Evaluation of a Diagnostic Decision Support System for the Triage of Patients in a Hospital Emergency Department

One of the biggest challenges for the management of the emergency department (ED) is to expedite the management of patients since their arrival for those with low priority pathologies selected by the classification systems, generating unnecessary saturation of the ED. Diagnostic decision support systems (DDSS) can be a powerful tool to guide diagnosis, facilitate correct classification and improve patient safety. Patients who attended the ED of a tertiary hospital with the preconditions of Manchester Triage system level of low priority (levels 3, 4 and 5), and with one of the five most frequent causes for consultation: dyspnea, chest pain, gastrointestinal bleeding, general discomfort and abdominal pain, were interviewed by an independent researcher with a DDSS, the Mediktor system. After the interview, we compare the Manchester triage and the final diagnoses made by the ED with the triage and diagnostic possibilities ordered by probability obtained by the Mediktor system, respectively. In a final sample of 214 patients, the urgency assignment made by both systems does not match exactly, which could indicate a different classification model, but there were no statistically significant differences between the assigned levels (S = 0.059, p = 0.442). The diagnostic accuracy between the final diagnosis and any of the first 10 Mediktor diagnoses was of 76.5%, for the first five diagnoses was 65.4%, for the first three diagnoses was 58%, and the exact match with the first diagnosis was 37.9%. The classification of Mediktor in this segment of patients shows that a higher level of severity corresponds to a greater number of hospital admissions, hospital readmissions and emergency screenings at 30 days, although without statistical significance. It is expected that this type of applications may be useful as a complement to the triage, to accelerate the diagnostic approach, to improve the request for appropriate complementary tests in a protocolized action model and to reduce waiting times in the ED

Editor’s Note

This special issue has been designed with the primary objective of demonstrating the diversity of fields where AI is used and, consequently, how it is gaining increasing importance as a tool for analysis and research. In this sense, there are works related to the following topics: the use of AI with the IoT, campaign management, topic models and fusion methods, sales forecasting, price forecasting for electricity market, NLP techniques in computational medicine, evaluation of patient triage in hospital emergency settings, algorithms for solving the assignment problem, scheduling strategy for scientific workflow, driver fatigue detection mechanisms, virtual reality and specialized training, image segmentation, web service selection, multimedia documents adaptation, 3D navigation in virtual environments, multi-criteria decision-making methods and emotional states classification

Using Diagnostic Decision Support Systems to Reduce Diagnostic Error: A Survey of Critical Care Physicians

The purpose of this study is to investigate the use of decisions support systems (DSS) by critical care physicians and to address the following questions: Does the use of a decision support system during diagnosis reduce diagnostic error and how are decision support systems used by critical care physicians? There are no studies that address these research questions in a clinical setting. The information assessment method (IAM) was used to guide the development of the survey questions. Critical care physicians from the University of Oklahoma Health Sciences Center were surveyed. Chi squared test for independence was used to determine the relationship between DSS use and diagnostic error rates. There were three main findings of the study: (1) use of a DSS by a critical care physician can decrease diagnostic error by up to 60%; (2) 56% of critical care physicians are using a DSS during diagnosis to learn something new, confirm something they already knew, and/or to reassure themselves; and (3) the increased use of a DSS by critical care physicians can lead to a decrease in the belief of the ability of a DSS to reduce diagnostic error.NoMax Chambers Librar

Evaluation of a Diagnostic Decision Support System for the Triage of Patients in a Hospital Emergency Department

One of the biggest challenges for the management of the emergency department (ED) is to expedite the management of patients since their arrival for those with low priority pathologies selected by the classification systems, generating unnecessary saturation of the ED. Diagnostic decision support systems (DDSS) can be a powerful tool to guide diagnosis, facilitate correct classification and improve patient safety. Patients who attended the ED of a tertiary hospital with the preconditions of Manchester Triage system level of low priority (levels 3, 4 and 5), and with one of the five most frequent causes for consultation: dyspnea, chest pain, gastrointestinal bleeding, general discomfort and abdominal pain, were interviewed by an independent researcher with a DDSS, the Mediktor system. After the interview, we compare the Manchester triage and the final diagnoses made by the ED with the triage and diagnostic possibilities ordered by probability obtained by the Mediktor system, respectively. In a final sample of 214 patients, the urgency assignment made by both systems does not match exactly, which could indicate a different classification model, but there were no statistically significant differences between the assigned levels (S = 0.059, p = 0.442). The diagnostic accuracy between the final diagnosis and any of the first 10 Mediktor diagnoses was of 76.5%, for the first five diagnoses was 65.4%, for the first three diagnoses was 58%, and the exact match with the first diagnosis was 37.9%. The classification of Mediktor in this segment of patients shows that a higher level of severity corresponds to a greater number of hospital admissions, hospital readmissions and emergency screenings at 30 days, although without statistical significance. It is expected that this type of applications may be useful as a complement to the triage, to accelerate the diagnostic approach, to improve the request for appropriate complementary tests in a protocolized action model and to reduce waiting times in the ED

Evaluation of a Diagnostic Decision Support System for the Triage of Patients in a Hospital Emergency Department

One of the biggest challenges for the management of the emergency department (ED) is to expedite the management of patients since their arrival for those with low priority pathologies selected by the classification systems, generating unnecessary saturation of the ED. Diagnostic decision support systems (DDSS) can be a powerful tool to guide diagnosis, facilitate correct classification and improve patient safety. Patients who attended the ED of a tertiary hospital with the preconditions of Manchester Triage system level of low priority (levels 3, 4 and 5), and with one of the five most frequent causes for consultation: dyspnea, chest pain, gastrointestinal bleeding, general discomfort and abdominal pain, were interviewed by an independent researcher with a DDSS, the Mediktor system. After the interview, we compare the Manchester triage and the final diagnoses made by the ED with the triage and diagnostic possibilities ordered by probability obtained by the Mediktor system, respectively. In a final sample of 214 patients, the urgency assignment made by both systems does not match exactly, which could indicate a different classification model, but there were no statistically significant differences between the assigned levels (S = 0.059, p = 0.442). The diagnostic accuracy between the final diagnosis and any of the first 10 Mediktor diagnoses was of 76.5%, for the first five diagnoses was 65.4%, for the first three diagnoses was 58%, and the exact match with the first diagnosis was 37.9%. The classification of Mediktor in this segment of patients shows that a higher level of severity corresponds to a greater number of hospital admissions, hospital readmissions and emergency screenings at 30 days, although without statistical significance. It is expected that this type of applications may be useful as a complement to the triage, to accelerate the diagnostic approach, to improve the request for appropriate complementary tests in a protocolized action model and to reduce waiting times in the ED