Maximizing Workforce Productivity

Healthcare Labor Optimization & Scheduling

AI generates predictive models that optimize work schedules and best-allocate resources based on both historical and real-time information. This ensures adequate and cost-effective coverage and continuity of care for patients, reducing inefficiencies for all stakeholders.

AI-driven predictive analytics models forecast future staffing needs based upon historical and real-time data on patient demographics, disease prevalence, and seasonal trends. Using this data, AI algorithms predict spikes in patient demand, anticipate staffing shortages, and recommend appropriate response measures. Non-healthcare related data sources are also be factored in, such as weather, social, or local (political) events that can have an impact on the influx of patients in a hospital. Healthcare provider schedules are designed with AI algorithms to consider constraints such as staffing ratios, regulatory requirements, and labor laws to ultimately maximize staff productivity, minimize employee burnout, and improve work-life balance. AI algorithms monitor workflows to identify bottlenecks and analyze resource utilization in the event that there is a new opportunity to streamline or reduce inefficiencies and improve the overall delivery of care. Administrative tasks related to labor scheduling such as data entry, appointment scheduling, documentation reconciliation can be automated with AI, ultimately reducing costs and freeing time for healthcare professionals to engage in higher-value activities.

Andersen Consulting Delivers

• Improved (cost) efficiency and productivity. Healthcare systems are able to allocate resources based upon patient flow and healthcare needs with the support of AI algorithms, which leads to successful maintenance of appropriate staffing levels across different departments and shifts. This benefits healthcare organizations with reduced overtime costs and improved delivery of care.
• Improved well-being of healthcare professionals. In considering employee preferences, skills, and patient demand, and staffing constraints, AI algorithms can design schedules that balance workload distribution and promote a healthier work-life balance, while reducing the administrative burden related to scheduling.
• Patient scheduling. Non-urgent and elective procedures are either rescheduled or moved to a different location away from the area that is critically affected.