Joint Patient Selection and Scheduling under No-Shows: Theory and Application in Proton Therapy

32 Pages Posted: 6 Jun 2019 Last revised: 28 Jul 2020

See all articles by Soroush Saghafian

Soroush Saghafian

Harvard University - Harvard Kennedy School (HKS)

Nikolaos Trichakis

Massachusetts Institute of Technology (MIT)

Ruihao Zhu

Massachusetts Institute of Technology (MIT) - School of Engineering

Helen Shih

Harvard University - Massachusetts General Hospital

Date Written: May 29, 2019

Abstract

Problem Definition: We study how to admit and schedule heterogeneous patients by using simple, interpretable, yet effective policies when capacity is scarce, no-show behavior is patient- and time-dependent, and overtime is costly.

Academic/Practical Relevance: Our work is motivated by the aforementioned operational challenges that typically face adopters of new technologies in the healthcare sector. We anchor our study on a partnership with the proton therapy center of Massachusetts General Hospital (MGH), which offers a new radiation technology for cancer patients.

Methodology: We formulate the problem as a nonlinear integer optimization problem. However, since the solution to this formulation lacks both tractability and interpretability, to be relevant to practice, we limit our study to simple and interpretable policies. In particular, we propose a simple index-based rule and derive analytical performance guarantees for it. We also calibrate our model using empirical data from our partner hospital, and conduct a series of experiments to evaluate the performance of our proposed policy under practical circumstances.

Results: The analytical performance guarantees and our numerical experiments demonstrate (a) the strong performance of the proposed policies, and (b) their robustness to various practical considerations (e.g., to potential misspecification of no-show probabilities).

Managerial Implications: Our results show that our proposed policy, despite being a simple and interpretable index-based rule, is capable of improving performance by about 20% at an organization such as MGH, and of delivering results that are not far from being optimal across a wide range of parameters that might vary between organizations. This suggests that the proposed policy can be viewed as an effective “one-fits-all" capacity allocation rule that can be used in a variety of environments in which operational challenges such as no-shows and overtime costs need to be navigated using simple and interpretable rules.

Keywords: appointment scheduling, non-monotone submodular maximization, no-shows, index rules

Suggested Citation

Saghafian, Soroush and Trichakis, Nikolaos and Zhu, Ruihao and Shih, Helen, Joint Patient Selection and Scheduling under No-Shows: Theory and Application in Proton Therapy (May 29, 2019). HKS Working Paper No. RWP19-019, Available at SSRN: https://ssrn.com/abstract=3396096 or http://dx.doi.org/10.2139/ssrn.3396096

Soroush Saghafian (Contact Author)

Harvard University - Harvard Kennedy School (HKS) ( email )

79 John F. Kennedy Street
Cambridge, MA 02138
United States

Nikolaos Trichakis

Massachusetts Institute of Technology (MIT) ( email )

77 Massachusetts Avenue
50 Memorial Drive
Cambridge, MA 02139-4307
United States

Ruihao Zhu

Massachusetts Institute of Technology (MIT) - School of Engineering ( email )

MA
United States

Helen Shih

Harvard University - Massachusetts General Hospital ( email )

55 Fruit Street Boston
Boston, MA 02114
United States

Here is the Coronavirus
related research on SSRN

Paper statistics

Downloads
61
Abstract Views
581
rank
388,893
PlumX Metrics