Mahsa Dehghani Soufi1, Taha Samad-Soltani1, Samad Shams Vahdati2, Peyman Rezaei-Hachesu3. 1. Department of Health Information Technology, School of Management and Medical Informatics, Tabriz University of Medical Sciences, Tabriz, Iran. 2. Road Traffic Injury Research Center, Department of Emergency Medicine, Tabriz University of Medical Sciences, Tabriz, Iran. 3. Department of Health Information Technology, School of Management and Medical Informatics, Tabriz University of Medical Sciences, Tabriz, Iran; Iranian Center of Excellence in Health Management, Tabriz University of Medical Sciences, Tabriz, Iran. Electronic address: rezaeip@tbzmed.ac.ir.
Abstract
OBJECTIVES: Fast and accurate patient triage for the response process is a critical first step in emergency situations. This process is often performed using a paper-based mode, which intensifies workload and difficulty, wastes time, and is at risk of human errors. This study aims to design and evaluate a decision support system (DSS) to determine the triage level. METHODS: A combination of the Rule-Based Reasoning (RBR) and Fuzzy Logic Classifier (FLC) approaches were used to predict the triage level of patients according to the triage specialist's opinions and Emergency Severity Index (ESI) guidelines. RBR was applied for modeling the first to fourth decision points of the ESI algorithm. The data relating to vital signs were used as input variables and modeled using fuzzy logic. Narrative knowledge was converted to If-Then rules using XML. The extracted rules were then used to create the rule-based engine and predict the triage levels. RESULTS: Fourteen RBR and 27 fuzzy rules were extracted and used in the rule-based engine. The performance of the system was evaluated using three methods with real triage data. The accuracy of the clinical decision support systems (CDSSs; in the test data) was 99.44%. The evaluation of the error rate revealed that, when using the traditional method, 13.4% of the patients were miss-triaged, which is statically significant. The completeness of the documentation also improved from 76.72% to 98.5%. CONCLUSIONS: Designed system was effective in determining the triage level of patients and it proved helpful for nurses as they made decisions, generated nursing diagnoses based on triage guidelines. The hybrid approach can reduce triage misdiagnosis in a highly accurate manner and improve the triage outcomes.
OBJECTIVES: Fast and accurate patient triage for the response process is a critical first step in emergency situations. This process is often performed using a paper-based mode, which intensifies workload and difficulty, wastes time, and is at risk of human errors. This study aims to design and evaluate a decision support system (DSS) to determine the triage level. METHODS: A combination of the Rule-Based Reasoning (RBR) and Fuzzy Logic Classifier (FLC) approaches were used to predict the triage level of patients according to the triage specialist's opinions and Emergency Severity Index (ESI) guidelines. RBR was applied for modeling the first to fourth decision points of the ESI algorithm. The data relating to vital signs were used as input variables and modeled using fuzzy logic. Narrative knowledge was converted to If-Then rules using XML. The extracted rules were then used to create the rule-based engine and predict the triage levels. RESULTS: Fourteen RBR and 27 fuzzy rules were extracted and used in the rule-based engine. The performance of the system was evaluated using three methods with real triage data. The accuracy of the clinical decision support systems (CDSSs; in the test data) was 99.44%. The evaluation of the error rate revealed that, when using the traditional method, 13.4% of the patients were miss-triaged, which is statically significant. The completeness of the documentation also improved from 76.72% to 98.5%. CONCLUSIONS: Designed system was effective in determining the triage level of patients and it proved helpful for nurses as they made decisions, generated nursing diagnoses based on triage guidelines. The hybrid approach can reduce triage misdiagnosis in a highly accurate manner and improve the triage outcomes.
Authors: David W Bates; David Levine; Ania Syrowatka; Masha Kuznetsova; Kelly Jean Thomas Craig; Angela Rui; Gretchen Purcell Jackson; Kyu Rhee Journal: NPJ Digit Med Date: 2021-03-19
Authors: Jhon Camacho; Manuela Zanoletti-Mannello; Zach Landis-Lewis; Sandra L Kane-Gill; Richard D Boyce Journal: J Med Internet Res Date: 2020-08-06 Impact factor: 5.428
Authors: Marta Fernandes; Rúben Mendes; Susana M Vieira; Francisca Leite; Carlos Palos; Alistair Johnson; Stan Finkelstein; Steven Horng; Leo Anthony Celi Journal: PLoS One Date: 2020-04-02 Impact factor: 3.240