Imagine a patient who doesn’t receive the optimal treatment in a hospital because it is understaffed. Or because the required professional is unavailable. If media reports and word-of-mouth anecdotes are to be believed, these incidents are not uncommon in contemporary healthcare. 

Staffing problems in hospitals and other healthcare settings have emerged as a challenge that directly affects the community. A Mercer report on the future of US healthcare predicts a nationwide worker shortage by 2028. The consulting firm highlights that this shortage could encompass 100,000 critical workers, including physicians and nurses.

Such shortfalls can manifest as missing specialists, unavailable professionals for urgent shifts, and overtime costs running through the roof. What if part of these staffing problems actually stems from discrepancies or poor interpretation of data?

Why Does Healthcare Experience Staffing Problems?

According to the US Bureau of Labor Statistics (BLS), 24 million professionals work in healthcare and social assistance. The bureau identified both clinical practitioners, such as doctors and technicians, and support staff.

Even more interestingly, the BLS shows that total employment increased substantially from 2004 to 2024. Over 2014–24, the growth rate was 13%. Much of this was led by healthcare and social assistance roles. Through 2024, the bureau predicted wage and salary employment growth of 8.4% for these sectors.

So far, so good. Where are the staffing problems coming from?

One may assume that the COVID-19 crisis, which caused unprecedented stress to healthcare workers, led to significant attrition and retention challenges. Indeed, several surveys of healthcare workers, particularly nurses, indicate high degrees of burnout. 

Many healthcare professionals consistently report struggling with heavy workloads and administrative challenges. A cross-sectional study published in Nursing Outlook in 2025 showed that workload and support factors impact the probability of burnout among staff.

But beyond this, we must not overlook that some seemingly staffing-related problems could be a visibility issue. 

What if the organization doesn’t have optimal scheduling, making shift allocations for employees highly challenging? What if essential workforce information is so fragmented across systems that managers cannot use it?

It has become imperative to identify the deeper causes of staffing problems in healthcare. This is the only way to address them. 

From Workforce Data to Intelligence

While investigating data issues in healthcare settings, one stark question comes up. Are workplaces using the available data effectively?

Consider the vast amounts of staff-related data available in hospitals. From shift schedules to overtime, and care-centric skills to availability for flexible hours, the data that hospital administration can access about its team is immense. However, these data points are often:

• Spread across different systems and hence, difficult to consolidate.
• Not considered for scheduling or identifying internal opportunities before hiring externally.
• Too complex to understand compared to the training (or lack of it) available to managers and leaders.

A data-driven solution to staffing gaps requires people with authority to convert raw data into intelligence. Typically, this needs simpler, more streamlined solutions for data retrieval, interpretation, and application. 

Advanced leadership training focused on delegation and resource allocation can also be helpful. For example, a program like a Master of Science in Nursing or an MSN clinical nurse leader course can help nursing professionals understand interdisciplinary teamwork. This allows them to optimize staffing by observing demand fluctuations between departments.

In the long run, leaders will need advocacy and change management skills to make data-led decisions part of the organization’s fabric. Cleveland State University reflects that developing policy-level skills and strategic thinking will become more important for future healthcare leaders. Managers with these competencies will be better equipped to identify employee availability and fitment for the task/shift at hand. 

Why Predictive Analytics Can Come in Handy

Another application of data for addressing staffing challenges may come through predictive analytics. Although this AI application may not have taken off at a breakneck pace like generative AI, it has the potential to transform staffing. 

The primary advantage of adopting predictive data is in minimizing the gap between planned and actual staffing.

Consider how convenient it would be if you could predict patient demand and match it to employee availability. What if you had a prior handle on sick calls and operational needs for the next day? Instead of basing decisions on what you see already played out, you will have more control over what may happen.

In 2025, a research team submitted a probabilistic nursing forecasting approach to the IEEE Engineering in Medicine and Biology Society (EMBC). The model used DeepAR to predict nursing workforce demand across hospital units and achieved considerable success. 

In North Little Rock, Baptist Health Hospital experimented with a similar initiative. They combined automation and predictive analytics to optimize patient flow and streamline discharges for everyone involved. 

“Before this transformation, our morning huddles were driven by spreadsheets and best guesses. Decisions about bed placement and discharges were essentially made in hindsight. Now, our teams come in with a clear, predictive view of who is likely to discharge, when those discharges will happen and where patients will go.” – Cody Walker, President, Baptist Health Hospital.

Although these approaches are nascent for now, healthcare may gradually start basing more clinical and staffing decisions on data. AI being part of the arsenal makes this likelihood stronger.

How Workforce Data Can Reveal the Real Staffing Challenge

FAQs

Q1: What is workforce intelligence in healthcare?

Workforce intelligence involves turning data such as schedules, availability, overtime, skills, and staffing demand into actionable insights. This helps leaders make workforce decisions.

Q2: Who should be involved in data-driven staffing decisions?

Staffing decisions can benefit from input across HR, operations, finance, scheduling, and clinical leadership. Involving multiple stakeholders lets you develop a broader view of workforce needs and constraints.

Q3: Can better workforce data reduce the need for external staffing?

Yes. Better visibility can help organizations identify internal workforce capacity before turning to external staffing. However, data alone cannot eliminate genuine workforce shortages or specialized skill gaps.

Prioritizing Shared Decision-Making:

As hospitals begin applying data more rigorously to staffing decisions, the healthcare community will also need to shift toward shared decision-making. Data-driven workforce technology must not remain limited to HR or scheduling teams. Instead, it becomes more useful when various departments provide actionable guidance, such as clinical leaders and finance experts. 

Applying data to tackle staffing challenges promises to be exciting and directly beneficial to employees and patients alike. It only makes sense that the system remains considerate and balanced across perspectives rather than resorting to myopia.