Does the CHDA Matter to Employers? Certification, Skills and Career Value
CHDA is recognized by employers,
but is the CHDA a universal requirement for healthcare data analyst jobs?
CHDA has relevance
and beyond traditional Health Information Management departments
In Part 1 of this series, “Certified Health Data Analyst (CHDA): The Value in Today’s Healthcare Analytics Workforce”, I examined the evolution of healthcare analytics, changes to the CHDA certification, and the improvement in first-time examination pass rates. Those developments reflect progress, but they also raise an important question: “What value does the CHDA bring to today's healthcare workforce?”
As healthcare organizations increasingly rely on data to guide clinical, financial, operational, and quality decisions, the demand for analytical knowledge extends beyond technical skills. For the Health Information professional, this creates opportunities to build upon existing knowledge of clinical documentation, coding and classification, reimbursement, data quality, governance, and informatics.
This second article examines:
— how employers recognize the CHDA,
— the competencies healthcare data analysts need beyond certification, and
— how analytics connects information across the healthcare continuum.
4. Is the CHDA Certification Valued by Healthcare Employers?
One of the questions I raised in my earlier discussion of the CHDA was whether the healthcare industry recognized the value of the certification. Improving pass rates and increasing the number of credentialed professionals are important, but professional certification must ultimately have relevance to the work being performed.
So, what does the employment market tell us today?
The postings I reviewed show that the CHDA is recognized by employers, particularly for positions involving healthcare analytics, quality improvement, population health, risk adjustment, claims, health information, and informatics. However, the CHDA is generally not a universal requirement for employment as a healthcare data analyst.
Instead, employers recognize the credential in several ways.
Some employers identify the CHDA as a preferred certification.
For example, two 2026 L.A. Care Health Plan postings, (viewed October 2026) Quality Improvement Analytics Data Analyst III and Authorizations and Claims Analytics Solutions Business Insights and Data Analyst III, list the CHDA among their preferred certifications.
Across the two postings, the CHDA appears alongside technical credentials in Tableau, Power BI, Snowflake, SQL, and Python, as well as healthcare-related credentials such as the Certified Professional in Healthcare Quality (CPHQ) and Managed Care Professional. This illustrates how employers may view healthcare analytics as requiring a combination of healthcare domain knowledge and technical analytical capability.
Other positions incorporate the CHDA more directly into professional development requirements. A 2026 Risk Adjustment Data Analyst position with Community First Health Plans/University Health requires healthcare data analysis experience and specifies that, depending upon the role, a coding certification such as the CCS and/or a data analytics certification such as the CHDA or Microsoft Power BI Data Analyst Associate must be obtained within two years of employment.
The CHDA also has relevance beyond traditional
Health Information Management departments.
For example, a Samford University faculty position in Health Informatics and Analytics identified the CHDA or a related specialty certification as preferred. A Washington State government healthcare analytics position also identified the CHDA among desired professional credentials for work involving healthcare data and regulatory reporting.
These examples suggest that the CHDA is recognized in selected healthcare analytics, informatics, education, and public-sector roles, although its importance varies by position and employer.
This distinction is important.
The CHDA is recognized in the healthcare employment market, but it is not the standard entry requirement for becoming a healthcare data analyst.
Employers continue to place substantial emphasis on education, healthcare experience, SQL and database skills, visualization tools such as Power BI and Tableau, statistical and analytical capabilities, healthcare data knowledge, and the ability to communicate findings to both technical and nontechnical audiences.
Although the CHDA is not universally required for employment, that does not diminish the value of the CHDA. Rather, it helps define where the certification fits within the healthcare environment.
A tool-specific technical certification may demonstrate proficiency with a particular analytical tool or technology. The CHDA represents something different: the application of analytics within the healthcare information environment.
AHIMA states “Individuals who earn the CHDA designation achieve recognition of their expertise and mastery in health data analysis, reporting, and business needs assessment.”
And, “This certification provides practitioners with the knowledge to acquire, manage, analyze, interpret, and transform data into accurate, consistent, and timely information while balancing the “big picture” strategic vision with day-to-day insights and details.” (accessed October 2026, reference link below).
For the Health Information professional, that distinction may be particularly relevant. Health Information professionals already work with many of the elements upon which healthcare analytics depends: clinical documentation, data quality, coding and classification, terminology, privacy, governance, reimbursement, electronic health information, and regulatory requirements.
The CHDA can provide a pathway for extending that knowledge into healthcare data analysis and demonstrating an additional area of competency.
However, I would not suggest that a professional pursue the CHDA simply because healthcare analytics is growing—or because another individual has earned the credential.
Certification is an investment of time, preparation, continuing education, and financial resources. Its value should be evaluated in relation to an individual's existing education, experience, responsibilities, and career direction.
Reviewing current job descriptions, as I did for this article (October 2026), can be particularly useful. Look beyond whether the letters CHDA appear in a job posting. Examine the responsibilities and competencies employers are requesting:
⚙️ What types of healthcare data will you analyze?
⚙️ What technical skills are expected?
⚙️ Is knowledge of clinical care, coding, claims, reimbursement, quality measures, or informatics important?
⚙️ Will you be expected to interpret results or simply produce them?
⚙️ Will you communicate findings to clinicians, executives, technical teams, payers, or other decision-makers?
⚙️ Does the CHDA body of knowledge complement the work you want to perform?
These questions bring me back to how I view professional certification today.
The more useful question may not be ⫸ “Do employers require the CHDA?”
The relevant question may be ⫸
“Does the CHDA strengthen the combination of healthcare knowledge, analytical capability, and professional experience required for the work I want to do?”
5. Healthcare Data Analyst Skills: Why Certification Alone Is Not Enough
Professional certification can demonstrate knowledge, commitment, and competency within a defined area of practice. However, certification alone does not establish proficiency in every competency required for a particular position.
Healthcare analytics is a multidisciplinary field. Today's healthcare data analyst may work with clinical, claims, financial, operational, population health, or patient-generated data—often combining information from multiple systems and sources. The skills required therefore extend beyond any single certification.
In two L.A. Care Health Plan Analyst III postings reviewed October, 2026, SQL, Tableau or Power BI, advanced statistical methods, version control, and data governance were required skills, along with knowledge of managed care concepts such as quality measures, utilization management, and claims operations. The CHDA appeared in both as a preferred certification, not a requirement.
Examples from five postings reviewed October 2026:
🌍 Technical and Analytical Skills
Healthcare data analysts increasingly need practical experience with the tools used to acquire, manage, analyze, and communicate data. Depending on the position and organization, these may include:
Excel and other spreadsheet applications
SQL and relational databases
Power BI, Tableau, or other data visualization tools
Statistical analysis and analytical methodologies
Python, R, SAS, or other analytical technologies
Data extraction, transformation, and validation
Version control and reproducible workflows (such as Git)
Data quality and data governance
Increasingly, AI-assisted analytical tools
The specific technologies will continue to change.
More important than mastery of one particular tool is the ability to understand analytical methods, evaluate results, and adapt as technologies evolve. For example, the rapid advancement of AI illustrates how quickly analytical technologies and required competencies can evolve.
🌍 Healthcare Domain Knowledge
Healthcare data have meaning because they represent patients, clinical conditions, services, treatments, utilization, outcomes, reimbursement, and healthcare operations. Analysts need sufficient healthcare knowledge to understand what they are measuring and whether the results make sense within the context in which the data were created.
Depending upon the role, this may require knowledge of:
• Clinical documentation • EHR data • ICD-10-CM • CPT/HCPCS • Claims and reimbursement • Revenue cycle • Quality measures • Population health • Healthcare operations • Regulatory requirements • SNOMED CT and other clinical terminologies • Interoperability and health information exchange • HEDIS/Stars and utilization management
This is an area where professionals entering analytics from Health Information Management, nursing, coding, informatics, quality, or other healthcare disciplines bring valuable domain knowledge to the analytical process.
🌍 Data Quality and Context
Before asking what the data show, the healthcare data analyst must often ask whether the data are appropriate for the question being investigated.
Where did the data originate?
How were the data collected?
What definitions and standards were used?
Are values missing or incomplete?
Are data from different systems actually comparable?
Were coding, documentation, or workflow changes introduced during the period being analyzed?
These questions can materially affect an analysis.
A sophisticated visualization or statistical model cannot compensate for data that are poorly defined, incomplete, incorrectly mapped, or inappropriate for the intended purpose.
Good analytics begins with understanding the data.
🌍 Communication and Data Storytelling
Analysis has limited value if the findings cannot be communicated to the people responsible for making decisions.
Healthcare data analysts frequently work across disciplines and may need to explain findings to clinicians, executives, financial teams, quality professionals, information technology teams, coders, payers, researchers, or other stakeholders.
That requires more than presenting numbers.
The analyst must be able to translate analytical findings into understandable information, explain limitations, distinguish correlation from causation, and communicate what the findings may mean for the organization or population being studied.
In many situations, the analyst becomes the bridge between data and decision-making.
🌍 Critical Thinking in the Age of AI
Artificial intelligence adds another dimension to the healthcare analyst's skill set.
AI can accelerate data preparation, generate code, identify patterns, assist with visualization, summarize results, and support increasingly sophisticated analyses. These capabilities can improve productivity, but they also increase the importance of validation and professional judgment.
Healthcare analysts need to understand both the data and the analytical process to recognize when an AI-generated result does not make sense.
The question is no longer simply, “Can I perform the analysis?”
It is also:
“Can I determine whether the analysis—and the information it produces—is appropriate, accurate, and meaningful?”
Certification as Part of Professional Development >>
This is where I see the CHDA fitting into the larger picture.
The certification provides a structured healthcare analytics body of knowledge. It can demonstrate that a professional has invested in developing competencies related to healthcare data acquisition, analysis, interpretation, reporting, governance, and business needs.
But the credential should complement—not replace—practical experience, technical development, healthcare domain knowledge, critical thinking, communication skills, and continuing education.
Healthcare analytics will continue to evolve. The tools we use today may not be the tools we use five years from now. New data sources will emerge, interoperability will continue to expand, and AI will assume a greater role in how healthcare information is processed and analyzed.
For the healthcare data analyst, continued learning is therefore not optional.
Certification can establish a foundation. Experience, application, and continued learning build upon it.
6. Healthcare Data Analysis Across the Health Information Continuum
This is also where I see the CHDA connecting with the broader health information profession.
Clinical documentation becomes coded and classified information. That information contributes to claims and reimbursement. Clinical, claims, financial, quality, and operational data enter data warehouses and analytical platforms. Information moves among healthcare organizations through increasingly interoperable systems.
Analytics sits across this continuum.
A diagnosis code, for example, is not simply a billing element. In different contexts it may become part of a quality measure, population health analysis, risk-adjustment methodology, utilization study, reimbursement model, research dataset, or a predictive model.
Likewise, clinical information collected within the electronic health record may ultimately support decisions far beyond the original patient encounter.
For the healthcare data analyst, understanding these relationships provides context that technology alone cannot supply and are particularly significant for the Health Information professional.
Clinical documentation, coding and classification, terminology, data integrity, and information governance provide the foundation for many downstream analytical activities. Errors or inconsistencies introduced early in the information process can affect quality reporting, reimbursement, population health measures, and organizational decisions. Understanding the movement of information across systems and healthcare functions enables analysts to evaluate not only what the data show, but whether those data accurately represent the clinical and operational activities being measured.
This is one reason healthcare analytics represents an important opportunity for professionals whose backgrounds include health information management, clinical care, coding, reimbursement, informatics, quality, or healthcare operations.
Healthcare data analysis therefore intersects naturally with health information management, clinical care, coding, reimbursement, informatics, interoperability, quality, and healthcare operations.
The knowledge represented by the CHDA can be applied across healthcare settings, including health systems, payer organizations, public health, consulting, and other environments where healthcare data are used to support clinical, financial, operational, quality, and population health decisions.
CHDA certification career value cannot be measured by certification alone. Its value depends upon how the knowledge represented by the credential combines with healthcare experience, technical capability, critical thinking, communication, and continued learning.
For the Health Information professional, healthcare analytics offers an opportunity to build upon knowledge already developed through working with clinical information, coding and classification, data quality, governance, reimbursement, and information systems.
Knowledge is cumulative
Education, experience, certification, and continued learning build upon one another. The CHDA can add knowledge and professional value—but that does not mean every professional needs the credential at the same point in their career.
The more important question is:
“Will the CHDA add value to my career at this time?”
Consider the knowledge you already have, the work you want to do, and the competencies you need to develop next for your career. Let these considerations be a significant factor in deciding to sit for the CHDA certification.
Conclusion: Connecting Healthcare Knowledge and Analytics
The value of the CHDA extends beyond the certification itself. Its relevance depends upon how healthcare analytics knowledge is combined with professional experience, technical capabilities, critical thinking, and the ability to interpret information within the healthcare environment.
For Health Information professionals, analytics offers an opportunity to build upon existing knowledge while developing new competencies that support clinical, financial, operational, and organizational decision-making.
Technology provides increasingly sophisticated analytical capabilities. Healthcare knowledge provides the context needed to interpret and apply the results.
In the final article in this series, I will look ahead to the relationship between the CHDA and artificial intelligence, the future of healthcare analytics, and what these changes may mean for the Health Information professional.
Link to the first article in the series: Certified Health Data Analyst (CHDA): The Value in Today’s Healthcare Analytics Workforce
AHIMA References:
American Health Information Management Association. (2026). Certified Health Data Analyst (CHDA). AHIMA. AHIMA — Certified Health Data Analyst (CHDA)
American Health Information Management Association. (2023). Certified Health Data Analyst (CHDA) Exam Content Outline. Effective February 1, 2024. AHIMA — CHDA Exam Content Outline
American Health Information Management Association. (2023). Certified Health Data Analyst (CHDA) Content Outline Crosswalk, 2016–2024. AHIMA — CHDA Content Outline Crosswalk
American Health Information Management Association. Certified Health Data Analyst Exam Pass Rates. Perspectives in Health Information Management. AHIMA Perspectives resource, since removed from AHIMA's site. Archived version captured July 6, 2022: AHIMA Perspectives — Historical CHDA Exam Pass Rates
L.A. Care Health Plan. (2026). Quality Improvement Analytics Data Analyst III [Job posting]. L.A. Care Health Plan Careers. Requisition ID 13206.
L.A. Care Health Plan. (2026). Authorizations and Claims Analytics Solutions Business Insights and Data Analyst III [Job posting]. L.A. Care Health Plan Careers. Requisition ID 13303.
***** The views in this article are my own and do not represent AHIMA. I am not writing or speaking on behalf of AHIMA. CHDA is a certification offered by AHIMA, and the pass-rate and exam information comes from AHIMA's published resources, cited above.
Te-Ar Healthcare Consulting is independent of AHIMA.
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