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On-line Multi-criteria Allocating System of Emergent Patients

On-line Multi-criteria Allocating System of Emergent Patients. Chao-Wen Chen, Ting-Wei Shiu, Yuh-Wen Chen. 1. Introduction. A large disaster always generates numerous trauma patients in a very short time.

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On-line Multi-criteria Allocating System of Emergent Patients

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  1. On-line Multi-criteria Allocating System of Emergent Patients Chao-Wen Chen,Ting-Wei Shiu, Yuh-Wen Chen

  2. 1. Introduction • A large disaster always generates numerous trauma patients in a very short time. • How to allocate these patients to appropriate hospitals for saving lives is an important issue. • We expect to design an allocation system for emergent patients by matching the supply side: hospitals and demand side: patient characteristics via internet. • This study proposes an multi-criteria allocation model for assigning emergent patients.

  3. 2. System Framework and Model • System • RFID • Affinity Set • TTAS • RTS

  4. System Framework

  5. RFID/Demonstration 2006-2011 2000-2005

  6. Affinity Set • Aset developed to deal the interaction of elements. • Affinity is a neutral term. • Many affinities could be found in practices: • Political party • Conference participants • …

  7. Indirect Affinity • As mentioned earlier, a close relationship between people or things that have similar qualities, structures, properties or features etc. we call this type of affinity indirect affinity. • Direct Affinity • Direct affinity is natural liking for or attraction to a person or a thing or an idea, etc. In direct affinity two elements are involved: the subjects of affinity and the affinity that takes place between them.

  8. TTAS • Taiwan Triage and Acuity Scale, TTAS • Based on the basis of Canadian Triage and Acuity Scale, CTAS

  9. RTS • Revised Trauma Score • A weighting combination of Glasgow Coma Scale (GCS), Systolic Blood Pressure (SBP), and Respiratory Rate (RR).

  10. Allocation Model • We use a network model based on affinity set to represent the decision process of allocating patients. • Maximizing the global affinity is to be achieved. • TTAS and RTS are obtained from the on-line trauma registry system. • The capability of each hospital and the vital signs/characteristics (TTAS, RTS) of each patient are simultaneously considered for matching. • Simulation results are visualized for each hospital.

  11. 3. The Model and Example • Find the hospital that maximizing the global affinity • Constrained to the decision process/flow chart • Considered criteria: • Supply side: number of doctors on call (C1), special life support (C2), number of available beds (C3), rank of hospital (C4), distance to hospital (C5), number of specialty doctor (C6) • Demand side: TTAS, RTS

  12. Project Network based on Affinity

  13. Resolution and Ranking

  14. User Interface (1)

  15. User Interface (2)

  16. Ranking Results

  17. 4. Conclusions and Recommendations • This is an interesting study for on-line MCDM. • The matching process for the supply side and demand side still needs sophisticated improvements. • Based on the registry system via internet, the value-added services of this study could be practically launched soon. • Cloud computing for assigning tremendous patients after a large disaster is possible in the very near future.

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