Infusion Management Software

The Vendor is required to provide a highly specialized, enterprise-grade solution designed to support the unique operational, financial, and patient care demands of our Infusion Center.
- Provide a cloud-based, purpose-built platform specifically designed for hospital-based infusion centers, with demonstrated success in optimizing complex oncology operations, and compatible out of the box with Oracle Cerner electronic health records (EHR).
- As infusion volumes continue to grow and treatments become more complex, there is an increasing need to maximize existing capacity, improve patient access to timely care, and ensure efficient use of nursing and chair resources.
- Provide to leverage advanced artificial intelligence, machine learning, and predictive analytics trained specifically on infusion center workflows to dynamically align scheduling with demand, reduce unused capacity, and proactively identify and resolve operational bottlenecks before they impact patient care or staff workload.
- Solution must deliver continuous, data-driven optimization of scheduling and resource allocation, not static or rules-based scheduling, and must demonstrate the ability to generate measurable outcomes, including increased treatment throughput, improved revenue capture, reduced overtime and operational inefficiencies, and enhanced patient satisfaction.
- Solution must meet all of the following minimum requirements:
•    Must be a dedicated infusion center optimization platform, not a general-purpose scheduling or analytics tool
•    Must leverage artificial intelligence, machine learning, and predictive analytics trained specifically on infusion center operational data to support continuous optimization
•    Must provide automated, ongoing schedule optimization, not one-time or static configuration
•    Must be out-of-the-box compatible with the Oracle Cerner EHR platform, with native, bi-directional integration embedded into clinical workflows
•    Must demonstrate prior successful deployment in infusion centers utilizing Oracle Cerner EHR
•    Must excel at predictive level-loading of daily schedules across nursing staff and chair resources, including the ability to flag future high-risk days and recommend preventive adjustments
•    Must be capable of identifying specific appointments that should be rescheduled to improve operational efficiency and patient experience
•    Must analyze historical utilization data, treatment duration variability, appointment types, and patient-specific needs to recommend optimal appointment times
•    Must include real-time, color-coded scheduling views that dynamically reflect overbooked, balanced, and underutilized capacity
•    Must include live operational dashboards comparing planned workflow vs. actual workflow in real time
•    Must provide proactive, actionable alerts to adjust appointment times, staffing assignments, or resource allocation
•    Must include a shared, real-time dashboard displaying chair status and nurse assignments
•    Must include automated workflow alerts tied to lab readiness and chart completion
•    Must provide 24/7 access to all system features with real-time data updates
- Implementation & Integration Requirements:
•    Must be fully integrated and operational with Oracle Cerner within 21 calendar days of implementation start, without requiring custom interface development or third-party middleware
•    Must commit to developing optimized daily appointment templates within 30 days of transmittal of initial data
•    Must provide at least one additional EHR integration at no cost within five (5) years in the event of an EHR transition
•    Implementation team must work closely with the Infusion Center to continuously refine scheduling templates based on real-time usage, performance, and operational changes
- Performance & Continuous Optimization:
•    Must be designed to optimize chair utilization continuously, not periodically
•    Must include the ability to generate daily, weekly, and monthly performance reports, including actionable recommendations for improvement
•    Must demonstrate the ability to identify unused capacity and quantify optimization opportunities
•    Must optimize scheduling templates using machine learning-driven updates at least annually, with preference for continuous learning models
•    Must provide ongoing optimization support to adapt to changes in staffing models, chair capacity, patient volume, and operational hours
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