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.

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.

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.

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=17,762). 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.