scholarly journals Best Paper Selection

2021 ◽  
Vol 30 (01) ◽  
pp. 175-175
Keyword(s):  
General Practice ◽  
Decision Support ◽  
Multicentre Study ◽  
Hospital Admission ◽  
Clinical Decision Support ◽  
Clinical Decision Support System ◽  
Clinical Decision Support Systems ◽  
Cluster Randomized Trial ◽  
Clinical Decision ◽  
Cluster Randomized

Wu G, Yang P, Xie Y, Woodruff HC, Rao X, Guiot J, Frix AN, Louis R, Moutschen M, Li J, Li J, Yan C, Du D, Zhao S, Ding Y, Liu B, Sun W, Albarello F, D’Abramo A, Schininà V, Nicastri E, Occhipinti M, Barisione G, Barisione E, Halilaj I, Lovinfosse P, Wang X, Wu J, Lambin P. Development of a clinical decision support system for severity risk prediction and triage of COVID-19 patients at hospital admission: an international multicentre study. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7331655/ Balestrieri M, Sisti D, Rocchi M, Rucci P, Simon G, Araya R, de Girolamo G. Effectiveness of clinical decision support systems and telemedicine on outcomes of depression: a cluster randomized trial in general practice. https://academic.oup.com/fampra/article/37/6/731/5882119

scholarly journals Effectiveness of clinical decision support systems and telemedicine on outcomes of depression: a cluster randomized trial in general practice

2020 ◽  
Vol 37 (6) ◽  
pp. 731-737
Author(s):  
Matteo Balestrieri ◽  
Davide Sisti ◽  
Marco Rocchi ◽  
Paola Rucci ◽  
Gregory Simon ◽  
...  
Keyword(s):  
Quality Of Life ◽  
Decision Support ◽  
Randomized Trial ◽  
Clinical Decision Support ◽  
Clinical Decision Support Systems ◽  
Cluster Randomized Trial ◽  
Clinical Decision ◽  
Medical Interview ◽  
Cluster Randomized

Abstract Background Computerized Clinical Decision Support Systems (CCDSS) are information technology tools, designed to improve clinical decision-making. Telemedicine is a health care service delivery using videoconferencing, telephone or messaging technologies. Objectives Our project aimed at testing the effectiveness of a composite CCDSS and telemedicine approach designed to treat depression in primary care. Methods This cluster randomized trial involved four GP clinics located in Northern Italy. Two clinics were assigned to the experimental protocol, and two served as controls. The study compared the telemedicine group (TG), in which GPs had access to a CCDSS platform, with the control group (CG) in which GPs provided treatment as usual (TAU). Patients scoring ≥11 on Patient Heath Questionnaire and ≥26 on the Inventory of Depressive Symptomatology-Self-Report were eligible for participation. Patients were also administered the World Health Organization Quality of Life-BREF to assess quality of life and Medical Interview Satisfaction Scale 21 to assess satisfaction with the medical interview. Results Overall, 2810 patients were screened and 66 in the experimental group and 32 in the CG passed the screening stages and met inclusion criteria. The percentage of remitters at 6 months was significantly higher in the TG than in the CG group (24.1% versus 3.1%, χ 2 = 6.6, P = 0.01). This difference remained significant after adjusting for baseline confounders. Physical and psychological quality of life improved significantly from baseline in both groups. Patients reported, on average, good satisfaction with the medical interview. Conclusions Our study showed that a combined CCDSS and telemedicine approach may be more effective than the TAU offered by GPs to patients with depression. Trial registration The trial was registered on https://clinicaltrials.gov/ on 5 October 2012 with identifier: NCT01701791. The first participant was enrolled on 5 May 2014 and the study was completed on May 2016.

scholarly journals Computerized Clinical Decision Support System for Prompting Brief Alcohol Interventions with Treatment Seeking Smokers: A Sex-Based Secondary Analysis of a Cluster Randomized Trial

2020 ◽  
Vol 17 (3) ◽  
pp. 1024 ◽  
Author(s):  
Nadia Minian ◽  
Anna Ivanova ◽  
Sabrina Voci ◽  
Scott Veldhuizen ◽  
Laurie Zawertailo ◽  
...  
Keyword(s):  
Decision Support ◽  
Clinical Decision Support ◽  
Support System ◽  
Clinical Decision Support System ◽  
Secondary Analysis ◽  
Cluster Randomized Trial ◽  
Clinical Decision ◽  
Alcohol Intervention ◽  
Brief Alcohol Intervention ◽  
Cluster Randomized

Although brief alcohol intervention can reduce alcohol use for both men and women, health care providers (HCPs) are less likely to discuss alcohol use or deliver brief intervention to women compared to men. This secondary analysis examined whether previously reported outcomes from a cluster randomized trial of a clinical decision support system (CDSS)—prompting delivery of a brief alcohol intervention (an educational alcohol resource) for patients drinking above cancer guidelines—were moderated by patients’ sex. Patients (n = 5702) enrolled in a smoking cessation program at primary care sites across Ontario, Canada, were randomized to either the intervention (CDSS) or control arm (no CDSS). Logistic generalized estimating equations models were fit for the primary and secondary outcome (HCP offer of resource and patient acceptance of resource, respectively). Previously reported results showed no difference between treatment arms in HCP offers of an educational alcohol resource to eligible patients, but there was increased acceptance of the alcohol resource among patients in the intervention arm. The results of this study showed that these CDSS intervention effects were not moderated by sex, and this can help inform the development of a scalable strategy to overcome gender disparities in alcohol intervention seen in other studies.

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.

Methodologies of Legacy Clinical Decision Support System

2017 ◽  
Vol 2 (2) ◽  
pp. 20-37
Author(s):  
Meenakshi Sharmi ◽  
Himanshu Aggarwal
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Support System ◽  
Clinical Decision Support System ◽  
Support Systems ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Research Area

Information technology playing a prominent role in the field of medical by incorporating the clinical decision support system (CDSS) in their routine practices. CDSS is a computer based interactive program to assist the physician to make the right decision at right time. Nowadays, clinical decision support systems are a dynamic research area in the field of computers, but the lack of understanding, as well as functions of the system, make adoption slow by physicians and patients. The literature review of this article focuses on the overview of legacy CDSS, the kind of methodologies and classifiers employed to prepare such a decision support system using a non-technical approach to the physician and the strategy-makers. This article provides understanding of the clinical decision support along with the gateway to physician, and to policy-makers to develop and deploy decision support systems as a healthcare service to make the quick, agile and right decision. Future directions to handle the uncertainties along with the challenges of clinical decision support systems are also enlightened in this study.

scholarly journals Evaluation Framework for Successful Artificial Intelligence–Enabled Clinical Decision Support Systems: Mixed Methods Study (Preprint)

2020 ◽  
Author(s):  
Mengting Ji ◽  
Georgi Z Genchev ◽  
Hengye Huang ◽  
Ting Xu ◽  
Hui Lu ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Decision Support ◽  
Clinical Decision Support ◽  
Support System ◽  
Clinical Decision Support System ◽  
Measurement Instrument ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
User Acceptance ◽  
Evaluation Framework

BACKGROUND Clinical decision support systems are designed to utilize medical data, knowledge, and analysis engines and to generate patient-specific assessments or recommendations to health professionals in order to assist decision making. Artificial intelligence–enabled clinical decision support systems aid the decision-making process through an intelligent component. Well-defined evaluation methods are essential to ensure the seamless integration and contribution of these systems to clinical practice. OBJECTIVE The purpose of this study was to develop and validate a measurement instrument and test the interrelationships of evaluation variables for an artificial intelligence–enabled clinical decision support system evaluation framework. METHODS An artificial intelligence–enabled clinical decision support system evaluation framework consisting of 6 variables was developed. A Delphi process was conducted to develop the measurement instrument items. Cognitive interviews and pretesting were performed to refine the questions. Web-based survey response data were analyzed to remove irrelevant questions from the measurement instrument, to test dimensional structure, and to assess reliability and validity. The interrelationships of relevant variables were tested and verified using path analysis, and a 28-item measurement instrument was developed. Measurement instrument survey responses were collected from 156 respondents. RESULTS The Cronbach α of the measurement instrument was 0.963, and its content validity was 0.943. Values of average variance extracted ranged from 0.582 to 0.756, and values of the heterotrait-monotrait ratio ranged from 0.376 to 0.896. The final model had a good fit (<i>χ<sub>26</sub><sup>2</sup></i>=36.984; <i>P</i>=.08; comparative fit index 0.991; goodness-of-fit index 0.957; root mean square error of approximation 0.052; standardized root mean square residual 0.028). Variables in the final model accounted for 89% of the variance in the user acceptance dimension. CONCLUSIONS User acceptance is the central dimension of artificial intelligence–enabled clinical decision support system success. Acceptance was directly influenced by perceived ease of use, information quality, service quality, and perceived benefit. Acceptance was also indirectly influenced by system quality and information quality through perceived ease of use. User acceptance and perceived benefit were interrelated.

scholarly journals Evaluation Framework for Successful Artificial Intelligence–Enabled Clinical Decision Support Systems: Mixed Methods Study

10.2196/25929 ◽  
2021 ◽  
Vol 23 (6) ◽  
pp. e25929
Author(s):  
Mengting Ji ◽  
Georgi Z Genchev ◽  
Hengye Huang ◽  
Ting Xu ◽  
Hui Lu ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Decision Support ◽  
Clinical Decision Support ◽  
Support System ◽  
Clinical Decision Support System ◽  
Measurement Instrument ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
User Acceptance ◽  
Evaluation Framework

Background Clinical decision support systems are designed to utilize medical data, knowledge, and analysis engines and to generate patient-specific assessments or recommendations to health professionals in order to assist decision making. Artificial intelligence–enabled clinical decision support systems aid the decision-making process through an intelligent component. Well-defined evaluation methods are essential to ensure the seamless integration and contribution of these systems to clinical practice. Objective The purpose of this study was to develop and validate a measurement instrument and test the interrelationships of evaluation variables for an artificial intelligence–enabled clinical decision support system evaluation framework. Methods An artificial intelligence–enabled clinical decision support system evaluation framework consisting of 6 variables was developed. A Delphi process was conducted to develop the measurement instrument items. Cognitive interviews and pretesting were performed to refine the questions. Web-based survey response data were analyzed to remove irrelevant questions from the measurement instrument, to test dimensional structure, and to assess reliability and validity. The interrelationships of relevant variables were tested and verified using path analysis, and a 28-item measurement instrument was developed. Measurement instrument survey responses were collected from 156 respondents. Results The Cronbach α of the measurement instrument was 0.963, and its content validity was 0.943. Values of average variance extracted ranged from 0.582 to 0.756, and values of the heterotrait-monotrait ratio ranged from 0.376 to 0.896. The final model had a good fit (χ262=36.984; P=.08; comparative fit index 0.991; goodness-of-fit index 0.957; root mean square error of approximation 0.052; standardized root mean square residual 0.028). Variables in the final model accounted for 89% of the variance in the user acceptance dimension. Conclusions User acceptance is the central dimension of artificial intelligence–enabled clinical decision support system success. Acceptance was directly influenced by perceived ease of use, information quality, service quality, and perceived benefit. Acceptance was also indirectly influenced by system quality and information quality through perceived ease of use. User acceptance and perceived benefit were interrelated.

scholarly journals Supporting care for suboptimally controlled type 2 diabetes mellitus in general practice with a clinical decision support system: a mixed methods pilot cluster randomised trial

BMJ Open ◽  
2020 ◽  
Vol 10 (2) ◽  
pp. e032594
Author(s):  
Mark E Murphy ◽  
Jenny McSharry ◽  
Molly Byrne ◽  
Fiona Boland ◽  
Derek Corrigan ◽  
...  
Keyword(s):  
Blood Pressure ◽  
General Practice ◽  
Decision Support ◽  
Decision Support System ◽  
Clinical Decision Support ◽  
Support System ◽  
Clinical Decision Support System ◽  
Randomised Trial ◽  
Clinical Decision

ObjectivesWe developed a complex intervention called DECIDE (ComputeriseD dECisIonal support for suboptimally controlleD typE 2 Diabetes mellitus in Irish General Practice) which used a clinical decision support system to address clinical inertia and support general practitioner (GP) intensification of treatment for adults with suboptimally controlled type2 diabetes mellitus (T2DM). The current study explored the feasibility and potential impact of DECIDE.DesignA pilot cluster randomised controlled trial.SettingConducted in 14 practices in Irish General Practice.ParticipantsThe DECIDE intervention was targeted at GPs. They applied DECIDE to patients with suboptimally controlled T2DM, defined as a glycated haemoglobin (HbA1c) ≥70 mmol/mol and/or blood pressure ≥150/95 mmHg.InterventionThe intervention incorporated training and a web-based clinical decision support system which supported; (i) medication intensification actions; and (ii) non-pharmacological actions to support care. Control practices delivered usual care.Primary and secondary outcome measuresFeasibility and acceptability was determined using thematic analysis of semi-structured interviews with GPs, combined with data from the DECIDE website. Clinical outcomes included HbA1c, medication intensification, blood pressure and lipids.ResultsWe recruited 14 practices and 134 patients. At 4-month follow-up, all practices and 114 patients were followed up. GPs reported finding decision support helpful navigating increasingly complex medication algorithms. However, the majority of GPs believed that the target patient group had poor engagement with GP and hospital services for a range of reasons. At follow-up, there was no difference in glycaemic control (−3.6 mmol/mol (95% CI −11.2 to 4.0)) between intervention and control groups or in secondary outcomes including, blood pressure, total cholesterol, medication intensification or utilisation of services. Continuation criteria supported proceeding to a definitive randomised trial with some modifications.ConclusionThe DECIDE study was feasible and acceptable to GPs but wider impacts on glycaemic and blood pressure control need to be considered for this patient population going forward.Trial registration numberISRCTN69498919

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