Infusion scheduling is more than just reserving chairs on a grid. Traditional methods often overlook real-world challenges like fluctuating treatment times, staff availability, and pharmacy delays, leading to inefficiencies and long wait times. This blog explores how a data-driven approach can optimize scheduling, improve patient flow, and create a more balanced workload for clinicians.
Pitfalls of traditional chair scheduling
Traditional scheduling methods, often used by Electronic Health Record (EHR) systems, rely on grid-based chair allocation and ‘if this, then that’ (IFTTT) rules. On the surface, this seems logical—patients are scheduled for set durations, chairs are assigned accordingly, and operating hours are fixed. However, this approach overlooks the complexities of real-world infusion centers, where variability in patient needs, staff availability, and workflow disruptions create significant scheduling challenges.
Scheduling by chair creates a visually organized plan, but it primarily considers individual chair occupancy rather than the broader dynamics of an infusion center’s workflow. By focusing only on the occupation of each chair individually, the whole picture of what is unfolding in the infusion center as a whole is missed. This approach does not consider the complex interplay of important components such as nurse availability, pharmacy preparation times, and patient arrival fluctuations. This results in a fundamentally incorrect solution.
Complexity of daily operations
While it is true that, in general, we know certain deterministic characteristics such as the number of chairs and operational hours of a center, there are a host of factors (e.g. late arrivals, delays in labs, delays in pharmacy, nurses calling in sick, clinics running late, uncertainty of treatment length) that contain a significant degree of randomness that massively impact the schedule.
For example, the uncertainty of treatment length—whether due to treatments running shorter than expected or longer because of patient reactions—can create significant disruptions in the planned grid schedule. Grid-based scheduling relies on precise start and end times, similar to reserving a tennis court. In practice, however, infusion appointments often deviate from their scheduled durations, with less than 50% starting and ending on time. When one patient’s treatment is delayed, the ripple effects can impact subsequent patients’ appointments, leading to prolonged wait times and underutilized chairs.
Additionally, scheduling based on chair count without considering staff availability and workload can lead to staffing issues. Chair schedules do not consider nurse touchtime or patient acuity. If the schedule does not account for these factors, nurses may be overworked or underutilized, affecting the quality of care and leading to staff burnout.
The complexity of infusion center operations and the interplay between different operational and clinical factors requires scheduling systems to incorporate the mathematics of probability theory and optimization to tackle the problem adequately.
A modern approach: mathematically optimized schedules
Optimizing infusion schedules is an extraordinarily complex problem. Even in a moderately sized center, the number of possible appointment arrangements is vast—more than the number of water and air molecules on planet earth plus the number of stars in the known universe—making it difficult for rule-based solutions to effectively address scheduling challenges. A more advanced mathematical approach is needed to navigate this complexity.
LeanTaaS’ expert data science team with deep technical expertise in mathematics, physics, and engineering, in partnership with clinical domain experts, have spent over a decade developing the state of the art solution. iQueue for Infusion Centers employs a data driven, mathematical approach to infusion center scheduling that has consistently delivered impressive results. The solution addresses the core issues in three key areas: demand forecasting, supply capacity forecasting, and process flows.
- Demand forecasting: Infusion centers experience significant variability between planned demand and actual demand. Factors such as oncologist schedules, practice mix, delayed arrivals, and patients varying treatment cycles contribute to this unpredictability. Patients are often on a 6-8 week cycle and rotate off their treatment, or they might have reactions that alter their treatment duration. iQueue uses pattern recognition and machine learning from historical data of prior appointments to forecast volume and mix by day of week for a given center. This helps to better predict and plan for actual demand.
- Supply capacity forecasting: Managing supply capacity in infusion centers is complex. Constraint-based optimization algorithms in iQueue consider various factors such as rosters, schedules, drugs, pharmacy preparation times, and more. By accounting for these constraints, iQueue ensures that the infusion center can operate smoothly, even when unexpected changes occur. This approach helps level load the chair utilization and manage nursing workload effectively.
- Process flows: The flow of patients through an infusion center can be impacted by numerous factors, including where and how patients check in, whether drugs are pre-mixed or mixed on demand, the layout of chairs (whether in a pod or spread across multiple floors), and how nurses are assigned. iQueue’s optimization algorithms take these variables into account, ensuring that the schedule accommodates the nuances of each center’s unique process flows. This results in more efficient operations and improved patient experience.
Why this matters
Doing this right has enormous value to your patients, your staff, and your institution. When infusion centers use iQueue, they can expect to see significant efficiency and throughput improvements, like a 15% increase in completed appointments and a 23% increase in daily completed patient hours. These results not only maximize the use of resources but also improve patient access to care.
For patients, the most noticeable benefit is a reduction in wait times, enhancing their overall experience.
For staff, particularly nurses, the optimization of schedules leads to a more balanced workload in terms of touch time, volume of patients, and acuity. This means less burnout and a more manageable pace of work, allowing them to provide better care without feeling overwhelmed.
Over the past decade, more than 800 infusion centers across over 100 leading health systems have successfully implemented this approach, demonstrating its long-term impact. These centers have shown significant improvements that have been sustained over a very long period of time. In particular, 80% of the National Comprehensive Cancer Network (NCCN) organizations have standardized on iQueue as have 60% of the National Cancer Institute (NCI) organizations.
In summary, iQueue offers a superior approach to that of traditional methods by employing machine learning, probabilistic models, and optimization algorithms, resulting in reduced wait times, significantly enhanced efficiency, and improved overall patient experience.
