The Decision Support System and Conventional Method of Telephone Triage by Nurses in Emergency Medical Services

2018 ◽  
Vol 14 (1) ◽  
pp. 77-88
Author(s):  
Mohammad Parvaresh Masoud ◽  
Mahdi Kashani Nejad ◽  
Hamid Darebaghi ◽  
Mohsen Chavoshi ◽  
Mahdi Farahani
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Cost Of Care ◽  
Telephone Triage ◽  
Emergency Nurses ◽  
Time Duration ◽  
Emergency Medical ◽  
Significant Difference ◽  
Quasi Experimental

The demand for medical emergencies often begins with a call to a dispatch center. An appropriate performance of this unit influences providing effective services significantly. This study aimed to compare the effect of using revised New Jersey telephone triage manual as a Decision Support System and the conventional methods on the time duration of mission taking and the performance of emergency medical dispatchers which were done by 115 emergency nurses of Qom, Iran, in 2012.This quasi-experimental study aimed to compare the effects of two methods on the performance of nurses of Qom. Conventional (September and October) and DSS data (December and January) were extracted and compared. November was skipped due to nurses' familiarity with the program. Performing the new method improved the overall performance significantly (P < 0.05). The DSS method increased the appropriate performance and decreased inappropriate performance significantly, but in the time duration of mission taking there was no a significant difference comparing two methods (p = 0.342). This method has been shown to improve both patient outcomes, as well as the cost of care.

scholarly journals A Clinical Feasibility Study of an Artificial Intelligence-Powered Clinical Decision Support System

2021 ◽  
Author(s):  
Christina Popescu ◽  
Grace Golden ◽  
David Benrimoh ◽  
Myriam Tanguay-Sela ◽  
Dominique Slowey ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Decision Support ◽  
Decision Support System ◽  
Clinical Decision Support ◽  
Support System ◽  
Clinical Decision Support System ◽  
Clinical Decision ◽  
Session Length ◽  
Positive Effects ◽  
Significant Difference

Objective: We examine the feasibility of an Artificial Intelligence (AI)-powered clinical decision support system (CDSS), which combines the operationalized 2016 Canadian Network for Mood and Anxiety Treatments guidelines with a neural-network based individualized treatment remission prediction. Methods: Due to COVID-19, the study was adapted to be completed entirely at a distance. Seven physicians recruited outpatients diagnosed with major depressive disorder (MDD) as per DSM-V criteria. Patients completed a minimum of one visit without the CDSS (baseline) and two subsequent visits where the CDSS was used by the physician (visit 1 and 2). The primary outcome of interest was change in session length after CDSS introduction, as a proxy for feasibility. Feasibility and acceptability data were collected through self-report questionnaires and semi-structured interviews. Results: Seventeen patients enrolled in the study; 14 completed. There was no significant difference between appointment length between visits (introduction of the tool did not increase session length). 92.31% of patients and 71.43% of physicians felt that the tool was easy to use. 61.54% of the patients and 71.43% of the physicians rated that they trusted the CDSS. 46.15% of patients felt that the patient-clinician relationship significantly or somewhat improved, while the other 53.85% felt that it did not change. Conclusions: Our results confirm the primary hypothesis that the integration of the tool does not increase appointment length. Findings suggest the CDSS is easy to use and may have some positive effects on the patient-physician relationship. The CDSS is feasible and ready for effectiveness studies.

A Method for Assessing the Impact of a Computer-Based Decision Support System on Health Care Outcomes

1992 ◽  
Vol 31 (01) ◽  
pp. 3-11 ◽  
Author(s):  
J. R. Lave ◽  
M. A. McNeil ◽  
R. A. Bankowitz
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Controlled Trial ◽  
Length Of Hospital Stay ◽  
Cost Of Care ◽  
Medical Decision ◽  
Diagnostic Uncertainty ◽  
Computer Based ◽  
The Impact

Abstract:The wide variation in utilization of diagnostic resources has not been decreased by the proliferation of new diagnostic technologies. We wish to test the hypothesis that the introduction of a medical decision support system into clinical practice could potentially lead to more efficient use of diagnostic information, and therefore lead to a reduction in overall laboratory use and cost of care. We have devised and are currently implementing a randomized controlled trial of a computer based decision support system, the University of Pittsburgh version of Quick Medical Reference (QMR).The main purpose of the study is to determine the effect of the QMR program on specific outcome measures: length of hospital stay, number and types of diagnostic tests ordered, and overall charges. An important part of this evaluation is relating the initial level of diagnostic uncertainty expressed by the admitting housestaff team to utilization of diagnostic resources. The purpose of this paper is to describe the methodology for carrying out this controlled trial, and to describe our initial experiences with its implementation.

Evaluating the Clinical Feasibility of an Artificial Intelligence-Powered Clinical Decision Support System for Depression Treatment: A Longitudinal Feasibility Study (Preprint)

2021 ◽  
Author(s):  
Christina Popescu ◽  
Grace Golden ◽  
David Benrimoh ◽  
Myriam Tanguay-Sela ◽  
Dominique Slowey ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Decision Support ◽  
Decision Support System ◽  
Clinical Decision Support ◽  
Support System ◽  
Clinical Decision Support System ◽  
Clinical Decision ◽  
Session Length ◽  
Positive Effects ◽  
Significant Difference

BACKGROUND Approximately two thirds of patients with major depressive disorder (MDD) do not achieve remission during their first treatment. There has been increasing interest in the use of digital, artificial intelligence (AI)-powered clinical decision support systems (CDSS) to assist physicians in their treatment selection and management, improving personalization and use of best practices such as measurement-based care. Previous literature shows that in order for digital mental health tools to be successful, the tool must be easy to use for patients and physicians and feasible within existing clinical workflows. OBJECTIVE We examine the feasibility of an AI-powered clinical decision support system, which combines the operationalized 2016 Canadian Network for Mood and Anxiety Treatments guidelines with a neural-network based individualized treatment remission prediction. METHODS Due to COVID-19, the study was adapted to be completed entirely at a distance. Seven physicians recruited outpatients diagnosed with MDD as per DSM-V criteria. Patients completed a minimum of one visit without the CDSS (baseline) and two subsequent visits where the CDSS was used by the physician (visit 1 and 2). The primary outcome of interest was change in session length after CDSS introduction, as a proxy for feasibility. Feasibility and acceptability data were collected through self-report questionnaires and semi-structured interviews. RESULTS Seventeen patients enrolled in the study; 14 completed. There was no significant difference between appointment length between visits (introduction of the tool did not increase session length). 92.31% of patients and 71.43% of physicians felt that the tool was easy to use. 61.54% of the patients and 71.43% of the physicians rated that they trusted the CDSS. 46.15% of patients felt that the patient-clinician relationship significantly or somewhat improved, while the other 53.85% felt that it did not change. CONCLUSIONS Our results confirm the primary hypothesis that the integration of the tool does not increase appointment length. Findings suggest the CDSS is easy to use and may have some positive effects on the patient-physician relationship. The CDSS is feasible and ready for effectiveness studies. CLINICALTRIAL NCT04061642

scholarly journals A Model-Based Real-Time Decision Support System for Irrigation Scheduling to Improve Water Productivity

Agronomy ◽  
2019 ◽  
Vol 9 (11) ◽  
pp. 686 ◽  
Author(s):  
Chen ◽  
Qi ◽  
Gui ◽  
Gu ◽  
Ma ◽  
...  
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Real Time ◽  
Support System ◽  
Deficit Irrigation ◽  
Water Productivity ◽  
Irrigation Scheduling ◽  
Cotton Field ◽  
Crop Water ◽  
Significant Difference

A precisely timed irrigation schedule to match crop water demand is vital to improving water use efficiency in arid farmland. In this study, a real-time irrigation-scheduling infrastructure, Decision Support System for Irrigation Scheduling (DSSIS), based on water stresses predicted by an agro-hydrological model, was constructed and evaluated. The DSSIS employed the Root Zone Water Quality Model (RZWQM2) to predict crop water stresses and soil water content, which were used to trigger irrigation and calculate irrigation amount, respectively, along with forecasted rainfall. The new DSSIS was evaluated through a cotton field experiment in Xinjiang, China in 2016 and 2017. Three irrigation scheduling methods (DSSIS-based (D), soil moisture sensor-based (S), and conventional experience-based (E)), factorially combined with two irrigation rates (full irrigation (FI), and deficit irrigation (DI, 75% of FI)) were compared. The DSSIS significantly increased water productivity (WP) by 26% and 65.7%, compared to sensor-based and experience-based irrigation scheduling methods (p < 0.05), respectively. No significant difference was observed in WP between full and deficit irrigation treatments. In addition, the DSSIS showed economic advantage over sensor- and experience-based methods. Our results suggested that DSSIS is a promising tool for irrigation scheduling.

The Clinical Decision Support System for Trauma Emergency Care: Development, Modification and Clinical Use (Preprint)

2021 ◽  
Author(s):  
Zhen Jian ◽  
Tao Lv ◽  
Rongguang Ao ◽  
Yongan Wang ◽  
Xinhua Jiang ◽  
...  
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Clinical Decision Support ◽  
Support System ◽  
Clinical Decision Support System ◽  
Life Support ◽  
Field Testing ◽  
Clinical Decision ◽  
Time Duration ◽  
Clinical Scenarios

BACKGROUND Background: Trauma remains an urgent social problem as the leading cause of death in persons 1 through 44 years of age. According Computerized clinical decision support system (CDSS) is a solution to promote Advanced trauma Life Support (ATLS) protocol for traumatic emergency management. OBJECTIVE Objectives: The objectives were to develop a CDSS for trauma management in emergency department (ED) based on ATLS algorithm, and evaluate its preliminary effectiveness for the treatment of patients admitted to the emergency room (ER) suffering from severe injuries. METHODS Methods: A design workshop was convened to discuss the goals and users’ needs of a CDSS based on ATLS algorithm and then worked through typical clinical scenarios involving trauma emergency. The prototype CDSS was developed and then refined based on scenarios in ED involving multiple injuries. field testing was conducted to point out the defects of the system's function in real clinical environment and further optimize it. A comparation of clinical outcomes before and after the implement of the CDSS was conducted to evaluate the effectiveness of the CDSS. RESULTS Results: A common design concept of Trauma First Aid (TFA) emerged after three rounds of discussions and storyboard design. Some bugs during the use process were fixed after case-based refinement. The CDSS workstations were divided into mobile terminals with a pad and medical workbenches with an all-in-one computer. Application programming interface (API) to get real-time access to electronic health record system and online specialist consultation were achieved by the CDSS after field testing. In the clinical study, a total of 176 consecutive trauma patients with a complete TFA file between June 2020 and June 2021 were compared with 235 traumatic patients in ER before the implement of TFA from June 2018 to June 2019. The missed injury rate dropped from 11% to 4% (p < 0.01) after the implement of TFA. Time duration from admission to physician's first order was longer in the TFA group (21±11min vs 11±13min ,p< 0.01). There was a higher rate of packed RBCs transfusions (69% vs 43%, p < 0.01) with less time duration from admission to the order of blood transfusion (35±37min vs 50±33min, p<0.05) in TFA group when compared with the control group. The consultation time of relevant department was much shorter in the TFA group (10±17min vs 25±33min, p<0.01). CONCLUSIONS Conclusions: TFA was a CDSS based on ATLS algorithm and assisted clinical decision for physicians in the ED to treat traumatic patients in the advanced life support stage. The initial clinical evaluation in a reginal trauma center showed practicality and effectiveness of the CDSS.

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