Operations research methods improve chemotherapy patient appointment scheduling

Patient queue analysis as a component of Lean Six Sigma improvement in healthcare processes: a case study from a chemotherapy day unit

PURPOSE

Oncology patients are a vulnerable group that faces multiple challenges, aggravated by long waiting times and service queues. This article aims to use Lean Six Sigma (LSS) to improve the chemotherapy preparation process and prospectively study the patient files' queue dynamics to prioritise process improvement remedies against adding resources strategy.

DESIGN/METHODOLOGY/APPROACH

Six Sigma methodology has been employed together with Lean tools and queue dynamics in a case study research in a chemotherapy day unit to define, measure, analyse, improve and control the problematic process. The study population involved all internal customers and a sample of external customers (n = 450). The study processes were measured by 25 data points.

FINDINGS

The most frequent problem was the "Long waiting time from oncologist assessment till receiving chemotherapy". Mean value-added time for chemotherapy preparation was 42 min, the defect was any patient's waiting time exceeding it. The average pre-intervention waiting time was 65.5 ± 27.20 min. The defect baseline sigma level was 0.78 sigma. Remedies involved assigning two pharmacists, arranging the pharmacy setting to satisfy chemotherapy preparation steps, adjusting the number of patients/hours, standardising patients' files interarrival time, delivering files to the pharmacy by piece, not by batch, and fixing the printers and landlines. Post-intervention mean patient waiting time was reduced significantly to 58.7 ± 23.44 min (p-value = 0.05), and the defect sigma level was raised to 0.91 sigma.

RESEARCH LIMITATIONS/IMPLICATIONS

This study draws attention to prioritising process improvement remedies in complex care settings with long queues.

SOCIAL IMPLICATIONS

This study enhances service delivery and customer satisfaction.

ORIGINALITY/VALUE

This study serves as one of the few publications to study patient queue behaviour as a part of LSS improvements in healthcare projects.

Online scheduling using a fixed template: the case of outpatient chemotherapy drug administration

In this paper, we use a fixed template of slots for the online scheduling of appointments. The template is a link between planning the service capacity at a tactical level and online scheduling at an operational level. We develop a detailed heuristic for the case of drug administration appointments in outpatient chemotherapy. However, the approach can be applied to online scheduling in other application areas as well. The desired scheduling principles are incorporated into the cost coefficients of the objective function of a binary integer program for booking appointments in the template, as requests arrive. The day and time of appointments are decided simultaneously, rather than sequentially, where optimal solutions may be eliminated from the search. The service that we consider in this paper is an example to show the versatility of a fixed template online scheduling model. It requires two types of resource, one of which is exclusively assigned for the whole appointment duration, and the other is shared among multiple appointments after setting up the service. There is high heterogeneity among appointments on a day of this service. The appointments may range from fifteen minutes to more than eight hours. A fixed template gives a pattern for the scheduling of possibly required steps before the service. Instead of maximizing the fill-rate of the template, the objective of our heuristic is to have high performance in multiple indicators pertaining to various stakeholders (patients, nurses, and the clinic). By simulation, we illustrate the performance of the fixed template model for the key indicators.

Patient Flow Analysis Using Real-Time Locating System Data: A Case Study in an Outpatient Oncology Center

PURPOSE:

Electronic health records (EHRs) have been mainly used to analyze bottlenecks in care processes of outpatient oncology clinics. However, EHR data lead to some limitations in understanding patient flow because they are manually entered and not updated in real time. Data generated from a real-time location system (RTLS) can supplement EHR data. This study aims to demonstrate how RTLS data combined with EHR data can be used to evaluate potential interventions to improve patient flow in an outpatient cancer center.

METHODS:

EHR and RTLS data obtained from a large cancer center in central Virginia were analyzed to estimate process times and determine the various patient paths patients follow during their visit for infusion. Using the input data, we developed a discrete-event simulation (DES) model and assessed 5 what-if scenarios involving changes in staff scheduling and care processes.

RESULTS:

Raw RTLS data including > 3.5 million observations were preprocessed to remove noise and extract meaningful information. The DES results showed that new nursing schedules for the infusion center and improved pharmacy processes have positive impacts on reducing patient waiting times by approximately 20% and overall length of stay by approximately 3.4% to 4.6%, compared with the current system.

CONCLUSION:

Combining EHR and RTLS data, we were able to capture dynamic aspects of patient flow more realistically. DES models that represent a complex system based on accurate input data can help decision making on determining operational changes to improve patient flow.

Scheduling patient appointment in an infusion center: a mixed integer robust optimization approach

Infusion centers are experiencing greater demand, resulting in long patient wait times. The duration of chemotherapy treatment sessions often varies, and this uncertainty also contributes to longer patient wait times and to staff overtime, if not managed properly. The impact of such long wait times can be significant for cancer patients due to their physical and emotional vulnerability. In this paper, a mixed integer programming infusion appointment scheduling (IAS) mathematical model is developed based on patient appointment data, obtained from a cancer center of an academic hospital in Central Virginia. This model minimizes the weighted sum of the total wait times of patients, the makespan and the number of beds used through the planning horizon. A mixed integer programming robust slack allocation (RSA) mathematical model is designed to find the optimal patient appointment schedules, considering the fact that infusion time of patients may take longer than expected. Since the models can only handle a small number of patients, a robust scheduling heuristic (RSH) is developed based on the adaptive large neighborhood search (ALNS) to find patient appointments of real size infusion centers. Computational experiments based on real data show the effectiveness of the scheduling models compared to the original scheduling system of the infusion center. Also, both robust approaches (RSA and RSH) are able to find more reliable schedules than their deterministic counterparts when infusion time of patients takes longer than the scheduled infusion time.

Efficiency Model of Cladribine Tablets Versus Infusion-Based Disease-Modifying Drugs for Patients with Relapsing-Remitting Multiple Sclerosis

INTRODUCTION

To develop a simulation model assessing the efficiency of using cladribine tablets versus infusion-based disease-modifying drugs (DMDs) for the treatment of relapsing-remitting multiple sclerosis (RRMS) from a facility perspective in the UK.

METHODS

A scheduling algorithm was developed to simulate day-case admissions and calculate the mean changes to resource use and time burden for patients in a facility that transitions from infusion-based treatments to cladribine tablets over 1 year. Model inputs and assumptions were based on previous research and expert opinion. Model validation and quality checks were performed and additional scenario analyses were also conducted.

RESULTS

The model successfully scheduled all infusion treatments in the base case and no patients were left off the schedule as a result of lack of capacity. Modeled base-case outcomes increased in future scenarios owing to a 35% increase in demand. The introduction of cladribine tablets reduced these impacts. Specifically, the difference in mean daily utilization was reduced in the future scenario from 13% to 3% as 8% of patients moved to cladribine tablets; annual administration costs decreased by 96% and annual time burden decreased by 90%. Results from additional scenarios showed the largest benefits from switching current infusion patients to cladribine tablets were realized in facilities having moderate to high resource utilization.

CONCLUSIONS

This model provides facility decision-makers the ability to assess the efficiency of using cladribine tablets rather than an infusion-based DMD. The simulation quantified the benefits gained from reducing the burden on facility resources by switching some patients with RRMS from infusion-based DMDs to cladribine tablets. Overall, modeled outcomes increased in future scenarios owing to an increase in demand, although the introduction of cladribine tablets reduced this impact.

A review on the use of operational research techniques in the medical records department

Introduction:

Various operational research (OR) techniques have been used in different areas of healthcare. One of the areas in which OR techniques can be effective is the medical records department (MRD). The aim of this study is to review the applications of OR in the management of MRD and its related processes.

Methods:

This is a review article. In order to collect data, English-language studies published between 2000 and 2018, related to the use of OR techniques in MRD, in the Medline, Science Direct, ProQuest and Web of science databases were investigated. From 1165 retrieved studies, 19 articles met the inclusion criteria and were included in the final review.

Results:

The results showed that different OR techniques such as linear programming, integer programming, simulation, hierarchical analysis process, etc. have been used in various aspects of the MRD and its ongoing processes such as improving efficiency, workload management, resource allocation, optimal scheduling of staff work hours, patient scheduling, patient admission and discharge.

Conclusion:

It can be concluded that if the managers and experts of MRD and health information management become familiar with the principles and techniques of OR and become aware of the importance of these techniques in improving efficiency of MRD, there is a hope that in the future these techniques will find their true place in MRD and ultimately enhance the quality of services provided to patients.

Chemotherapy scheduling template development using an optimization approach

PURPOSE

The purpose of this paper is to develop a chemotherapy scheduling template that accounts for nurse resource availability and patient treatment needs to alleviate the mid-day patient load and provide quality services for patients.

DESIGN/METHODOLOGY/APPROACH

Owing to treatment complexity in chemotherapy administration, nurses are required at the beginning, end and during treatment. When nurses are not available to continue treatment, the service is compromised, and the resource constraint is violated, which leads to inevitable delay that risks service quality. Consequently, an optimization method is used to create a scheduling template that minimizes the violation between resource assignment and treatment requirements, while leveling patient load throughout a day. A case study from a typical clinic day is presented to understand current scheduling issues, describe nursing resource constraints, and develop a constraint-based optimization model and leveling algorithm for the final template.

FINDINGS

The approach is expected to reduce the variation in the system by 24 percent and result in five fewer chemo chairs used during peak hours. Adjusting staffing levels could further reduce resource constraint violations and more savings on chair occupancy. The actual implementation results indicate a 33 percent reduction on resource constraint violations and positive feedback from nursing staff for workload.

RESEARCH LIMITATIONS/IMPLICATIONS

Other delays, including laboratory test, physician visit and treatment assignment, are potential research areas.

ORIGINALITY/VALUE

The study demonstrates significant improvement in mid-day patient load and meeting treatment needs using optimization with a unique objective.

Chemotherapy appointment scheduling and daily outpatient-nurse assignment

Chemotherapy planning and patient-nurse assignment problems are complex multiobjective decision problems. Schedulers must make upstream decisions that affect daily operations. To improve productivity, we propose a two-stage procedure to schedule treatments for new patients, to plan nurse requirements, and to assign the daily patient mix to available nurses. We develop a mathematical formulation that uses a waiting list to take advantage of last-minute cancellations. In the first stage, we assign appointments to the new patients at the end of each day, we estimate the daily requirement for nurses, and we generate the waiting list. The second stage assigns patients to nurses while minimizing the number of nurses required. We test the procedure on realistically sized problems to demonstrate the impact on the cost effectiveness of the clinic.

A decision support system for real-time scheduling of multiple patient classes in outpatient services

We propose a methodology to provide real-time assistance for outpatient scheduling involving multiple patient types. Schedulers are shown how each prospective placement in the appointment book would impact a day's operational performance for patients and providers. Rooted in prior literature and analytical findings, the information provided to schedulers about vacant slots is based on the probabilities that the calling patient, the already-existing appointments, and the session-end time will be unduly delayed. The information is updated in real-time before and after every new booking; calculations are driven by each patient type's historical consultation times and no-show data, and implemented via a simulation tool based on the underlying analytical methodology. Our findings lead to practical guidelines for dynamically constructing templates that provide allowances for different consultation durations, service time variability, no-show rates, and provider-driven performance targets for patient waiting and provider overtime. Extensions to healthcare batch scheduling applications such as radiology, surgery, or chemotherapy-where patient mixes may be known in advance-are suggested as future research opportunities since avoiding session overtime and procedures' completion time delays involve similar considerations.

Operational research techniques applied throughout cancer care services: a review

Cancer is a disease affecting increasing numbers of people. In the UK, the proportion of people affected by cancer is projected to increase from 1 in 3 in 1992, to nearly 1 in 2 by 2020. Health services to tackle cancer can be grouped broadly into prevention, diagnosis, staging, and treatment. We review examples of Operational Research (OR) papers addressing decisions encountered in each of these areas. In conclusion, we find many examples of OR research on screening strategies, as well as on treatment planning and scheduling. On the other hand, our search strategy uncovered comparatively few examples of OR models applied to reducing cancer risks, optimising diagnostic procedures, and staging. Improvements to cancer care services have been made as a result of successful OR modelling. There is potential for closer working with clinicians to enable the impact of other OR studies to be of greater benefit to cancer sufferers.

Alternative Outpatient Chemotherapy Scheduling Method to Improve Patient Service Quality and Nurse Satisfaction

INTRODUCTION

Optimal scheduling and calendar management in an outpatient chemotherapy unit is a complex process that is driven by a need to focus on safety while accommodating a high degree of variability. Primary constraints are infusion times, staffing resources, chair availability, and unit hours.

METHODS

We undertook a process to analyze our existing management models across multiple practice settings in our health care system, then developed a model to optimize safety and efficiency. The model was tested in one of the community chemotherapy units. We assessed staffing violations as measured by nurse-to-patient ratios throughout the workday and at key points during treatment. Staffing violations were tracked before and after the implementation of the new model.

RESULTS

The new model reduced staffing violations by nearly 50% and required fewer chairs to treat the same number of patients for the selected clinic day. Actual implementation results indicated that the new model leveled the distribution of patients across the workday with an 18% reduction in maximum chair utilization and a 27% reduction in staffing violations. Subsequently, a positive impact on peak pharmacy workload reduced delays by as much as 35 minutes. Nursing staff satisfaction with the new model was positive.

CONCLUSION

We conclude that the proposed optimization approach with regard to nursing resource assignment and workload balance throughout a day effectively improves patient service quality and staff satisfaction.

Capacity planning and appointment scheduling for new patient oncology consults

To ensure that patients receive timely access to care, it has become increasingly important to use existing care provider capacity as efficiently as possible and to make informed capacity planning decisions. To support this decision-making process at a regional cancer center in British Columbia (Canada), we undertook a simulation and optimization based study that investigated the simultaneous impact of the available number of new patient consultation slots, appointment scheduling policies and oncologist specialization configurations on the timeliness of patient access to care and physician workload. The key contribution of this paper is the methodological framework it provides to decision makers who manage specialty clinics to ensure that they are using their resources efficiently and making informed strategic short- and mid-term capacity planning decisions for new patient demand.

Chemotherapy appointment scheduling under uncertainty using mean-risk stochastic integer programming

Oncology clinics are often burdened with scheduling large volumes of cancer patients for chemotherapy treatments under limited resources such as the number of nurses and chairs. These cancer patients require a series of appointments over several weeks or months and the timing of these appointments is critical to the treatment's effectiveness. Additionally, the appointment duration, the acuity levels of each appointment, and the availability of clinic nurses are uncertain. The timing constraints, stochastic parameters, rising treatment costs, and increased demand of outpatient oncology clinic services motivate the need for efficient appointment schedules and clinic operations. In this paper, we develop three mean-risk stochastic integer programming (SIP) models, referred to as SIP-CHEMO, for the problem of scheduling individual chemotherapy patient appointments and resources. These mean-risk models are presented and an algorithm is devised to improve computational speed. Computational results were conducted using a simulation model and results indicate that the risk-averse SIP-CHEMO model with the expected excess mean-risk measure can decrease patient waiting times and nurse overtime when compared to deterministic scheduling algorithms by 42 % and 27 %, respectively.