The power of predictive analytics may just be the best kept secret in healthcare. But it probably won’t be for long.
That’s because predictive analytics enable healthcare organizations to master one of the industry’s biggest problems – accurately forecasting patient demand and staffing need. The healthcare workforce can constitute 50%-60% of budgetary expenditures for healthcare organizations, according to Fitch Ratings. With such a considerable investment, healthcare organizations should want to engage in workforce planning that’s strategic, precise and accurate. But, most do not make use of predictive analytics and advanced labor management to achieve those goals.
This technology, which can forecast staffing needs up to 120 days in advance of the shift, can reduce overtime and bonus pay and streamline staffing for healthcare organizations around the country. This saves client organizations between 4%-7% of total labor costs, according to Avantas, a company that develops and provides predictive analytics for healthcare. But it’s not only about money. Healthcare organizations that implement predictive analytics in scheduling and staffing are finding that it improves staff morale, retention, and open shift coverage, which are shown to in turn improve patient satisfaction.
What is Predictive Analytics?
The use of predictive analytics to forecast future resource needs and other factors is not a new concept. It has been used for years in industries such as manufacturing, high tech, and retail to save time and money and to improve performance. A technology-based platform, predictive analytics uses techniques such as data mining, algorithms and computer modeling to analyze past data to make predictions about future events. These techniques now are being applied to healthcare workforce scheduling and staffing.
A key tenet of this technology in healthcare is its customized approach. Rather than only using industry-wide averages, forecasts for patient demand and staffing need are based on historical trend data collected directly from the healthcare provider. Advanced mathematical techniques are used to forecast future patient volumes; these forecasts are reviewed and recalibrated on a weekly basis to improve accuracy as the shift approaches. The result is a unique forecast of patient volumes and staffing needs that is specific for each unit and the organization, and that is actionable every day.
Healthcare systems that use predictive analytics by Avantas found the demand forecasting to be extremely accurate and cost effective. A major southern healthcare system achieved a 98% accuracy rate at predicting staffing needs 30 days out from the shift, while another, larger organization reported substantial cost savings: $7.2 million annual savings in contract labor costs at the flagship hospital alone; $8 million annual savings tied to centralized resource management; and $8 million annual savings due to increased staff retention. Avantas is an AMN Healthcare company.
In general, healthcare enterprises that have implemented Avantas predictive analytics and advanced labor management model have reduced their overall labor spending by 4% to 7%. Despite such impressive numbers, the use of predictive analytics in healthcare scheduling and staffing is only now gaining traction. Part of the slow uptake appears to be a lack of awareness.
Nurse Managers Interested, But Unaware of Technology Solution
In hospitals and other healthcare organizations, the scheduling of nurses and other personnel is mostly accomplished using unsophisticated scheduling tools without any forecasting of patient demand.
This situation was clearly reflected in a recent survey of nurse managers by AMN Healthcare and Avantas, Predictive Analytics in Healthcare: Optimizing Nurse Staffing in an Era of Workforce Shortages. When asked how they currently handle staff scheduling, responses from nurse managers revealed widespread use of outmoded scheduling tools: 24% use paper-based scheduling and staffing tools, 19% use simple digital spreadsheets, and 23% don’t use any scheduling tools at all.
In the same survey, nearly all nurse managers said that predictive analytics would be helpful to them in the scheduling and staffing. But 80% of them also said they did not know that such technology-enabled products existed.
Staffing Struggles Impact Morale and Quality
The Predictive Analytics in Healthcare survey also shows another critical reason why it’s so important to solve nurse scheduling and staffing problems. Many nurse managers expressed serious concerns about the effect on staff morale, patient safety and care quality.
Among the survey’s findings on the impact of scheduling and staffing problems:
- Nearly 70% of nurse managers say they are very concerned about the impact on patient satisfaction
- Approximately 55% say they are very concerned about the effect on quality of care
- Nearly half say they are very concerned about medical errors, staff turnover and budgetary issues
- 94% say that understaffing caused by scheduling and staffing problems hurts staff morale
The Way Forward
Many healthcare leaders recognize that current approaches to nurse scheduling and staffing are deficient in many ways. But they may not realize the extent to which these problems can drain financial resources, reduce employee morale, and threaten patient care quality and safety. The first step is recognizing that the technology to accurately predict future demand and staffing need is available to healthcare organizations today, and take the action to learn more about it. Those healthcare enterprises that have already adopted these strategies are reaping numerous benefits for their staff, patients and bottom line.
Dan White is President of Strategic Workforce Solutions for AMN Healthcare.