Visual Management of Nursing Schedules for Workforce Balancing and Reduction of Overtime Coverage: A Longitudinal Quality Improvement Study

Technical Summary: Visual Management of Nursing Schedules for Workforce Balancing and Reduction of Overtime Coverage

Problem Statement
Hospitals face a persistent operational challenge: maintaining safe staffing levels while controlling labor costs. In nursing, factors such as absenteeism, vacancies, turnover, and daily demand fluctuations frequently necessitate overtime coverage, leading to unplanned staffing expenditures and reactive management. While visual management is a recognized tool in Lean Healthcare for increasing operational transparency, there is a scarcity of empirical literature describing its specific impact on workforce balancing and the reduction of overtime-related costs. The core problem addressed is the lack of timely visibility into staffing deficits and surpluses, which prevents proactive internal redistribution and forces reliance on costly overtime.

Methodology
This study is a single-center, longitudinal quality improvement project conducted at a private tertiary hospital in Goiânia, Brazil. The research followed the SQUIRE 2.0 framework for reporting healthcare improvement interventions.

• Setting: The intervention monitored 64 beds across inpatient wards, a general intensive care unit, a coronary care unit, an emergency department, and supporting clinical areas. The nursing workforce consisted of 160 professionals (132 technicians and 28 registered nurses).
• Intervention: Initiated in September 2025, the intervention introduced a visual workforce management system. This system integrated data on nursing schedules, absenteeism, hospital occupancy, internal staff redistribution, overtime coverage events, and associated expenditures into a unified visual process. The operational logic posited that visual monitoring would enable early identification of imbalances, facilitating structured internal redistribution before overtime became necessary.
• Data and Analysis: The study utilized routinely collected institutional operational data. August 2025 served as the baseline month, with September 2025 through May 2026 constituting the post-implementation follow-up. The primary outcome was the monthly number of overtime coverage events; secondary outcomes included overtime expenditure, hospital occupancy, absenteeism, and staff redistribution rates. Due to the availability of only one pre-intervention month, the analysis relied on descriptive monthly statistics rather than interrupted time-series modeling, interpreting results as temporal associations.

Key Results
The implementation of the visual management system was associated with a substantial reduction in both the frequency and cost of overtime coverage, despite sustained hospital occupancy.

• Overtime Reduction: Compared to the baseline of 143 events (BRL 32,693.00), the post-implementation period saw a mean reduction of 83.6% in coverage events (averaging 23.4 events/month) and an 81.4% reduction in costs (averaging BRL 6,071.42/month).
• Performance Extremes: The lowest point occurred in April 2026 with 13 events (BRL 3,699.00). While May 2026 showed a rebound to 28 events, this figure remained 80.4% below the baseline.
• Operational Context: These reductions occurred while mean post-implementation hospital occupancy remained high at 82.27% (compared to 80.48% at baseline) and weighted absenteeism was low at 1.37%.
• Drivers of Overtime: Analysis of the baseline and follow-up data identified "open positions" (staff departure) and "medical leave" as the primary drivers of overtime events, accounting for over 60% of occurrences. The General Intensive Care Unit and the 4th-floor inpatient ward were the units generating the highest coverage costs.

Key Contributions

• Empirical Evidence: The study provides empirical data linking visual management in nursing to significant financial and operational improvements, a dimension often under-described in Lean Healthcare literature.
• Operational Mechanism: It demonstrates that reducing overtime does not necessarily require reducing the workforce or demand. Instead, it validates a mechanism where improved operational visibility allows for better capacity matching and structured internal redistribution of existing staff.
• Management Infrastructure: The intervention established a "control tower" for nursing resources, connecting schedules, absences, occupancy, and costs to support proactive decision-making rather than reactive coverage.

Significance and Claims
The authors claim that visual workforce management is a scalable, lower-cost strategy for hospitals to address staffing imbalances. The study supports the plausibility that improved visibility and structured redistribution contribute to better workforce balancing.

However, the paper maintains a modest stance regarding causality. The authors explicitly state that because only one pre-intervention month was available and no concurrent control group was used, the results should be interpreted as a temporal association rather than definitive causal evidence. They acknowledge limitations, including the lack of patient safety or care quality indicators in the current dataset and the reliance on local operational definitions. The study concludes that while the findings are promising, future evaluations should incorporate broader indicators such as patient safety, care quality, and staff experience to fully assess the intervention's impact.