational analytics courses, which provided him with foundational knowledge essential for his later work in healthcare research and technology development. 

Research and Early Innovations 

In 2013, Kumar served as a Research Associate II at the University of Michigan Medical School. He conducted contextual inquiry interviews with patients, providers, and public health practitioners to support patient empowerment within a Learning Health System. His work resulted in a publication titled “Leveraging Contextual Inquiry Methods to Empower Patients in a Learning Health System,” which was presented at the 48th Hawaii International Conference on System Sciences (HICSS) in 2015. 

Professional Experience at Henry Ford Health System 

Kumar joined Henry Ford Health System in 2014 as a Senior Informatics Analyst, where he developed and managed cloud and machine learning systems. His work focused on areas such as pharmacy services, enterprise analytics, and medical group operations. 

In 2016, Kumar developed and patented a method using autoencoder neural networks to analyze medication administration records, aiming to identify potential drug diversion cases by floor nurses. This innovation was documented in the U.S. Patent titled “Prescription Medication Diversion Analysis and Reporting” (US 2019/0355461 A1). 

Contributions to Population Health and Equity 

From 2019 to 2024, Kumar managed informatics for Henry Ford Health as part of a multi-site program funded by the Centers for Disease Control and Prevention (CDC) and administered by the American College of Preventive Medicine (ACPM). The program focused on preventing, detecting, and controlling hypertension among African American men in the Detroit area. The team’s efforts were recognized by The Joint Commission as a finalist for The Bernard J. Tyson National Award for Excellence in Pursuit of Healthcare Equity in 2023. 

Founding CareFrame 

In February 2024, Kumar founded CareFrame, a technology company dedicated to transforming healthcare through autonomous quantitative research directed by human expertise. Leveraging his extensive experience in healthcare informatics and system development, he aims to address the challenges researchers encounter in accessing and analyzing complex healthcare data. 

Planning, Design, and Execution – The Development of CareFrame 

CareFrame is developing technology that automates the planning, design, and execution of comprehensive study protocols. The platform emphasizes scalable quantitative research through discretized autonomous pipelines in healthcare. By processing and organizing large datasets into structured knowledge graphs, CareFrame seeks to make healthcare data more accessible and actionable. 

Recognizing that a single hospital can generate millions of rows of data daily—with complex dependencies and unique distributions—CareFrame aims to fully utilize this information. Traditional tools often struggle with such scale, leaving potential insights untapped. CareFrame addresses this by using scalable, autonomous pipelines to process data efficiently, minimizing human biases and resource constraints. 

A key feature of CareFrame is its ability to create logic-based and statistical models of studies, allowing researchers to assess the impact of various interventions on health outcomes. This capability enables the exploration of different scenarios and a better understanding of the broader context before deploying resources and applying findings in real-world settings. The system’s discretized pipeline approach facilitates precise planning and refinement of studies, leading to more reliable and actionable results. 

Commitment to Innovation and Collaboration 

Kumar’s career reflects a steady commitment to improving healthcare through technology and collaboration. With over 15 years of experience, including a decade focused on machine learning solutions for electronic medical record databases, he brings substantial expertise to CareFrame. His hands-on experience in developing systems and his contributions to population health initiatives demonstrate his ability to lead complex, technology-driven projects. 

By connecting isolated data silos and encouraging interdisciplinary collaboration, Sanjeev Kumar aims to support researchers and accelerate advancements in healthcare. CareFrame offers a comprehensive solution to enhance healthcare research and practice through scalable support and improved data accessibility. Utilizing native AI technologies, the platform addresses key challenges in research and collaboration, promoting a more equitable and efficient approach to healthcare advancement. 

Learn More 

To learn more about how CareFrame is advancing healthcare research, visit CareFrame.ai. 

link

Leave a Reply

Your email address will not be published. Required fields are marked *