Health data is pervasive in healthcare today, with many organizations seeking to use that data to improve care and operations, thus increasing the need for health data analysts. To find out what it takes to be a great health data analyst, we gathered feedback from a panel of experienced analysts who have worked in a wide range of healthcare environments.
- Laura Blabac, MA, MS, RHIA, CHDA – Lead Tech Product Manager, UnitedHealth Group
- Amber Epperson, RHIA, CHDA, COC – Data Quality Specialist, KONZA National Network
- Benjamin Kaltz, RHIA – Sr. Business Data Analyst, Cooper University Health Care
- Nikky Lei, RHIA – Clinical Data Manager/Analyst, Abbott
Their thoughts are summarized below. .
Interest in Being a Health Data Analyst
All of the interviewees mentioned the ability to impact healthcare as being a primary driver of interest in health data analysis. The analysts use data to tell stories, identifying insights and trends important to their organizations. Here are some examples:
- Ben shared that being an analyst offers the opportunity to blend his interest in data and healthcare and he now applies his health information management (HIM) skills in his revenue cycle operations and analytics position.
- Nikky’s interest in data analysis grew from her early medical record jobs and working with data at a medical device company.
- Amber learned about the importance of data quality through her work as a coding manager where she saw how different types of data (e.g., clinical documentation, provider alerts, coding, reimbursement revenue, quality metrics) are dependent on one another.
- Laura has always been interested in the “stories” told by health records and realized that the ability to manage, collect, and understand that data would grow in importance.
Important Skills for Health Data Analysts
All agreed that being a health data analyst requires both hard (i.e., technical) and soft (e.g., communication) skills. Each of the interviewees had examples to share in these skill areas.
- Ben mentioned how communication skills were very important to working with stakeholders on projects and that strong Excel skills were a must. He is working on becoming more proficient with SQL.
- Nikky feels that, as a data analyst, attention to detail and patience are important soft skills. She noted that SQL and Excel are your good friends. Work experience and reflecting on mistakes help develop those skills over time. Communication skills are required when reporting to audiences with different levels of knowledge.
- Amber shared that curiosity is important, as is creativity, critical thinking, and being detail-oriented. She said it is key to have a solid understanding of the data points being reviewed and to be able to translate a set of data into actionable information, by way of the many types of processing languages. She has taken online courses for learning advanced Excel functions and is currently working on learning the NCQA Data Aggregator Validation program.
- Laura felt that the most important skills are being able to use analytical and data visualization tools and languages, such as Excel and Tableau, and R, SQL, and Python. Even more essential is the ability to see patterns and trends and to be able to show and communicate them. Early on, she focused on becoming an expert in Excel and learning how to clean data, as well as creating pivot tables and visualizations. She hopes to learn more about R, given its use in supporting data science and research.
Being a Great Health Data Analyst
A key point the interviewees made was that the best health data analysts are well-versed in working with a wide range of data and really understand how data drives decision-making. Ben noted how managing lots of data at one time is very important as well as clearly indicating the context and data source(s). Nikki felt that being open-minded and open to learning things out of the scope of daily tasks helps make a great data analyst.
Amber shared that to be a great health data analyst one should know the basic terminology/nomenclature systems and how they are used; have a baseline understanding of the insurance landscape, including incentive payment programs and what the “Triple Aim” seeks to do; have the drive to stay up-to-date with ever-evolving regulatory programs; and lastly, have a grasp on the ways in which data is stored and shared within the electronic health record.
Laura thinks the best data analysts have the ability to really understand the data, to get into the meaning of each data element, and to understand the quality, structures and interrelationships of data, across systems—the skill of data profiling. Good data analysts also know how to listen to subject matter experts and to present data as it exists—the skill of data visualization. Lastly, and equally important, the best data analysts learn how to allow the data to tell its story, paying attention to bias and finding ways to reduce it.
What You Like Most
The group agreed that health data analysts love to dig into the data to find the story behind the numbers. Laura noted how being a health data analyst is a real “roll up your sleeves and dig in” job. Ben really enjoys working with teams and people from other departments such as coding, billing, revenue integrity, and healthcare access. He says, “It’s exciting to see how your contributions to a project can impact so many different service areas throughout an organization, both from a final outcomes perspective, as well as process improvements.” Amber mentioned how every day is a challenge but very fulfilling, as she is able to use her skillset to bring value to healthcare in a unique and needed way. Laura also shared that she personally enjoys the process of cleaning data and getting it to a point where it can be analyzed—it satisfies her “data neat freak” streak. All agreed that a health data analyst wears many hats and can contribute in many ways.
All recognized that the volume of healthcare data today has grown tremendously and that is one of the biggest challenges to being a health data analyst: working with all this data. Amber agreed noting that the volume of data to be reviewed, cleaned, and interpreted is extensive. Additionally, interfaced systems can cause issues with data processing and translation, leading to confusion. Nikki felt that analyst roles can be demanding as analysts can get ad hoc requests from many team members at once. Time management is critical.
Another challenge is data silos. Ben shared that even with an EHR system that is very robust, his health organization, like many others, experiences issues with data silos. Work is underway to have a data lake infrastructure, but there are certainly difficulties when working with disparate systems and trying to integrate all of the clinical, financial, and administrative data needed.
Laura felt that one of the most challenging things about being a health data analyst is trying to influence an organization to value data management. While organizations value having data, ensuring and enforcing good data management practices across an organization is not always given the same value or attention. When organizations allow the creation of data in silos, don’t utilize standard terminologies or ontologies, or are not diligent in day-to-day management, it can —and usually does—create data duplication and a significant amount of dirty data, making the ability to do good analysis difficult, if not in some cases impossible. Data governance is equally as important.
Career Advice for Others Interested in Becoming a Health Data Analyst
All interviewees felt their HIM education and RHIA credential provided a solid foundation in health data and helped prepare them for the work they do today. Ben shared that his HIM education helped him to see the bigger picture of the healthcare system and develop critical-thinking skills. Amber emphasized that health information professionals have a unique skill set that can pave the way to the data analyst role.
The group also had some tips for getting started in an analyst role. Nikki suggested trying to work with data to see if you like it—there are many analytics courses and datasets available to help get some practice and understand the work. Laura advised learning how to speak “healthcare” fluently and becoming a subject matter expert in a variety of clinical and administrative operational areas that create or use data. It is also important to start to build your technical skills, at the very least in Excel and SQL. Working on practice projects is a great way to do this and build a portfolio.
Learn more on the AHIMA Data Analytics page.