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.

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.

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.

Characterizing Decision Support Telemedicine Systems

2006 ◽  
Vol 45 (05) ◽  
pp. 523-527 ◽  
Author(s):  
A. Abu-Hanna ◽  
B. Nannings
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Literature Search ◽  
Support Systems ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Problem Dimension ◽  
Process Dimension

Summary Objectives: Decision Support Telemedicine Systems (DSTS) are at the intersection of two disciplines: telemedicine and clinical decision support systems (CDSS). The objective of this paper is to provide a set of characterizing properties for DSTSs. This characterizing property set (CPS) can be used for typing, classifying and clustering DSTSs. Methods: We performed a systematic keyword-based literature search to identify candidate-characterizing properties. We selected a subset of candidates and refined them by assessing their potential in order to obtain the CPS. Results: The CPS consists of 14 properties, which can be used for the uniform description and typing of applications of DSTSs. The properties are grouped in three categories that we refer to as the problem dimension, process dimension, and system dimension. We provide CPS instantiations for three prototypical applications. Conclusions: The CPS includes important properties for typing DSTSs, focusing on aspects of communication for the telemedicine part and on aspects of decisionmaking for the CDSS part. The CPS provides users with tools for uniformly describing DSTSs.

scholarly journals The acceptance of clinical decision support systems among clinicians in the treatment of neck and/or back pain

2020 ◽  
Vol 30 (Supplement_5) ◽  
Author(s):  
S M Jansen-Kosterink ◽  
M Cabrita ◽  
I Flierman
Keyword(s):  
Back Pain ◽  
Decision Support ◽  
Focus Groups ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Support Systems ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Case Based Reasoning ◽  
Online Questionnaire

Abstract Background Clinical Decision Support Systems (CDSSs) are computerized systems using case-based reasoning to assist clinicians in making clinical decisions. Despite the proven added value to public health, the implementation of CDSS clinical practice is scarce. Particularly, little is known about the acceptance of CDSS among clinicians. Within the Back-UP project (Project Number: H2020-SC1-2017-CNECT-2-777090) a CDSS is developed with prognostic models to improve the management of Neck and/or Low Back Pain (NLBP). Therefore, the aim of this study is to present the factors involved in the acceptance of CDSSs among clinicians. Methods To assess the acceptance of CDSSs among clinicians we conducted a mixed method analysis of questionnaires and focus groups. An online questionnaire with a low-fidelity prototype of a CDSS (TRL3) was sent to Dutch clinicians aimed to identify the factors influencing the acceptance of CDSSs (intention to use, perceived threat to professional autonomy, trusting believes and perceived usefulness). Next to this, two focus groups were conducted with clinicians addressing the general attitudes towards CDSSs, the factors determining the level of acceptance, and the conditions to facilitate use of CDSSs. Results A pilot-study of the online questionnaire is completed and the results of the large evaluation are expected spring 2020. Eight clinicians participated in two focus groups. After being introduced to various types of CDSSs, participants were positive about the value of CDSS in the care of NLBP. The clinicians agreed that the human touch in NLBP care must be preserved and that CDSSs must remain a supporting tool, and not a replacement of their role as professionals. Conclusions By identifying the factors hindering the acceptance of CDSSs we can draw implications for implementation of CDSSs in the treatment of NLBP.

scholarly journals Applicability of Clinical Decision Support in Management among Patients Undergoing Cardiac Surgery in Intensive Care Unit: A Systematic Review

2021 ◽  
Vol 11 (6) ◽  
pp. 2880
Author(s):  
Miguel Pereira ◽  
Patricia Concheiro-Moscoso ◽  
Alexo López-Álvarez ◽  
Gerardo Baños ◽  
Alejandro Pazos ◽  
...  
Keyword(s):  
Cardiac Surgery ◽  
Intensive Care ◽  
Decision Support ◽  
Clinical Decision Support ◽  
Web Of Science ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Real Time Information ◽  
Time Information ◽  
Improve Patient

The advances achieved in recent decades regarding cardiac surgery have led to a new risk that goes beyond surgeons' dexterity; postoperative hours are crucial for cardiac surgery patients and are usually spent in intensive care units (ICUs), where the patients need to be continuously monitored to adjust their treatment. Clinical decision support systems (CDSSs) have been developed to take this real-time information and provide clinical suggestions to physicians in order to reduce medical errors and to improve patient recovery. In this review, an initial total of 499 papers were considered after identification using PubMed, Web of Science, and CINAHL. Twenty-two studies were included after filtering, which included the deletion of duplications and the exclusion of titles or abstracts that were not of real interest. A review of these papers concluded the applicability and advances that CDSSs offer for both doctors and patients. Better prognosis and recovery rates are achieved by using this technology, which has also received high acceptance among most physicians. However, despite the evidence that well-designed CDSSs are effective, they still need to be refined to offer the best assistance possible, which may still take time, despite the promising models that have already been applied in real ICUs.

scholarly journals Barriers and facilitators to the adoption of electronic clinical decision support systems: a qualitative interview study with UK general practitioners

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Elizabeth Ford ◽  
Natalie Edelman ◽  
Laura Somers ◽  
Duncan Shrewsbury ◽  
Marcela Lopez Levy ◽  
...  
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
General Practitioners ◽  
Clinical Decision Support ◽  
Support Systems ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Structured Interview ◽  
Qualitative Interview Study ◽  
High Level

Abstract Background Well-established electronic data capture in UK general practice means that algorithms, developed on patient data, can be used for automated clinical decision support systems (CDSSs). These can predict patient risk, help with prescribing safety, improve diagnosis and prompt clinicians to record extra data. However, there is persistent evidence of low uptake of CDSSs in the clinic. We interviewed UK General Practitioners (GPs) to understand what features of CDSSs, and the contexts of their use, facilitate or present barriers to their use. Methods We interviewed 11 practicing GPs in London and South England using a semi-structured interview schedule and discussed a hypothetical CDSS that could detect early signs of dementia. We applied thematic analysis to the anonymised interview transcripts. Results We identified three overarching themes: trust in individual CDSSs; usability of individual CDSSs; and usability of CDSSs in the broader practice context, to which nine subthemes contributed. Trust was affected by CDSS provenance, perceived threat to autonomy and clear management guidance. Usability was influenced by sensitivity to the patient context, CDSS flexibility, ease of control, and non-intrusiveness. CDSSs were more likely to be used by GPs if they did not contribute to alert proliferation and subsequent fatigue, or if GPs were provided with training in their use. Conclusions Building on these findings we make a number of recommendations for CDSS developers to consider when bringing a new CDSS into GP patient records systems. These include co-producing CDSS with GPs to improve fit within clinic workflow and wider practice systems, ensuring a high level of accuracy and a clear clinical pathway, and providing CDSS training for practice staff. These recommendations may reduce the proliferation of unhelpful alerts that can result in important decision-support being ignored.

Massive transfusion experience, current practice and decision support: A survey of Australian and New Zealand anaesthetists

2021 ◽  
pp. 0310057X2097403
Author(s):  
Brenton J Sanderson ◽  
Jeremy D Field ◽  
Lise J Estcourt ◽  
Erica M Wood ◽  
Enrico W Coiera
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Clinical Decision Support ◽  
Support System ◽  
Clinical Decision Support System ◽  
Response Rate ◽  
Massive Transfusion ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Transfusion Practice

Massive transfusions guided by massive transfusion protocols are commonly used to manage critical bleeding, when the patient is at significant risk of morbidity and mortality, and multiple timely decisions must be made by clinicians. Clinical decision support systems are increasingly used to provide patient-specific recommendations by comparing patient information to a knowledge base, and have been shown to improve patient outcomes. To investigate current massive transfusion practice and the experiences and attitudes of anaesthetists towards massive transfusion and clinical decision support systems, we anonymously surveyed 1000 anaesthetists and anaesthesia trainees across Australia and New Zealand. A total of 228 surveys (23.6%) were successfully completed and 227 were analysed for a 23.3% response rate. Most respondents were involved in massive transfusions infrequently (88.1% managed five or fewer massive transfusion protocols per year) and worked at hospitals which have massive transfusion protocols (89.4%). Massive transfusion management was predominantly limited by timely access to point-of-care coagulation assessment and by competition with other tasks, with trainees reporting more significant limitations compared to specialists. The majority of respondents reported that they were likely, or very likely, both to use (73.1%) and to trust (85%) a clinical decision support system for massive transfusions, with no significant difference between anaesthesia trainees and specialists ( P = 0.375 and P = 0.73, respectively). While the response rate to our survey was poor, there was still a wide range of massive transfusion experience among respondents, with multiple subjective factors identified limiting massive transfusion practice. We identified several potential design features and barriers to implementation to assist with the future development of a clinical decision support system for massive transfusion, and overall wide support for a clinical decision support system for massive transfusion among respondents.

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