Bridging clinical expertise with data & AI to build better healthcare
Your essential guide to healthcare analytics: bridging clinical knowledge and data skills for transforming patient care.
Hi, I'm Chad You (尤哥) — "Shovel Seller MD".
With over 10 years of hands-on experience shipping medical AI in US hospitals and currently serving as Senior Product Manager at Vanderbilt University Medical Center (VUMC) Health Policy, where I lead the Clinical Research Data Core, I've seen firsthand how powerful the intersection of clinical domain expertise and modern data/AI skills can be.
This handbook is the resource I wish I had when I started my journey in healthcare analytics and AI. It draws inspiration from resources like Zack Wilson's Data Engineer Handbook and the Data Roles Continuum, recognizing that many healthcare professionals (regardless of title — analyst, scientist, or engineer) are effectively analytics engineers in practice.
The goal: Equip you with the business knowledge, hard technical skills, and soft skills needed to turn raw healthcare data into real impact — from clinical quality improvement to population health management and value-based care.
I'm now focusing more on "selling shovels" for the 2025-2026 medical LLM gold rush through actionable playbooks, code, and frameworks (check my Substack for weekly insights). This handbook remains a community resource and is open to contributions.
- Clinicians transitioning into data/AI roles
- Data professionals entering healthcare (analysts, engineers, scientists)
- Product managers and leaders in health tech/hospitals
- Anyone wanting to bridge clinical context with technical execution
- Chapter 1: Healthcare System Fundamentals
- Ecosystem overview, key stakeholders (providers, payers, patients, regulators)
- Healthcare business models and revenue cycle management
- Chapter 2: Healthcare Data Landscape
- Types of healthcare data
- Data standards and conversions — HL7, FHIR, SNOMED CT, ICD, CPT, and interoperability
- Data governance, HIPAA, and privacy considerations
- Chapter 3: Healthcare Analytics Use Cases
- Clinical quality improvement, population health, financial optimization, operational efficiency
- Value-based care, provider lookup, and real-world case studies
- Chapter 4: Healthcare Data Architecture
- Data warehousing, data lakes, EHR integration strategies
- Upcoming: Data modeling, SQL for healthcare, visualization, advanced analytics/ML
- Collaboration with stakeholders, project management, translating business questions into analytics, ethical considerations
(Chapters 5–12 are planned and will be expanded based on community input.)
Curated resources including:
- Books (e.g., Healthcare Analytics Made Simple, AI in healthcare titles)
- Courses, blogs, newsletters, journals
- Open datasets (CMS, CDC, etc.)
- Recommended repos, projects (like Tuva), tools, and social accounts
A core focus is converting messy, siloed clinical data (from EHRs and other sources) into standardized, analytics-ready formats. This includes:
- Mapping between proprietary systems and standards like FHIR
- ETL processes for claims, clinical, and operational data
- Ensuring compliance while enabling insights
- Start with the Table of Contents and
healthcare-knowledge.md - Dive into practical chapters based on your needs
- Explore the appendices for deeper learning and tools
- Star the repo if you find it useful, and consider contributing!
This is a living, community-driven project (88+ commits and growing). Contributions are welcome — whether it's:
- Expanding chapters
- Adding case studies or code examples
- Fixing typos or improving explanations
- Suggesting new resources
Open an issue or submit a PR. Let's build better healthcare analytics together.
- Website: chadyou.com
- X (Twitter): @youcc
- Substack (Shovel Seller MD — medical LLM playbooks): chadyou.substack.com
- LinkedIn: Search "Chad You" or check my profile
- Other repos: Maps/visualization experiments, etc.
I'm based in Tennessee, USA, and practice Aikido when not wrangling healthcare data.
MIT License — feel free to use, adapt, and build upon this work.
"Selling shovels" for the medical AI and analytics gold rush since 2025.
Made with ❤️ for the healthcare data community.