How Analytics Solves Staffing Shortages and Optimizes Workforce Planning

Workforce analytics can be a valuable tool for addressing staffing shortages in the healthcare sector. By relying on predictive data tools, organizations can stay ahead of the industry’s persistent labor challenges.

Understanding the Impact of Staffing Shortages on the Healthcare Industry

Healthcare organizations are facing significant staffing shortages across the industry. If current patterns hold, there will be a deficit of more than 140,000 physicians in 2038 and more than 245,000 licensed practical nurses.

In 2028, the number of LPNs is forecast to meet 83% of the need in the United States. and just 70% in 2038.

Rural practices, where employee recruitment is more difficult, are especially vulnerable to inadequate staffing.

Key drivers behind staffing shortages

Several factors have contributed to the growing need for healthcare employees. An aging patient population with more complex health issues means greater demand for healthcare workers to care for them. At the same time, the healthcare workforce population is growing older and many employees are approaching retirement, reducing the availability of healthcare staff.

The Covid-19 pandemic prompted the exodus of many healthcare workers and their departures placed further strain on those remaining, leading to more burnout and employee dissatisfaction. Burnout, often caused by long hours and the emotional toll of working in a high-stress environment, is an ongoing challenge for healthcare employee retention that causes many to leave the profession. A 2023 study reported a burnout rate exceeding 90% among nurses. Meanwhile, education pipelines are challenged by capacity limitations, restricting the number of students who can enter the healthcare field.

Economic consequences of workforce deficiencies

Heavy turnover is costly to healthcare operations. When a registered nurse (RN) leaves their position, average costs exceed $61,000, and departures can add up to more than $4 million a year for average hospitals. Recruitment costs, training expenses, overtime pay to fill staffing gaps, and reliance on traveling professionals further adds to expenses. Inadequate staffing can also impair patient care, putting more financial strain on a healthcare organization and the system overall.

Challenges of traditional workforce planning

Traditional workforce planning is mostly reactive, with administrators identifying past issues and determining how to resolve them. Filling in vacancies after they happen, responding to patient demand after it declines or accelerates, or realizing gaps in skills after they emerge can lead to rushed hiring and inconsistent operations. All of this impacts patient care and financial sustainability.

Leveraging Predictive Analytics for Workforce Optimization

Predictive analytics helps organizations transform to proactive workforce planning.

Introduction to predictive workforce analytics

Predictive workforce analytics utilizes historical data and statistical algorithms to determine probable outcomes. In healthcare, predictive workforce analytics can be assessed to estimate patient volume, workforce needs, and turnover rates. By using data to solve staffing shortages, administrators can prepare for anticipated workforce needs before they become setbacks and can respond more rapidly to staff challenges when they occur.

Methods for predicting employee turnover

• Analyze historical workforce data to identify those at risk of departure.
• Assess characteristics and patterns among those workers who leave, such as wages, career development, performance, and job satisfaction.
• Employee surveys can provide feedback and serve as a data source on workforce environments, work demands and employee sentiments.

Case studies of predictive analytics successes

1. Cleveland Clinic Medina Hospital: The emergency department at the Ohio hospital relied on two years of patient data assessing the busiest times of the day and how many patients were leaving before being seen. That data helped administrators make decisions on where to add staff. Based on the analysis, the organization closed some rooms on Saturdays and shifted nurse and paramedic schedules, leading to a 70% drop in the number of patients who left without being seen.
2. Mercy: The St. Louis-based hospital system relied on an AI-powered workforce technology to schedule nurses more efficiently. The tool lowered labor costs, reduced time spent on scheduling, and decreased reliance on travel nurses, saving $30 million across the 50-hospital system, according to Healthcare IT News. Agency staff fell by 17 percentage points, time spent on staffing fell by 20%, and turnover dropped by 8%.

Implementing Data-Driven Workforce Planning

Workforce planning analytics are essential to an organization’s success. By relying on data analytics, healthcare organizations can make smarter decisions that support efficiency, boost employee morale and retention, sustain quality care for patients, and reduce unnecessary costs.

Steps for effective workforce planning

• Know your demand: Gather data on patient volumes and patient needs and on which departments are facing growth or declines in demand. Assess local patient trends and seasonal trends. Artificial intelligence (AI) tools can analyze anticipated outcomes based on these data inputs, according to the American Health Association.
• Identify risks: Organizations should prepare for turnover and retirement risks, as well as gaps in competency and skill levels.
• Track outcomes: Track employee turnover and retention after implementing data-driven approaches. This allows organizations to determine which tools have been most successful and where to invest further.

Role of AI in optimizing workforce scheduling

AI can play a critical role in workforce scheduling, by reducing time spent on assigning staff schedules and streamlining the entire process. This frees up resources for other, more meaningful duties within a healthcare organization, increases efficiencies and transparency, and boosts morale and retention, all leading to financial stability.

Tools and technologies for workforce analytics

Predictive analytics for staffing and AI-driven scheduling platforms bring automation to shift assignments, helping to reduce workloads for administrators and ensure sufficient staffing levels. With workforce planning tools, organizations can anticipate shifts in demand and further tailor staffing to patient needs.

• Predicting turnover: AI models assess employee surveys on engagement, work stress levels, and absenteeism to detect signs of burnout.
• Candidate screening: AI tools can quickly identify top applicants by finding keywords or required experience in resumes, which allows administrators to focus more on other tasks.
• Optimized scheduling: Scheduling platforms collect data on employee shift preferences and capacity, as well as anticipated patient levels and needs to create balanced and efficient schedules. Healthcare workers’ well-being is supported while organizations avoid costly overstaffing and inadequate staffing that impairs care.  

Benefits of Data-Driven Workforce Strategies

Data-driven strategies can save time for administrators, reduce redundancies, and ensure sufficient staffing.

Reducing employee turnover

Supporting employees through predictive analysis can guard against burnout and decrease turnover. Technology platforms can identify what characteristics lead to strong or weak performance and when employees may be a risk of leaving. Data from exit surveys can illuminate patterns of dissatisfaction and their roots. Those insights can allow managers to determine what supports would be most useful in boosting performance or in retaining employees. Rather than being guided by intuition, their actions are grounded in data.

Enhancing operational efficiency

Data-driven staffing decisions can minimize workforce disruption and create the most efficient staffing levels, cutting unnecessary costs related to both overstaffing and turnover.

Achieving sustainable business growth

A supported staff improves patient care and satisfaction and reduces employee turnover, which ultimately yields business growth and financial strength.  

Future Outlook: Trends in Workforce Analytics

The rise of AI in workforce solutions

AI workforce optimization is increasingly assisting with staffing solutions, streamlining processes through automation and freeing up staff and resources for other healthcare duties.

Preparing businesses for future workforce needs

As the healthcare industry faces immense workforce challenges, AI technology can provide meaningful supports that promote efficiencies, cost savings, and patient satisfaction. By identifying and preparing for industry changes, employees can be more insulated from burnout.

Long-term strategy over short-term solutions

Organizations have traditionally reacted to workforce challenges by responding with short-term solutions. With predictive workforce planning, organizations are better equipped to plan ahead, increase efficiencies, and respond to challenges before they arrive.