Grant: Discovery Grants Program - Individual
University of Ontario Institute of Technology
$26,000.00
Service Operations Management in the Mental Health Care Sector
FY 2023-24
Summary
This research will investigate statistical and mathematical models to develop insights into planning and management of waiting lists in general, and mental health care systems in particular. Common operational aspects of mental health services include: 1) a patient (customer) is "assigned" to a clinician (server) to receive treatment services during a random number of visits; 2) psychiatric patients have high no-show rates in their treatment sessions; and 3) psychiatric treatment plans are highly variable in terms of duration and the distribution of their outcomes. Informed by these common aspects, I will investigate methods to forecast demand and waiting time for mental health care services and to shed light on factors that drive exceptionally long waiting times for psychiatric patients. Using the mathematical models, the impacts of operational decisions, for example, capacity cap on the number of patients assigned to a clinicians, and aspects by which patients should be clustered to reduce the variability in waiting times, will be investigated. I will validate my mathematical models using empirical data. At an academic level, this research program will contribute to the fields of operations research, operations management in the health care and service sectors, and psychiatric care planning and management. It will address important issues regarding the modeling of queueing systems with returning heterogeneous customers that are assigned to heterogeneous servers to receive service during a random number of interactions. At a practical level, this research will aid decision makers in the field of mental health care with their planning methods. The need for improvements in management of waiting lists has become even more evident in the context of increased demand for psychiatric care during the current pandemic, due to high levels of stress in family and work settings and extended loneliness. These improvements will be essential for the post-Covid-19 era. Psychiatric patients in particular, and customers receiving service in general, in Canada and elsewhere, will benefit from planning that matches realities more effectively and efficiently.
Program
Discovery Grants Program - Individual
Location
Ontario, Canada
Recipient Type
Academia
Type
Grant
Source
NSERC's Awards Data
As published
Application id
RGPIN-2021-03499
Name nom
Rastpour, Amir
Department département
Faculty of Business and Information Technology
Organization id
19222
Institution établissement
University of Ontario Institute of Technology
Province en
Ontario
Province fr
Ontario
Country en
CANADA
Country fr
CANADA
Fiscal year exercice financier
2023
Competition year année de concours
2021
Award amount
26000
Program id
RGPIN
Program name en
Discovery Grants Program - Individual
Program name fr
Programme de subventions à la découverte - individuelles
Group en
DISCOVERY RESEARCH
Group fr
RECHERCHE AXÉE SUR LA DÉCOUVERTE
Committee code
1509
Committee name en
Civil, Industrial and Systems Engineering
Committee name fr
Génie civil, industriel et des systèmes
Area of application code
1200
Area of application group en
Advancement of knowledge
Area of application group fr
Avancement des connaissances
Area of application en
Advancement of knowledge
Area of application fr
Avancement des connaissances
Research subject code
1606
Research subject group en
Industrial engineering
Research subject group fr
Operations management
Research subject en
Génie industriel
Research subject fr
Gestion de la production
Application title
Service Operations Management in the Mental Health Care Sector
Keyword
Demand prediction; Mental Health Care; Operations management; Operations research; Queueing theory; Waiting list management
Application summary
This research will investigate statistical and mathematical models to develop insights into planning and management of waiting lists in general, and mental health care systems in particular. Common operational aspects of mental health services include: 1) a patient (customer) is "assigned" to a clinician (server) to receive treatment services during a random number of visits; 2) psychiatric patients have high no-show rates in their treatment sessions; and 3) psychiatric treatment plans are highly variable in terms of duration and the distribution of their outcomes. Informed by these common aspects, I will investigate methods to forecast demand and waiting time for mental health care services and to shed light on factors that drive exceptionally long waiting times for psychiatric patients. Using the mathematical models, the impacts of operational decisions, for example, capacity cap on the number of patients assigned to a clinicians, and aspects by which patients should be clustered to reduce the variability in waiting times, will be investigated. I will validate my mathematical models using empirical data. At an academic level, this research program will contribute to the fields of operations research, operations management in the health care and service sectors, and psychiatric care planning and management. It will address important issues regarding the modeling of queueing systems with returning heterogeneous customers that are assigned to heterogeneous servers to receive service during a random number of interactions. At a practical level, this research will aid decision makers in the field of mental health care with their planning methods. The need for improvements in management of waiting lists has become even more evident in the context of increased demand for psychiatric care during the current pandemic, due to high levels of stress in family and work settings and extended loneliness. These improvements will be essential for the post-Covid-19 era. Psychiatric patients in particular, and customers receiving service in general, in Canada and elsewhere, will benefit from planning that matches realities more effectively and efficiently.