Robust Overbooking for No-Shows and Cancellations in Healthcare DOI Creative Commons
Feng Xiao, Kin Keung Lai, Chun Kit Lau

et al.

Mathematics, Journal Year: 2024, Volume and Issue: 12(16), P. 2563 - 2563

Published: Aug. 19, 2024

Any country’s healthcare system is vital for its progress, quality of life, and long-term viability. During the pandemic, many developed countries encountered challenges differing degrees in administration their systems. The overloading services common, leading to prolonged waiting times medical services. Thus, wastage hospital resources must be taken seriously. In this paper, we examine problem no-shows cancellations outpatient clinics. By examining literature drawing from practical industry experience, uncover operational procedures these We then suggest a robust optimization strategy overbooking, incorporating both conventional overbooking model resilient approach. proposed seeks address substantial uncertainties parameters during pandemic. Taking into account risk aversion, develop an optimal policy that considers associated costs. primary contribution lies introducing alternative approach manage uncertainty through utilization technique.

Language: Английский

Predictive Optimization of Patient No-Show Management in Primary Healthcare Using Machine Learning DOI
Andrés Leiva-Araos, C. Contreras, Hemani Kaushal

et al.

Journal of Medical Systems, Journal Year: 2025, Volume and Issue: 49(1)

Published: Jan. 14, 2025

Language: Английский

Citations

1
A Systematic Review for Classification and Selection of Deep Learning Methods DOI Creative Commons

Nisa Aulia Saputra,

Lala Septem Riza, Agus Setiawan

et al.

Decision Analytics Journal, Journal Year: 2024, Volume and Issue: 12, P. 100489 - 100489

Published: June 5, 2024

The effectiveness of deep learning in completing tasks comprehensively has led to a rapid increase its usage. Deep encompasses numerous diverse methods, each with own distinct characteristics. aim this study is synthesize existing literature order classify and identify an appropriate method for given task. A systematic review was conducted as comprehensive study, utilizing spanning from 2012 2024. findings revealed that plays significant role eight main tasks, including prediction, design, evaluation assessment, decision-making, creating user instructions, classification, identification, models. various such Convolutional Neural Networks (CNN), Recurrent (RNN), Autoencoders (AE), Generative Adversarial (GAN), (DNN), Backpropagation (BP), Feed-Forward (FFNN), different confirmed. These provide researchers understanding selecting effective methods specific tasks.

Language: Английский

Citations

4
A Next Available Appointment (NAA) Tool to Better Manage Patient Delay Risk and Patient Scheduling Expectations in Specialist Clinics DOI Creative Commons
Vahid Riahi, David A. Rolls, Ibrahima Diouf

et al.

The International Journal of Health Planning and Management, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 23, 2025

ABSTRACT Every year there are approximately 3 million new outpatient specialist clinic appointments at local hospital networks in Victoria, Australia. Growing daily demand for these services leads to high‐volume waiting lists and therefore long appointment delays patients. This phenomenon emphasises the importance of providing analytics tools assist with list management clinics. In this paper, we developed a novel Next Available Appointment (NAA) tool, clinicians manage delayed‐appointment risk improve patient experience by aligning expected actual day appointment. The NAA uses simulation determine earliest available week on or after timeframe requested clinician, considering current future planned clinician availability. It was validated using years historical information across several scenarios chosen capture operational diversity. As practical example, scenario implementation within clinic's setting achieved simulated reduction overdue from 41% 25% (i.e., 47,000 over years). We also provided early details tool currently underway.

Language: Английский

Citations

0
A Home Healthcare Routing-Scheduling Optimization Model Considering Time-Balancing and Outsourcing DOI Creative Commons
Shabnam Rekabi, Babak Moradi,

Farima Salamian

et al.

Supply Chain Analytics, Journal Year: 2024, Volume and Issue: 7, P. 100077 - 100077

Published: Aug. 13, 2024

Language: Английский

Citations

2
Robust Overbooking for No-Shows and Cancellations in Healthcare DOI Creative Commons
Feng Xiao, Kin Keung Lai, Chun Kit Lau

et al.

Mathematics, Journal Year: 2024, Volume and Issue: 12(16), P. 2563 - 2563

Published: Aug. 19, 2024

Any country’s healthcare system is vital for its progress, quality of life, and long-term viability. During the pandemic, many developed countries encountered challenges differing degrees in administration their systems. The overloading services common, leading to prolonged waiting times medical services. Thus, wastage hospital resources must be taken seriously. In this paper, we examine problem no-shows cancellations outpatient clinics. By examining literature drawing from practical industry experience, uncover operational procedures these We then suggest a robust optimization strategy overbooking, incorporating both conventional overbooking model resilient approach. proposed seeks address substantial uncertainties parameters during pandemic. Taking into account risk aversion, develop an optimal policy that considers associated costs. primary contribution lies introducing alternative approach manage uncertainty through utilization technique.

Language: Английский

Citations

1