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Difference between revisions of "IS480 Team wiki: 2014T1 Code Blue Project Overview"

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| style="padding-left: 20px;" | The goal of this project is to develop a simulation tool which provides a graphical visualization of the dynamic queue management framework which can be implemented in the hospitals’ emergency department (ED). The framework addresses the complex challenges faced by hospital to achieve a desired service level for the patients (e.g. LOS of 90% of patients must be within x minutes). Focused on managing patient queue dynamically before doctor consultation, the project shall implement the dynamic patient-prioritization strategies. The strategies make use of several greedy algorithms such as Shortest-Consultation-Time-First (SCON) or Shortest-Remaining-Time-First (SREM) to improve on patients’ length of stay (LOS) in the ED.
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| style="padding-left: 20px;" | The goal of this project is to develop a simulation tool which provides a graphical visualization of the dynamic queue management framework which can be implemented in the hospitals’ Emergency Department (ED). The framework addresses the complex challenges faced by hospital to achieve a desired service level for the patients (e.g. LOS of 90% of patients must be within x minutes). Focused on managing patient queue dynamically before doctor consultation, the project shall implement the dynamic patient-prioritization strategies. The strategies make use of several greedy algorithms such as Shortest-Consultation-Time-First (SCON) or Shortest-Remaining-Time-First (SREM) to improve on patients’ length of stay (LOS) in the ED.
  
 
The aim of this tool is firstly to allow healthcare practitioners to better understand and visualize the mechanism and effects of the proposed strategies; secondly to appreciate how existing data in the database can aid automation and improve operation; and finally to allow visual interaction with a simulation tool.
 
The aim of this tool is firstly to allow healthcare practitioners to better understand and visualize the mechanism and effects of the proposed strategies; secondly to appreciate how existing data in the database can aid automation and improve operation; and finally to allow visual interaction with a simulation tool.
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In the existing literature, addressing the issue of long waiting times in an ED often takes the form of single-faceted queue management strategies that are either from a demand perspective or from a supply perspective. From the demand perspective, there is work on queue design such as priority queues, or queue control strategies such as a fast-track system and demand restriction through ambulance diversion. However, they may not sufficiently leverage insights that can be derived from both historical and real-time data (Kar Way Tan, 2013).  
 
In the existing literature, addressing the issue of long waiting times in an ED often takes the form of single-faceted queue management strategies that are either from a demand perspective or from a supply perspective. From the demand perspective, there is work on queue design such as priority queues, or queue control strategies such as a fast-track system and demand restriction through ambulance diversion. However, they may not sufficiently leverage insights that can be derived from both historical and real-time data (Kar Way Tan, 2013).  
  
Therefore, an integrated framework that manages queues dynamically in the ED from both the demand and supply perspectives by leveraging historical data and real-time data is being proposed and research on by our sponsor, Assistant Professor (Practice) Kar Way.
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Therefore, an integrated framework that manages queues dynamically in the ED from both the demand and supply perspectives by leveraging historical data and real-time data is being proposed by our sponsor, Assistant Professor (Practice) Kar Way.
 
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<li>Predictive Module</li>
 
<li>Predictive Module</li>
 
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<p>For a more detailed scope, please refer to our [https://wiki.smu.edu.sg/is480/IS480_Team_wiki%3A_2014T1_Code_Blue_scope project scope].</p>
 
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== <div style="background: #000066; padding: 12px; font-weight: bold; font-size: 60%; line-height: 0.5em;"><font face="Arial" color="white">Process in the Emergency Department</font></div> ==
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[[Image:codeblue_ed_process.png|left|500px]]
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<p>To set the context, it is important to understand the process that is happening in the Emergency Department of a hospital. </p>
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<p>Patients will first make the necessary registration upon their visit and an initial assessment at the triage. Subsequently, they would proceed to queue for the consultation with the doctor.The doctor would determine if further tests and treatment is needed for the patient after the initial consultation. If not, the patient is immediately discharged or admitted into one of the wards. If they do need additional treatments, they would receive the necessary treatments and rejoin the queue to consult the doctor for a final review before being discharged/admitted.</p>
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<b>How does the proposed strategies works?</b>
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<p>SCON seeks to dynamically arrange the queue(right after a doctor is available) with patients of the shortest consultation time to be first in the queue in increasing order.</p>
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<p>SREM seeks to dynamically arrange the queue with patients with the shortest remaining time to the targeted length-of-stay(LOS) first in line of the queue. Patients will likewise be arranged in increasing order.</p>
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<p>The mixed method will use both of the strategies mentioned simultaneously to arrange the queue. </p>
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<p>The 3 strategies overall aims to optimally shortened the overall patient's length-of-stay across the board. </p>
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== <div style="background: #000066; padding: 12px; font-weight: bold; font-size: 60%; line-height: 0.5em;"><font face="Arial" color="white">References</font></div> ==
 
== <div style="background: #000066; padding: 12px; font-weight: bold; font-size: 60%; line-height: 0.5em;"><font face="Arial" color="white">References</font></div> ==
 
Papers referenced:
 
Papers referenced:

Revision as of 00:47, 13 August 2014

Code Blue 1.jpg
Home The Team Project Overview Project Management Project Documentation Project Resources
Project Description Project Scope Technologies X-factor


Aim-icon.png The goal of this project is to develop a simulation tool which provides a graphical visualization of the dynamic queue management framework which can be implemented in the hospitals’ Emergency Department (ED). The framework addresses the complex challenges faced by hospital to achieve a desired service level for the patients (e.g. LOS of 90% of patients must be within x minutes). Focused on managing patient queue dynamically before doctor consultation, the project shall implement the dynamic patient-prioritization strategies. The strategies make use of several greedy algorithms such as Shortest-Consultation-Time-First (SCON) or Shortest-Remaining-Time-First (SREM) to improve on patients’ length of stay (LOS) in the ED.

The aim of this tool is firstly to allow healthcare practitioners to better understand and visualize the mechanism and effects of the proposed strategies; secondly to appreciate how existing data in the database can aid automation and improve operation; and finally to allow visual interaction with a simulation tool.

The project will focus on the demand perspective of the integrated framework.


Ed-waiting-time.png Hospitals are facing increasing challenges in today's emergency department due to growth in patients demand for services and limited capacity in resource allocation. (D. J. Medeiros, Eric Swenson & Christopher DeFlitch, 2008). Long waiting times and a lack of doctors are often observed and experienced.

In the existing literature, addressing the issue of long waiting times in an ED often takes the form of single-faceted queue management strategies that are either from a demand perspective or from a supply perspective. From the demand perspective, there is work on queue design such as priority queues, or queue control strategies such as a fast-track system and demand restriction through ambulance diversion. However, they may not sufficiently leverage insights that can be derived from both historical and real-time data (Kar Way Tan, 2013).

Therefore, an integrated framework that manages queues dynamically in the ED from both the demand and supply perspectives by leveraging historical data and real-time data is being proposed by our sponsor, Assistant Professor (Practice) Kar Way.


Scope-icon.png The high level scope of the project comprises of the following module:
  • Simulation Module
  • Reporting Module
  • Predictive Module

For a more detailed scope, please refer to our project scope.

Process in the Emergency Department

Codeblue ed process.png

To set the context, it is important to understand the process that is happening in the Emergency Department of a hospital.

Patients will first make the necessary registration upon their visit and an initial assessment at the triage. Subsequently, they would proceed to queue for the consultation with the doctor.The doctor would determine if further tests and treatment is needed for the patient after the initial consultation. If not, the patient is immediately discharged or admitted into one of the wards. If they do need additional treatments, they would receive the necessary treatments and rejoin the queue to consult the doctor for a final review before being discharged/admitted.


How does the proposed strategies works?

SCON seeks to dynamically arrange the queue(right after a doctor is available) with patients of the shortest consultation time to be first in the queue in increasing order.

SREM seeks to dynamically arrange the queue with patients with the shortest remaining time to the targeted length-of-stay(LOS) first in line of the queue. Patients will likewise be arranged in increasing order.

The mixed method will use both of the strategies mentioned simultaneously to arrange the queue.

The 3 strategies overall aims to optimally shortened the overall patient's length-of-stay across the board.

References

Papers referenced:

  • Kar Way Tan (2013) Dynamic Queue Management for Hospital Emergency Room Service.
  • D. J. Medeiros, Eric Swenson & Christopher DeFlitch (2008) Improving Patient Flow in a Hospital Emergency Department, Proceedings of the 2008 Winter Simulation Conference.

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