Hostname: page-component-76fb5796d-5g6vh Total loading time: 0 Render date: 2024-04-25T09:51:18.856Z Has data issue: false hasContentIssue false
  • English
  • Français

Quantitative Evaluation for Uncertainty of Information About Patients’ Injury Severity in a Hospital Disaster: A Simulation Study Using Shannon’s Information Theory

Published online by Cambridge University Press:  29 June 2015

Yasuhiko Ajimi ,
Masaru Sasaki ,
Yasuyuki Uchida ,
Masayasu Gakumazawa ,
Katsunori Sasaki ,
Takashi Fujita  and
Tetsuya Sakamoto
Yasuhiko Ajimi*
Affiliation:
Department of Emergency Medicine, School of Medicine, Teikyo University, Tokyo, Japan
Masaru Sasaki
Affiliation:
Tokyo Metropolitan Hiroo Hospital, Tokyo, Japan
Yasuyuki Uchida
Affiliation:
Department of Emergency Medicine, School of Medicine, Teikyo University, Tokyo, Japan
Masayasu Gakumazawa
Affiliation:
Department of Emergency Medicine, School of Medicine, Teikyo University, Tokyo, Japan
Katsunori Sasaki
Affiliation:
Department of Emergency Medicine, School of Medicine, Teikyo University, Tokyo, Japan
Takashi Fujita
Affiliation:
Department of Emergency Medicine, School of Medicine, Teikyo University, Tokyo, Japan
Tetsuya Sakamoto
Affiliation:
Department of Emergency Medicine, School of Medicine, Teikyo University, Tokyo, Japan
*
Correspondence: Yasuhiko Ajimi, MD, DMSc Trauma & Resuscitation Center Department of Emergency Medicine School of Medicine, Teikyo University 2-11-1 Kaga, Itabashi-ku, Tokyo 173-8606, Japan E-mail: relievedtemple436@gmail.com
Get access Rights & Permissions [Opens in a new window]

Abstract

Introduction

Reducing uncertainty about information on injury severity or number of patients is an important concern in managing equipment and rescue personnel in a disaster setting. A simplified disaster model was designed using Shannon’s Information Theory to study the uncertainty of information in a triage scenario.

Hypothesis

A disaster triage scene with a specific number of injured patients represents a source of information regarding the extent of patients’ disability. It is possible to quantify uncertainty of information regarding patients’ incapacity as entropy if the information source and information arising from the source in Information Theory can be adapted to the disaster situation and the information on patients’ incapacity that arises.

Methods

Five different scenarios of a fire disaster in a hospital were modeled. Information on patients’ extent of impairment was converted to numerical values in relation to available equipment and the number of rescue personnel. Victims were 10 hospitalized patients with conditions of unknown severity. Triage criteria were created arbitrarily and consisted of four categories from Level 1 (able to walk) to Level 4 (cardiac arrest). The five situations were as follows: (1) Case 1: no triage officer; (2) Case 2: one triage officer; (3) Case 3: one triage officer and a message that six patients could walk; (4) Case 4: one triage officer and a message that all patients could obey commands; and (5) Case 5: one triage officer and a message that all patients could walk. Entropy in all cases and the amount of information newly given in Cases 2 through 5 were calculated.

Results

Entropies in Cases 1 through 5 were 5.49, 2.00, 1.60, 1.00, and 0.00 bits/symbol, respectively. These values depict the uncertainty of probability of the triage categories arising in each situation. The amount of information for the triage was calculated as 3.49 bits (ie, 5.49 minus 2.00). In the same manner, the amount of information for the messages in Cases 3 through 5 was calculated as 0.4, 1.0, and 2.0 bits, respectively. These amounts of information indicate a reduction in uncertainty regarding the probability of the triage levels arising.

Conclusion

It was possible to quantify uncertainty of information about the extent of disability in patients at a triage location and to evaluate reduction of the uncertainty by using entropy based on Shannon’s Information Theory.

AjimiY, SasakiM, UchidaY, GakumazawaM, SasakiK, FujitaT, SakamotoT. Quantitative Evaluation for Uncertainty of Information About Patients’ Injury Severity in a Hospital Disaster: A Simulation Study Using Shannon’s Information Theory. Prehosp Disaster Med. 2015;30(4):1-4.

Keywords

amount of informationentropyinformation managementInformation Theorytriage system
Type
Original Research
Information
Prehospital and Disaster Medicine , Volume 30 , Issue 4 , August 2015 , pp. 351 - 354
Copyright
© World Association for Disaster and Emergency Medicine 2015 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

1. Shannon, CE. A mathematical theory of communication. Bell System Tech J. 1948;27(379-423):623-656.CrossRefGoogle Scholar
2. Merriam-Webster Dictionary Web site. http://www.merriam-webster.com/dictionary/bit. Accessed December 25, 2015.Google Scholar
3. Li, X, Yu, S, Chen, H, Lu, C, Zhang, K, Li, F. Cardiovascular autonomic function analysis using approximate entropy from 24-h heart rate variability and its frequency components in patients with Type 2 diabetes. J Diabetes Invest. 2015;6(2):227-235.CrossRefGoogle ScholarPubMed
4. Fonseca, A, Boboeva, V, Brederoo, S, Baggio, G. Disrupting morphosyntactic and lexical semantic processing has opposite effects on the sample entropy of neural signals. Brain Res. 2015;1604:1-14.CrossRefGoogle ScholarPubMed
5. Benson, M, Koenig, KL, Schultz, CH. Disaster triage: START, then SAVE—a new method of dynamic triage for victims of a catastrophic earthquake. Prehosp Disaster Med. 1996;11(2):117-124.CrossRefGoogle ScholarPubMed
6. Pierce, JR, (ed). An Introduction to Information Theory, 2nd revised ed. Mineola, New York USA: Dover Publications, Inc; 2014: 78-116.Google Scholar
7. Harrell, WA, Boisvert, JA. An information theory analysis of duration of lifeguards’ scanning. Percept Mot Skills. 200;97(1):129-134.CrossRefGoogle Scholar
8. Guastello, SJ. Self-organization and leadership emergence in emergency response teams. Nonlinear Dynamics Psychol Life Sci. 2010;14(2):179-204.Google ScholarPubMed