Using Data to Drive Workforce Changes

By Jackie Larson, Executive Vice President, Aya Advisory Solutions

Over the past two years, many healthcare systems short on staff and high on census have relied on contingent labor to meet the needs of their communities. As we move towards a post-acute phase of the pandemic, what can healthcare systems looking to implement flexible workforce solutions do to understand the right staffing mix to sustainably help their patients? The answer lies in a hospital’s data. No two hospitals are the same, and the outdated assumption of the “ideal mix” — 85:15 core staff to contingency — won’t necessarily meet patient demand.

Healthcare workforce experts can thoroughly analyze data to help hospitals craft unique staffing strategies. These strategies can effectively utilize hospitals’ existing resources and design workforce roadmaps that work now and into the future. Often, hospitals don’t have the right types and layers of contingency resources to draw on. By digging into data, hospitals can create more nimble and flexible resources that can ebb and flow with demand.

Know your Data, Know your Workforce

The more data hospitals have access to the easier it is to identify opportunities to optimize workforces with staffing gaps. Potential metrics ideal for analysis can include workforce data, detailed spend information and instances when hospitals or units couldn’t handle a patient load or cancel services. Analyzing this data can help hospitals establish frameworks to build staffing models for a more flexible and nimbler workforce — including the optimal size of internal float pools to cover demand.

Using predictive analytics, this data can also provide projections for hospitals’ long-term needs, like how much contingent labor they should anticipate. For example, health systems with seasoned workforces  with a high amount of PTO accrual that must be used by the end of the year will have a higher demand for contingency labor than systems with a higher mix of lower tenured staff. By improving the utilization of its hospitals’ staffing resources, these health systems can remove the burden on core staff to fill vacancies, thus reducing burnout and increasing retention.

Get Buy-In

Simply getting a data report doesn’t solve workforce challenges. Rather, hospitals need the right executive sponsors and stakeholders to commit to improvement and give permission to act. If a hospital’s data shows they need to hire 100 nurses into its resource pool, they need the power to do so. Most successful clients have an engaged executive team and a culture of change and readiness. From front-line staff to the C-suite, every level needs to be willing to embed strategy recommendations based on data to get a hospital to a better state.

Timeline to Results

Transformative change is not easy, and the road to improvement can seem daunting. However, after collecting and analyzing data, a hospital can begin implementing quick fixes with results in just four to six weeks with sustainable results coming within 12-18 months.

At Aya Healthcare, our Advisory Solutions team works with clients throughout the process — capturing feedback, measuring results and pivoting as needed to ensure the long-term success of a program. There is no cookie-cutter solution for every hospital, but data and deep analytics can forecast optimal staffing levels for robust, flexible workforces that improve clinical, financial and operational outcomes.

About the Author

Jackie Larson is a healthcare industry veteran and recognized thought leader. With more than 20 years in the industry, she has provided guidance and support to hundreds of hospitals on a range of issues including workforce optimization, productivity, labor pool and incentives, system integration, resource management and business analytics.

Jackie is a sought-after writer and speaker on healthcare staffing and workforce optimization topics. She has been featured at national conferences and is a regular contributor to Becker’s Hospital Review and the Huffington Post, among other publications.