Healthcare organizations are collecting unprecedented volumes of data, yet most non-technical professionals—quality managers, care coordinators, clinical leaders, and early-career analysts—lack the foundational skills to transform that data into actionable insights.

Existing resources assume prior knowledge of programming, statistics, or data science, leaving a critical gap between executive-level overviews and deeply technical references. Data Fluent fills this gap by providing a ground-up education in healthcare analytics for readers without technical backgrounds. Written by a clinician who successfully transitioned to analytics, the book demystifies data concepts through practical healthcare examples—pharmacy claims analysis, quality measurement, population health metrics, and predictive modeling. Unlike tool-centric guides, this book emphasizes analytical thinking: teaching readers to ask the right questions, recognize data quality issues, and critically evaluate results before acting on them. What makes this book unique is its 'Gut Check' philosophy—the discipline of pausing to verify that analytical outputs make real-world sense before drawing conclusions. This approach addresses a critical problem in healthcare analytics: technically correct analyses that produce clinically absurd results because analysts lacked domain context to question their work. The book bridges technical execution with healthcare intuition, producing analysts who can both run queries and recognize when something doesn't add up.

Aayush Sisodia is a Business Analytics Advisor at Cigna/Evernorth Health Services, where he specializes in pharmacy claims analytics and population health measurement. His unique career trajectory—from clinical dentistry to health informatics to healthcare analytics—gives him a distinctive perspective on bridging the gap between clinical practice and data-driven decision-making.

He holds a Bachelor of Dental Surgery (BDS), a Master of Science in Health Informatics (MSHI), and is currently completing his MBA. Previously, he served as Population Health Data Manager at Maimonides Medical Center in Brooklyn, NY, where he led quality measurement initiatives and built analytics capabilities for value-based care programs.

His research interests include healthcare AI bias, medication adherence analytics, and digital health interventions. He has developed several healthcare technology applications, including TeachBack Engine (health literacy verification) and EOB Lens (medical bill explanation). His published and under-review work addresses racial bias in clinical AI systems, GLP-1 medication policy frameworks, and voice biomarkers for mental health monitoring.