$1.2M Revenue Boost and $500K Cost Savings per OR per year with Opmed.ai

Enter Opmed.ai, a solution that combines artificial intelligence and network science to optimize OR scheduling. This innovative technology was recently tested in an major hospital, aiming to refine OR scheduling, enhance block utilization, and optimize overall planning to increase revenue, reduce costs, and streamline staffing requirements.

Smart OR Planning: A Three-Pronged Approach

At the core of OR efficiency lies meticulous planning. The OR plan is built around cases, each representing a specific operation scheduled within a predicted timeslot. Cases are assigned resources such as rooms, staff, and equipment, and grouped into blocks allocated to surgeons. 

Opmed.ai's Adaptive OR Optimization Engine takes the lead by offering a tailored solution to enhance the planning process, concentrating on three key components:

1. Case Length Prediction: The software predicts the duration of each case more accurately than traditional estimates by considering a multitude of co-dependent factors like who is the surgeon, what is the staff composition, and even the specific time of the day in which the case is scheduled.
2. Block Utilization: Predicting in advance whether a surgeon will fill their block. The potential underutilized block time offers an opportunity to advance other procedures in the waitlist.
3. Optimization Targets:some text
   1. Revenue: Maximizes revenue by condensing cases into a tightly packed schedule, creating white space for additional cases.
   2. Cost: Reduces operational costs by freeing up ORs and minimizing staff shifts and overtime.
   3. Staff Allocation: Efficiently allocates staff resources to cover all cases with fewer nurses and anesthesiologists.

The Real-Life Case Study

Opmed.ai’s OR optimization engine was put to the test over a seven month period. The trial aimed to enhance OR scheduling in the following ways:

• Predict case lengths more accurately
• Improve block utilization
• Overall optimization targeting revenue, cost, and staffing efficiencies.

Methodology

• Data Collection: Two years of historical OR data (2021-2022) were used to train Opmed.ai's models on case length prediction and block utilization.
• Implementation Period: The technology was applied over a seven-month period (January to July 2023), during which all components of the system were actively used to manage the OR schedule.

Optimization Strategies

Opmed.ai employed optimization strategies targeting revenue, cost, and staff allocation:

• Revenue optimization focused on condensing cases into a tightly packed schedule to allow fitting more cases and thus maximize revenue generation.
• Cost optimization aimed to reduce operational expenses by freeing up ORs and aligning white space with staff shifts to minimize overtime.
• Staff optimization prioritized efficient allocation of nursing and anesthesiology resources, aiming to cover all cases with fewer staff members.

Key Results

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• Case Length Prediction: Opmed.ai's predictions were successful in 70% of cases, reducing mean error by 40% compared to hospital estimates.
• Block Utilization: Identified an average of 7,500 underutilized minutes per OR per year.
• Revenue Optimization: Estimated potential revenue boost of $1.2 million per OR per year.
• Cost Optimization: Achieved savings of $500,000 per OR per year.
• Staff Optimization: Freed up approximately 80 anesthesiologists and 50 nurses per OR per year.

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These results were obtained against one of the most efficient private hospitals in the country. And yet, still, despite this hospital’s well-established effective planning and utilization, our optimization detected vast untapped opportunities for improvement. 

Accurate Case Length Prediction

40% reduction in estimation error compared to the hospital's own estimates, translating to approximately 4,000 minutes of misallocated time per OR per year.

Opmed.ai's algorithm significantly improved the accuracy of predicting case durations compared to traditional hospital estimates. By considering various factors such as surgeon identity, staff composition, and historical data, Opmed.ai achieved a 70% success rate in predicting case lengths.

• 70% Success Rate: Comparing Opmed.ai’s estimates for case length with those currently used by the hospital schedulers, we found that in 70% of the time Opmed.ai’s predictions were more accurate by a significant margin. On an average week the total prediction error over all cases was reduced from ∼3,100 to ∼1,900 minutes, a saving of ∼1,200 minutes per week. 
• Estimation Error Reduced by 40%: Compared to traditional hospital estimates, Opmed.ai managed to reduce the average estimation error in predicting surgery durations by 40%. 
• Detecting ~4,000 Misallocated Minutes per OR per Year: These are instances where the actual duration of surgeries differed significantly from the initial estimates, leading to inefficiencies in scheduling and resource allocation.

Block Utilization Analysis

On average, approximately 7,500 underutilized minutes per OR per year, were identified, presenting opportunities for increased productivity.

Surgeons sometimes don't use all their scheduled surgery time. Opmed.ai analyzed block utilization patterns to identify instances of underutilized block time. 

Opmed.ai analyzed block utilization patterns and found an average of 7,500 minutes of underutilized time per operating room each year. These are periods where scheduled surgeries did not fully utilize the allocated time slots, representing opportunities for increased productivity and efficiency.

By forecasting potential white space in surgeons' schedules, Opmed.ai could reallocate this time for additional procedures, thereby turning idle time into productive OR time.

Revenue Optimization

Opmed.ai's technology demonstrated significant financial benefits for the hospital, including an estimated $1.2 million increase in revenue and $500,000 in cost savings per OR per year. 

Opmed.ai optimizes the way in which all cases are arranged to help the hospital increase its revenue and cut costs. Even small changes, like starting a surgery a bit earlier or later, or shifting a block to a different room, could help the hospital use its time and space better. Mainly, the optimized OR plans take advantage of all the misallocated time scattered throughout the day to generate meaningful time slots for additional surgeries.

Cost Optimization

Saving $500,000 per OR per Year

Through efficient resource allocation and scheduling, Opmed.ai achieved cost savings of $500,000 per operating room per year. These savings result from reduced overtime, optimal utilization of staff and equipment, and minimized operational expenses.

Optimizing Staff

Additionally, the optimization strategies freed up approximately 80 anesthesiologists and 50 nurses per OR per year, enhancing staff efficiency and potentially reducing burnout.

By streamlining scheduling and improving efficiency, Opmed.ai freed up approximately 80 anesthesiologists and 50 nurses per operating room annually. This optimization enables better utilization of staff resources, reduces workload, and potentially mitigates burnout, leading to improved staff satisfaction and patient care.

A New Era in OR Scheduling 

In the high-pressure environment of hospital operating rooms (ORs), efficiency is not just a goal; it's a necessity. Delays or disruptions can cascade, affecting patient outcomes, staff morale, and the hospital's bottom line. 

By leveraging the power of AI and network science, Opmed.ai has shown that it's possible to transform OR management, making them more efficient, cost-effective, and patient-centered. 

These results underscore the importance of leveraging advanced technologies like artificial intelligence and network science to optimize healthcare operations, ultimately leading to improved patient care and financial sustainability for hospitals.