HeaderSIS.jpg

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

From IS480
Jump to navigation Jump to search
Line 54: Line 54:
 
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.
 
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.
 
|-
 
|-
| colspan="2" | <hr>
+
 
|-
 
| [[Image:scope-icon.png|150px]]
 
| style="padding-left: 20px;" | The scope of the project includes the following feature set/module:
 
<ul>
 
    <li>
 
      <b>Simulation Input Management</b>
 
      <ul>
 
        <li>UI for managing simulation settings such as the probability of patient to go through laboratory procedure and probability of  different laboratory result simulated.</li>
 
        <li>UI for user to select and input simulation decision parameters such as the type of prioritization strategy, service rate, type of simulation to run and the period to run.</li>
 
        <li>Upload historical data file.</li>
 
      </ul>
 
    </li>
 
    <li><b>Dynamic Patient-Prioritization Strategy Engine</b>
 
      <ul>
 
          <li>Implementation of the strategy Shortest-Consultation-Time-First (SCON). This strategy focuses on patient's estimated length of consultation to determine his/her priority in the queue.</li>
 
          <li>Implementation of the strategy Shortest-Remaining-Time-First (SREM). This strategy focuses on using the patient's remaining length of stay period to determine his/her priority in the queue.</li>
 
          <li>Implementation of the hybrid strategy Mixed Strategy. This strategy uses both aspects from the SCON and SREM strategy.</li>
 
      </ul>
 
    </li>
 
    <li><b>Visualization of Simulation</b>
 
      <ul>
 
          <li>Running the simulation in animation mode.</li>
 
          <li>Running the simulation in play-back mode.</li>
 
      </ul>
 
    </li>
 
    <li><b>Reporting</b>
 
      <ul>
 
          <li>Dashboard-like reporting tool which display the simulation outcome and evaluation of the strategies, such as No.of patients served within target length of stay, No. of patients pre-empted in the queue</li>
 
          <li>Exporting of the report to PDF format by the user</li>
 
      </ul>
 
    </li>
 
    <li><b>Predictive Analytics [X-Factor]</b>
 
      <ul>
 
        <li>To be completed</li>
 
      </ul>
 
    </li>
 
</ul>
 
 
|}
 
|}
 
== <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> ==

Revision as of 22:27, 11 July 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 a dynamic queue management framework for 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 and research on by our sponsor, Assistant Professor (Practice) Kar Way.

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.

Images in this page taken from: