Predictive analytics is helping healthcare organizations optimize clinical staffing
As the healthcare landscape transforms, healthcare leaders are under pressure to control costs and drive better outcomes. Insurance expansion resulting in rising patient volumes is further straining the ability to provide quality care. Rising quickly on healthcare leadership wish lists is optimizing their clinical staffing. Not only does staffing account for over 50 percent of the operating cost of the average hospital, having the right clinician at the right place and time can also have a significant impact on patient care.
A majority of hospital administrators today schedule based on expected patient volumes and level of acuity. However, the data used for staffing is based on historical trends and can be highly inaccurate, resulting in overstaffing or understaffing, Understaffing can negatively impact health outcomes and lead to patient dissatisfaction. Conversely, overstaffing can lead to medical waste and cost inefficiencies. New value-based care models are driving the need to improve the accuracy of clinical staffing. Successful value-based care models are highly dependent on getting the right clinical staff mix, since achieving cost efficiencies while meeting quality metrics can make or break the model.
In the past few years, the advancement of predictive analytics has improved hospital staffing. Predictive analytics can enable hospitals to accurately predict patient demand, staff the appropriate clinician, anticipate point-of-care needs and improve the scheduling process. An optimal staffing predictive model should aggregate data from multiple sources to capture insights from wait times, staff competencies, demographics, historical trends, holidays, geography, seasonal conditions, patient patterns, acuity levels and so on. Models can also incorporate local factors that can cause a spike in ER visits such as sporting events or weather.
As an example, PinnacleHealth (a 577-bed hospital in Harrisburg, Pennsylvania) was challenged with maintaining an optimal clinical staff-to-patient ratio and was often overstaffed at its nursing stations. The hospital would anticipate a high level of patient volume that did not materialize. Conversely, when PinnacleHealth was understaffed during an unanticipated patient surge, it had to pay overtime to maintain ideal staff-to-patient ratios. PinnacleHealth is projecting that a substantial reduction in overtime hours should result in a significant ROI/cost savings.