2 results
Primary Triage in a Mass-casualty Event Possesses a Risk of Increasing Informational Confusion: A Simulation Study Using Shannon’s Entropy
- Yasuhiko Ajimi, Masaru Sasaki, Yasuyuki Uchida, Ichiro Kaneko, Shinya Nakahara, Tetsuya Sakamoto
-
- Journal:
- Prehospital and Disaster Medicine / Volume 31 / Issue 5 / October 2016
- Published online by Cambridge University Press:
- 05 August 2016, pp. 498-504
- Print publication:
- October 2016
-
- Article
- Export citation
-
Introduction
Primary triage in a mass-casualty event setting using low-visibility tags may lead to informational confusion and difficulty in judging triage attribution of patients. In this simulation study, informational confusion during primary triage was investigated using a method described in a prior study that applied Shannon’s Information Theory to triage.
HypothesisPrimary triage using a low-visibility tag leads to a risk of informational confusion in prioritizing care, owing to the intermingling of pre- and post-triage patients. It is possible that Shannon’s entropy evaluates the degree of informational confusion quantitatively and improves primary triage.
MethodsThe Simple Triage and Rapid Treatment (START) triage method was employed. In Setting 1, entropy of a triage area with 32 patients was calculated for the following situations: Case 1 – all 32 patients in the triage area at commencement of triage; Case 2 – 16 randomly imported patients to join 16 post-triage patients; Case 3 – eight patients imported randomly and another eight grouped separately; Case 4 – 16 patients grouped separately; Case 5 – random placement of all 32 post-triage patients; Case 6 – isolation of eight patients of minor priority level; Case 7 – division of all patients into two groups of 16; and Case 8 – separation of all patients into four categories of eight each. In Setting 2, entropies in the triage area with 32 patients were calculated continuously with each increase of four post-triage patients in Systems A and B (System A – triage conducted in random manner; and System B – triage arranged into four categories).
ResultsIn Setting 1, entropies in Cases 1-8 were 2.00, 3.00, 2.69, 2.00, 2.00, 1.19, 1.00, and 0.00 bits/symbol, respectively. Entropy increased with random triage. In Setting 2, entropies of System A maintained values the same as, or higher than, those before initiation of triage: 2.00 bits/symbol throughout the triage. The graphic waveform showed a concave shape and took 3.00 bits/symbol as maximal value when the probability of each category was 1/8, whereas the values in System B showed a linear decrease from 2.00 to 0.00 bits/symbol.
ConclusionInformational confusion in a primary triage area measured using Shannon’s entropy revealed that random triage using a low-visibility tag might increase the degree of confusion. Methods for reducing entropy, such as enhancement of triage colors, may contribute to minimizing informational confusion.
,Ajimi Y ,Sasaki M ,Uchida Y ,Kaneko I ,Nakahara S .Sakamoto T Primary Triage in a Mass-casualty Event Possesses a Risk of Increasing Informational Confusion: A Simulation Study Using Shannon’s Entropy . Prehosp Disaster Med.2016 ;31 (5 ):498 –504 .
Quantitative Evaluation for Uncertainty of Information About Patients’ Injury Severity in a Hospital Disaster: A Simulation Study Using Shannon’s Information Theory
- Yasuhiko Ajimi, Masaru Sasaki, Yasuyuki Uchida, Masayasu Gakumazawa, Katsunori Sasaki, Takashi Fujita, Tetsuya Sakamoto
-
- Journal:
- Prehospital and Disaster Medicine / Volume 30 / Issue 4 / August 2015
- Published online by Cambridge University Press:
- 29 June 2015, pp. 351-354
- Print publication:
- August 2015
-
- Article
- Export citation
-
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.
HypothesisA 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.
MethodsFive 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.
ResultsEntropies 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.
ConclusionIt 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.
,Ajimi Y ,Sasaki M ,Uchida Y ,Gakumazawa M ,Sasaki K ,Fujita T .Sakamoto T 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 .