# Vaccination Schedules Recommended by the Centers for Disease Control and Prevention: From Human-Readable to Machine-Processable

## TL;DR

This paper describes converting CDC vaccination schedules into machine-readable rules to improve clinical decision support systems.

## Contribution

The novel contribution is creating and sharing ontology-based, machine-processable immunization rules in multiple formats.

## Key findings

- 465 rules for 19 vaccines across 13 categories were developed and shared on GitHub.
- Rules were validated using cross-review and tested with HL7 FHIR resources.
- Publicly available rules can aid health IT, education, and clinical institutions.

## Abstract

Background: Reusable, machine-processable clinical decision support system (CDSS) rules have not been widely achieved in the medical informatics field. This study introduces the process, results, challenges faced, and lessons learned while converting the United States of America Centers for Disease Control and Prevention (CDC)-recommended immunization schedules (2022) to machine-processable CDSS rules. Methods: We converted the vaccination schedules into tabular, charts, MS Excel, and clinical quality language (CQL) formats. The CQL format can be automatically converted to a machine-processable format using existing tools. Therefore, it was regarded as a machine-processable format. The results were reviewed, verified, and tested. Results: We have developed 465 rules for 19 vaccines in 13 categories, and we have shared the rules via GitHub to make them publicly available. We used cross-review and cross-checking to validate the CDSS rules in tabular and chart formats. The CQL files were tested for syntax and logic with hypothetical patient HL7 FHIR resources. Our rules can be reused and shared by the health IT industry, CDSS developers, medical informatics educators, or clinical care institutions. The unique contributions of our work are twofold: (1) we created ontology-based, machine-processable, and reusable immunization recommendation rules, and (2) we created and shared multiple formats of immunization recommendation rules publicly which can be a valuable resource for medical and medical informatics communities. Conclusions: These CDSS rules can be important contributions to informatics communities, reducing redundant efforts, which is particularly significant in resource-limited settings. Despite the maturity and concise presentation of the CDC recommendations, careful attention and multiple layers of verification and review are necessary to ensure accurate conversion. The publicly shared CDSS rules can also be used for health and biomedical informatics education and training purposes.