Optimizing an Adolescent Hybrid Telemedical Mental Health Service Through Staff Scheduling Using Mathematical Programming: Model Development Study

Abstract

Journal of Medical Internet Research - International Scientific Journal for Medical Research, Information and Communication on the Internet #Preprint #PeerReviewMe: Warning: This is a unreviewed preprint. Readers are warned that the document has not been peer-reviewed by expert/patient reviewers or an academic editor, may contain misleading claims, and is likely to undergo changes before final publication, if accepted, or may have been rejected/withdrawn. Readers with interest and expertise are encouraged to sign up as peer-reviewer, if the paper is within an open peer-review period. Please cite this preprint only for review purposes or for grant applications and CVs (if you are the author). Background: According to the WHO, globally, one in seven 10 to 19-year-olds experiences a mental disorder, accounting for 13% of the global burden of disease in this age group. Half of all mental illnesses begin by the age of 14 years old and some of them with severe presentations must be admitted to hospitals and assessed by highly skilled mental health care practitioners. Objective: Telehealth can be used to assess young people and children remotely and ultimately save travel costs for the health service. The aim of this paper is to share insights how we developed a decision support tool to assign staff to days and hospital locations where adolescent mental health patients are assessed face to face, or, where possible, patients are seen through video consultation. Methods: To model the problem, we used integer linear programming, a technique which is used in mathematical modelling. The model is implemented using an Open Source solver back-end. Results: In our case study, we focus on real-world demand coming from different hospital sites in UK’s National Health Service (NHS). We incorporate our model into a decision support tool and solve a realistic test instance using Microsoft Excel and the Open Source solver back-end. Conclusions: Our approach can be used by NHS managers to better match capacity and location-dependent demand within an increasing need for hybrid telemedical services and the aims to reduce travelling and the carbon footprint within healthcare organizations.