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AI and employee retention in Healthcare

By Steve Cavolick

There is a worker shortage in America.

Despite the end of COVID stimulus packages, many businesses struggle to find enough employees to operate at maximum efficiency. You may have noticed signs in store windows asking for help, heard about large sign-on bonuses in radio or tv ads, or just had to wait longer than usual for service while dining in a restaurant.

Getting people back to work to fill vacant positions is one part of the puzzle, but employers of all kinds are also dealing with a higher than normal churn rate this year. One of the industries being hit hardest with employee turnover is healthcare.

The state of Mississippi now has 2,000 fewer nurses than it did at the beginning of the year and a survey from the American Association of Critical-Care Nurses indicates that nearly two-thirds of nurses have considered leaving the profession.

Replacing front-line healthcare workers is bad for business for two reasons: cost and patient outcomes. The online recruiter for startup and tech companies, BuiltIn, estimates that filling technical positions, such as those for healthcare providers, costs between 100-150% of the employee’s salary. While high turnover is associated with decreased morale and lower productivity in most businesses, the stakes are higher in healthcare. Increased turnover there can negatively impact patient care, such as having more patient falls and medication errors.

The environment in healthcare is fast-paced and demanding, which leads to burnout. One way to reduce worker attrition is to determine the triggers of burnout and predict which workers are likely to become crispy. Fixing the problem should be a two-pronged approach that involves traditional analytics to monitor employment satisfaction metrics and AI to determine the drivers of burnout.

Traditional analytics and information management techniques can be used to:

  • Benchmark retention and churn rates.
  • Analyze anonymous survey feedback from employees.
  • Extract data (including sentiment) from exit interviews.

AI could be used to not only detect who is likely to give their notice and the drivers of burnout, but to automate and simplify the activities that are causing the burnout. Some ways that AI can help: 

  • Intelligent Scheduling: Data pertaining to working shifts, breaks taken, and attendance, for starters, can be feature engineered to make ML models that predict burnout more accurately.
  • Clinical Data Input: AI automation does not get bored with repetitive administrative tasks (or develop carpal tunnel syndrome), which allows staff to focus more on people and personalizing care.
  • Silent ICU (future): Staff can be exposed to as many as 350 audible alarms per patient per day. Having AI-augmented analytics present advanced visualizations, and responding with observations or commands without keystrokes could reduce stress by creating nearly silent ICUs.

The cost of replacing a healthcare worker is high, but when you factor in the impact workers have on productivity, performance, and patient outcomes, those costs spike considerably. AI can help reduce the drag churn has on your bottom line and clinical outcomes.

If you are ready to build AI applications to help retain your workers and improve results, our data science team can help. If you’re not quite there yet, the LRS Big Data and Analytics group has over 20 years of experience implementing applications in advanced analytics, information management, and data warehousing. Not sure how to get started? Our strategic offerings can help you align business and technology teams, discover the right use case, and determine an ROI. If you are interested in understanding how we can help you find value in your data, please fill out the form below to request a meeting.

About the author

Steve Cavolick is a Senior Solution Architect with LRS IT Solutions. With over 20 years of experience in enterprise business analytics and information management, Steve is 100% focused on helping customers find value in their data to drive better business outcomes. Using technologies from best-of-breed vendors, he has created solutions for the retail, telco, manufacturing, distribution, financial services, gaming, and insurance industries.