Healthcare providers and payers are in stiff competition for health information technology vendors to hire experienced data scientists and machine learning experts in a very competitive job market, according to a recent study published in the Journal of the American Medical Informatics Association (JAMIA). (Academic.oup.com)
Healthcare systems, insurance companies, and vendors all are fighting for experienced data scientists who have advanced knowledge of common programming languages and machine learning development, the report revealed. (Healthitanalytics.com)
All Types of Organizations Need to Leverage Big Data
Companies of all types want to leverage their big data in a more effective manner, according to study author Melanie Meyer, a health informatics professional from the University of Massachusetts Lowell. These employers need to find more talented big data staff to meet their data analytics goals.
In healthcare, where most data is unstructured and hard to access and analyze, many stakeholders are trying to find staff who can understand how to put together meaningful information from heterogeneous and fragmented healthcare data.
The defining characteristic of a true data scientist, explained Meyer, is the ability to think broadly as well as deep for at least one area of the field, such as big data or statistics.
Given the nature of this complex work, data science is a very creative and collaborative field, Meyers says. These data scientists often work in team environments to find the most innovative ways to finish projects. Data communication is also a vital skill. It includes a high level of expertise with visualization tools that are used to explore raw data as part of the data science collaborative process.
Data Science Shortages Are Constant in Healthcare
Talent shortages in the big data area are a constant problem, but it is not from a lack of effort. In a 90-day period from February-April 2018, Meyer saw 200 healthcare-related data science jobs on one employment website.
About 40% of the 198 job openings were posted by consultants and vendors. Eighteen percent were posted by health insurance companies, and 16% were from health systems.
Interestingly, biotech, pharmaceutical and research companies only posted a few listings. Individual hospitals and doctors’ groups only posted about six listings.
Almost 40% of the job listings were for a person who could aid in performance improvement, especially regarding quality measurement, patient outcomes, and financial improvements. Some of the listings mentioned population health management projects and clinical decision support work.
Twenty-five percent of the postings wanted professionals with product development skills, and 7% wanted people with innovation talent.
Meyer noted that jobs in health systems usually focused on performance improvement, and vendor positions were more about product development. Vendors are typically looking for experts in digital health, claims analytics, natural language processing, and behavioral health solutions.
Many Job Advertisements By Insurance Companies Lacked Focus
Meyer found it interesting that most of the ads offered by insurance companies were not looking for a specific data skillset. This could indicate that payers are looking for broader support of current data science projects. Some bigger name health systems, such as those related to academic medical centers, wanted to fill jobs in well-established data science or artificial intelligence departments.
Data scientist jobs at healthcare systems are often found in departments such as clinical strategy, enterprise analytics, informatics, or population health. At insurance companies, the jobs are in departments such as clinical analytics or corporate analytics.
Companies are generally looking for people with mid-level or senior-level experience in the data sciences. Sixty-three percent of all the job listings were for the mid-career data scientist, and 30% wanted more senior professionals.
Approximately ⅔ of the job listings wanted three to 10 years of work experience. Statistics expertise was especially in demand, as was R, data storytelling, Python, and machine learning.
Most of the jobs listed wanted a bachelor’s degree, but some wanted a master’s degree in data analytics. Desired degrees included quantitative fields, including statistics, engineering, and computer science.
Senior Positions Sought by Vendors and Insurance Companies
Many vendors and insurance companies wanted data professionals for senior-level roles. This trait may indicate these stakeholders are more advanced than the providers. Or, they are taking a different approach to build data skills in their organizations.
All data science jobs need proficiency in programming languages and a good understanding of statistics, but companies across industries also are looking for ‘softer’ skills to complement those skills.
Meyer said that the importance of data communication should always be emphasized. For instance, communicating findings and storytelling were among the most required skills for many tech positions. Working well with a large team of people from different departments was often a common requirement, which means the candidate must have a well-rounded skill set that is not just focused on technology.
Problem-solving skills also were listed, as this ensures the professional candidate can handle challenges involved in working on business problems in creative ways. Being exposed to real-world problems helps the professional to develop data skills.
Organizations that want to bring on board data science experts to support their machine learning and analytics projects will need to look for candidates that have a balance of mathematical and interpretive skills.
Some stakeholders may need to offer higher salary packages to bring in such high-value professionals, but the investment here could be worth it. The market for data analytics skills continues to get stronger.
Developing a strong support team of qualified, experienced, and creative data scientists will better position payers, providers and vendors for success in a very competitive environment. With the proper staff in place, companies will be able to pull actionable insights from their massive data assets.