scholarly journals A Novel Case Base Reasoning and Frequent Pattern Based Decision Support System for Mitigating Software Risk Factors

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 102278-102291
Author(s):  
Muhammad Asif ◽  
Jamil Ahmed
Keyword(s):  
Risk Factors ◽  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Frequent Pattern ◽  
Software Risk ◽  
Case Base ◽  
Case Base Reasoning

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 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 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 A Medical Decision Support System to Assess Risk Factors for Gastric Cancer Based on Fuzzy Cognitive Map

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Seyed Abbas Mahmoodi ◽  
Kamal Mirzaie ◽  
Maryam Sadat Mahmoodi ◽  
Seyed Mostafa Mahmoudi
Keyword(s):  
Risk Factors ◽  
Gastric Cancer ◽  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Healthcare Professionals ◽  
Fuzzy Cognitive Maps ◽  
Medical Decision ◽  
Risk Level ◽  
Medical Decision Support

Gastric cancer (GC), one of the most common cancers around the world, is a multifactorial disease and there are many risk factors for this disease. Assessing the risk of GC is essential for choosing an appropriate healthcare strategy. There have been very few studies conducted on the development of risk assessment systems for GC. This study is aimed at providing a medical decision support system based on soft computing using fuzzy cognitive maps (FCMs) which will help healthcare professionals to decide on an appropriate individual healthcare strategy based on the risk level of the disease. FCMs are considered as one of the strongest artificial intelligence techniques for complex system modeling. In this system, an FCM based on Nonlinear Hebbian Learning (NHL) algorithm is used. The data used in this study are collected from the medical records of 560 patients referring to Imam Reza Hospital in Tabriz City. 27 effective features in gastric cancer were selected using the opinions of three experts. The prediction accuracy of the proposed method is 95.83%. The results show that the proposed method is more accurate than other decision-making algorithms, such as decision trees, Naïve Bayes, and ANN. From the perspective of healthcare professionals, the proposed medical decision support system is simple, comprehensive, and more effective than previous models for assessing the risk of GC and can help them to predict the risk factors for GC in the clinical setting.

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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