CHDA, AI, and the Future of Healthcare Analytics
In AHIMA’s examination content outline for the Certified Health Data Analyst credential, artificial intelligence (AI) appears as "one task among nine",
in one of six domains.
So what does a healthcare analytics certification mean in an AI-enabled workplace?
This is Part 3 of my series on the CHDA credential. In Part 1, I examined how the CHDA has changed, including updates to the certification and improved first-time examination pass rates. In Part 2, I looked at how employers recognize the CHDA and the skills healthcare data analysts need beyond certification. This final article looks ahead, as AI changes how healthcare data are analyzed and interpreted.
This article examines:
— how AHIMA’s updated CHDA examination content outline addresses AI,
— what additional competencies healthcare data analysts will need, and
— what will determine the CHDA’s future relevance for the Health Information Professional.
7. CHDA and AI in Healthcare Analytics
Perhaps the most significant change between my earlier discussion of the CHDA and today’s healthcare environment is the rapid expansion of AI. How does the CHDA body of knowledge relate to AI, and what does this mean for the future of the Health Information Professional?
AI is changing how healthcare data are collected, processed, analyzed, interpreted, and used to support decisions. Understanding these developments requires more than familiarity with emerging technologies. It also requires healthcare domain knowledge, analytical judgment, data governance, and the ability to evaluate whether information is accurate, appropriate, and meaningful.
AI and the CHDA Body of Knowledge:
AHIMA’s revised CHDA examination content outline, effective February 1, 2024, explicitly includes the ability to differentiate applications of artificial intelligence in healthcare under Domain 1, Foundational Knowledge of Analytics in Healthcare.
Although AI is explicitly identified within Domain 1, competencies addressed throughout the examination framework also relate to AI-enabled healthcare analytics. These include understanding healthcare data sources, analytical methodologies, data quality, interpretation, reporting, and governance. The outline shows where they appear:
Domain 1 – levels of analysis (descriptive, diagnostic, predictive, and prescriptive), alongside the AI task.
Domain 2 – creating an analysis business plan.
Domain 3 – data lineage and evaluating data quality.
Domain 4 – identifying trends, patterns, and anomalies.
Domain 5 – explaining assumptions and limitations and communicating findings to stakeholders.
Domain 6 – key health data laws such as HIPAA and the need for audit logs.
These are skills a professional would draw on when evaluating an AI-generated result, though the outline does not frame them as AI skills.
Figure 1. CHDA exam domains by exam weight and their relevance to evaluating AI-assisted analysis. Source: AHIMA CHDA Exam Content Outline (effective February 1, 2024). AI-related skill labels are the author’s interpretation.
It is worth being precise about scope. Within Domain 1, AI is one task among nine, at the level of differentiating applications. Competencies such as model validation, bias, and explainability are not listed in the outline, which is why they appear below as areas for continued learning.
The CHDA is a healthcare analytics certification, not an AI certification. Its value in an AI-enabled environment lies in the healthcare knowledge and analytical judgment professionals bring to evaluating and applying AI-generated information.
AI Creates New Learning Requirements:
As AI technologies evolve, CHDA professionals, like other healthcare analysts, will need to develop additional AI-related competencies through continuing education and practical experience.
Depending upon the Health Information Professional’s responsibilities, this may include understanding:
AI and machine learning concepts and their appropriate applications.
Model validation, performance measurement, and monitoring.
Bias, representativeness, and potential effects on healthcare populations.
Transparency, explainability, and limitations of AI-generated findings.
Privacy, security, governance, and regulatory responsibilities.
Human oversight and the appropriate use of AI-supported recommendations.
These competencies are not necessarily mastered through CHDA certification alone. They also depend on familiarity with the technologies used within an organization.
The Value of CHDA Knowledge in an AI-Enabled Environment:
The CHDA’s relevance to AI should not be measured solely by the number of AI-related topics included in the certification examination.
Its value also lies in the broader healthcare analytics knowledge needed to ask appropriate questions, understand data sources, evaluate analytical findings, and communicate information that supports meaningful decisions.
Technology enables increasingly sophisticated analysis.
Human knowledge guides the interpretation, evaluation, and responsible application of the results.
The CHDA can contribute to this foundation of healthcare analytics knowledge, but certification alone does not determine how effectively professionals apply that knowledge.
8. The Future of the CHDA and Healthcare Analytics
The CHDA certification has undergone important changes in recent years. According to AHIMA, first-time pass rates rose from 48% in 2023 to 54% in 2025, and the number of first-time testers grew from 54 to 114 over the same period (reference 1). The examination framework has also been updated to reflect evolving healthcare analytics knowledge and competencies, including AI.
These developments provide an opportunity to consider not only how the certification has changed, but also what will determine its future relevance to the healthcare workforce.
An Evolving Body of Knowledge:
The updated CHDA examination framework incorporates healthcare datasets, classification systems and terminologies, revenue cycle management, electronic health records, informatics, data governance, analytical methodologies, and applications of AI.
These are not isolated areas of knowledge. They reflect the interconnected environment in which healthcare data analysts work.
Healthcare data originate from an expanding range of sources, including clinical care, EHRs, claims, laboratories, imaging, registries, public health systems, wearable devices, and patient-generated information.
Organizations are also using increasingly sophisticated analytical technologies to transform these data into information that supports clinical, financial, operational, quality, and population health decisions.
For the CHDA to remain relevant, its body of knowledge must continue to reflect how healthcare data are generated, managed, analyzed, interpreted, and used.
Employer Recognition and Professional Value:
The future of the CHDA cannot be measured solely by examination pass rates or the number of credential holders. An important consideration is whether healthcare organizations recognize the knowledge represented by the certification and how that knowledge contributes to the work being performed.
As discussed in Part 2, selected healthcare employers identify the CHDA as a preferred or desired certification. Although the CHDA is not consistently identified as a required credential, the competencies associated with the CHDA have practical applications across healthcare organizations.
These competencies support healthcare data analysis, data quality, clinical and operational performance measurement, financial analysis, and evidence-informed decision-making. For employers, the value extends beyond the credential itself to how professionals apply their knowledge to organizational priorities and challenges.
Certification represents a defined body of knowledge, while professional value is further established through experience, performance, and meaningful contributions to healthcare decisions and outcomes.
Opportunities for the Health Information Professional:
The evolving healthcare analytics environment creates opportunities for Health Information Professionals to build upon their existing knowledge. HI Professionals have long contributed to the quality, integrity, classification, governance, privacy, and appropriate use of health information. These responsibilities remain important as healthcare organizations expand their use of analytics and AI.
Understanding how information originates, moves between systems, and is used for different purposes provides valuable context for evaluating analytical findings.
For professionals interested in healthcare analytics, the CHDA may offer a structured pathway for expanding analytical knowledge and applying existing healthcare competencies in new roles. However, it is not the only pathway. Career development may also involve technical training, additional education, practical projects, professional experience, or other certifications. The appropriate direction depends upon an individual’s existing knowledge, responsibilities, professional interests, and career goals.
AHIMA’s Role in Sustaining the CHDA Certification:
As the healthcare industry evolves, AHIMA has an ongoing responsibility to maintain the relevance, credibility, and professional recognition of the CHDA for prospective candidates and current credential holders alike. Doing so requires continuous evaluation of its competencies, examination content, educational resources, and alignment with emerging workforce needs, including AI, advanced analytics, data governance, and interoperability.
From my professional perspective, AHIMA should consider the following priorities to strengthen the CHDA certification and support its credential holders:
Publish the evidence employers and candidates need. Continue to provide reports annually on first-time pass rates along with cohort sizes and present this information in a multi-year format. Include a brief summary for employers regarding the competencies covered by CHDA and the analytics job functions they are associated with.
Build an AI-focused learning pathway for credential holders. The outline addresses AI at the level of differentiating applications. Continuing education on model validation, bias, explainability, and human oversight, ideally eligible for recertification credit, would close the gap between the outline and the competencies this article describes.
Keep candidate resources current and signal how the outline will evolve. Publish when the content outline is next scheduled for review and how AI content may change, with study guidance updated to match.
The long-term success of the CHDA depends on a shared commitment. AHIMA must provide leadership in maintaining and communicating the credential’s relevance, while CHDA-certified professionals must continue developing and applying their knowledge as healthcare data, technology, and analytical practices evolve.
Looking Ahead:
Healthcare analytics will continue to evolve as new technologies, data sources, analytical methods, and organizational requirements emerge. For Health Information Professionals, the opportunity extends beyond earning another credential. It involves recognizing the value of existing knowledge, identifying new competencies, and participating in the changing ways healthcare information is analyzed and used.
Take-away for the CHDA and the Health Information Professional; CHDA with AI and the future for Healthcare
The discussion in this article brings us back to three important questions:
1) How does the CHDA body of knowledge relate to AI?
The CHDA is not an AI certification. However, its emphasis on healthcare data, analytical methodologies, interpretation, reporting, and governance provides a foundation for understanding and evaluating AI-supported analytics. AHIMA’s inclusion of AI applications in the updated examination framework reflects the growing relationship between AI and healthcare analytics.
2) What will determine the future relevance of the CHDA?
The certification’s future value will depend on more than examination pass rates or the number of credential holders. Continued alignment with healthcare analytics practices, employer recognition, and the practical application of its competencies will be important in determining its relevance to the healthcare workforce.
3) What does this mean for the Health Information Professional?
As healthcare organizations adopt increasingly sophisticated analytical technologies, professionals will need to combine existing healthcare knowledge with new technical and analytical capabilities. The CHDA may support that development, but it is one of several pathways for expanding professional knowledge and career opportunities.
Figure 2. Take-away for CHDA credential and the Future for Health Information Professionals.
Knowledge is cumulative. The knowledge developed through education, clinical practice, health information, coding, analytics, and professional experience does not become less valuable as technology evolves. It becomes the foundation upon which new competencies are developed.
As artificial intelligence changes how healthcare information is analyzed and used, the responsibility of healthcare professionals is not simply to keep pace with technology, but to understand, evaluate, and guide its meaningful application.
Technology Enables. Human Knowledge Guides.
CHDA with AI and the Future of Healthcare
Connecting healthcare analytics, artificial intelligence, and professional knowledge
to the evolving needs of healthcare.
Disclosure: I have held the CHDA credential since 2015 and volunteer for AHIMA. This series reflects my own professional views and my review of AHIMA’s published materials and employer job postings, not an official AHIMA position.
I am not writing or speaking on behalf of AHIMA. CHDA is a certification offered by AHIMA.
TE-AR Healthcare Consulting is independent of AHIMA.
References are cited below.
AHIMA References accessed October 2026:
American Health Information Management Association. (2026). Certified Health Data Analyst (CHDA).
https://www.ahima.org/certification-careers/certifications-overview/chda
American Health Information Management Association. (2023). Certified Health Data Analyst (CHDA) Exam Content Outline, effective February 1, 2024.
https://www.ahima.org/media/lflpvl4k/chda-exam-content-outline_final_11-2023.pdf
American Health Information Management Association. (2023). Certified Health Data Analyst (CHDA) Content Outline Crosswalk, 2016–2024.
https://www.ahima.org/media/42hdf2we/chda-crosswalk_final_11-2023.pdf
4. American Health Information Management Association. (2022). CCHIIM Candidate Guide. Updated March 24, 2022.https://www.ahima.org/media/1d0bdghm/revised-candidate-guide-2-24-25.pdf
Link to the first article in the series: Certified Health Data Analyst (CHDA): The Value in Today’s Healthcare Analytics Workforce
Link to the second article in the series: Does the CHDA Matter to Employers? Certification, Skills and Career Value
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