AI Models for Patient Volume Prediction in Hospital ADT Units

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    Executive Summary:

    In response to the pervasive challenge of reactive decision-making within the healthcare landscape, a major U.S. healthcare provider partnered with Factspan. Facing critical challenges like delayed decision-making affected patient care, operations, and finances. To remedy this, the institution actively sought a solution that would mitigate the negative impact and restore stability. The partnership resulted in an advanced AI-driven system, employing predictive analytics to streamline admissions, transfers, and discharges.

    With the deployment of time series forecasting models and a user-friendly Tableau Dashboard, the Factspan initiative not only addressed immediate operational challenges but marked a pivotal shift towards proactive decision-making. This transformative project laid the groundwork for a data-driven operational framework, amplifying patient care, fortifying financial stability, and positioning the institution as an innovative leader in healthcare analytics.

    Project Highlights:
    • Achieved 95% accuracy in volume predictions with healthcare analytics
    • Streamlined staffing response time by 30%
    • Realized $1M annual savings through optimized resource allocation
    • Enhanced workload management efficiency by 25% with predictive analytics
    • Realized a 40% increase in operational productivity
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