Ingrid Vliegen

Designing cyclic appointment schedules for outpatient clinics with scheduled and unscheduled patient arrivals

We present a methodology to design appointment systems for outpatient clinics and diagnostic facilities that offer both walk-in and scheduled service. The developed blueprint for the appointment schedule prescribes the number of appointments to plan per day and the moment on the day to schedule the appointments. The method consists of two models; one for the day process that governs scheduled and unscheduled arrivals on the day and one for the access process of scheduled arrivals. Appointment schedules that balance the waiting time at the facility for unscheduled patients and the access time for scheduled patients are calculated iteratively using the outcomes of the two models. Two methods to calculate appointment schedules, complete enumeration and a heuristic procedure, are compared in various numerical experiments. Furthermore, an appointment schedule for the CT-scan facility at the Academic Medical Center Amsterdam, The Netherlands, is developed to demonstrate the practical merits of the methodology. The method is of general nature and can therefore also be applied to scheduling problems in other sectors than health care.

ORchestra: an online reference database of OR/MS literature in health care

We introduce the categorized reference database ORchestra, which is available online at http://www.utwente.nl/choir/orchestra/.

Individual risk profiling for breast cancer recurrence: towards tailored follow-up schemes.

Background:Breast cancer follow-up is not tailored to the risk of locoregional recurrences (LRRs) in individual patients or as a function of time. The objective of this study was to identify prognostic factors and to estimate individual and time-dependent LRR risk rates.Methods:Prognostic factors for LRR were identified by a scoping literature review, expert consultation, and stepwise multivariate regression analysis based on 5 years of data from women diagnosed with breast cancer in the Netherlands in 2005 or 2006 (n=17u2009762). Inter-patient variability was elucidated by examples of 5-year risk profiles of average-, medium-, and high-risk patients, whereby 6-month interval risks were derived from regression estimates.Results:Eight prognostic factors were identified: age, tumour size, multifocality, gradation, adjuvant chemo-, adjuvant radiation-, hormonal therapy, and triple-negative receptor status. Risk profiles of the low-, average-, and high-risk example patients showed non-uniform distribution of recurrence risks (2.9, 7.6, and 9.2%, respectively, over a 5-year period).Conclusion:Individual risk profiles differ substantially in subgroups of patients defined by prognostic factors for recurrence and over time as defined in 6-month time intervals. To tailor follow-up schedules and to optimise allocation of scarce resources, risk factors, frequency, and duration of follow-up should be taken into account.

Stochastic integer programming for multi-disciplinary outpatient clinic planning

Scheduling appointments in a multi-disciplinary clinic is complex, since coordination between disciplines is required. The design of a blueprint schedule for a multi-disciplinary clinic with open access requirements requires an integrated optimization approach, in which all appointment schedules are jointly optimized. As this currently is an open question in the literature, our research is the first to address this problem. This research is motivated by a Dutch hospital, which uses a multi-disciplinary cancer clinic to communicate the diagnosis and to explain the treatment plan to their patients. Furthermore, also regular patients are seen by the clinicians. All involved clinicians therefore require a blueprint schedule, in which multiple patient types can be scheduled. We design these blueprint schedules by optimizing the patient waiting time, clinician idle time, and clinician overtime. As scheduling decisions at multiple time intervals are involved, and patient routing is stochastic, we model this system as a stochastic integer program. The stochastic integer program is adapted for and solved with a sample average approximation approach. Numerical experiments evaluate the performance of the sample average approximation approach. We test the suitability of the approach for the hospital’s problem at hand, compare our results with the current hospital schedules, and present the associated savings. Using this approach, robust blueprint schedules can be found for a multi-disciplinary clinic of the Dutch hospital.

Improving the design and operation of an integrated emergency post via simulation

In the Netherlands, patients with an acute care demand after office hours often wrongly choose to visit the emergency department (ED), while they could have visited the general practitioners’ post (GPP). This may lead to overcrowding and increased costs. In this paper, we focus on an Integrated Emergency Post (IEP) at a Dutch hospital, where the ED and the GPP have been merged into a single point of access for patients. To find the optimal process design for this new IEP, we use computer simulation incorporating patient preferences. We define many potential interventions, and compare these by categorizing and grouping them, and sequentially withdrawing ineffective interventions, while accounting for possible interaction effects. Results show a sustainable solution for all stakeholders involved, reducing patients’ length of stay up to 17%. Based on these results, an intervention has been trialled in practice, showing a decrease in patient LOS.

Multi-disciplinary planning in health care: a review

Multi-disciplinary planning in health care is an emerging research field that applies to many health care areas with similar underlying planning characteristics. We provide a review of the literature and describe cross-relations between different applications. We identify multiple fields to classify the literature upon. These fields relate to the system characteristics, decision characteristics, and applicability. The relevant papers for each of these fields are discussed, which provides a broad and thorough overview of the present research, and guides readers towards identifying the applicable literature for their research based on the characteristics of their problem. Furthermore, we disclose research gaps and present open challenges for further research.

Stratified breast cancer follow-up using a partially observable Markov decision process

Frequency and duration of follow-up for patients with breast cancer is still under discussion. Current follow-up consists of annual mammography for the first five years after treatment and does not depend on the personal risk of developing a locoregional recurrence (LRR) or second primary tumor. Aim of this study is to gain insight in how to allocate resources for optimal and personal follow-up. We formulate a discrete-time Partially Observable Markov Decision Process (POMDP) with a finite horzion in which we aim to maximize the total expected number of quality-adjusted life years (QALYs). Transition probabilities were obtained from data from the Netherlands Cancer Registry (NCR). Twice a year the decision is made whether or not a mammography will be performed. Recurrent disease can be detected by both mammography or women themselves (self-detection). The optimal policies were determined for three risk categories based on differentiation of the primary tumor. Our results suggest a slightly more intensive follow-up for patients with a high risk and poorly differentiated tumor, and a less intensive schedule for the other risk groups.

Game—The BedGame—A Classroom Game Based on Real Healthcare Challenges

The BedGame is a classroom game to introduce Operations Management (OM) in healthcare, more specifically to introduce the effects of centralized versus decentralized planning, and the concepts of variability and queueing theory. In the BedGame, players assign medical and surgical specialties to nursing wards to obtain a balanced bed distribution, while fulfilling as many of the specialty-specific requirements as possible. The game was first designed to support decision making in a hospital in The Netherlands, and afterwards converted to a classroom game. The game has been successfully used in several courses at the University of Twente including “Operations Management in Health Care” (undergraduate), “Quantitative Methods for Operations Management in Health Care” (graduate), and a course on patient logistics for healthcare professionals. The online appendices are available at https://doi.org/10.1287/ited.2016.0172.

Predicting turnaround time reductions of the diagnostic track in the histopathology laboratory using mathematical modelling

Background Pathology departments face a growing volume of more and more complex testing in an era where healthcare costs tend to explode and short turnaround times (TATs) are expected. In contrast, the histopathology workforce tends to shrink, so histopathology employees experience high workload during their shifts. This points to the need for efficient planning of activities in the histopathology laboratory, to ensure an equal division of workload and low TATs, at minimum costs. Methods The histopathology laboratory of a large academic hospital in The Netherlands was analysed using mathematical modelling. Data were collected from the Laboratory Management System to determine laboratory TATs and workload performance during regular working hours. A mixed integer linear programme (MILP) was developed to model the histopathology processes and to measure the expected performance of possible interventions in terms of TATs and spread of workload. Results The MILP model predicted that tissue processing at specific moments during the day, combined with earlier starting shifts, can result in up to 25% decrease of TATs, and a more equally spread workload over the day. Conclusions Mathematical modelling can help to optimally organise the workload in the histopathology laboratory by predicting the performance of possible interventions before actual implementation. The interventions that were predicted by the model to have the highest performance have been implemented in the histopathology laboratory of University Medical Center Utrecht. Further research should be executed to collect empirical evidence and evaluate the actual impact on TAT, quality of work and employee stress levels.

Risk-based breast cancer follow-up stratified by age

Although personalization of cancer care is recommended, current follow‐up after the curative treatment of breast cancer is consensus‐based and not differentiated for base‐line risk. Every patient receives annual follow‐up for 5 years without taking into account the individual risk of recurrence. The aim of this study was to introduce personalized follow‐up schemes by stratifying for age. Using data from the Netherlands Cancer Registry of 37 230 patients with early breast cancer between 2003 and 2006, the risk of recurrence was determined for four age groups (<50, 50‐59, 60‐69, >70). Follow‐up was modeled with a discrete‐time partially observable Markov decision process. The decision to test for recurrences was made two times per year. Recurrences could be detected by mammography as well as by self‐detection. For all age groups, it was optimal to have more intensive follow‐up around the peak in recurrence risk in the second year after diagnosis. For the first age group (<50) with the highest risk, a slightly more intensive follow‐up with one extra visit was proposed compared to the current guideline recommendation. The other age groups were recommended less visits: four for ages 50‐59, three for 60‐69, and three for ≥70. With this model for risk‐based follow‐up, clinicians can make informed decisions and focus resources on patients with higher risk, while avoiding unnecessary and potentially harmful follow‐up visits for women with very low risks. The model can easily be extended to take into account more risk factors and provide even more personalized follow‐up schedules.