3 Ways AI, ML, and Predictive Analytics Can Help Solve the Nursing Crisis

The nursing profession is in crisis. According to McKinsey, over 30% of surveyed nurses said they may leave their current patient care jobs in the next year, and for inpatient nurses it’s higher at 45%. Meanwhile, the average professional tenure of nurses dropped from 3.6 years to 2.8 years between 2020 and 2023. These alarming trends have healthcare systems on red alert. Ninety-four percent of surveyed health system senior executives said the nursing shortage is critical. And the American Association of Colleges of Nursing expects the scarcity to worsen as baby boomers age and the need for healthcare grows. 

The main reasons why nurses are leaving their jobs are well documented: Nurses don’t feel valued by their organization, are inadequately compensated, have no work-life balance, and are asked to carry unmanageable workloads. Healthcare organizations must take proactive steps to handle these issues or risk declining patient outcomes, increased costs associated with backfilling and hiring contract staff, potential legal liability, and a cascading negative effect on doctors and other staff. Meanwhile, the nursing shortage also presents a public health crisis, as it affects access to and quality of patient care for all of us.

Although data alone may not be able to solve the nursing crisis, it can make a significant impact. While there are many ideas on the table about the reasons and solutions for the shortage, data has the potential to pinpoint exactly what’s happening and provide leaders with concrete insights to steer effective decision-making. Organizations with the right data foundation in place can harness advanced analytics such as AI and machine learning (ML) to alleviate the burden carried by these critically important healthcare workers.

Healthcare organizations need to understand the factors that contribute to nursing attrition and get ahead of the problem. To do so, they can develop a data model that uses AI with ML capabilities to produce predictive analytics. The data could include number of years on staff, salary and pay raises, average monthly hours, and even indicators such as demographics or clinic location (Is the clinic in a heavily trafficked area? Does it have limited parking?). Data sources could include payrolls, performance appraisals, exit interviews, and employee satisfaction surveys. For example, the model may predict that there is a 25% nurse turnover rate in the next year. The model can also identify related risk factors within the subset of nurses likely to leave. 

Based on this information, the model can help organizations identify areas to apply targeted remedial actions and interventions for individuals and groups. For example, administrators can offer more personal days, provide wellness resources, increase salaries, or add a bonus. They can also use the data insights to change hiring practices, improve physical working conditions, or implement team-building exercises. With a comprehensive range of data and the application of advanced analytics, organizations will be able to make better decisions about which interventions to invest in and implement. Predictive analytics can also continuously improve the accuracy of predictions based on additional data collected from ongoing experience.

Unmanageable workload is a top indicator of nursing attrition. Other factors that lead to burnout include last-minute schedule changes, nurses being asked to perform tasks outside of their typical scope, and a lack of experienced or specialty staff on duty. 

Hospital systems can better manage nurses’ workloads by using predictive analytics to determine future staffing needs. Models can include data such as historical patterns, seasonal trends, population growth, and staff vacancies or vacation schedules. By determining and planning staffing and schedules months ahead, systems can better shift resources where needed to reduce stress. Analytics can determine when a specific clinic or nurse group is busiest and develop strategies to reduce their workload. These include having healthcare workers with specific skill sets onsite.

Healthcare workers are drowning in an unending deluge of low-value tasks. They spend on average 34% of their day on administrative tasks such as data entry and calls to insurance companies, which is a major contributor to burnout. Meanwhile, a recent Nature study highlighted how the high level of multitasking that nurses must perform can negatively impact patient care and safety. 

AI and ML can’t take the place of healthcare workers; however, these tools can help nurses complete processes that complement patient care. Natural language processing transcription applications can take the place of data entry. AI can make sure medical charts have the adequate level of coding for billing, and help optimize hospital bed allocation. ML can parse through the different signals coming in and help nurses prioritize patient care. Organizations can also use advanced supply chain analytics to optimize the delivery of drugs and equipment that are in short supply, and help nurses take the frustration out of getting the right supplies to the right patient. With these tools, nurses can focus mainly on patient care and avoid burnout.

Data is the lifeblood of advanced analytics. To enable tools such as AI and ML, healthcare organizations need a modern data platform that enables them to access and collect data into one place, share it securely and seamlessly, scale to handle the massive amounts of information coming in from different sources, and power data management and analysis tools. Once organizations have these capabilities in hand, they can start to attract and retain nurses, increase job satisfaction, and enable better, safer patient care.