2 min read

Can health systems afford AI talent?

Can health systems afford AI talent? unknown

Health systems are rapidly incorporating artificial intelligence into their operating models and clinical care delivery. Many are partnering with digital health companies or pioneering AI-driven EHR applications with external tech support.

 

Others are building internal teams to set their artificial intelligence strategy and propel their organizations into the future. Many of the AI positions are garnering six-figure salaries, which can be tough for hospitals and health systems to manage with tight margins and multiple strategic priorities. But as AI becomes more ubiquitous in healthcare, can systems afford not to invest in it?

Health systems on the forefront of artificial intelligence are appointing chief AI officers and bringing on AI engineers and other talent. In the last year, Phoenix-based Mayo Clinic Arizona, UC Davis Health in Sacramento, Calif., and UC San Diego Health all brought on chief AI officers. According to Bloomberg, chief AI officers could command $1 million salaries.

When joining Mayo Clinic Arizona in September, Bhavik Patel, MD, said the system was just starting to "scratch the surface" of AI in medicine and shared plans to develop AI models to automate information from medical information to support patient decision-making and guidance of care.

UC Davis brought on Optum's former AI chief to lead the system's efforts to establish a strategy in the area and oversee data-sharing initiatives. Dennis Chornenky will also bring on his consulting technology firm Domelabs AI to boost the system.

Building an AI team takes commitment and resources. AI positions are highly specialized and require knowledge in building and managing data, according to a report from Washington, D.C.-based Nexford University. Machine learning engineers, AI engineers and data scientists are often part of internal AI teams. But competition is high for people with these skill sets. The average salaries for common AI roles are:

  • Machine learning engineer: $109,143
  • AI engineer: $160,757
  • Data scientist: $65,674
  • Computer vision engineer: $168,803
  • AI research scientist: $115,443
  • Natural language processing engineer: $86,193

Health systems may decide to bring on an artificial intelligence consultant to develop their strategies, but it will not be cheap. The average salary of an AI consultant is $124,843, according to Nexford.

Rochester, Minn.-based Mayo Clinic is hiring an AI systems engineering manager to provide strategic direction on regulatory compliance for AI model development and implementation and consult with stakeholders on best practices with artificial intelligence. The health system is requiring a good amount of experience and healthcare knowledge for this role and listed the annual salary range as $250,000 to more than $500,000.

Cedars-Sinai in Los Angeles is hiring multiple AI roles, including a programmer and analyst who has experience in leveraging artificial intelligence to support business objectives and provide midlevel software development expertise. The health system is offering $85,900 to $137,300 in annual base pay for the role.

New York City-based Mount Sinai Health System is also building an internal AI team, with a full-time position open for a data engineer to support research performed in its computational pathology lab. Salary for the role ranges from $90,000 to $135,285 and could include bonuses or incentives.

KLAS reported in September that IT and software development have increased as a strategic priority for health systems in the last year, with 20% saying it is a No. 1 priority. Right now the top three spending areas for IT are revenue cycle management, clinical workflow optimization and patient engagement. Just 6% said they have a generative AI strategy, and one-third of respondents said they lacked clinical expertise to adopt AI.

Health systems noted the top three use cases for AI right now are clinical decision support tools, predictive analytics and research stratification as well as clinical workflow optimization and automation.