Clinical Decision Support for Immunization (CDSi)
Immunization clinical decision support (CDS), more commonly referred to as evaluation and forecasting, is an automated process that determines the recommended immunizations needed for a patient and delivers these recommendations to the healthcare provider. Health Information Systems (HIS) – which can include Health Information Exchanges (HIEs), Immunization Information Systems (IIS) and Electronic Health Records (EHRs) – provide healthcare providers with immunization evaluation and forecasting tools designed to automatically determine the recommended immunizations needed when a patient presents for vaccination. These recommendations are developed by the Advisory Committee on Immunization Practices (ACIP). ACIP is a federal advisory committee responsible for providing expert external advice and guidance to the Director of the Centers for Disease Control and Prevention (CDC) and the Secretary of the U.S. Department of Health and Human Services (DHHS) on use of vaccines and related agents for control of vaccine-preventable disease in the United States. Recommendations include age for vaccine administration, number of doses, dosing interval, and precautions and contraindications.
After ACIP recommendations are published, technical and clinical subject matter experts (SMEs) work to interpret and integrate them into their evaluation and forecasting engines. New ACIP schedule changes are currently communicated only through clinical language, in publications like the Morbidity and Mortality Weekly Report (MMWR) and the Epidemiology and Prevention of Vaccine-Preventable Diseases (“The Pink Book”). The translation of that clinical language into technical logic that can be processed within evaluation and forecasting engines is a time-consuming and complex process that happens mostly independently within the different HIS. Due to the challenge of interpreting clinically-written ACIP recommendations, CDS engine outputs often varied and did not always match the expectations of clinical SMEs.
To this end, the Clinical Decision Support for Immunization (CDSi) project has developed and maintains resources to aid HIS implementers align with the current ACIP recommendations. These resources are summarized in the following introductory materials.
- CDSi Mini Guide [8 pages] – The CDSi mini-guide provides a high-level view of the CDSi resources in an easy to read and short format.
- CDSi Infographic [1 page] – The CDSi infographic provides a graphical view of the CDSi resources in a 1-page format.
- Clinical Decision Support for Immunization (CDSi): Logic Specification for ACIP Recommendations, version 4.3 [2.4 MB, 147 pages] Updated September 2021
9 chapters, including an executive summary, and 7 appendices
- Supporting Data Version 4.36 [2 MB] Updated September 2022
These supporting data are the attributes and specific values required to support evaluation and forecasting as defined in the Logic Specification.
The CDSi Logic Specification and Supporting Data provide an implementation-neutral foundation for development and maintenance of CDS engines. They capture ACIP recommendations in an unambiguous manner and improve both the uniform representation of vaccine decision guidelines, as well as the ability to automate vaccine evaluation and forecasting.
The target audience for the Logic Specification and Supporting Data includes business and/or technical implementers of immunization CDS engines. These implementers may support any system with an immunization evaluation and forecasting engine, including but not limited to IIS.
The Logic Specification and the Supporting Data were developed to be as implementation-neutral as possible to support those currently with or without complete evaluation and forecasting engines as they:
- Refine, extend, or develop their implementation;
- Clarify their understanding of immunization rules; and
- Troubleshoot and verify correct implementation of immunization rules.
The CDSi test cases are comprised of two spreadsheets. The first spreadsheet covers routine age-based recommendations for both childhood and adults. The second spreadsheet covers recommendations where an underlying condition (e.g., risk factor) may result in an immunity, contraindication, or indication to vaccinate.
- CDSi Test Cases – Healthy Childhood and Adult, version 4.27 – Excel Updated September 2022
- CDSi Test Cases – Underlying Conditions, version 4.3 – Excel Updated October 2021
In an effort to improve ACIP recommendation adoption rates, the CDSi project is providing pre-release materials for the yearly flu recommendation. The pre-release resources are based on the information provided to the CDSi project team by the CDC subject matter experts prior to the official MMWR publication. Once the official MMWR is published, the pre-release material will be removed, appropriately updated, and released as part of an official CDSi version. It must be understood that changes are likely to occur between the pre-release material and official version, as new information becomes available.
- No pre-release material at this time
To learn about key concepts of the CDSi Logic Specification, Supporting Data, and Testing Cases, see the CDSi training materials.
Please email any questions to: firstname.lastname@example.org.
Registered users can now access COVID-19 vaccine lot numbers and expiration dates provided to CDC by the vaccine manufacturer. COVID-19 Vaccine Lot Number and Expiration Date Tool
This short video provides an introduction to the Clinical Decision Support for Immunization (CDSi) project.
This short video explains the basic concepts of Evaluation and Forecasting within the CDSi project. The video describes how an implementer would use both Supporting Data and the Logic Specification together to first determine if a vaccine dose administered was either valid or invalid and then forecast the next recommended dose for the patient.
This short video explains the basic concepts found in the CDSi Logic Specification. The video describes each chapter of the Logic Specification and the techniques used within each chapter to document the terminology, process flow, and decision making.