Understanding the Stochastic Behavior of Procedures Helps to Improve Efficiency Within the Cath Lab

To improve the accuracy of cath lab scheduling, Pieter Stepaniak, PhD, advocates taking into account the mean time of a cardiologist-procedure combination, rather than the mean time for a coronary angiography procedure as performed by all cardiologists. 

While visiting some cath labs in Europe, I noticed that they are not always organized in an efficient way, as opposed to, for example, operating rooms. Some cath labs had too much capacity and organized their work in such a way that staff had to work frequent overtime. The construction of schedules was based not on clear algorithms, but on historical experience (feeling). Observing this same phenomenon in our cath lab, we attempted to find an answer to an important and most fundamental question: that of the statistical distribution of our cath lab procedures. Once this information is known, it makes scheduling of cath lab procedures much easier. Because there was no relevant literature, we decided to perform an analysis based on our cath lab data. The results of this study are presented in Cardiology Journal [in press].¹ In this article, we describe the importance of our study and provide some recommendations.

As is commonly known, cath labs are capital- and labor-intensive departments, and thus financially important for the hospital. Due to the increasing prevalence of cardiovascular diseases, as well as the rapidly expanding absolute numbers and types of catheter procedures, the demand for catheterization procedures is increasing.² By 2030, 40.5% of the United States population is expected to have some form of cardiovascular disease.² Based on this information, it is expected that in the future, the demand for cath labs (today, relatively scarce and costly) will also increase. For this reason, cath labs should be used in an optimal manner. Cath lab utilization should be maximized, and idle periods (i.e., under-utilized time or high turnover times) and work outside regular hours (i.e., over-utilized time) should be minimized. It has been shown that frequent work beyond scheduled hours does not only lead to overtime costs, but also to intangible costs resulting from dissatisfaction and reduced motivation of staff.³ Overtime work is one of the primary reasons for nurses to terminate employment² and scheduling conflicts are a major cause of nursing staff turnover.⁴ Therefore, management should aim for maximal use of available cath lab time, given the aforementioned constraints. In the literature, various studies have reported different methods used to improve cath lab efficiency.^5-10 Procedures performed in cath labs are stochastic (involving a random variable, chance, or probability), as compared to procedures in the operation room (OR), which has been statistically modeled by many studies.^11-16 Similar to the OR, efficient use of cath labs crucially depends on estimated case durations. Statistical models may help to improve estimated case duration to support management in the cost-effective use of expensive surgical resources.¹⁶ 

Understanding the stochastic behavior of cath lab procedures is of utmost importance for several reasons. The Institute of Medicine (IoM) report “Crossing the Quality Chasm: A New Health System for the 21st Century,” discusses many problems in the quality of the United States’ health care delivery system.¹⁷ The report suggests that health care should be safe, effective, patient-centered, timely, efficient and equitable. According to the IoM, timely access to care means reducing waits and possible harmful delays for both those who receive and those who deliver care. One of the most serious problems has to do with timely access to hospital services. Problems involving access to care manifest themselves in a variety of forms, including rejection of patients. Because not all patients are equal, variances in treatment duration exist. From a cath lab perspective, managing fluctuating treatment durations through the use of statistical models will increase efficient care. From an operational point of view, a good statistical model makes procedure times more predictable by making a more accurate estimation of the procedural duration. Next, it helps manage the variation in cath lab procedures. The goal is to contribute to reliable cath lab schedules. Reliable schedules allow for the efficient use of scarce cath lab capacity. The chances of overused cath lab time and cancelled cases are reduced. Reducing overtime is important, because overtime is costly. If cases are not well scheduled, there is a chance that scheduled patients will be cancelled and may stay longer in the hospital. Prolonged hospital stay does not only lead to elevated costs and hence less revenue, but also to lower patient satisfaction. As cath labs are scarce and hence have limited capacities, the information from our study may be used to achieve schedules that are more efficient. Examples of useful actions include scheduling the same cases consecutively in fast-track pathways^18-19 and scheduling cases in specific rooms²⁰.

Practical use in a clinical setting

Today at our center, we make elective schedules and take into account the underlying distribution (and hence mean time) of a cardiologist-procedure combination, rather than the mean time for a coronary angiography procedure performed by all cardiologists. In sequencing cases, we also avoid scheduling three procedures consecutively that are highly skewed. In Table 1, we present an example of how procedures per cardiologist were scheduled for a Monday morning. The ‘before’ column shows, per cardiologist, the type of scheduled procedures and the average total time needed to complete the schedule. The ‘after’ column is the result after our study results were implemented. For example, cardiologist John needed 371 minutes on average, whereas only 300 minutes were assigned as block time. After analyzing the data, we found that for John, the ablation slow pathway and diagnostic electrophysiology procedure were relatively highly skewed and the mean time of his procedure was higher than for his peers. After consulting with him, we decided to reschedule the diagnostic electrophysiology procedure on Monday to another day, and adjust his procedure time to be more customized to his actual workflow. This resulted in a fall of the average needed capacity to complete cases on a particular day (from 371 minutes to 314 minutes). For the other cardiologists, we performed additional scheduling interventions based on individual mean “cardiologist-procedure combination” data, resulting in less overtime. 

Applying the knowledge we gained from our analysis to our department ultimately resulted in less overtime and a reduction in the number of cancelled patients. Also, teams experienced less stress to finish the schedule on time. In our experience, key factors when implementing this method of scheduling include:

1. Leadership from both cardiologists and management, focusing on patient-centered care and efficient usage of costly and relative scarce cath lab capacity;
2. A data management system which will help management to further optimize cath lab efficiency;
3. Last, but not least, a scheduler who is empowered by management to make efficient schedules for the patients’ sake.

Dr. Pieter Stepaniak can be contacted at pieter.stepaniak@gmail.com.

References

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