Improving resource capacity planning in hospitals with business approach

Despite a rich literature on organizational capacity (OC) in voluntary sports clubs (VSCs), few studies have examined OC building and its long-term sustainability. Against this background, the authors identified changes in OC among VSCs that participated in a club development program and examined the sustainability of these changes. The authors collected survey data 9 months after participation comparing the participating VSCs (n = 62) with similar nonparticipating VSCs (n = 64). A selection of the participating VSCs was then contacted 3–4 years later for a follow-up survey (n = 48) and focus group interviews (n = 5). The results show that (a) significant differences in human resource capacity, planning and development capacity, and infrastructure and process capacity were visible between the participating and nonparticipating VSCs, and that (b) certain changes in OC remain in the clubs 3–4 years after participation. A sustainable change was that core volunteers related differently to the work in their respective VSCs.

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Predicting Patient Length of Stay Using Artificial Intelligence to Assist Health Care Professionals in Resource Planning and Scheduling Decisions

Journal of Global Information Management ◽

10.4018/jgim.20220901oa01 ◽

2022 ◽

Vol 30 (8) ◽

pp. 0-0

Keyword(s):

Artificial Intelligence ◽

Length Of Stay ◽

Health Care Professionals ◽

Capacity Planning ◽

Resource Planning ◽

Absolute Error ◽

Decision Makers ◽

Planning And Scheduling ◽

Healthcare Facilities ◽

Resource Capacity Planning

Artificial Intelligence (AI) significantly revolutionizes and transforms the global healthcare industry by improving outcomes, increasing efficiency, and enhancing resource utilization. The applications of AI impact every aspect of healthcare operation, particularly resource allocation and capacity planning. This study proposes a multi-step AI-based framework and applies it to a real dataset to predict the length of stay (LOS) for hospitalized patients. The results show that the proposed framework can predict the LOS categories with an AUC of 0.85 and their actual LOS with a mean absolute error of 0.85 days. This framework can support decision-makers in healthcare facilities providing inpatient care to make better front-end operational decisions, such as resource capacity planning and scheduling decisions. Predicting LOS is pivotal in today’s healthcare supply chain (HSC) systems where resources are scarce, and demand is abundant due to various global crises and pandemics. Thus, this research’s findings have practical and theoretical implications in AI and HSC management.

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Planning Capacity for Mental Health and Addiction Services in the Emergency Department: A Discrete-Event Simulation Approach

Journal of Healthcare Engineering ◽

10.1155/2019/8973515 ◽

2019 ◽

Vol 2019 ◽

pp. 1-11 ◽

Cited By ~ 3

Author(s):

Deyvison T. Baia Medeiros ◽

Shoshana Hahn-Goldberg ◽

Dionne M. Aleman ◽

Erin O’Connor

Keyword(s):

Mental Health ◽

Emergency Department ◽

Discrete Event Simulation ◽

Capacity Planning ◽

Discrete Event ◽

Minimal Impact ◽

Event Simulation ◽

Number Of Patients ◽

Ed Visits ◽

Resource Capacity

Ontario has shown an increasing number of emergency department (ED) visits, particularly for mental health and addiction (MHA) complaints. Given the current opioid crises Canada is facing and the legalization of recreational cannabis in October 2018, the number of MHA visits to the ED is expected to grow even further. In face of these events, we examine capacity planning alternatives for the ED of an academic hospital in Toronto. We first quantify the volume of ED visits the hospital has received in recent years (from 2012 to 2016) and use forecasting techniques to predict future ED demand for the hospital. We then employ a discrete-event simulation model to analyze the impacts of the following scenarios: (a) increasing overall demand to the ED, (b) increasing or decreasing number of ED visits due to substance abuse, and (c) adjusting resource capacity to address the forecasted demand. Key performance indicators used in this analysis are the overall ED length of stay (LOS) and the total number of patients treated in the Psychiatric Emergency Services Unit (PESU) as a percentage of the total number of MHA visits. Our results showed that if resource capacity is not adjusted, ED LOS will deteriorate considerably given the expected growth in demand; programs that aim to reduce the number of alcohol and/or opioid visits can greatly aid in reducing ED wait times; the legalization of recreational use of cannabis will have minimal impact, and increasing the number of PESU beds can provide great aid in reducing ED pressure.

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