Operations Research Frameworks for Improving Make-Ahead Drug Policies at Outpatient Chemotherapy Infusion Centers
Richardson, Donald
2019
Abstract
Outpatient chemotherapy infusion is one of the most common forms of treatment used to cure, control, and ease symptoms of cancer. Patients who require outpatient chemotherapy infusion undergo lengthy and physically demanding infusion sessions over the course of their treatment. While the frequency and duration of visits vary by patient, drug, and cancer type, most patients will require several treatments over the course of months or years to complete their regimen/treat their disease. Receiving infusion is just one part of the complex treatment process. Patients may have their blood work done, wait for the results to process, visit their oncologist, wait on their order to be placed by their oncologist and prepared by the pharmacy, and then have the infusion administered by infusion clinic staff. Each step introduces randomness which can lead to propagated delays. These delays negatively affect patients as well as clinical operation cost and staff workload. We focus on optimizing drug preparation at the pharmacy to reduce patient delays. Drugs can be prepared the morning before patients arrive to prevent the patient from waiting the additional time needed to prepare their prescribed drugs in addition to any other wait time incurred during peak pharmacy hours. However, patients scheduled for outpatient chemotherapy infusion sometimes may need to cancel at the last minute even after arriving for their appointment (i.e. patient may be deemed too ill to receive treatment). This results in the health system incurring waste cost if the drug was made ahead since the drugs are patient specific and have a short shelf life. Infusion centers must implement policies to balance this potential waste cost with the time savings for their patients and staff. In support of this effort, this dissertation focuses on methods and strategies to improve the process flow of chemotherapy infusion outpatients by optimizing pharmacy make-ahead policies. We propose using three different methods which build upon each other. First we develop a predictive model which utilizes patient-specific data to estimate the probability that a patient will defer or not show for treatment on a given day. Generally, the ability to generate high-quality predictions of patient deferrals can be highly valuable in managing clinical operations, such as scheduling patients, determining which drugs to make before patients arrive, and establishing the proper staffing for a given day. We also introduce how the patient-specific probability of deferral can help determine a ``general rule of thumb" policy for what should be made ahead on a given day. Next we utilize these probabilities in two integer programming models. These multi-criteria optimization models prioritize which and how many drugs to make ahead given a fixed window of time. This is done with the dual objectives of reducing the expected waste cost as well as the expected value of reduced patient waiting time. Lastly, we utilize simulation to better quantify the impact of our proposed policies. We show that making chemotherapy drugs ahead of an infusion appointment not only benefit the patient they are prescribed for but also subsequent patients due to the decrease load (i.e., reduced blocking) on the pharmacy system as a whole. Each method utilizes electronic medical record data from the University of Michigan Rogel Cancer Center (UMRCC) but may be generalized to any cancer center infusion clinic.Subjects
Linear Integer Programming, Simulation, Predictive Modeling, Cancer Center Pharmacy
Types
Thesis
Metadata
Show full item recordCollections
Remediation of Harmful Language
The University of Michigan Library aims to describe library materials in a way that respects the people and communities who create, use, and are represented in our collections. Report harmful or offensive language in catalog records, finding aids, or elsewhere in our collections anonymously through our metadata feedback form. More information at Remediation of Harmful Language.
Accessibility
If you are unable to use this file in its current format, please select the Contact Us link and we can modify it to make it more accessible to you.