scholarly journals Alert Override Patterns With a Medication Clinical Decision Support System in an Academic Emergency Department: Retrospective Descriptive Study

10.2196/23351 ◽  
2020 ◽  
Vol 8 (11) ◽  
pp. e23351 ◽  
Author(s):  
Junsang Yoo ◽  
Jeonghoon Lee ◽  
Poong-Lyul Rhee ◽  
Dong Kyung Chang ◽  
Mira Kang ◽  
...  
Keyword(s):  
Risk Factors ◽  
Emergency Department ◽  
Logistic Regression ◽  
Decision Support ◽  
Decision Support System ◽  
Clinical Decision Support ◽  
Support System ◽  
Clinical Decision Support System ◽  
Clinical Decision ◽  
Broad Perspective

Background Physicians’ alert overriding behavior is considered to be the most important factor leading to failure of computerized provider order entry (CPOE) combined with a clinical decision support system (CDSS) in achieving its potential adverse drug events prevention effect. Previous studies on this subject have focused on specific diseases or alert types for well-defined targets and particular settings. The emergency department is an optimal environment to examine physicians’ alert overriding behaviors from a broad perspective because patients have a wider range of severity, and many receive interdisciplinary care in this environment. However, less than one-tenth of related studies have targeted this physician behavior in an emergency department setting. Objective The aim of this study was to describe alert override patterns with a commercial medication CDSS in an academic emergency department. Methods This study was conducted at a tertiary urban academic hospital in the emergency department with an annual census of 80,000 visits. We analyzed data on the patients who visited the emergency department for 18 months and the medical staff who treated them, including the prescription and CPOE alert log. We also performed descriptive analysis and logistic regression for assessing the risk factors for alert overrides. Results During the study period, 611 physicians cared for 71,546 patients with 101,186 visits. The emergency department physicians encountered 13.75 alerts during every 100 orders entered. Of the total 102,887 alerts, almost two-thirds (65,616, 63.77%) were overridden. Univariate and multivariate logistic regression analyses identified 21 statistically significant risk factors for emergency department physicians’ alert override behavior. Conclusions In this retrospective study, we described the alert override patterns with a medication CDSS in an academic emergency department. We found relatively low overrides and assessed their contributing factors, including physicians’ designation and specialty, patients’ severity and chief complaints, and alert and medication type.

scholarly journals Alert Override Patterns With a Medication Clinical Decision Support System in an Academic Emergency Department: Retrospective Descriptive Study (Preprint)

2020 ◽  
Author(s):  
Junsang Yoo ◽  
Jeonghoon Lee ◽  
Poong-Lyul Rhee ◽  
Dong Kyung Chang ◽  
Mira Kang ◽  
...  
Keyword(s):  
Risk Factors ◽  
Emergency Department ◽  
Logistic Regression ◽  
Decision Support ◽  
Decision Support System ◽  
Clinical Decision Support ◽  
Support System ◽  
Clinical Decision Support System ◽  
Clinical Decision ◽  
Broad Perspective

BACKGROUND Physicians’ alert overriding behavior is considered to be the most important factor leading to failure of computerized provider order entry (CPOE) combined with a clinical decision support system (CDSS) in achieving its potential adverse drug events prevention effect. Previous studies on this subject have focused on specific diseases or alert types for well-defined targets and particular settings. The emergency department is an optimal environment to examine physicians’ alert overriding behaviors from a broad perspective because patients have a wider range of severity, and many receive interdisciplinary care in this environment. However, less than one-tenth of related studies have targeted this physician behavior in an emergency department setting. OBJECTIVE The aim of this study was to describe alert override patterns with a commercial medication CDSS in an academic emergency department. METHODS This study was conducted at a tertiary urban academic hospital in the emergency department with an annual census of 80,000 visits. We analyzed data on the patients who visited the emergency department for 18 months and the medical staff who treated them, including the prescription and CPOE alert log. We also performed descriptive analysis and logistic regression for assessing the risk factors for alert overrides. RESULTS During the study period, 611 physicians cared for 71,546 patients with 101,186 visits. The emergency department physicians encountered 13.75 alerts during every 100 orders entered. Of the total 102,887 alerts, almost two-thirds (65,616, 63.77%) were overridden. Univariate and multivariate logistic regression analyses identified 21 statistically significant risk factors for emergency department physicians’ alert override behavior. CONCLUSIONS In this retrospective study, we described the alert override patterns with a medication CDSS in an academic emergency department. We found relatively low overrides and assessed their contributing factors, including physicians’ designation and specialty, patients’ severity and chief complaints, and alert and medication type.

scholarly journals Impact of a Web-Based Clinical Decision Support System to Assist Practitioners in Addressing Physical Activity and/or Healthy Eating for Smoking Cessation Treatment: Protocol for a Hybrid Type I Randomized Controlled Trial

10.2196/19157 ◽  
2020 ◽  
Vol 9 (9) ◽  
pp. e19157
Author(s):  
Nadia Minian ◽  
Mathangee Lingam ◽  
Rahim Moineddin ◽  
Kevin E Thorpe ◽  
Scott Veldhuizen ◽  
...  
Keyword(s):  
Physical Activity ◽  
Risk Factors ◽  
Smoking Cessation ◽  
Decision Support ◽  
Decision Support System ◽  
Clinical Decision Support ◽  
Support System ◽  
Clinical Decision Support System ◽  
Clinical Decision ◽  
Smoking Cessation Program

Background Modifiable risk factors such as tobacco use, physical inactivity, and poor diet account for a significant proportion of the preventable deaths in Canada. These factors are also known to cluster together, thereby compounding the risks of morbidity and mortality. Given this association, smoking cessation programs appear to be well-suited for integration of health promotion activities for other modifiable risk factors. The Smoking Treatment for Ontario Patients (STOP) program is a province-wide smoking cessation program that currently encourages practitioners to deliver Screening, Brief Intervention, and Referral to treatment for patients who are experiencing depressive symptoms or consume excessive amounts of alcohol via a web-enabled clinical decision support system. However, there is no available clinical decision support system for physical inactivity and poor diet, which are among the leading modifiable risk factors for chronic diseases. Objective The aim of this study is to assess whether adding a computerized/web-enabled clinical decision support system for physical activity and diet to a smoking cessation program affects smoking cessation outcomes. Methods This study is designed as a hybrid type 1 effectiveness/implementation randomized controlled trial to evaluate a web-enabled clinical decision support system for supporting practitioners in addressing patients’ physical activity and diet as part of smoking cessation treatment in a primary care setting. This design was chosen as it allows for simultaneous testing of the intervention, its delivery in target settings, and the potential for implementation in real-world situations. Intervention effectiveness will be measured using a two-arm randomized controlled trial. Health care practitioners will be unblinded to their patients’ treatment allocation; however, patients will be blinded to whether their practitioner receives the clinical decision support system for physical activity and/or fruit/vegetable consumption. The evaluation of implementation will be guided by the Reach, Effectiveness, Adoption, Implementation, and Maintenance (RE-AIM) framework. Results Recruitment for the primary outcome of this study is ongoing and will be completed in November 2020. Results will be reported in March 2021. Conclusions The findings of the study will provide much needed insight into whether adding a computerized/web-enabled clinical decision support system for physical activity and diet to a smoking cessation program affects smoking cessation outcome. Furthermore, the implementation evaluation would provide insight into the feasibility of online-based interventions for physical activity and diet in a smoking cessation program. Addressing these risk factors simultaneously could have significant positive effects on chronic disease and cancer prevention. Trial Registration ClinicalTrials.gov NCT04223336; https://clinicaltrials.gov/ct2/show/NCT04223336 International Registered Report Identifier (IRRID) DERR1-10.2196/19157

scholarly journals A Clinical Decision Support System for Prediction of Postpartum Hemorrhage in Vaginal Birth

2021 ◽  
Author(s):  
Jingxuan Yang ◽  
Peng Guo ◽  
Yingli Song ◽  
Lingli Han ◽  
Xiaoyu Yang ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Decision Support ◽  
Decision Support System ◽  
Vaginal Delivery ◽  
Clinical Decision Support ◽  
Support System ◽  
Postpartum Hemorrhage ◽  
Clinical Decision Support System ◽  
Clinical Decision ◽  
Multi Layer Perceptron

Abstract Objective The morbidity and mortality caused by postpartum hemorrhage has been increased since 2016 in China, in addition, promoting vaginal delivery is an important task in China currently. This study aimed to develop a clinical decision support system (CDSS) to predict postpartum hemorrhage among vaginal delivery women. Design: A retrospective cohort study. Methods We performed a retrospective analysis of medical records among 1587 vaginal delivery women, who had visited the obstetrics clinic at the Third Affiliated Hospital of Zhengzhou University from 2018 to 2020, these women then were randomly divided into a training set (70%), a validation set (15%) and a test set (15%). We adopted a univariate logistic regression model to select the significant features (P < 0.01). Afterward, we trained several artificial neural networks and binary logistic regression to predict the postpartum hemorrhage, the neural networks included multi-layer perceptron (MLP), back propagation (BP) and radial basis function (RBF). In order to compare and identify the most accurate network, we used the confusion matrix and the receiver operating characteristic (ROC) curve. We finally developed a clinical decision support system based on the most accurate network. All statistical analyses were performed by IBM SPSS (version 20), and MATLAB 2013b software was applied to develop the clinical decision support system. Results Initially, 45 potential variables were addressed by the univariate logistic regression, 16 significant predictors were then selected to enter the binary logistic regression and neural networks (P-value < 0.01). After validation, the best performing model was the multi-layer perceptron network with the highest discriminative ability (AUC 0.862, 95% CI 0.838–0.887). Followed by the back propagation model (AUC 0.866; 95% CI 0.842–0.890), the logistic regression model (AUC 0.856; 95% CI 0.832–0.880). The radial basis function model (AUC 0.845; 95% CI 0.820–0.870) had lower discriminative ability. Conclusion In summary, in terms of predicting postpartum hemorrhage, the multi-layer perceptron network performed better than the back propagation network, logistic regression model, and radial basis function network. The developed clinical decision support system based on the multi-layer perceptron network is expected to promote early identification of postpartum hemorrhage in vaginal delivery women, thereby improve the quality of obstetric care and the maternal outcome.

scholarly journals Effect of Computerized Physician Order Entry and Clinical Decision Support System on Adverse Drug Events Prevention in the Emergency Department: A Systematic Review

2020 ◽  
pp. 875512252095816
Author(s):  
Sadrieh Hajesmaeel Gohari ◽  
Kambiz Bahaadinbeigy ◽  
Shahrad Tajoddini ◽  
Sharareh R. Niakan Kalhori
Keyword(s):  
Emergency Department ◽  
Decision Support ◽  
Decision Support System ◽  
Clinical Decision Support ◽  
Support System ◽  
Clinical Decision Support System ◽  
Clinical Decision ◽  
Computerized Physician Order Entry ◽  
Order Entry ◽  
Physician Order Entry

Objective: An adverse drug event (ADE) is an injury resulting from a medical intervention related to a drug. The emergency department (ED) is a ward vulnerable to more ADEs because of overcrowding. Information technologies such as computerized physician order entry (CPOE) and clinical decision support system (CDSS) may decrease the occurrence of ADEs. This study aims to review research that reported the evaluation of the effectiveness of CPOE and CDSS on lowering the occurrence of ADEs in the ED. Data Sources: PubMed, EMBASE, and Web of Science databases were used to find studies published from 2003 to 2018. The search was conducted in November 2018. Study Selection and Data Extraction: The search resulted in 1700 retrieved articles. After applying inclusion and exclusion criteria, 11 articles were included. Data on the date, country, type of system, medication process stages, study design, participants, sample size, and outcomes were extracted. Data Synthesis: Results showed that CPOE and CDSS may prevent ADEs in the ED through significantly decreasing the rate of errors, ADEs, excessive dose, and inappropriate prescribing (in 54.5% of articles); furthermore, CPOE and CDSS may significantly increase the rate of appropriate prescribing and dosing in compliance with established guidelines (45.5% of articles). Conclusion: This study revealed that the use of CPOE and CDSS can lower the occurrence of ADEs in the ED; however, further randomized controlled trials are needed to address the effect of a CDSS, with basic or advanced features, on the occurrence of ADEs in the ED.

scholarly journals Impact of a Web-Based Clinical Decision Support System to Assist Practitioners in Addressing Physical Activity and/or Healthy Eating for Smoking Cessation Treatment: Protocol for a Hybrid Type I Randomized Controlled Trial (Preprint)

2020 ◽  
Author(s):  
Nadia Minian ◽  
Mathangee Lingam ◽  
Rahim Moineddin ◽  
Kevin E Thorpe ◽  
Scott Veldhuizen ◽  
...  
Keyword(s):  
Physical Activity ◽  
Risk Factors ◽  
Smoking Cessation ◽  
Decision Support ◽  
Decision Support System ◽  
Clinical Decision Support ◽  
Support System ◽  
Clinical Decision Support System ◽  
Clinical Decision ◽  
Smoking Cessation Program

BACKGROUND Modifiable risk factors such as tobacco use, physical inactivity, and poor diet account for a significant proportion of the preventable deaths in Canada. These factors are also known to cluster together, thereby compounding the risks of morbidity and mortality. Given this association, smoking cessation programs appear to be well-suited for integration of health promotion activities for other modifiable risk factors. The Smoking Treatment for Ontario Patients (STOP) program is a province-wide smoking cessation program that currently encourages practitioners to deliver Screening, Brief Intervention, and Referral to treatment for patients who are experiencing depressive symptoms or consume excessive amounts of alcohol via a web-enabled clinical decision support system. However, there is no available clinical decision support system for physical inactivity and poor diet, which are among the leading modifiable risk factors for chronic diseases. OBJECTIVE The aim of this study is to assess whether adding a computerized/web-enabled clinical decision support system for physical activity and diet to a smoking cessation program affects smoking cessation outcomes. METHODS This study is designed as a hybrid type 1 effectiveness/implementation randomized controlled trial to evaluate a web-enabled clinical decision support system for supporting practitioners in addressing patients’ physical activity and diet as part of smoking cessation treatment in a primary care setting. This design was chosen as it allows for simultaneous testing of the intervention, its delivery in target settings, and the potential for implementation in real-world situations. Intervention effectiveness will be measured using a two-arm randomized controlled trial. Health care practitioners will be unblinded to their patients’ treatment allocation; however, patients will be blinded to whether their practitioner receives the clinical decision support system for physical activity and/or fruit/vegetable consumption. The evaluation of implementation will be guided by the Reach, Effectiveness, Adoption, Implementation, and Maintenance (RE-AIM) framework. RESULTS Recruitment for the primary outcome of this study is ongoing and will be completed in November 2020. Results will be reported in March 2021. CONCLUSIONS The findings of the study will provide much needed insight into whether adding a computerized/web-enabled clinical decision support system for physical activity and diet to a smoking cessation program affects smoking cessation outcome. Furthermore, the implementation evaluation would provide insight into the feasibility of online-based interventions for physical activity and diet in a smoking cessation program. Addressing these risk factors simultaneously could have significant positive effects on chronic disease and cancer prevention. CLINICALTRIAL ClinicalTrials.gov NCT04223336; https://clinicaltrials.gov/ct2/show/NCT04223336 INTERNATIONAL REGISTERED REPORT DERR1-10.2196/19157

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