Optimizing the hospital workforce in a rapidly changing environment

The traditional approach of using the midnight census as the chief measure of patient demand can obscure more granular trends measured in hours or minutes.

Whether it’s delivering millions of packages on time to customers’ doorsteps, managing thousands of flights in multiple time zones, or deploying military personnel across the globe, organizations in numerous industries tap into the power of logistics science and optimization modeling every day. But for hospitals and health systems, the challenge of getting the right nurse to the right tower for the next shift without exceeding the staffing budget or upsetting the nurse-to-patient ratio is perennially frustrating.

Recent shifts in the healthcare industry have further complicated the challenge of accurately budgeting, scheduling, deploying and assigning clinical staff. Growing health systems that acquire new hospitals may need to recalibrate their staffing for increased needs. And organizations that are scaling back inpatient capacity while expanding their presence in clinics and other community-based outpatient settings amid industry-wide changes in healthcare delivery must make similar adjustments.

As workforces evolve, the traditional approach of using the midnight census as the chief measure of patient demand can obscure more granular trends measured in hours or minutes. Without access to accurate, timely data, hospitals run the risk of over-staffing, and the prospect of tens of millions of dollars in increased costs.At the other end of the spectrum, under- staffing can jeopardize clinical quality, staff engagement and patient satisfaction.

Emerging workforce optimization modeling can help organizations review years of staffing and budgeting information on data points ranging from hourly patient demand to clinical time entry to bed capacity to nurse-to-patient ratios. The process is intended to uncover potential inefficiencies and changes in operation that might require modifications in work rules or scheduling protocols.

For example, high vacancy rates can lead to higher proportions of costly overtime payments. The modeling also allows organizations to quantify the impact of work rules governing parameters including shift lengths, start times and weekend shifts. For example, organizations that only require clinical staff to work every third weekend instead of every two weekends might need — depending on the modeling — a significantly higher number of full-time employees (FTEs) in order to fill each weekend shift. And population health initiatives that are intended to drive down inpatient utilization may require flexible models to adequately staff community- based facilities beyond the four walls of the hospital.

Hospitals and health systems can use the insights from modeling exercises to calculate the impact of everything from changes to weekend staffing schedules to the creation of new float pools. Ultimately, the process is intended to help organizations sustain or improve performance in clinical outcomes, labor costs and employee engagement, without having to sacrifice results in one area to achieve results elsewhere.

For example, one large Midwest health system, which was organized under a loose federation model, operated for years without standard, system-wide work rules. At one point, the system had 350 work rules in place for 11 hospitals. Using the optimization process, an interdisciplinary team was able to pare down the list to 50 new standards for use by the entire system. When the standardization process began, the system scored in the 39th percentile of employee satisfaction on surveys developed by the Agency for Healthcare Research and Quality (AHRQ). Three and half years later, the system scored in the 75th percentile on the AHRQ survey, while saving $8 million in spending on overtime and agency pay over two years.

Achieving meaningful results of that nature requires genuine buy-in and engagement from multiple internal stakeholders, including the chief nursing officer, the chief finance officer, human resources leaders, schedulers and frontline nursing staff. Initial meetings might help each department understand how their counterparts traditionally perceive and measure staffing, and determine that appropriate staffing levels have been met.

For example, while members of the finance department might view staffing through the lens of a given number of FTEs in a unit, frontline staff are more likely to focus on the number of patients they see in a given day. And the chief nursing officer often ends up mediating between both groups.

When optimization efforts are inclusive of a wide range of viewpoints and needs, they can deliver results that satisfy multiple internal stakeholders. Finance leaders might appreciate greater alignment between projecting staffing budgets and actual results, while frontline nurses may enjoy greater predictability in the day- to-day staffing of their particular unit. Ideally, workforce optimization can help organizations adapt and thrive in an ever-shifting environment, and achieve results ranging from improved financial performance to a more engaged clinical staff.