The healthcare industry has spent the better part of the last decade building new and revamping old information systems. It has created new ways to collect, store and share data and has been hyper-focused on what comes next for that data. But as the industry has done so, one thing that has become clear is the need for more talent capable of working with that data.
The need for data science professionals is growing alongside the rate at which technology invades healthcare. A 2019 analysis of health data scientist job postings published in the Journal of the American Medical Informatics Association (JAMIA) highlighted the ferociousness with which healthcare providers, IT vendors and insurance companies are competing for data science talent.
The skills most sought after include knowledge of common programming languages and machine learning development. These organizations are looking to make better use of the data they have by structuring and analyzing it to create stories from data that is diverse in content and fragmented throughout systems.
The Skills, Experience Gap
The reason the competition is so fierce is that organizations are having a difficult time conquering the data skills gap. Part of it comes down to the development of competencies for health data science positions to find people who can glean insights from operational, financial and clinical data. Another part of it comes down to concerns over budget and a lack of trust of technology from executives.
Of the listings analyzed by JAMIA, 37% were seeking someone who could help them analyze data relating to quality of care, patient outcomes and financial performance. Population health and clinical decision support initiatives were also top of mind. It’s worth noting that only 25% of them were seeking someone with product development skills.
For the vendors, claims analytics, behavioral health expertise and natural language processing were areas where interest was the highest.
The gap is partly down to experience being an issue. Of the positions listed, 63% were seeking a professional who was “mid-career” or in other words, has at least three to 10 years of relevant experience working in data analytics, particularly in areas involving statistics, storytelling through data, machine learning and Python.
Furthermore, an additional 30% of listings were seeking someone for more senior roles than mid-level. For young talent, finding roles in data science positions can be tough given that they don’t fit the experience profile. To help overcome this, obtaining further education could help alleviate some of the need for experience.
The Right Education
While data science jobs require technical skill such as an understanding of programming languages and statistics, many also seek candidates with the soft skills and broader knowledge of healthcare processes and requirements to work within or manage interdisciplinary teams.
The need to communicate data across the spectrum of care and between teams whose knowledge of health IT systems may be limited is steadily growing. Knowing how to identify goals and plug them into broader organizational strategies is something that vendors and insurance companies in particular are looking for, according to the JAMIA analysis.
A Master of Science in Health Informatics (MSHI) or a concentration of an MSHI degree such as Healthcare Analytics could help prove to an employer that you have the relevant knowledge to fill a mid-level position. For someone in a mid-level position looking to move into a more senior role, a graduate education is likely required or, at the very least, a good thing to have in proving you have the qualifications to take on such a role.