Why do health systems struggle to efficiently utilize assets? The fundamental problem is one of matching a volatile, unpredictable demand for services with the constrained availability of supply. Yesterday’s tools are not adequate to address this supply-demand issue.
Cancer centers face these operational challenges on a daily basis, resulting in unacceptable patient wait times, lack of available appointments, and nurses who can’t get off the floor for their breaks.
During this webinar, you’ll hear from leaders at Johns Hopkins Medicine and University of Kansas Cancer Center about how they’ve applied predictive analytics with machine learning to improve capacity, chair utilization, and increase both patient and nurse satisfaction at their health systems.
Join this webinar and understand:
- How applying machine learning, mathematical algorithms and predictive analytics to health system operations can streamline efficiencies for both patients and clinicians
- Insights from leading health system executives on the digital transformation that brought substantial outcomes across their organization such as Johns Hopkins’ experiencing a year-over-year decrease in drug wait time and seeing a 14% growth in patient volumes during COVID at one location, and University of Kansas Cancer Center seeing a reduction in average chair wait time by 32% and a 19% increase in average daily completed volumes
