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Applying Discrete Event Simulation to Reduce Patient Wait Times and Crowding: The Case of a Specialist Outpatient Clinic with Dual Practice System
Long wait times and crowding are major issues affecting outpatient service delivery, but it is unclear how these affect patients in dual practice settings. This study aims to evaluate the effects of changing consultation start time and patient arrival on wait times and crowding in an outpatient clin...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8871892/ https://www.ncbi.nlm.nih.gov/pubmed/35206804 http://dx.doi.org/10.3390/healthcare10020189 |
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author | Fun, Weng Hong Tan, Ee Hong Khalid, Ruzelan Sararaks, Sondi Tang, Kar Foong Ab Rahim, Iqbal Md. Sharif, Shakirah Jawahir, Suhana Sibert, Raoul Muhammad Yusof Nawawi, Mohd Kamal Mohd |
author_facet | Fun, Weng Hong Tan, Ee Hong Khalid, Ruzelan Sararaks, Sondi Tang, Kar Foong Ab Rahim, Iqbal Md. Sharif, Shakirah Jawahir, Suhana Sibert, Raoul Muhammad Yusof Nawawi, Mohd Kamal Mohd |
author_sort | Fun, Weng Hong |
collection | PubMed |
description | Long wait times and crowding are major issues affecting outpatient service delivery, but it is unclear how these affect patients in dual practice settings. This study aims to evaluate the effects of changing consultation start time and patient arrival on wait times and crowding in an outpatient clinic with a dual practice system. A discrete event simulation (DES) model was developed based on real-world data from an Obstetrics and Gynaecology (O&G) clinic in a public hospital. Data on patient flow, resource availability, and time taken for registration and clinic processes for public and private patients were sourced from stakeholder discussion and time-motion study (TMS), while arrival times were sourced from the hospital’s information system database. Probability distributions were used to fit these input data in the model. Scenario analyses involved configurations on consultation start time/staggered patient arrival. The median registration and clinic turnaround times (TT) were significantly different between public and private patients (p < 0.01). Public patients have longer wait times than private patients in this study’s dual practice setting. Scenario analyses showed that early consultation start time that matches patient arrival time and staggered arrival could reduce the overall TT for public and private patients by 40% and 21%, respectively. Similarly, the number of patients waiting at the clinic per hour could be reduced by 10–21% during clinic peak hours. Matching consultation start time with staggered patient arrival can potentially reduce wait times and crowding, especially for public patients, without incurring additional resource needs and help narrow the wait time gap between public and private patients. Healthcare managers and policymakers can consider simulation approaches for the monitoring and improvement of healthcare operational efficiency to meet rising healthcare demand and costs. |
format | Online Article Text |
id | pubmed-8871892 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-88718922022-02-25 Applying Discrete Event Simulation to Reduce Patient Wait Times and Crowding: The Case of a Specialist Outpatient Clinic with Dual Practice System Fun, Weng Hong Tan, Ee Hong Khalid, Ruzelan Sararaks, Sondi Tang, Kar Foong Ab Rahim, Iqbal Md. Sharif, Shakirah Jawahir, Suhana Sibert, Raoul Muhammad Yusof Nawawi, Mohd Kamal Mohd Healthcare (Basel) Article Long wait times and crowding are major issues affecting outpatient service delivery, but it is unclear how these affect patients in dual practice settings. This study aims to evaluate the effects of changing consultation start time and patient arrival on wait times and crowding in an outpatient clinic with a dual practice system. A discrete event simulation (DES) model was developed based on real-world data from an Obstetrics and Gynaecology (O&G) clinic in a public hospital. Data on patient flow, resource availability, and time taken for registration and clinic processes for public and private patients were sourced from stakeholder discussion and time-motion study (TMS), while arrival times were sourced from the hospital’s information system database. Probability distributions were used to fit these input data in the model. Scenario analyses involved configurations on consultation start time/staggered patient arrival. The median registration and clinic turnaround times (TT) were significantly different between public and private patients (p < 0.01). Public patients have longer wait times than private patients in this study’s dual practice setting. Scenario analyses showed that early consultation start time that matches patient arrival time and staggered arrival could reduce the overall TT for public and private patients by 40% and 21%, respectively. Similarly, the number of patients waiting at the clinic per hour could be reduced by 10–21% during clinic peak hours. Matching consultation start time with staggered patient arrival can potentially reduce wait times and crowding, especially for public patients, without incurring additional resource needs and help narrow the wait time gap between public and private patients. Healthcare managers and policymakers can consider simulation approaches for the monitoring and improvement of healthcare operational efficiency to meet rising healthcare demand and costs. MDPI 2022-01-19 /pmc/articles/PMC8871892/ /pubmed/35206804 http://dx.doi.org/10.3390/healthcare10020189 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Fun, Weng Hong Tan, Ee Hong Khalid, Ruzelan Sararaks, Sondi Tang, Kar Foong Ab Rahim, Iqbal Md. Sharif, Shakirah Jawahir, Suhana Sibert, Raoul Muhammad Yusof Nawawi, Mohd Kamal Mohd Applying Discrete Event Simulation to Reduce Patient Wait Times and Crowding: The Case of a Specialist Outpatient Clinic with Dual Practice System |
title | Applying Discrete Event Simulation to Reduce Patient Wait Times and Crowding: The Case of a Specialist Outpatient Clinic with Dual Practice System |
title_full | Applying Discrete Event Simulation to Reduce Patient Wait Times and Crowding: The Case of a Specialist Outpatient Clinic with Dual Practice System |
title_fullStr | Applying Discrete Event Simulation to Reduce Patient Wait Times and Crowding: The Case of a Specialist Outpatient Clinic with Dual Practice System |
title_full_unstemmed | Applying Discrete Event Simulation to Reduce Patient Wait Times and Crowding: The Case of a Specialist Outpatient Clinic with Dual Practice System |
title_short | Applying Discrete Event Simulation to Reduce Patient Wait Times and Crowding: The Case of a Specialist Outpatient Clinic with Dual Practice System |
title_sort | applying discrete event simulation to reduce patient wait times and crowding: the case of a specialist outpatient clinic with dual practice system |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8871892/ https://www.ncbi.nlm.nih.gov/pubmed/35206804 http://dx.doi.org/10.3390/healthcare10020189 |
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