Effect of Clinical Decision Support Systems on the Antithrombotic Treatment for Atrial Fibrillation Patients: A Systematic Review and Meta-analysis (Preprint)

2021 ◽  
Author(s):  
Xueying Ru ◽  
Yunhui Ma ◽  
Tianhao Wang ◽  
Zhigang Pan
Keyword(s):  
Atrial Fibrillation ◽  
Decision Support ◽  
Clinical Decision Support ◽  
Primary Outcome ◽  
Meta Analysis ◽  
Intervention Group ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Control Group ◽  
Antithrombotic Treatment

BACKGROUND Atrial fibrillation (AF) is one of the most common arrhythmogenic diseases with high risk of disability and mortality, thereby greatly reducing the quality of life. Thromboembolic prophylaxis plays an essential role in AF therapy. Clinical decision support systems (CDSS) is available for management of AF patients with regard to antithrombotic treatment. OBJECTIVE To systematically review the association between clinical decision support systems (CDSS) and the antithrombotic treatment for the management of atrial fibrillation (AF) patients. METHODS We searched the electronic databases PubMed, MEDLINE., Embase, The Cochrane Library, and Biosis Preview for published randomized controlled trials (RCTs) on the relationship between CDSS and the management of AF patients from inception to April 2021. Two researchers screened these studies independently, extracted data, assessed the risk of bias and evaluated the CDSS features. The primary outcome was the proportion of antithrombotic treatment prescriptions in agreement with recommendations in the guidelines, and the secondary outcome was stroke morbidity and the incidence of adverse events. Meta-analysis was done using Revman5.4.1 and Stata16.1. RESULTS We included six RCTs, involving 20,562 subjects (11,334 in the intervention group and 9,228 in the control group). The 14.265 subjects had a primary outcome (7,930 in the intervention group and 6,335 in the control group). The proportion of antithrombotic treatment prescriptions in agreement with recommendations in the guidelines in the intervention group was slightly higher than that in the control group (RR=1.03, 95% CI: 1.01–1.05, P<.001). Stroke morbidity was not significantly different (RR=1.07, 95% CI: 0.94–1.22, P=.33), but adverse events were lower in the intervention group than that in control group (RR=0.79, 95% CI :0.64–0.98, P=.03). We detected no publication bias for the primary outcome in the meta-analysis (P=.89 for the Egger test and P=.81 for Begg’s test). CONCLUSIONS The use of CDSS improved physicians’ compliance with AF guidelines for antithrombotic treatment and decreased adverse events, but did not lower the stroke morbidity.

Computerized Clinical Decision Support Systems and Antibiotic Prescribing: A Systematic Review and Meta-analysis

2019 ◽  
Vol 41 (3) ◽  
pp. 552-581 ◽  
Author(s):  
Eduardo Carracedo-Martinez ◽  
Christian Gonzalez-Gonzalez ◽  
Antonio Teixeira-Rodrigues ◽  
Jesus Prego-Dominguez ◽  
Bahi Takkouche ◽  
...  
Keyword(s):  
Systematic Review ◽  
Decision Support ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Support Systems ◽  
Meta Analysis ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Antibiotic Prescribing

scholarly journals Implementation of clinical decision support system for anticoagulant prescribing for patients with deep vein thrombosis

2020 ◽  
Vol 75 (1) ◽  
pp. 69-76
Author(s):  
Anton A. Chernov ◽  
E. B. Kleymenova ◽  
Dmitry A. Sychev ◽  
Liubov P. Yashina ◽  
Maria D. Nigmatkulova ◽  
...  
Keyword(s):  
Decision Support ◽  
Clinical Decision Support ◽  
Anticoagulant Therapy ◽  
Clinical Guidelines ◽  
Oral Anticoagulants ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Control Group ◽  
Hemorrhagic Complication ◽  
Vein Thrombosis

Background: Physicians adherence to recommendations for appropriate antithrombotic therapy of venous thromboembolism (VTE) can reduce the risk of recurrent VTE, pulmonary hypertension, bleeding and other adverse events. Clinical decision support systems (CDSS) are shown to increase physicians adherence to clinical guidelines. Aims: To assess effectivenes and safety of CDSS for anticoagulant prescribing for inhospital patients with VTE. Methods: A prospective cohort study was conducted in a Moscow general hospital from 06.30.2017 to 06.23.2018 to compare physicians compliance with clinical guidelines for DVT anticoagulant therapy, the rate of drug errors and direct costs of anticoagulant therapy before and after CDSS implementation (55 patients in control group and 49 in experimental group). Results: The rate of anticoagulant prescribing for patients with DVT did not alter significantly after CDSS implementation (96% compared with 91% before CDSS), but physicians compliance with recommendations on anticoagulant dosage increased from 32.7% to 73.5% (p = 0.0003) with corresponding decrease in the rate of anticoagulant prescribing errors from 1.35 to 0.65 per 1 patient (p = 0.0005). The length of stay and hemorrhagic complication rate did not differ between control and experimental groups. LMWH replacement with new oral anticoagulants has reduced the cost of anticoagulant therapy for 1 patient from 11.800 rubles (IQR = 7000) to 5.430 rubles (IQR = 5700) (p 0.005). Conclusions: СDSS can increase physicians adherence to recommended anticoagulant therapy for patients with DVT: to prevent unreasonable under-/overdosing or prolongation of anticoagulant therapy. CDSS for DVT drug therapy can be economically feasible.

scholarly journals Computerised clinical decision support systems and absolute improvements in care: meta-analysis of controlled clinical trials

BMJ ◽  
2020 ◽  
pp. m3216 ◽  
Author(s):  
Janice L Kwan ◽  
Lisha Lo ◽  
Jacob Ferguson ◽  
Hanna Goldberg ◽  
Juan Pablo Diaz-Martinez ◽  
...  
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Support Systems ◽  
Meta Analysis ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Clinical Endpoints ◽  
Study Characteristics ◽  
Meta Regression

Abstract Objective To report the improvements achieved with clinical decision support systems and examine the heterogeneity from pooling effects across diverse clinical settings and intervention targets. Design Systematic review and meta-analysis. Data sources Medline up to August 2019. Eligibility criteria for selecting studies and methods Randomised or quasi-randomised controlled trials reporting absolute improvements in the percentage of patients receiving care recommended by clinical decision support systems. Multilevel meta-analysis accounted for within study clustering. Meta-regression was used to assess the degree to which the features of clinical decision support systems and study characteristics reduced heterogeneity in effect sizes. Where reported, clinical endpoints were also captured. Results In 108 studies (94 randomised, 14 quasi-randomised), reporting 122 trials that provided analysable data from 1 203 053 patients and 10 790 providers, clinical decision support systems increased the proportion of patients receiving desired care by 5.8% (95% confidence interval 4.0% to 7.6%). This pooled effect exhibited substantial heterogeneity (I 2 =76%), with the top quartile of reported improvements ranging from 10% to 62%. In 30 trials reporting clinical endpoints, clinical decision support systems increased the proportion of patients achieving guideline based targets (eg, blood pressure or lipid control) by a median of 0.3% (interquartile range −0.7% to 1.9%). Two study characteristics (low baseline adherence and paediatric settings) were associated with significantly larger effects. Inclusion of these covariates in the multivariable meta-regression, however, did not reduce heterogeneity. Conclusions Most interventions with clinical decision support systems appear to achieve small to moderate improvements in targeted processes of care, a finding confirmed by the small changes in clinical endpoints found in studies that reported them. A minority of studies achieved substantial increases in the delivery of recommended care, but predictors of these more meaningful improvements remain undefined.

scholarly journals Effectiveness of an Advanced Clinical Decision Support System on Clinical Decision-Making Skills in a Call Center Medication Therapy Management Pharmacy Setting: A Pilot Study

Pharmacy ◽  
2020 ◽  
Vol 8 (4) ◽  
pp. 228
Author(s):  
Jennifer M. Bingham ◽  
Veronique Michaud ◽  
Jacques Turgeon ◽  
David R. Axon
Keyword(s):  
Decision Support ◽  
Clinical Decision Support ◽  
Call Center ◽  
Intervention Group ◽  
Clinical Decision ◽  
Control Group ◽  
Clinical Recommendations ◽  
Medication Therapy ◽  
Post Test ◽  
Pharmacy Setting

(1) Background: There is limited evidence related to the efficacy of advanced clinical decision support systems (CDSS) on the quantity of high-quality clinical recommendations in a pharmacy-related medication therapy management (MTM) setting. The study aimed to assess the effect of an advanced CDSS on the quantity of relevant clinical pharmacist recommendations in a call center MTM setting. (2) Methods: This pre-test/post-test with comparator group study compared clinical skills assessment scores between certified MTM pharmacists in March 2020. A Wilcoxon Signed Rank test assessed the difference between pre- and post-test scores in both groups. (3) Results: Of 20 participants, the majority were less than 40 years old (85%) with a Doctor of Pharmacy degree (90%). Nine were female. Intervention group participants had less than three years of experience as a pharmacist. The control group had less than three years (40%) or seven to ten years (40%) of experience. There was a significant increase in intervention group scores between pre- (median = 3.0, IQR = 3.0) and post-test segments (median = 6.5, IQR = 4.0, p = 0.02). There was no significant change between control group pre- and post-test segments (p = 0.48). (4) Conclusion: Pharmacist exposure to an advanced CDSS was associated with significantly increased quantity of relevant clinical recommendations in an MTM pharmacy setting.

scholarly journals Impact of electronic clinical decision support on adherence to guideline-recommended treatment for hyperlipidaemia, atrial fibrillation and heart failure: protocol for a cluster randomised trial

BMJ Open ◽  
2017 ◽  
Vol 7 (12) ◽  
pp. e019087 ◽  
Author(s):  
Maya Elizabeth Kessler ◽  
Rickey E Carter ◽  
David A Cook ◽  
Daryl Jon Kor ◽  
Paul M McKie ◽  
...  
Keyword(s):  
Heart Failure ◽  
Atrial Fibrillation ◽  
Decision Support ◽  
Clinical Decision Support ◽  
Primary Outcome ◽  
Point Of Care ◽  
Decision Support Tool ◽  
Clinical Decision ◽  
Support Tool ◽  
Clinical Decision Support Tool

IntroductionClinical practice guidelines facilitate optimal clinical practice. Point of care access, interpretation and application of such guidelines, however, is inconsistent. Informatics-based tools may help clinicians apply guidelines more consistently. We have developed a novel clinical decision support tool that presents guideline-relevant information and actionable items to clinicians at the point of care. We aim to test whether this tool improves the management of hyperlipidaemia, atrial fibrillation and heart failure by primary care clinicians.Methods/analysisClinician care teams were cluster randomised to receive access to the clinical decision support tool or passive access to institutional guidelines on 16 May 2016. The trial began on 1 June 2016 when access to the tool was granted to the intervention clinicians. The trial will be run for 6 months to ensure a sufficient number of patient encounters to achieve 80% power to detect a twofold increase in the primary outcome at the 0.05 level of significance. The primary outcome measure will be the percentage of guideline-based recommendations acted on by clinicians for hyperlipidaemia, atrial fibrillation and heart failure. We hypothesise care teams with access to the clinical decision support tool will act on recommendations at a higher rate than care teams in the standard of care arm.Ethics and disseminationThe Mayo Clinic Institutional Review Board approved all study procedures. Informed consent was obtained from clinicians. A waiver of informed consent and of Health Insurance Portability and Accountability Act (HIPAA) authorisation for patients managed by clinicians in the study was granted. In addition to publication, results will be disseminated via meetings and newsletters.Trial registration numberNCT02742545.

Diagnostic accuracy in Family Medicine residents using a clinical decision support system (DXplain): a randomized-controlled trial

Diagnosis ◽  
2018 ◽  
Vol 5 (2) ◽  
pp. 71-76 ◽  
Author(s):  
Adrian Israel Martinez-Franco ◽  
Melchor Sanchez-Mendiola ◽  
Juan Jose Mazon-Ramirez ◽  
Isaias Hernandez-Torres ◽  
Carlos Rivero-Lopez ◽  
...  
Keyword(s):  
Decision Support ◽  
Family Medicine ◽  
Diagnostic Accuracy ◽  
Clinical Decision Support ◽  
Medical Practice ◽  
Intervention Group ◽  
Diagnostic Errors ◽  
Clinical Decision ◽  
Control Group ◽  
Family Medicine Residents

Abstract Background: Clinical reasoning is an essential skill in physicians, required to address the challenges of accurate patient diagnoses. The goal of the study was to compare the diagnostic accuracy in Family Medicine residents, with and without the use of a clinical decision support tool (DXplain http://www.mghlcs.org/projects/dxplain). Methods: A total of 87 first-year Family Medicine residents, training at the National Autonomous University of Mexico (UNAM) Postgraduate Studies Division in Mexico City, participated voluntarily in the study. They were randomized to a control group and an intervention group that used DXplain. Both groups solved 30 clinical diagnosis cases (internal medicine, pediatrics, gynecology and emergency medicine) in a multiple-choice question test that had validity evidence. Results: The percent-correct score in the Diagnosis Test in the control group (44 residents) was 74.1±9.4 (mean±standard deviation) whereas the DXplain intervention group (43 residents) had a score of 82.4±8.5 (p<0.001). There were significant differences in the four knowledge content areas of the test. Conclusions: Family Medicine residents have appropriate diagnostic accuracy that can improve with the use of DXplain. This could help decrease diagnostic errors, improve patient safety and the quality of medical practice. The use of clinical decision support systems could be useful in educational interventions and medical practice.

scholarly journals Effectiveness and harms of clinical decision support systems for referral within chronic pain practice: protocol for a systematic review and meta-analysis

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Hervé Tchala Vignon Zomahoun ◽  
Regina Visca ◽  
Nicole George ◽  
Sara Ahmed
Keyword(s):  
Systematic Review ◽  
Chronic Pain ◽  
Decision Support ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Support Systems ◽  
Meta Analysis ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Randomized Controlled

Abstract Background Chronic pain is a common public health problem with negative consequences for individuals and societies. Fortunately, interdisciplinary chronic pain management has been shown to be effective for improving patients’ outcomes and strongly recommended in clinical practice guidelines. Appropriate referral within the healthcare system based on individuals’ needs and available services is essential to optimise health-related outcomes and maximise resources. Clinical decision support systems have been shown to be effective for supporting healthcare professionals in different practices. However, there is no knowledge synthesis on clinical decision support systems for referral within chronic pain practice. We aim to identify the clinical decision support systems for referral within chronic pain practices and assess their content, effectiveness, harms, and validation parameters. Methods Using the methodology of Cochrane reviews, we will perform a systematic review and meta-analysis based on studies meeting the following criteria: Population, patients with chronic pain and/or healthcare professionals working in chronic pain; Intervention, clinical decision support systems for referral within chronic pain practice; Comparison, any other clinical tool, any usual care or practices; Outcomes, clinical outcomes of patients measuring how patients feel, function or survive including benefits, adverse effects, continuity of care, care appropriateness, care satisfaction, quality of life, healthcare professional performance, and cost outcomes; and Study design: randomized controlled trials, non-randomized controlled trials, before and after controlled studies and interrupted time series. We will search relevant literature with the support of an information specialist using Medline, Embase, PsycInfo, CINHAL, Web of Science and Cochrane Library from their inception onwards. Two reviewers will independently complete study selection, data extraction and risk of bias assessment. We will analyse data to perform both narrative syntheses and meta-analysis if appropriate. Discussion Findings of this review will contribute to enhancing chronic pain care and research. Clinical decision support systems identified as effective in this review can be investigated for implementation in clinical practice and impact on improving patient, clinical and health system outcomes. Clinical decision support systems not yet ready for implementation that require further improvement will also be identified. Systematic review registration PROSPERO registration: CRD42020158880.

Architectures for Architects

1993 ◽  
Vol 32 (01) ◽  
pp. 12-13 ◽  
Author(s):  
M. A. Musen
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Support Systems ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Design And Development

Abstract:Response to Heathfield HA, Wyatt J. Philosophies for the design and development of clinical decision-support systems. Meth Inform Med 1993; 32: 1-8.

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