Welcome
The Public Health Automation Clinic is a free, community-driven initiative that helps public health professionals automate the repetitive, manual work that consumes their days. Whether you are an epidemiologist renaming hundreds of files by hand, a registrar copy-pasting data between incompatible systems, or an analyst reformatting the same spreadsheet every reporting cycle, this resource is for you.
The Public Health Automation Clinic is a work in progress. The examples and solutions throughout are designed to be anonymized and generalizable so that anyone facing a similar problem can benefit, regardless of their specific team, agency, or jurisdiction.
Feedback and submissions are welcome as this resource continues to evolve. See Chapter 1: The Automation Clinic for how to submit your own problem.
0.1 The Challenge
Public health professionals are drowning in tasks that machines should handle. Every hour spent on a task a script could handle in seconds is an hour not spent on disease surveillance, community outreach, or data interpretation.
The barrier is rarely the solution itself. It is knowing that the solution exists, and having someone with the right skills translate the problem into code. That is what the Public Health Automation Clinic aims to address.
The guiding principle is simple: if you are doing the same thing twice, find a way to make the computer do it.
Automation is the medium-term fix for public health professionals stuck in inefficient systems. Scripts, pipelines, and APIs can eliminate mechanical tasks and free people to think. But the long-term answer requires thinking in terms of architecture: interoperability standards like HL7 and FHIR, governance reform, and incorporating these skills into how we prepare public health professionals for the workplace.
This book focuses on the practical, immediate wins. The broader vision of systems architecture, interoperability, and workforce education is explored in the companion resource, Bridgeframe.
0.2 The Solution
The Public Health Automation Clinic provides:
- Practical automation solutions built with R, Python, and the Posit ecosystem (RStudio, Quarto, Shiny)
- A free intake process where you describe a manual problem and receive a working, documented solution
- A growing solutions library organized by the flow of data, from desktop operations to analysis and reporting
- Local-first tools that run on your computer, with no cloud AI services or commercial software required
- Anonymized, published solutions so the entire public health workforce benefits from each submission
0.3 Who This Is For
- Epidemiologists, registrars, and data managers who perform repetitive data tasks that could be scripted
- IT business analysts working in public health who need to demonstrate the value of automation to program teams
- Project managers overseeing health IT implementations who want to streamline reporting and project tracking
- Data scientists and informaticians working with epidemiological systems who want reusable, shareable code
- Students in health informatics or public health programs looking for practical examples of applied automation
0.4 How to Use This Book
This book is organized into three parts, with appendices, to take you from understanding the problem to submitting your own automation challenge and eventually contributing back to the community.
0.4.1 Part I: The Clinic
- The Automation Clinic — Why automation matters for public health. Focus areas and the local-first philosophy. The free intake model: how to submit, what qualifies, submission formats and examples, what to expect. Paid services for urgent or evolving needs.
0.4.2 Part II: Desktop Automation
File renaming, format conversion, directory organization, batch operations on local files. The starting point for most automation journeys.
- Placeholder subchapters will be added as solutions are developed.
0.4.3 Part III: Data Access and Integration
Connecting to databases, working with APIs (Google Sheets, REDCap, CDC data portals), importing and exporting data, transferring files between systems.
- Placeholder subchapters will be added as solutions are developed.
0.4.4 Part IV: Data Cleaning and Transformation
Standardizing messy datasets, deduplication, edit rule validation, exception reporting. Turning raw data into analysis-ready inputs.
- Placeholder subchapters will be added as solutions are developed.
0.4.5 Part V: Data Analysis and Reporting
Recurring surveillance analyses, reproducible reports with Quarto and R Markdown, interactive dashboards with Shiny, automated chart and table generation.
- Placeholder subchapters will be added as solutions are developed.
0.4.6 Part VI: Business Process Automation
Project status reports, grant milestone tracking, workload distribution, cross-agency data exchange. Automating the work around the analytical work.
- Placeholder subchapters will be added as solutions are developed.
0.4.7 Part VII: The Automation Mindset
- From Manual to Automated — The optimization hierarchy (eliminate, automate, standardize). How to think about your own workflows. Getting started with scripting.
- Building a Community of Practice — Contributing solutions. The pattern library vision. Workforce development. Public GitHub repository plans.
- Automation and Public Health Education — Gaps in MPH curricula. R and Python as general-purpose tools beyond statistics. The case for teaching “automating the work around the work.”
0.4.8 Appendices
- A: Tool Recommendations — Commercial vs Open Source/Public Health tools comparison.
- B: Submission Templates and Examples — Detailed templates for User Story, GPS, and Situational Protocol formats.
- D: Development Tools and AI Transparency — How this book is built, the role of AI in development, and the commitment to local-first, AI-independent solutions.
- C: Glossary — Key terms for automation, public health data, and the tools ecosystem.
This book is a work in progress. The current published content covers Chapter 1: The Automation Clinic, which contains the complete clinic model, intake process, and submission guidance. Additional chapters will be developed as the solutions library grows and community submissions shape priorities.
If you have a repetitive task you want automated, start with Chapter 1 and then submit the intake form or open a GitHub Issue. If you want to understand the philosophy and scope first, continue reading this preface.
0.5 Local-First Philosophy
The clinic prioritizes solutions that run on your computer or local server. A script you can execute on your own machine is more reliable, efficient, and sustainable than a workflow that depends on a cloud-based large language model.
AI tools may be used in the development and documentation of solutions, but the solutions themselves will not require AI or LLM access to run. A well-written R or Python script that processes 50,000 records deterministically on your laptop will always be more dependable than sending that data to a remote API.
0.6 Building a Community of Practice
The long-term vision extends beyond individual problem-solving. Each submission and solution contributes to a pattern library of common public health automation needs, reusable code templates that can be adapted across jurisdictions, and a feedback loop that identifies the most impactful areas for tool development.
The Public Health Automation Clinic GitHub repository is the home for collaboration. Public health professionals can contribute solutions, suggest improvements, or report issues directly through GitHub Issues. This is especially useful for those comfortable working in the open and who want to engage with the development process directly.