Could Healthcare Innovation Be Hindered By a Data Scientist Shortage?

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Like many other industries, healthcare has become increasingly reliant on digital data. This trend has birthed many positive improvements in care quality, affordability, efficiency, and more, but this innovation may be slowing. A shortage of healthcare data science workers is looming.

Demand for STEM jobs has outpaced supply for years, but the recent data revolution has pushed this further. As it becomes harder and harder to find qualified data scientists, medical advancements may not be able to maintain their current pace.

Data science may drive the future, but that won’t happen with data alone. The industry needs experts who can help them capitalize on it in hopes that this data scientist shortage doesn’t last forever.

As of 2020, there was a data scientist shortage of 250,000 workers. That year, there were three times as many postings for data science jobs as there were searches for them. These listings had grown 39% over the prior year, and as these skills have grown more in-demand, these trends have only grown.

The implications of this shortage are more extreme in healthcare data science than in many other sectors. An estimated 30% of warehoused data comes from the medical industry, leaving the field with far more demand for data experts than others. Since improvements in that data cache could save $300 billion annually, this demand will likely only grow.

It doesn’t help that the medical industry is also relatively new to data science. Many healthcare organizations have only recently switched to digital, data-driven technologies, leaving them with a lack of experience. Consequently, it’s harder to train up talent from within the industry to serve these growing needs.

So, why is the healthcare data science shortage such an issue? The short answer is that the medical field has a much more urgent need for data scientists than other industries. While data science can help manufacturers outperform competitors, it can save lives and protect sensitive information in medicine.

Healthcare data is sensitive, yet it has to move between various parties. For example, payors have to disclose financial information with medical insurers under the No Surprises Act. Transferring or analyzing this sensitive data is challenging, requiring expert input.

The medical industry’s high security and precision needs typically exceed those of other industries. As a result, a lack of data science talent has a more extreme impact.

Data science is also starting to play a role in medical care itself. Analytics can provide practical insights for decision-making, like weighing patient needs to determine optimal resource distribution. Without reliable data analytics, hospitals may not deliver resources where they’re most needed. Those mistakes could lead to reduced care quality or even cost lives.

Healthcare data science could also create more accurate machine learning algorithms to help diagnose patients. These innovations could help provide more accurate personalized care, leading to better health outcomes. However, without data scientists, hospitals will be unable to create and fine-tune these models.

The healthcare data science talent shortage is concerning, but there are some solutions. As data science has grown, more automated tools have emerged to streamline the process. These tools can accomplish what took data scientists days in a few minutes, helping workers perform more work.

Implementing automated data gathering, cleansing, and organizing tools can save healthcare data science a lot of time. Data scientists can then spend more of their time on value-adding work, turning this data into actionable insights and tools.

Making data science a more accessible field can also help. Easier-to-use tools, user-friendly interfaces, and more education options can make it easier to learn data science concepts and skills. Medical organizations could then train existing employees to fulfill their data science needs, reducing the need for outside hires.

Ready-to-use healthcare databases have also become more common. Using these pre-cleaned, anonymous data sets to train AI models removes a considerable portion of the process. As a result, healthcare organizations can dive into data science much faster, requiring fewer experts.

The medical industry has only scratched the surface of healthcare data science’s potential. As more organizations rely on this field, demand for data scientists will keep growing. While this demand may outpace supply for a while, this trend won’t likely persist forever.

Data science is becoming more accessible and efficient by the day. If medical businesses can capitalize on these trends, they can mitigate the data scientist shortage and make the most of their data.