Leveraging integrated workforce data is integral to transforming healthcare performance

Today’s healthcare organizations produce large quantities of workforce data, which leaders have traditionally used to maintain compliance and ensure correct pay. Yet with rising labor expenses, staffing challenges, and growing complexity in operations, leaders now look for innovative methods to maximize the benefits of this data and boost organizational performance.

Merging workforce data with other datasets is a key strategic asset for any organization, delivering end-to-end intelligence that enables a continuous workforce optimization cycle. By integrating operational, financial and clinical data, health systems can:

• Facilitate proactive workforce planning
• Provide real-time operational guidance
• Transform raw labor information into actionable insights

By aligning daily operational activities with financial performance and clinical needs, healthcare organizations can optimize productivity, control expenses and elevate the patient experience, all while supporting staff well-being and retention initiatives.

Following is a five-part blueprint for developing a workforce optimization cycle in your organization, along with insight into why it’s necessary and the risks of inaction.

Make the case for data integration

The healthcare industry faces unprecedented workforce pressures, necessitating the need for new approaches to workforce management. These pressures include:

• Demand volatility caused by fluctuations in census, acuity and seasonal patterns
• Fragmented data systems separating clinical, labor, operational and financial information
• High turnover and burnout, particularly among frontline caregivers
• Skyrocketing labor costs driven by overtime, agency usage and benefit inflation
• Widening span of accountability for nurse managers and department supervisors

Although health systems gather extensive workforce data, they still often depend on basic or retrospective methods for planning labor and tracking productivity.

For example, many hospitals staff units based on average daily census, head count or midnight census even though those approaches don’t fully capture the complexity of patients’ needs and often result in over- or under-staffing. To measure productivity, hospitals use metrics like hours per patient per day, worked hours per unit of service, or relative value units per physician FTE, but again, these metrics don’t always reflect patient complexity, care coordination work or time spent documenting.

“Organizations spend a lot of time collecting data, but they aren’t able to tap into the power of that data,” said Jackie Blanchard, DNP, RN, chief nurse executive at UKG. “While basic methods of measuring labor and productivity may have worked in the past, they are no longer sufficient or sustainable in the current healthcare landscape.”

Now, healthcare leaders are seeking integrated, analytics-based approaches that promote greater efficiency and enterprise-level labor planning.

Michael Kimball, senior director of management engineering at St. Luke’s University Health Network, is one of those leaders.

“When you’re in an organization of 20,000 employees, and people start to accrue small amounts of incidental time, it hits your finances,” he said. “We’re trying to analyze our data, see what we can extract, and then redefine our policies to match our expectations. We want to know what this data could be telling us about our employees, our policies and/or our managers.”

Kimball says this important work comes in the wake of tackling bigger issues like ballooning labor costs stemming from the COVID pandemic when the health system relied primarily on nursing staff agencies. The system’s labor committee — a group of individuals focused on workforce optimization — largely addressed that problem.

“We put goals and projections into our budget, so we had hard-wired expectations to control costs,” he said.

Shanon Fucik, chief nurse executive at University of Missouri Health Care, also is driven by a desire to leverage the workforce most effectively and efficiently. By integrating data, the health system discovered that expensive incidental overtime was mainly caused by nurses skipping lunch breaks and some working more than their full-time hours, while others worked less than their assigned FTE.

“Now, we’re really focused on creating standards around ensuring everyone takes a break and works to their full FTE,” she said.

Incidental overtime is one of many reasons why organizations implement a workforce optimization cycle, says Christopher Haviland, BSN, MS, RN, workforce business consultant, healthcare at UKG.

“It’s about being able to derive greater insights from the data to achieve strategic goals whether they be growing the organization in a fiscally responsible way, containing labor costs, forecasting staffing needs or improving patient care quality,” Haviland said.

Connect labor usage to patient volume and cost

Unlike traditional healthcare labor analytics that rely heavily on simplistic ratios (e.g., FTEs per adjusted patient day or hours per patient day), an integrated approach inherent in the workforce optimization cycle surfaces underlying complexity by linking labor performance directly to:

• Department-level cost structures
• Patient acuity scores
• Patient and volume demand
• Patient census
• Throughput metrics

This is the approach that Mount Sinai Health System takes as it strives to improve demand forecasting and create more flexible staffing models, according to Vincent Tamarro, executive vice president and CFO.

“From a finance perspective, one of our priorities is integrating data across several systems so we can create a much clearer picture of what’s happening operationally,” he said. “That means bringing together information from the electronic health record, workforce scheduling systems, HR and payroll platforms, and the financial system. When those datasets are connected, you can start to align staffing levels much more closely with the true drivers of demand — things like patient census, patient acuity, case mix index and service line volumes.”

With this integration, organizations can calculate:

• Labor productivity in both hours and dollars
• True, fully loaded labor cost at a granular level
• Unit-level cost drivers and service-line variability

“This unified cost-volume-labor view enables leaders to identify where labor spend is justified and where operational inefficiencies inflate costs,” Blanchard said.

Leverage integrated data to empower frontline staff

Empowering employees to seamlessly handle common tasks — schedule swaps, time-off requests, attendance inquiries — reduces friction and improves engagement. With decision automation and workload reduction, supervisors gain more time for leadership activities that will allow them to focus on their frontline staff who, in turn, can provide higher-quality care to patients.

Real-time decision support using integrated data provides:

• Alerts for late arrivals, early departures and unexpected gaps in staffing
• Automated support for approvals and simple employee requests
• Guidance on break timing and shift distribution
• Recommendations for allocating staff to higher-need areas

Staff empowerment is a critical priority, said Kimball of St. Luke’s University Health Network. To promote it, the system:

• Feeds workforce data into the EHR so managers can assign nurses to patients shortly before work shifts begin
• Leverages a central nursing office so it can deploy staff to specific need-based locations
• Presents real-time data about projected overtime to managers at the point of care so they can make cost-effective staffing decisions

Mount Sinai recently leveraged real-time data during a five-week nursing strike when labor costs fluctuated quickly due to hiring temporary contract nurses.

“It was about so much more than the numbers,” Tamarro said. “It was about making sure we weren’t impacting patient care or sacrificing quality, safety and outcomes.

“One of the insights we gained fairly quickly was that while some units truly needed the additional staffing because of patient acuity, other areas had lower census or fluctuating demand,” Tamarro said. “By reviewing productivity data daily with nursing leadership and operations, we were able to redeploy staff and better align staffing with patient volumes across units rather than relying solely on additional contract labor.”

These nimble operational decisions would have been nearly impossible without real-time labor and productivity data as well as strong cross-functional communication, he said.

“Leadership was able to move quickly, allocate resources where they were most needed and balance financial stewardship with the mission of patient care,” he said.

Use a data-driven planning model to forecast labor needs

Healthcare labor planning often relies on static staffing grids or FTE estimates that fail to reflect real-time operational complexity. However, a data-driven planning model uses:

• Absence trends
• Actual time worked
• Historical volume and acuity/workload patterns
• Seasonal demand fluctuations
• Skill-mix requirements

This enables health systems to:

• Accurately predict staffing needs by unit and shift
• Build schedules aligned to real operational demand
• Reduce reliance on premium labor and agency staff
• Model “what-if” scenarios to stress-test strategic initiatives with the highest impact on productivity (e.g., modeling scenarios for an expanded service line or a prolonged uptick in acuity)

The result of using a data-driven planning model is a proactive, precise labor plan that improves care quality while controlling labor costs.

“By improving workforce planning and using better demand forecasting, we can stabilize our core workforce and gradually reduce reliance on those premium labor sources,” Tamarro said. “We’re partnering with operations on a much more regular cadence to review insights and adjust staffing plans, optimize float pools or redeploy staff across units as volumes change.”

Commit to continuous operational improvement

The workforce optimization cycle is not a one-time project — it is a continuous operational cycle requiring organizations to:

• Analyze current performance and identify cost, staffing and workflow opportunities
• Execute better schedules, leading to more efficient labor use
• Feed improved operational data into a more accurate labor forecast
• Guide supervisors and empower staff to take action in real time
• Link financial and clinical data to understand true productivity and cost drivers
• Measure results and repeat

Over time, organizations improve:

• Enterprise-level planning accuracy
• Labor cost predictability
• Patient experience and care outcomes
• Staff satisfaction and retention
• Workforce efficiency

Here are several ways innovative organizations leverage workforce data to their advantage:

Anticipate workforce needs. Mount Sinai combines workforce analytics (e.g., turnover trends, retirement projections, skill mix, productivity metrics) with operational and clinical data (e.g., patient volumes, acuity, service line growth) to anticipate needs rather than react to shortages. This includes investing in retention programs, career development and pipeline partnerships. Similarly, as demographic shifts occur in the community, the health system alters its strategy based on the service lines it predicts will grow or contract. For example, if data shows an aging patient population requiring specialized care, it can proactively expand advanced practice provider roles or cross-train nurses in those areas before gaps emerge.

Understand how staffing decisions affect quality and patient/caregiver experience. Mount Sinai combines workforce data with quality metrics, patient experience data and staff engagement indicators.

“The goal is to ensure that efforts to improve efficiency are also supporting safe staffing levels, strong clinical outcomes and a sustainable work environment for caregivers,” Tamarro said.

Lower spans of control while containing costs. University of Missouri Health Care leverages integrated workforce data along with acute care nursing and ambulatory restructures to lower spans of control while saving money.

“We have a standard now,” Fucik said. “If we purchase or partner with another organization, we can apply that standard. If the unit hits a certain number of FTEs, for example, we’ll add an assistant nurse manager. We worked with our finance and HR partners to understand the broader landscape.”

The future of healthcare workforce management

Leveraging integrated data analytics ensures organizations have the right people in the right place at the right time, according to Greg Damron, CFO of University of Missouri Health Care.

“Staffing changes have vast impacts on care quality, the care experience, the safety on the unit and so much more,” he said. “CFOs really need to work with nursing leaders to get the numbers right. There’s the micro: How well are we delivering the workforce we have when we need it? Then there’s the macro: How do we hire and retain the staff we’ll need as we look ahead?”

Looking ahead — not behind — is the only option, St. Luke’s Kimball said.

“We’re really interested in watching where AI goes with all of this and how AI will make some of this easier,” he said. “We’re making very important decisions based on this data. As the accuracy evolves, how can we leverage the powers of AI to do some of this work?”

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Benefits of the workforce optimization cycle

When organizations optimize their workforce using integrated data analytics inherent in the workforce optimization cycle, they’re able to:

1. Control labor costs by reducing incidental overtime and reliance on premium labor
2. Forecast staffing needs more accurately, enabling proactive planning
3. Improve patient care quality, safety and efficiency
4. Mitigate the financial and operational impacts of sudden workforce challenges (e.g., workforce challenges associated with labor strikes, natural disasters and public health crisis)
5. Support strategic growth using a cost-conscious strategy

What is the workforce optimization cycle?

The workforce optimization cycle creates a data-driven ecosystem that improves labor efficiency while elevating the quality of care by leveraging the following capabilities:

• Cost and productivity integration: Streamline operations and maximize efficiency by connecting labor usage to patient volume and cost.
• Operational insight and real-time decision support: Guide supervisors and empower staff by understanding what is happening real time (and why).
• Strategic workforce planning. Forecast and optimize future labor needs using integrated data that reflects real-time operational complexity.

The limitations of today’s workforce data

The biggest limitation of today’s workforce data? Raw metrics alone do not drive improvement.

• Today’s healthcare organizations capture data on:
• Call-outs and unplanned absences
• Floating and cross-unit staffing
• Overtime usage
• Premium pay and differentials
• Role distribution and coverage patterns
• Schedule adherence
• Shift start/stop times

About UKG

UKG is the workforce operating platform that puts workforce understanding to work. With the world’s largest collection of workforce insights and people-first AI, our ability to reveal unseen ways to build trust, amplify productivity, and empower talent is unmatched. It’s this expertise that equips our customers with the intelligence to solve any challenge in any industry — because great organizations know their workforce is their competitive edge.

_{This published piece is provided solely for informational purposes. HFMA does not endorse the published material or warrant or guarantee its accuracy. The statements and opinions by participants are those of the participants and not those of HFMA. References to commercial manufacturers, vendors, products, or services that may appear do not constitute endorsements by HFMA.}