Title: Matching Supply and Demand for Medical Practices (Panel Management, Controlling Indirect Waiting Times and Dynamic Appointment Scheduling)
Speaker: Dr Anne Zander – Karlsruher Institut fur Technolgie
Date & Time: 3pm, Wednesday 14 March
Location: Room 439-201, Engineering Science, 70 Symonds St, Auckland.
In my talk, I will cover three topics that I consider to be part of the main topic “Matching Supply and Demand for Medical Practices”. In all three cases, we consider a medical practice, e.g., a practice with one general practitioner.
In Panel Management, we consider a patient panel consisting of those patients that visit the physician on a regular basis. The research question is “How to manage the patient panel in order to achieve a balance between supply and demand now and in the future?” We consider the problem dynamically as we take into account that patients of different age visit the physician with varying frequencies. Considering a time capacity for the physician, we build integer linear programs to determine balanced panels, the mix of patients to be included to reach a balanced panel, and to decide about admission for a specific patient request to enter the panel. We test and compare the solutions of those programs using simulation.
Indirect waiting times or access times of patients are an important indicator for the quality of care of a physician. Indirect waiting times are mainly influenced by the panel size, i.e., the number of patients regularly visiting the physician. To study the nature of this influence, we develop an M/D/1/K/N queueing model in which we include now-shows and rescheduling. In contrast to previous work, we assume that panel patients do not make new appointments if they are already waiting. For a given panel size, we calculate the steady state probabilities for the indirect queue length and further aspects such as the effective arrival rate of patients. We compare those results to the outcomes of a simulation and show that the simplifications we used in the analytical model are justified. The queueing model can help physicians to decide on a panel size threshold in order to maintain a predefined service level with respect to indirect waiting times.
In Dynamic Appointment Scheduling, we want to offer a requesting patient an appointment in real time where we maximize schedule utilization. Hereby, we take different service durations and time preferences of patients into account. We model the problem exactly as a Markov decision process and present a heuristic for realistic problem instances that solves a stochastic ILP. In a simulation, we test and compare the heuristic to simpler strategies.
Title: Queueing Models for Healthcare Capacity Planning
Speaker: Peter T. Vanberkel
Affiliation: Department of Industrial Engineering, Dalhousie University
Date & Time: 3pm, Wednesday 7 March
Location: Room 303-310, Faculty of Science, University of Auckland.
In this seminar I will present two studies of capacity planning problems which we investigate using queueing theory. To foster collaboration, I will emphasize and discuss extensions and next steps.
In the first study, we develop queuing network models to determine the appropriate number of patients to be managed by a single oncologist. This is often referred to as a physician’s panel size. The key features that distinguish our study of oncology practices from other panel size models are high patient turnover rates, multiple patient and appointment types and follow-up care. The paper develops stationary and non-stationary queuing network models corresponding to stabilized and developing practices, respectively. These models are used to determine new patient arrival rates that ensure practices operate within certain performance thresholds. Extensions to this work are needed to account for collaborative practices where patients with co-morbidities are followed by multiple care providers.
In the second study, we investigate a novel Emergency Department (ED) replacement found in rural communities in Nova Scotia, Canada. Staffed by a paramedic and a registered nurse, and overseen by physician via telephone, Collaborative Emergency Centres (CECs) have replaced traditional physician-led EDs overnight. To determine if CECs are suitable in larger communities we model the flow of patients and analyze the resulting performance with Lindley’s recursion. The analysis, done with simulation, shows that a CECs success depends on the relationship between the demand for primary care appointments and the supply of primary care appointments. Furthermore, we show that larger communities can successfully use CECs but that there are diminishing returns. I’m interested in extending this work such that the analysis of Lindley’s recursion can be completed without simulation.
Title: Computational Modelling and Data Science at CSIRO Data61
Speaker: Simon Dunstall – CSIRO
Date & Time: 11am, Friday 9 March
Location: Room 439-201, Engineering Science, 70 Symonds St, Auckland.
Abstract: In this seminar I will present on three interrelated topics associated with applied science and technology development being undertaken in the Decision Sciences program of CSIRO Data61, and from which stem some openly-available tools and useful data science ideas. The first topic is that of mixing large-scale numerical simulations and statistics as part of a multi-year initiative in quantitatively assessing risks associated with forest fires started by power infrastructure, the second topic is on novel data science approaches to fire fighting resource planning, and the third topic is on CSIRO’s Workspace platform which supports high-productivity scientific applications development and is being used inside and outside CSIRO for a wide range of applications including Industry4.0 projects with major manufacturers, natural hazard numerical simulation tools, and virtual coaching tools for Olympic watersports.
Bio: Simon Dunstall is the research program leader for the Decision Sciences program in Data61, and the immediate past-president of the Australian Society of Operations Research. Data61 is the mathematics, statistics and ICT business unit of CSIRO in Australia, and Decision Sciences is one of five programs in Data61 which span a total of 450 R&D staff. Decision Sciences’ mission is to build domain-specific, user-oriented, computationally-intensive decision support systems for established international markets and emerging high-tech industries. In doing this we extend and utilise our scientifically-advanced numerical simulation engines, solvers and software libraries, and high productivity / HPC application-development toolkits. We also participate from in the national conversation about the ethics and impacts of digital technologies, and we apply economics, data science and social sciences to directly demonstrate and facilitate positive community and societal benefits from digital technology.