Essential Skill and Knowledge Required for Health Data Professionals: A Content Analysis of Job Advertisements

Abstract

Healthcare organizations rely on skilled health data professionals to enhance organizational effectiveness and patient care. This study analyzes recent job postings to identify the prevalent skills, competencies, and technical skills that healthcare organizations are looking for when hiring health data professionals. A content analysis of 34 unique job postings provides key insights into the skill sets and knowledge necessary to fulfill these roles. The findings revealed a diverse range of skills, including analytics, SQL proficiency, business acumen, data visualization, and essential soft skills such as problem solving, interpersonal communication, and project management. Additionally, the education requirements indicate a need for bachelor’s degrees or higher for these positions. These findings serve as a valuable resource for both educators and employers in guiding curriculum development and refining hiring practices.   

Key Words: skill sets; knowledge, skills, ability and other (KSAO); health information; content analysis; data analysis; data analyst

Introduction

Health data professionals' skill sets, and underlying knowledge are important to individuals and organizations. For many healthcare workers, developing these skills and learning specialized knowledge is important to an individual’s success and pivotal in creating an organization's effectiveness¹. Recognizing the importance of employing skilled data analysts will enable healthcare organizations to manage resources more effectively by utilizing real-time patient data, allowing better and quicker decision-making based on the latest available information².

A clear definition of data analysis is needed to understand what data analytics skills are required and most valued by employers³. Data analysis is “the process of systematically collecting, cleaning, transforming, describing, modeling, and interpreting data, generally employing statistical techniques”⁴. According to a 2022 study, the most commonly mentioned skills included expertise in statistical software and R, SAS, Excel, and SPSS packages. Structured data management, such as SQL, and data preparation using SQL were also listed as skills⁵. Nontechnical skills such as critical thinking, project management, practical use of querying tools, and quantitative and qualitative analysis knowledge are needed⁶. In addition, soft skills such as communication, decision-making, and organization are essential for optimal job performance⁷.

Health data analyst professionals should be well-versed in the concept of big data and its relationship with artificial intelligence (AI), machine learning, and other areas that are helpful in the health care environment⁸. Big data analytics depends on the accuracy of the data input and the efficacy of the collected data. Big data challenges include the storing, searching, capturing, sharing, and analyzing of the data. Also addressed are the challenges of real-time processing, privacy and security, and the diverse forms of healthcare data such as biomedical signals (e.g., blood pressure, blood sugar levels, etc.), genomic data, physiological sensing data, biomedical images, and social media⁹. Big data analytics and the use of AI may have a significant impact in the area of disease diagnosis and identifying potential treatments by developing better diagnostic tests and predicting future outcomes¹⁰.

To support organizations’ enhancement of the quality, efficiency, and safety of patient care through the use of robust databases for the collection and analysis of data, a digitally literate and competent workforce must be developed^11,12. The evolution in data collection methods, frequency, and volume has transformed the technologies and techniques for data analysis¹³. Data analyst professionals are at the forefront of these data collection and analysis efforts. Researchers note that while business analysts, data analysts, business intelligence analysts, and data scientists share similar requirements, there are notable differences in the specific skills each role demands¹⁴.  

The emerging digital technologies and rapid adoption of AI in healthcare have led to a dramatic change in the demand for digital analytic skills and the data analyst profession (DAP)¹⁵.   Adopting AI tools can support health data professionals in analyzing large datasets quickly and efficiently. These tools have the unique ability to identify inconsistencies in data and deliver dashboards and visualizations, as seen in tools like Tableau AI.

Understanding the qualifications, skills, and knowledge necessary for a healthcare data analyst requires identifying the top skills and domain expertise, alongside recognizing which skills can be enhanced through academic curricula and additional training¹⁶. Organizations who have difficulty determining the digital competencies and analytic skills required are seeking creative ways to develop and grow their own workforce talent ¹⁶. Educational programs have increased offerings in data analytics whether it be through a degree or certificate in an attempt to meet employer’s needs¹⁷. This report further supports curricula development in health information (HI) and data analytics educational programs by identifying critical skills that are currently in demand. Furthermore, the insights from this content analysis could aid employers in designing a more structured list of skills and job requirements specific to the field of data analytics ¹⁷.

Objectives:

1. To analyze the content of recent HI-related job postings in order to identify the most commonly requested skills, competencies, and technical proficiencies.
2. To categorize and quantify the prevalent knowledge, skills, and technological requirements for data analytics roles within the healthcare industry based on current job advertisements.

Methods

Study Design

This research employed a systematic approach to collect and analyze data relevant to healthcare data analytics roles, with the primary goal of providing comprehensive insights into the knowledge, skills, abilities, and other characteristics (KSAOs) sought by healthcare organizations when recruiting data professionals. To achieve this objective, an observational cross-sectional, qualitative study design was utilized to assess the prevailing skills and technology requirements for data analytics roles within the healthcare industry. This approach allowed for a snapshot of the current landscape, facilitating the identification of trends and patterns in the qualifications and competencies sought by healthcare employers. Importantly, since the data used in this study were drawn from the public domain, no Institutional Review Board (IRB) approval was necessary, in accordance with ethical standards for research involving publicly available information.

Data Collection

The study sourced data from online job advertisements for health-related data analyst positions posted within the 60-day period between July 1, 2023, and August 31, 2023. To ensure a comprehensive dataset, searches were conducted on prominent national and regional job posting websites, such as Indeed, LinkedIn, Monster, Glassdoor, and individual health system career portals.

Relevant postings were identified through the utilization of specific keywords, targeting job titles containing "data analyst" and at least one of the following health-related terms: "health," "healthcare," "hospital," or "medical." Further refinement was achieved by applying additional filters to restrict results to job postings published on or after July 1, 2023.

The data collection process engaged a team of seven researchers, each assigned distinct segments of the identified postings for meticulous review and manual data extraction. Critical information pertaining to job responsibilities, mandatory qualifications, competencies, and technical skills outlined in each job advertisement was systematically extracted. To facilitate subsequent qualitative analysis, this information was organized into standardized Excel extraction sheets.

This observational study design facilitated the acquisition of contextual insights into contemporary data analytics hiring demands and trends within the healthcare job industry, both at the national and regional levels. The objective of our content analysis was to identify the most frequently requested knowledge, skills, experience levels, and technical requirements sought by healthcare organizations when recruiting data professionals.

In total, 34 job postings that satisfied our inclusion criteria were identified. Each selected job advertisement furnished the following information, which was systematically recorded in our data extraction sheets:

• Weblink: The direct URL linking to the original online job posting.
• Job Title: The job title exactly as stated in the posting.
• Responsibilities: A compilation of key responsibilities and duties associated with each advertised data analyst role.
• Qualifications: The minimum required qualifications, competencies, training, skills, certifications, knowledge areas, and technical proficiencies, presented verbatim from the job posting.

Data Analysis

For data analysis, the collected information was first entered and organized into an Excel file, ensuring the integrity of the dataset. Subsequently, Atlas.ti, a computer-assisted qualitative analysis software (CAQDAS), was employed. The qualitative content analysis was conducted inductively, with job postings systematically coded to identify recurring themes and categories related to KSAOs required for health data analytics roles.

Under the guidance of the lead investigator, job responsibilities and qualifications from each posting were annotated with appropriate codes and categories. Rigorous checks for intercoder agreement were performed on a subsample of 10 advertisements to ensure consistency and reliability.

The coded data were then categorized into broader groups, aligning with conventions used by human resource professionals, encompassing KSAOs. Frequencies of major code groups were calculated to quantify the prevalence of specific requirements across the dataset. Additionally, a sub-analysis was conducted to differentiate between baseline KSAOs needed for analyst-level positions and those required for senior or executive roles.

Results

At the end of the data collection, 35 job advertisements for healthcare data analysts had been collected. One advertisement was a duplicate and not evaluated, leaving 34 unique job postings from across the United States. The advertisements collected came from employers in 17 states, with two advertisements not indicating the employer location. The top three results (see Figure 1) were from Illinois (seven), California (five), and New York (three).

The top three sectors of employers in this group were healthcare providers (13), insurers (seven), and healthcare consultants (six) which is displayed in Figure 2.

Knowledge, Skills, and Abilities

The knowledge, skills, and abilities (KSA) included in the healthcare data analyst advertisements were compiled. This compilation of KSAs from the advertisements provide insight into the requirement sought by employers. The results are listed alphabetically in Table 1. This table represents the number of occurrences (the frequency and number of times the term was mentioned). The case illustrates the number of job advertisements (34 job advertisements). Table 1 results show the prevalence of terms in percentages.    

Terms referring to analytics, business skills, and structured query language (SQL) were found in 71 percent of the job advertisements. For example, the top five frequently used terms are analytics (71 percent), business skills (71 percent), SQL (71 percent), report writing (62 percent), and statistics (56 percent).

These are included in the top five above, interpersonal skills and report writing were the next most frequently occurring items found in 62 percent of the advertisements. Terms relating to statistics (56 percent) were the next most frequently occurring, while data quality and problem solving each were mentioned in 53 percent of the advertisements. Terms relating to design of items such as models, research, reports and others were found in 50 percent of the postings and data visualization in 47 percent. The terms arranged by frequency of occurrence are displayed in Table 2. The percentage represents the number of cases divided by the total number of cases 34.

Terms such as visualization and data sets represent key aspects of the skill set and knowledge required in healthcare data analytics positions. For example, data visualization was observed in 47 percent  of the advertisements demonstrating the role in using visualization tools and presenting data in a manner that can be used for decision making. Data sets were found in 44 percent of the advertisements showing another important role for the healthcare data analyst.  

The results from the compiled list of KSAs shows the diverse nature of the healthcare data analyst job. Skills such as data analysis, business intelligence, and technical proficiency in programming languages and statistical software are important to the role. Technical knowledge such as understanding advanced technical concepts and knowledge of regulations, policies, and legislation related to healthcare, and data management are needed. Abilities such as meeting deadlines, time management, customer service, teamwork, and the ability to communicate effective have been identified in the advertisements. A guide of how the KSA terms were grouped and titled for analysis is included in Table 3. 

Education

The education requirements for each healthcare data analyst advertisement were also evaluated. Multiple advertisements indicated both a minimum and preferred educational requirement, and all requirements mentioned are included in the results. The most common requirement was a bachelor’s degree, mentioned in 68 percent of the advertisements. A master’s degree being preferred was the second most occurring, being in 21 percent of the advertisements. An educational requirement not being specified was the third highest frequency at 15 percent. This category indicates the educational requirement was not explicitly mentioned. The associate degree category, while less common compared to bachelor’s degree, was identified in 6 percent of the job postings. Job advertisements may include more than one level of education; for example, bachelor’s degree required and/or master’s degree preferred. The count represents the frequency of the educational level listed in the job advertisement. The percentage is calculated using the total count over the number of cases (45). The results of educational requirements are displayed in Table 4.

The data in Table 4 compare the educational requirements of senior-level healthcare data analysts and healthcare analysts. The analyst-level healthcare analyst requires a bachelor's degree in 17 out of 25 cases, while a smaller proportion prefer candidates with a master's degree or higher (4 out of 25). The senior-level analyst positions require a bachelor's degree (6 out of 9) and a master's degree (3 out of 9). Both analyst and senior-level analyst positions require candidates to have at least a bachelor's degree. A slightly higher preference for candidates with higher education qualifications (master's degree) is shown in senior-level analyst advertisements.

A comparison of the top five KSAs was reviewed. In the analyst-level position, the data shows analytics skills are the highest, with 80 percent of the positions listing this skill. SQL skills are also important, with 68 percent of positions requiring this skill. Business skills and report writing abilities are mentioned in 64 percent of the analyst-level positions, while interpersonal skills (60 percent) and data quality and problem-solving abilities are mentioned in 56 percent of the advertisements.

Business skills are ranked first for the senior analyst position, with 89 percent of the positions requiring expertise in this area. SQL remains the second top-ranked category, with 78 percent of the senior analyst roles listing this in the advertisement. Analytics skills (67 percent), statistical knowledge (67 percent), and data sets (56 percent) are listed in the ad for the senior-level position.

Overall, both analyst-level and senior-level analyst positions prioritize technical knowledge, skills, and abilities (such as analytics, SQL, and statistics) and other skills, such as interpersonal skills and problem-solving. Table 5 also displays a comparison of KSA for each group.

Discussion

This study assessed the knowledge, skills, abilities, and other characteristics (KSAOs) required for health data analytics jobs using data extracted from online job postings. The data collected from these job postings was used to determine the skills, competencies, and technical proficiencies most commonly required or desired for health data analytics jobs. In addition, the data was further categorized and quantified to highlight requirements for various job levels. This information can be helpful not only for job seekers but also for HI educators in determining what skills should be taught in HI higher education programs.

There were a large variety of key terms used in the job listings for health data analytics jobs, however, after analysis there were some trends that were noted. When the key terms were further coded and grouped, it was easy to see that there were both hard and soft KSAOs required for data analytics jobs. Some of the more commonly required or desired skills were basic business skills, including interpersonal skills, problem solving, report writing and analysis, presentation skills, project management, and other administrative skills. Another area of commonly required or desired skills was technical analytic knowledge, including business intelligence, data quality, data set and database use, SQL, Tableau and other statistical software, programming, a strong statistical knowledgebase, and data visualization and design.

While many HI professionals or new graduates may have skills or training in some of these areas, additional education or training may be required. It is noted that the vast majority of the job listings required a bachelor’s degree or above. While bachelor’s HI programs are required to teach basic management theories and skills, this study shows that it is essential that bachelor’s and master’s HI programs include some of the more specific skills needed for data analytics jobs, such as project management, report writing and analysis, and presentation skills. In addition, a continued focus on soft KSAOs will help prepare students for the data analytics workforce.

Also, further skills may be needed in the technical aspects of data analytics. In order to determine the needed breadth and depth of such skills, the data was analyzed further. In evaluating the need for skills by level of job it is noted that skills such as specific business intelligence and statistical software, data visualization and design, SQL, and information and data literacy are needed for many jobs. These are specific skills that may not be covered in some baccalaureate level HI programs. Therefore, such educational programs should consider adding these or strengthening the curriculum in these areas in order to adequately prepare students for data analytics jobs. Job seekers wishing to move into higher level data analytics positions may wish to pursue additional training, certification, or micro credentials in these areas.

Although this job skills analysis provides valuable information for job seekers and educational programs, it is important to note that many HI professionals already possess these skills. It is crucial not overlook the fact that many HI professionals are uniquely equipped to leverage their understanding of healthcare, health informatics, and data analysis to support future systems such as AI applications. Unlike information technology programs, HI programs train their students to use the diverse skills identified in this study. Simply put, these students will become the HI professionals with the expertise to enhance, manage, and train future AI systems. Health data analytic skills will be essential not just to automate or replace systems but to improve healthcare systems. The results highlight knowledge and skills that could be added to or strengthened in HI program curriculum.

Limitations

This study does have its limitations. It relies on a random selection of job postings, without employing purposeful or systematic sampling methods, which constrains the generalizability of the findings. The sample size further restricts the breadth of application of the results. Moreover, the data was collected at a specific moment in time, adding another layer of limitation to the study’s applicability over time.

Lastly, this cross-sectional study is limited in examining changes over time, particularly in the recent years when health IT and AI have transformed and developed significantly. Future research could include longitudinal studies to capture the dynamic nature of job market needs over time.

Conclusions

The current technology driven healthcare workplace is quickly embracing artificial intelligence and machine learning which will lead to a remarkable increase in the demand for employees with advanced data analytics knowledge and skills. This study considered the developing skillsets required for health data analytics jobs by extracting related key words and phrases from online job postings. Skills such as business intelligence and statistical software application, data visualization and design, SQL, and information and data literacy were found to be preferred or required for most of the jobs reviewed. In order to enhance health organizations’ efforts in patient safety and quality of care, a knowledgeable and capable workforce must be educated on the use of robust databases for the collection and analysis of data and the visualization of data to empower sound decision making. Further study could provide more detail as to the level of skill or depth of knowledge required in the various skill sets for employees to be successful or to move upwards in their data analytics career. Through educational curriculum updates and strategies, the HI workforce will need to be ready for the challenges found in the digital age of healthcare. 

References

1. Skhvediani, A., Sosnovskikh, S., Rudskaia, I., & Kudryavtseva, T. (2022). Identification and comparative analysis of the skills structure of the data analyst profession in Russia. Journal of Education for Business. 97(5), 295–304. https://doi.org/10.1080/08832323.2021.1937018

2. Tenali, N., & Babu, G. R. M. (2023). A systematic literature review and future perspectives for handling big data analytics in COVID-19 diagnosis. New Generation Computing. 41(2), 243-280. https://doi.org/10.1007/s00354-023-00211-8

3. Verma, A., Yurov, K. M., Lane, P. L., & Yurova, Y. V. (2019). An investigation of skill requirements for business and data analytics positions: A content analysis of job advertisements. Journal of Education for Business. 94(4), 243–250. https://doi.org/10.1080/08832323.2018.1520685

4. Eldridge, S. (2023, November 4). Data analysis. Encyclopedia Britannica. https://www.britannica.com/data-analysis

5. Skhvediani, A., Sosnovskikh, S., Rudskaia, I., & Kudryavtseva, T. (2022). Identification and comparative analysis of the skills structure of the data analyst profession in Russia. Journal of Education for Business. 97(5), 295–304. https://doi.org/10.1080/08832323.2021.1937018

6. White, S. (2016). (3^rd Ed.)  A Practical Approach to Analyzing Healthcare Data. AHIMA: Chicago.

7. Verma, A., Yurov, K. M., Lane, P. L., & Yurova, Y. V. (2019). An investigation of skill requirements for business and data analytics positions: A content analysis of job advertisements. Journal of Education for Business. 94(4), 243–250. https://doi.org/10.1080/08832323.2018.1520685

8. Jimenez, G., Spinazze, P., Matchar, D., Huat, G. K. C., van der Kleij, R. M. J. J., Chavannes, N. H., & Car, J. (2020). Digital health competencies for primary healthcare professionals: A scoping review. International Journal of Medical Informatics. 143, 104260. https://doi.org/10.1016/j.ijmedinf.2020.104260

9. Awrahman, B. J., Aziz Fatah, C., & Hamaamin, M. Y. (2022). A review of the role and challenges of big data in healthcare informatics and analytics. Computational Intelligence and Neuroscience. 2022.1–10. https://doi.org/10.1155/2022/5317760

10. Tenali, Nagamani, and G. Rama Mohan Babu. “A Systematic Literature Review and Future Perspectives for Handling Big Data Analytics in COVID-19 Diagnosis.” New Generation Computing 41, no. 2 (March 16, 2023): 243–80. https://doi.org/10.1007/s00354-023-00211-8.

11. Awrahman, B. J., Aziz Fatah, C., & Hamaamin, M. Y. (2022). A review of the role and challenges of big data in healthcare informatics and analytics. Computational Intelligence and Neuroscience. 2022.1–10. https://doi.org/10.1155/2022/5317760

12. Jimenez, G., Spinazze, P., Matchar, D., Huat, G. K. C., van der Kleij, R. M. J. J., Chavannes, N. H., & Car, J. (2020). Digital health competencies for primary healthcare professionals: A scoping review. International Journal of Medical Informatics. 143, 104260. https://doi.org/10.1016/j.ijmedinf.2020.104260

13. Gardiner, A., Aasheim, C., Rutner, P., & Williams, S. (2018). Skill requirements in big data: A content analysis of job advertisements. Journal of Computer Information Systems. 58(4), 374–384. https://doi.org/10.1080/08874417.2017.1289354

14. Verma, A., Yurov, K. M., Lane, P. L., & Yurova, Y. V. (2019). An investigation of skill requirements for business and data analytics positions: A content analysis of job advertisements. Journal of Education for Business. 94(4), 243–250. https://doi.org/10.1080/08832323.2018.1520685

15. Skhvediani, A., Sosnovskikh, S., Rudskaia, I., & Kudryavtseva, T. (2022). Identification and comparative analysis of the skills structure of the data analyst profession in Russia. Journal of Education for Business. 97(5), 295–304. https://doi.org/10.1080/08832323.2021.1937018

16. Meyer, M. A. (2019). Healthcare data scientist qualifications, skills, and job focus: A content analysis of job postings. Journal of the American Medical Informatics Association. 26(5), 383–391. https://doi.org/10.1093/jamia/ocy181

17. Verma, A., Yurov, K. M., Lane, P. L., & Yurova, Y. V. (2019). An investigation of skill requirements for business and data analytics positions: A content analysis of job advertisements. Journal of Education for Business. 94(4), 243–250. https://doi.org/10.1080/08832323.2018.1520685

Figure 1: Healthcare Data Analyst Employer Locations

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Figure 2: Healthcare Data Analyst Employer Sector

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Table 1: Frequently occurring key terms

Term	Occurrences	Cases	Percentage
Administrative skills	18	10	29
Analytics	55	24	71
Billing and Claims Knowledge	11	8	24
Business Skills	62	24	71
Business Intelligence	14	10	29
Change Management	4	2	6
Coding	1	1	3
Data Quality	29	18	53
Data Set	19	15	44
Database	20	11	32
Decision Support	3	2	6
Design	26	17	50
Information and Data literacy	21	14	41
Interpersonal Skills	30	21	62
Leadership	9	4	12
Legal, Regulatory, & Policy	18	7	21
Presentation Skills	21	12	35
Problem Solving	24	18	53
Program Development	2	1	3
Programming Language	11	8	24
Independent Worker	8	8	24
Project Management	14	10	29
Report Writing and Analysis	47	21	62
Research Proposal	4	3	9
SQL	39	24	71
Statistical Software	28	14	41
Statistics	36	19	56
Tableau	17	14	41
Technical Skills	21	12	35
Testing	14	5	15
Training	5	4	12
Visualization	45	16	47

 

Table 2: Top 15 terms by count

Items Ranked by top terms	Cases	Percentage
Analyze (analytics)	24	71
Business Skills	24	71
SQL	24	71
Report Writer	21	62
Interpersonal Skills	20	59
Statistics	19	56
Data Quality	18	53
Problem Solving	18	53
Design	17	50
Visualization	16	47
Data Set	15	44
Information and Data Literacy	14	41
Statistical Software	14	41
Tableau	14	41
Presentation	12	35
Technical Skills	12	35

 

Table 3: Knowledge, skills, abilities, & other (KSAOs)

Category	Key Terms/Description
Administrative Skills	Meeting deadlines, time management, customer service, teamwork, distributes communication/distributes reports
Analyze (Analytics)	“Analyzes data”; analysis, predictive modeling, analyzes large data sets. This can include interpret complex data sets, identify trends, provide actionable insights.
Business Skills	Business requirements, stakeholder collaboration, departmental collaboration. These skills include but are not limited to the following: understanding healthcare operations, financial principles, management, business communication, business collaborations, and strategic planning.
Business Intelligence	Power BI, creates dashboards
Data Quality	Performs data integrity
Data Sets	Analyze, manager, work, or develop code sets
Design	Design predictive models, design approach for automation, design analytic tools, design reports, design or building content, design implement solutions, design/develop data processes
Information and Data literacy	Managing data, compiling data, storing data, search and finding, information process, digital content
Interpersonal Skills	Communication verbal, written, and professionalism
Leadership	Provider leadership, help solve problem and manage , administer public health policy, provider leadership/direction, oversee programs
Technical (Knowledge)	Creates data marts, testing, application layer testing, create and understand advance, access data warehouse, extract data, debug, and investigate problems.
Legal, Regulatory, Policy	Monitor regulations. Analyze the impact of policy, administer public health policy, conduct analysis of policy, provide advice on the development of policy, monitor or review legal, regulatory, or legislation. Develop materials to explain policy, respond to congressional inquiries, understand Medicare policies, knowledge of state regulations
Problem Solving	Ability to problem solve, problem solving, solve company problems, gain insight into key business problems, etc.
Project Management	Life cycle, status reports
Presentation Skills	Ability to present to and communicate data, presentation, develop presentation for leadership, delivering presentation, present data
Programming Language	SQL, Java, programming language
SQL	Search for the acronym “SQL” term used in the job advertisement
Statistical Software	SAS, R, Python, statistical software
Statistics	Statistics or statistical
Tableau	Search for the term Tableau
Report Writing (Analysis and Development)	Write comprehensive reports, develop reports, analyze and develop routine reports, AQ of existing report, prepare accurate reports
Visualization	Search for the term visualization or graphical representation of information and data

 

Table 4: Education

Education Level	Count	Percentage	Analyst	Senior Analyst
Education Not Included	5	15	5	0
Education Associate Degree	2	6	1	1
Education Bachelor’s Degree	23	68	17	6
Education Master’s Degree or Preferred	9	26	6	3
Education Doctoral or Professional Degree	1	3	1	0

Note:  Total job advertisements = 34. The count represents the number of times the educational level was mentioned in the advertisement. Several advertisements had multiple educational levels. Some advertisements had no educational level noted.

Table 5: Comparison of the senior-level and analyst-level top five knowledge, skills, and abilities per category.

Category	Top five ranked skills
	1	2	3	4	5
Analyst-level	Analytics (80%)	SQL skills (68%)	Business  skills  & Report Writing  (64%)	Interpersonal (60%)	Data Quality & problem solving (56%)
Senior-level Analyst	Business  skills  (89%)	SQL (78%)	Analytics  skills  (67%)	Statistics (67%)	Data sets (56%)

 

Author Biographies

Cathy A. Flite, PhD, RHIA, FAHIMA, is an associate professor at Temple University’s Department of Health, Administration and Policy in Philadelphia, PA.

Susan L Foster, EdD, MBA, RHIA, CHPS, CHC, CHPC, CIPP/US, CC, FAHIMA, is a program director and professor at Florida SouthWestern State College’s School of Health Professions in Fort Myers, FL.

Shannon H. Houser, PhD, MPH, RHIA, FAHIMA, is a professor at the University of Alabama at Birmingham’s Department of Health Services Administration in Birmingham, AL.

T.J. Hunt, PhD, RHIA, CHDA, FAHIMA is an associate professor in the Heath Informatics Department at Rutgers University in New Brunswick, NJ.

Lakesha Kinnerson, MPH, RHIA, is an assistant professor at Samford University’s Department of Healthcare Administration and Informatics in Birmingham, AL.

Angela Morey, PhD, RHIA, CPHIMS, is a professor at the University of North Georgia’s Department of Health Administration in Dahlonega, GA.

Jennifer L. Peterson, PhD, RHIA, ODS, is a professor and the program director for Health Informatics and Management in the department of health sciences at Illinois State University in Normal, IL.

Roberta Darnez Pope, MSHI, RHIA, is a health information management instructor in the College of Health and Human Services, Department of Public Health at Western Kentucky University in Bowling Green, KY.