scholarly journals Improved Appropriateness of Advanced Diagnostic Imaging After Implementation of Clinical Decision Support Mechanism

2021 ◽  
Author(s):  
Leonid L. Chepelev ◽  
Xuan Wang ◽  
Benjamin Gold ◽  
Clara-Lea Bonzel ◽  
Frank Rybicki Jr ◽  
...  
Keyword(s):  
Nuclear Medicine ◽  
Logistic Regression ◽  
Decision Support ◽  
Clinical Decision Support ◽  
Clinical Decision ◽  
Chronological Order ◽  
Advanced Imaging ◽  
Support Mechanism ◽  
Wald Tests ◽  
Imaging Appropriateness

AbstractThe Protecting Access to Medicare Act (PAMA) mandates clinical decision support mechanism (CDSM) consultation for all advanced imaging. There are a growing number of studies examining the association of CDSM use with imaging appropriateness, but a paucity of multicenter data. This observational study evaluates the association between changes in advanced imaging appropriateness scores with increasing provider exposure to CDSM. Each provider’s first 200 consecutive anonymized requisitions for advanced imaging (CT, MRI, ultrasound, nuclear medicine) using a single CDSM (CareSelect, Change Healthcare) between January 1, 2017 and December 31, 2019 were collected from 288 US institutions. Changes in imaging requisition proportions among four appropriateness categories (“usually appropriate” [green], “may be appropriate” [yellow], “usually not appropriate” [red], and unmapped [gray]) were evaluated in relation to the chronological order of the requisition for each provider and total provider exposure to CDSM using logistic regression fits and Wald tests. The number of providers and requisitions included was 244,158 and 7,345,437, respectively. For 10,123 providers with ≥ 200 requisitions (2,024,600 total requisitions), the fraction of green, yellow, and red requisitions among the last 10 requisitions changed by +3.0% (95% confidence interval +2.6% to +3.4%), −0.8% (95% CI −0.5% to −1.1%), and −3.0% (95% CI 3.3% to −2.7%) in comparison with the first 10, respectively. Providers with > 190 requisitions had 8.5% (95% CI 6.3% to 10.7%) more green requisitions, 2.3% (0.7% to 3.9%) fewer yellow requisitions, and 0.5% (95% CI −1.0% to 2.0%) fewer red (not statistically significant) requisitions relative to providers with ≤ 10 requisitions. Increasing provider exposure to CDSM is associated with improved appropriateness scores for advanced imaging requisitions.

scholarly journals Accuracy and Effects of Clinical Decision Support Systems Integrated With BMJ Best Practice–Aided Diagnosis: Interrupted Time Series Study (Preprint)

2019 ◽  
Author(s):  
Liyuan Tao ◽  
Chen Zhang ◽  
Lin Zeng ◽  
Shengrong Zhu ◽  
Nan Li ◽  
...  
Keyword(s):  
Time Series ◽  
Logistic Regression ◽  
Decision Support ◽  
Clinical Decision Support ◽  
Best Practice ◽  
Clinical Decision Support Systems ◽  
Interrupted Time Series ◽  
Clinical Decision ◽  
Interrupted Time Series Analysis ◽  
Aided Diagnosis

BACKGROUND Clinical decision support systems (CDSS) are an integral component of health information technologies and can assist disease interpretation, diagnosis, treatment, and prognosis. However, the utility of CDSS in the clinic remains controversial. OBJECTIVE The aim is to assess the effects of CDSS integrated with British Medical Journal (BMJ) Best Practice–aided diagnosis in real-world research. METHODS This was a retrospective, longitudinal observational study using routinely collected clinical diagnosis data from electronic medical records. A total of 34,113 hospitalized patient records were successively selected from December 2016 to February 2019 in six clinical departments. The diagnostic accuracy of the CDSS was verified before its implementation. A self-controlled comparison was then applied to detect the effects of CDSS implementation. Multivariable logistic regression and single-group interrupted time series analysis were used to explore the effects of CDSS. The sensitivity analysis was conducted using the subgroup data from January 2018 to February 2019. RESULTS The total accuracy rates of the recommended diagnosis from CDSS were 75.46% in the first-rank diagnosis, 83.94% in the top-2 diagnosis, and 87.53% in the top-3 diagnosis in the data before CDSS implementation. Higher consistency was observed between admission and discharge diagnoses, shorter confirmed diagnosis times, and shorter hospitalization days after the CDSS implementation (all <italic>P</italic>&lt;.001). Multivariable logistic regression analysis showed that the consistency rates after CDSS implementation (OR 1.078, 95% CI 1.015-1.144) and the proportion of hospitalization time 7 days or less (OR 1.688, 95% CI 1.592-1.789) both increased. The interrupted time series analysis showed that the consistency rates significantly increased by 6.722% (95% CI 2.433%-11.012%, <italic>P</italic>=.002) after CDSS implementation. The proportion of hospitalization time 7 days or less significantly increased by 7.837% (95% CI 1.798%-13.876%, <italic>P</italic>=.01). Similar results were obtained in the subgroup analysis. CONCLUSIONS The CDSS integrated with BMJ Best Practice improved the accuracy of clinicians’ diagnoses. Shorter confirmed diagnosis times and hospitalization days were also found to be associated with CDSS implementation in retrospective real-world studies. These findings highlight the utility of artificial intelligence-based CDSS to improve diagnosis efficiency, but these results require confirmation in future randomized controlled trials.

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 A 12-hospital prospective evaluation of a clinical decision support prognostic algorithm based on logistic regression as a form of machine learning to facilitate decision making for patients with suspected COVID-19

PLoS ONE ◽  
2022 ◽  
Vol 17 (1) ◽  
pp. e0262193
Author(s):  
Monica I. Lupei ◽  
Danni Li ◽  
Nicholas E. Ingraham ◽  
Karyn D. Baum ◽  
Bradley Benson ◽  
...  
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Decision Support ◽  
Real Time ◽  
Acute Care ◽  
Clinical Decision Support ◽  
Invasive Mechanical Ventilation ◽  
Clinical Decision ◽  
Hospital System ◽  
Temporal Validation

Objective To prospectively evaluate a logistic regression-based machine learning (ML) prognostic algorithm implemented in real-time as a clinical decision support (CDS) system for symptomatic persons under investigation (PUI) for Coronavirus disease 2019 (COVID-19) in the emergency department (ED). Methods We developed in a 12-hospital system a model using training and validation followed by a real-time assessment. The LASSO guided feature selection included demographics, comorbidities, home medications, vital signs. We constructed a logistic regression-based ML algorithm to predict “severe” COVID-19, defined as patients requiring intensive care unit (ICU) admission, invasive mechanical ventilation, or died in or out-of-hospital. Training data included 1,469 adult patients who tested positive for Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) within 14 days of acute care. We performed: 1) temporal validation in 414 SARS-CoV-2 positive patients, 2) validation in a PUI set of 13,271 patients with symptomatic SARS-CoV-2 test during an acute care visit, and 3) real-time validation in 2,174 ED patients with PUI test or positive SARS-CoV-2 result. Subgroup analysis was conducted across race and gender to ensure equity in performance. Results The algorithm performed well on pre-implementation validations for predicting COVID-19 severity: 1) the temporal validation had an area under the receiver operating characteristic (AUROC) of 0.87 (95%-CI: 0.83, 0.91); 2) validation in the PUI population had an AUROC of 0.82 (95%-CI: 0.81, 0.83). The ED CDS system performed well in real-time with an AUROC of 0.85 (95%-CI, 0.83, 0.87). Zero patients in the lowest quintile developed “severe” COVID-19. Patients in the highest quintile developed “severe” COVID-19 in 33.2% of cases. The models performed without significant differences between genders and among race/ethnicities (all p-values > 0.05). Conclusion A logistic regression model-based ML-enabled CDS can be developed, validated, and implemented with high performance across multiple hospitals while being equitable and maintaining performance in real-time validation.

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 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 Joint Design with Providers of Clinical Decision Support for Value-Based Advanced Shoulder Imaging

2020 ◽  
Vol 11 (01) ◽  
pp. 142-152
Author(s):  
Michael C. Brunner ◽  
Scott E. Sheehan ◽  
Eric M. Yanke ◽  
Dean F. Sittig ◽  
Nasia Safdar ◽  
...  
Keyword(s):  
Decision Support ◽  
Shoulder Pain ◽  
Clinical Decision Support ◽  
Clinical Decision ◽  
The United States ◽  
Medical Providers ◽  
Barriers To Adoption ◽  
Advanced Imaging ◽  
Shoulder Imaging ◽  
Magnetic Resonance Imaging Mri

Abstract Background Provider orders for inappropriate advanced imaging, while rarely altering patient management, contribute enough to the strain on available health care resources, and therefore the United States Congress established the Appropriate Use Criteria Program. Objectives To examine whether co-designing clinical decision support (CDS) with referring providers will reduce barriers to adoption and facilitate more appropriate shoulder ultrasound (US) over magnetic resonance imaging (MRI) in diagnosing Veteran shoulder pain, given similar efficacies and only 5% MRI follow-up rate after shoulder US. Methods We used a theory-driven, convergent parallel mixed-methods approach to prospectively (1) determine medical providers' reasons for selecting MRI over US in diagnosing shoulder pain and identify barriers to ordering US, (2) co-design CDS, informed by provider interviews, to prompt appropriate US use, and (3) assess CDS impact on shoulder imaging use. CDS effectiveness in guiding appropriate shoulder imaging was evaluated through monthly monitoring of ordering data at our quaternary care Veterans Hospital. Key outcome measures were appropriate MRI/US use rates and transition to ordering US by both musculoskeletal specialist and generalist providers. We assessed differences in ordering using a generalized estimating equations logistic regression model. We compared continuous measures using mixed effects analysis of variance with log-transformed data. Results During December 2016 to March 2018, 569 (395 MRI, 174 US) shoulder advanced imaging examinations were ordered by 111 providers. CDS “co-designed” in collaboration with providers increased US from 17% (58/335) to 50% (116/234) of all orders (p < 0.001), with concomitant decrease in MRI. Ordering appropriateness more than doubled from 31% (105/335) to 67% (157/234) following CDS (p < 0.001). Interviews confirmed that generalist providers want help in appropriately ordering advanced imaging. Conclusion Partnering with medical providers to co-design CDS reduced barriers and prompted appropriate transition to US from MRI for shoulder pain diagnosis, promoting evidence-based practice. This approach can inform the development and implementation of other forms of CDS.

Effect of Clinical Decision Support on Appropriateness of Advanced Imaging Use Among Physicians-in-Training

2019 ◽  
Vol 212 (4) ◽  
pp. 859-866 ◽  
Author(s):  
Jashvant Poeran ◽  
Lisa J. Mao ◽  
Nicole Zubizarreta ◽  
Madhu Mazumdar ◽  
Bruce Darrow ◽  
...  
Keyword(s):  
Decision Support ◽  
Clinical Decision Support ◽  
Clinical Decision ◽  
Advanced Imaging

Imaging Appropriateness and Implementation of Clinical Decision Support

2015 ◽  
Vol 12 (6) ◽  
pp. 601-603 ◽  
Author(s):  
Giles W. Boland ◽  
Jeffrey Weilburg ◽  
Richard Duszak
Keyword(s):  
Decision Support ◽  
Clinical Decision Support ◽  
Clinical Decision ◽  
Imaging Appropriateness

scholarly journals Radiomics Analysis for Clinical Decision Support in Nuclear Medicine

2019 ◽  
Vol 49 (5) ◽  
pp. 438-449 ◽  
Author(s):  
Abdalla Ibrahim ◽  
Martin Vallières ◽  
Henry Woodruff ◽  
Sergey Primakov ◽  
Mohsen Beheshti ◽  
...  
Keyword(s):  
Nuclear Medicine ◽  
Decision Support ◽  
Clinical Decision Support ◽  
Clinical Decision
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