15.1 Overview
Public health professionals spend countless hours on repetitive, manual data tasks: renaming files, reformatting spreadsheets, copy-pasting between systems, and running the same analysis steps every reporting cycle. These tasks are tedious, error-prone, and solvable with short scripts or freely available tools.
The Public Health Automation Clinic is a free, community-driven initiative from Intersect Collaborations that addresses this gap. You describe a repetitive task; we develop a solution using free, open source tools (primarily R, Python, and the Posit ecosystem); and we publish it as an anonymized, reusable resource so anyone facing a similar problem can benefit.
The clinic focuses on data management and analysis tasks, including data extraction, cleaning, validation, automated reporting, and dashboard creation, as well as business process tasks like grant milestone tracking, file organization, and format conversion.
The Automation Clinic now has its own dedicated site with full details on the initiative, submission guidelines, example problems, and the solution library:
15.2 How It Connects to Bridgeframe
The Automation Clinic is a practical extension of the process optimization principles discussed in Section 14.1.2. Where that chapter establishes the conceptual framework (eliminate, automate, standardize), the clinic provides a mechanism for putting it into practice.
The clinic also demonstrates several Bridgeframe concepts in action:
- Structured problem descriptions use the same requirement formats (User Stories, GPS, Situational Protocols) introduced in Section 7.1.6.4
- Solutions prioritize local-first, open source tools, consistent with the tool comparison philosophy in this toolkit
- All solutions are published as reusable resources, building a community of practice around public health automation
15.3 CancerSurv Example
In the CancerSurv project, registrars identified several tasks suited to the Automation Clinic model:
- File renaming: An R script replaced manual renaming of lab report PDFs from system-generated filenames to the
[PatientID]_[FacilityCode]_[ReportDate].pdfconvention. - Data quality checks: A Python script automated 15 edit rules (e.g., diagnosis date precedes treatment date) and generated an exception report in under two seconds.
- Quarterly report assembly: An R Markdown script merged data from three source workbooks into a summary template, reducing a full-day task to five minutes.
- Project status reporting: An R script parsed GanttProject XML exports and generated formatted Quarto reports for the monthly steering committee.
Each solution required less than a day to develop and saved the registry team dozens of hours per quarter.
15.4 Get Involved
- Browse the full initiative: Visit the Public Health Automation Clinic for submission instructions, example problems, and the solution library
- Paid consulting: For urgent, complex, or organization-specific needs, contact Intersect Collaborations
- Training: Intersect Collaborations offers a course, “Automating Public Health Analytics with R, Quarto, and Windows Tools,” for professionals looking to build automation skills