In the Baltimore-Washington metropolitan area, a healthcare organization provides easy access to care with over 400,000 annual patient visits. At its infusion center, a team of specially trained registered nurses works closely with physicians and pharmacists to anticipate the special needs of patients. They strive to minimize the waiting time spent there and make the patient experience as convenient as possible.
From March to September of 2021, the median time between check-in and the start of Primary Medication in the infusion center was 136 minutes (2.3 hours). These long cycle times were causing delays in treatment, limited the number of patients that could be treated at the clinic, and were having a negative impact on the patient’s experience. The healthcare organization’s goal was to reduce the median time to 90 minutes (33% decrease) in the near term and 60 minutes (56% decrease) in the long term. This would decrease cycle times so more patients could be seen while also reducing unnecessary and long wait times.
To achieve these goals, the healthcare organization needed to determine specific causes of delays in patient cycle time and model how different scenarios could help achieve cycle time targets. By identifying the bottlenecks and issues that hinder patient throughput, the organization could then determine how to best utilize its limited resources. The healthcare organization was seeking a simulation tool that would allow them to evaluate flow and test scenarios in a safe digital twin.
The healthcare organization partnered with BigBear.ai to leverage Process Simulator™, their discrete event simulation software, which predicts the behavior of current and future processes. BigBear.ai’s solutions account for variability based on actual data, real-world complexities, and resource constraints.
When value-stream mapping its current process through the simulation tool, the healthcare organization identified three key areas impacted by waste: electronic orders or defects, the pharmacy, and scheduling. A lack of standardization for orders was leading to an overwhelming amount of rework. When pharmacy orders needed to be reworked or changed, they could be delayed by up to three hours. Similarly, orders and protocols that changed could result in rescheduling and canceling appointments that lead to defects in processing. The processes for rescheduling and canceling appointments were leading to defects. Patient arrival variations further exacerbated the inefficient schedules and processes, not to mention verification requirements, staffing limitations, and work queues only further making things more complicated.
To meet its near and long-term goals, the healthcare organization needed to reduce the number of order errors, decrease the number of same-day labs, and optimize patient scheduling. Using BigBear.ai’s discrete event simulation software, the healthcare organization was able to quantify the bottlenecks in the process, understand the current process capability, and test potential solutions to understand the overall impact on KPIs.
The healthcare organization began simulating different schedule templates to optimize patient throughput testing its current capacity against different schedule templates to identify opportunities for improvement. Based on the simulation analysis from the discrete event simulation software, the healthcare organization discovered a new schedule template with the potential to improve throughput by 33%.
The healthcare organization collaborated with the providers and the pharmacy to standardize their approach to orders to ensure that the right patient received the right drug at the right dose, through the right route, and at the right time. By adding an MD note documenting the reason for a regimen or dosage change, there was the potential to drastically increase patient flow. Working with IT colleagues, the team also developed a report that evaluates ordered labs to identify outlier values and the patients who don’t have their required bloodwork completed prior to the scheduled clinic visit. By making this key information available to providers, the negative impact on the cycle times will be eliminated and will accelerate the process.
Based on the analysis from BigBear.ai’s Process Simulator, the healthcare organization can reduce patient and pharmacy cycle times by more than 50%. The reduction in cycle times will increase the average number of patients capable of receiving treatment by 15 patients per day. That’s more than 5,000 additional patients the infusion center could treat in a year.
This new form of lean management puts the patient first, adding value from their perspective while eliminating and improving processes that don’t. The healthcare organization’s leaders and staff are aligned around a shared vision that drives results for the organization’s priorities while improving staff satisfaction and engagement.
“BigBear.ai’s discrete event simulation software has met our needs and surpassed expectations in its scalability, learning curve, and outstanding customer service,” said the Director of Process Improvement at the healthcare organization.
The healthcare organization is currently evaluating additional opportunities for AI / ML technology in emergency department triage, imaging, ambulance clinics, pharmacy, procedural areas, ancillary services, and even food service.