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MedMover : AI-Driven Patient Transport Optimization for Hospitals.��

Hackburst 2024 by QBurst

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Know Us

Jithin Vinod�Software Engineer Student

Joseph Zacharia Sunny�Data Analyst�( Canadian PR Holder )

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Challenges in Hospital Patient Transfers

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  • Inefficient Transfers: Delays from manual allocation and unpredictable demand.
  • Long Wait Times: Affects patient care and satisfaction.
  • Emergency Delays: Lack of dynamic prioritization in critical situations.
  • Congestion: Inefficient routing leads to hallway bottlenecks.
  • Unbalanced Workloads: Transporter task loads aren't evenly distributed.

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Existing QBurst Solution

  • Real-Time Tracking: RTLS monitors transporters and equipment.
  • Automated Allocation: Assigns transporters based on proximity.
  • Improved Efficiency: Reduces delays in patient transfers.
  • Challenges: Still faces issues with emergency response, routing, and equipment downtime.

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Enhancing Patient Transfers with �Generative AI & RTLS

  • AI-Powered Predictive Scheduling: Our solution anticipates peak demand periods and pre-allocates transporters accordingly, minimizing delays during high-demand periods.
  • Dynamic Route Optimization: Using AI-generated heatmaps, we provide real-time updates to transporters, avoiding congested areas and improving transfer speed.
  • Predictive Maintenance: Leveraging AI to predict equipment failures, we proactively schedule maintenance, reducing equipment downtime.
  • AI-Driven Workload Balancing: Optimizes task distribution among transporters, ensuring a more even workload and preventing burnout.
  • Emergency Prioritization: Features like Code Red Allocation reassign transporters during emergencies, prioritizing critical cases and improving response times.

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Expected Outcomes and Benefits

  • Faster Emergency Response: Prioritization ensures critical patients are transferred with minimal delay.
  • Reduced Patient Wait Times: Predictive scheduling and dynamic routing cut unnecessary delays, leading to faster transfers.
  • Optimized Resource Utilization: AI-driven workload balancing and predictive maintenance improve the efficiency of transporters and equipment.
  • Improved Patient Outcomes: Faster response times, better prioritization, and fewer delays lead to enhanced patient care and satisfaction.

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