Application Of Clinical Decision Support System Research Articles

Design and evaluation of a Mobile-Based decision support system to enhance lung transplant candidate assessment and management: knowledge translation integrated with clinical workflow

BackgroundAccurate and timely decision-making in lung transplantation (LTx) programs is critical. The main objective of this study was to develop a mobile-based evidence-based clinical decision support system (CDSS) to enhance the management of lung transplant candidates.MethodAn iterative participatory software development process was employed to develop the ImamLTx CDSS. This study was accomplished in three phases. First, required data and standard clinical workflow were identified according to the literature review and expert consensus. Second, a rule-based knowledge-based CDSS application was developed. In the third phase, this CDSS was evaluated. The evaluation was done using the standard Post-Study System Usability Questionnaire (PSSUQ 18.3) and ten usability heuristics factors for user interface design.ResultsAccording to expert consensus, fifty-five data items were identified as essential data sets using the Content Validity Ratio (CVR) formula. By integrating information flow in clinical practices with clinical protocols, more than 450 rules and 500 knowledge statements were extracted. This CDSS provides clinical decision support on an Android platform regarding inclusion and exclusion referral criteria, optimum transplant time based on the type of lung disease, findings of initial assessment, and the overall evaluation of lung transplant candidates. Evaluation results showed high usability ratings due to the fact provided accuracy and sensitivity of this lung transplant CDSS with the information quality domain receiving the highest score (6.305 from 7).ConclusionThrough a stepwise approach, the ImamLTx CDSS was developed to provide LTx programs with timely patient data access via a mobile platform. Our results suggest integration with existing workflow to support clinical decision-making and provide patient-specific recommendations.

Effect of an artificial intelligence-assisted tool on non-valvular atrial fibrillation anticoagulation management in primary care: protocol for a cluster randomized controlled trial

BackgroundAtrial fibrillation (AF) is one of the most common cardiac arrhythmia diseases. Thromboembolic prophylaxis plays an essential role in AF therapy, but at present, general practitioners (GPs) are presumed to lack the knowledge and enthusiasm for AF management. Clinical decision support systems (CDSS), assisted by artificial intelligence, help primary care providers (PCPs) make quick, individualized, and correct clinical decisions. This primary aim of the study is to identify whether the promotion of the CDSS would improve the primary care provided to patients with AF. The secondary objectives are mainly to assess the health-economic and clinical benefits from using the CDSS, and the improvement of GPs’ AF management capability.MethodsThis study will be a prospective cluster randomized controlled trial, conducted among 14 community health centers in Shanghai which were randomized as the intervention group and control group in a ratio of 1:1. The intervention group will use the CDSS in the consultation of patients with AF and the control group will maintain their usual care. The trial will include 498 patients with AF and the follow-up period will be 12 months. The primary outcome is set as the proportion of antithrombotic treatment prescriptions in agreement with recommendations in the latest China’s AF-related guidelines. The secondary outcomes are the frequency of consultation, the compliance rate of international normalized ratio (INR) in patients with warfarin, stroke morbidity, treatment compliance, medication satisfaction, and the cost-benefit analysis. Per-protocol (PP) analysis and the intention-to-treat (ITT) analysis will be conducted.DiscussionThis study aims to identify whether the application of CDSS to manage patients with AF in China’s community health centers would bring benefits for patients, physicians, and health economics.Trial registrationRegistry name: 非瓣膜性房颤社区AI辅助管理工具研发及推广效果研究 (Development and promotion of an AI-assisted tool for NVAF management in primary care); registry number: ChiCTR2100052307; registration date: Nov. 22nd, 2021; http://www.chictr.org.cn/showproj.aspx?proj=133849.

Diagnosing, Managing, and Controlling COVID-19 using Clinical Decision Support Systems: A Study to Introduce CDSS Applications

Coronavirus disease (COVID-19) as an emerging disease decreases security among people from different countries. Sense of security can be raised via quick diagnosis of COVID-19, and its management and control using clinical decision support systems (CDSS) to prevent further spread of the disease. So, the aim of this study is to identify and introduce the applications of a CDSS in the diagnosis, management, and control of COVID-19. This cross-sectional study was conducted to identify and introduce the applications of CDSS in the diagnosis, management, and control of COVID-19. Based on the results of some meetings with infectious disease specialists and a general practitioner as well as reviewing the related literature, information about COVID-19 and CDSS was obtained. Then based on the information obtained, a questionnaire was designed electronically and distributed in a two-round Delphi method among 19 experts in the three fields of medical informatics, health information management, and infectious disease specialists. According to the literature and expert opinions, 35 applications of CDSS applications were identified in the four main groups of “diagnosis”, “medication”, “monitoring”, and “health services”. Eventually, a collective agreement was reached on 30 applications in the first and second rounds of Delphi. Among all the applications, the highest means were assigned to “monitoring the vital signs” and “helping diagnose infections and damaged lung tissue through CT scan”. Introducing these applications can provide general, basic knowledge of the design and implementation of clinical decision support systems in the real world to prevent irreversible complications and even many people’s death.

Advantages and Functions of Clinical and Decision Support Systems

Clinical Decision Support Systems (CDSSs) signify the framework shift in the medical sector in the modern age. CDSSs are utilized in augmenting healthcare facilities in the process of making complex clinical decisions. Since the first application of CDSSs in the 80s, the framework has witnessed significant transformation. The frameworks are now administered through electronic healthcare records with complex capacities. Irrespective of these complex advancements, there are existing questions concerning the impacts of CDSSs on service providers, healthcare costs, and patients’ records. There are many published texts concerning the success stories of CDSSs, but significant setbacks have proved that CDSSs are not without any potential risks. In this research, we provide critical analysis on the application of CDSSs in clinical setting, integrating various forms, present application cases with proven effectiveness, potential harms and common pitfalls. We therefore conclude with evidence-centered recommendation for mitigating the issues of CDSSs maintainability, evaluation, implementation and designing.

Clinical Decision Support Systems and Computerized Provider Order Entry: Contributions from 2020.

SummaryObjectives: To summarize research contributions published in 2020 in the field of clinical decision support systems (CDSS) and computerized provider order entry (CPOE), and select the best papers for the Decision Support section of the International Medical Informatics Association (IMIA) Yearbook 2021. Methods: Two bibliographic databases were searched for papers referring to clinical decision support systems. From search results, section editors established a list of candidate best papers, which were then peer-reviewed by seven external reviewers. The IMIA Yearbook editorial committee finally selected the best papers on the basis of all reviews including the section editors’ evaluation. Results: A total of 1,919 articles were retrieved. 15 best paper candidates were selected, the reviews of which resulted in the selection of two best papers. One paper reports on the use of electronic health records to support a public health response to the COVID-19 pandemic in the United States. The second paper proposes a combination of CDSS and telemedicine as a technology-based intervention to improve the outcomes of depression as part of a cluster trial. Conclusions: As shown by the number and the variety of works related to clinical decision support, research in the field is very active. This year's selection highlighted the application of CDSS to fight COVID-19 and a combined technology-based strategy to improve the treatment of depression.

Appropriateness of Overridden Alerts in Computerized Physician Order Entry: Systematic Review.

BackgroundThe clinical decision support system (CDSS) has become an indispensable tool for reducing medication errors and adverse drug events. However, numerous studies have reported that CDSS alerts are often overridden. The increase in override rates has raised questions about the appropriateness of CDSS application along with concerns about patient safety and quality of care.ObjectiveThe aim of this study was to conduct a systematic review to examine the override rate, the reasons for the alert override at the time of prescribing, and evaluate the appropriateness of overrides.MethodsWe searched electronic databases, including Google Scholar, PubMed, Embase, Scopus, and Web of Science, without language restrictions between January 1, 2000 and March 31, 2019. Two authors independently extracted data and crosschecked the extraction to avoid errors. The quality of the included studies was examined following Cochrane guidelines.ResultsWe included 23 articles in our systematic review. The range of average override alerts was 46.2%-96.2%. An average of 29.4%-100% of the overrides alerts were classified as appropriate, and the rate of appropriateness varied according to the alert type (drug-allergy interaction 63.4%-100%, drug-drug interaction 0%-95%, dose 43.9%-88.8%, geriatric 14.3%-57%, renal 27%-87.5%). The interrater reliability for the assessment of override alerts appropriateness was excellent (kappa=0.79-0.97). The most common reasons given for the override were “will monitor” and “patients have tolerated before.”ConclusionsThe findings of our study show that alert override rates are high, and certain categories of overrides such as drug-drug interaction, renal, and geriatric were classified as inappropriate. Nevertheless, large proportions of drug duplication, drug-allergy, and formulary alerts were appropriate, suggesting that these groups of alerts can be primary targets to revise and update the system for reducing alert fatigue. Future efforts should also focus on optimizing alert types, providing clear information, and explaining the rationale of the alert so that essential alerts are not inappropriately overridden.

Validation of a Smartphone Application for Assessment of the Risk of Venous Thromboembolism in Medical Patients

Objective: To validate a smartphone application-based clinical decision support system (CDSS) for risk assessment of venous thromboembolism (VTE) in hospitalized medical patients. Setting: The medical department of a teaching hospital in Jeddah, Saudi Arabia. Participants: 97 patients, admitted to the medical ward during the month of December 2015. Aim of the study: A smart phone application based CDSS was developed to assess the risk of VTE in medical patients. Patient data were collected and risk of VTE was calculated simultaneously by the application and by an expert hematologist. The aim of the study was to determine agreement between both sources. Results: Ninety-seven patients, mean age 50 (19-92) and 51% (50) males were included. Forty-eight percent of patients were stratified as high-risk score based on CDSS compared to forty-seven percent based on expert opinion. There was a strong agreement between the CDSS and the expert hematologist opinion, on the indication and type of prophylaxis suggested for patients with a high-risk score (Chi square 86.7 and 155, p= 0.000, p=0.000, respectively). There was also an agreement with expert hematologist on the need for thromboprophylaxis as well as the type of prophylaxis (Chi square 86.7 and 1.55 respectively). Conclusion: Smartphone application-based CDSS shows a strong agreement with expert opinion for determination of VTE risk score in medical patients. Thus, the smart-phone based CDSS application has the potential to improve physician compliance with guidelines and reduce inappropriate use of thromboprophylaxis, therefore improving clinical care.

Decision Support in Dermatology and Medicine: History and Recent Developments

This article is focused on diagnostic decision support tools and will provide a brief history of clinical decision support (CDS), examine the components of CDS and its associated terminology, and discuss recent developments in the use and application of CDS systems, particularly in the field of dermatology. For this article, we use CDS to mean an interactive system allowing input of patient-specific information and providing customized medical knowledge-based results via automated reasoning, for example, a set of rules and/or an underlying logic, and associations.

Creating safer decision support systems for paediatric emergency care: the Paeds ED project

IntroductionPaediatric emergency care requires precision in prescribing but this can be prone to error. Traditional ‘aide memoires’ for resuscitation of ill and injured children have limitations. We identified a need...

Development strategy and potential impact on medication safety for clinical rules: the lithium case

Introduction: Prescribing, administration and monitoring of drugs are complex and error sensitive processes. Physicians have to take into account many drug- and patient specific characteristics continuously during therapy. Appropriate use of information technology, especially the introduction of clinical decision support systems (CDSS) can substantially reduce medication error rates and improve patient safety [1, 2]. Strategies for the development of clinical rules used in these CDSS systems are not available. Purpose: The aim of this study was: (1) to evaluate a strategy for the development of clinical rules and (2) to determine the potential impact on medication safety of these clinical rules. Lithium therapy was chosen as a test concept. Methods: For the development of clinical rules we followed a Plan – Do – Check – Act (PDCA) cycle with a panel of experts. Standard literature sources were screened for patient characteristics, monitoring parameters and other characteristics that have to be taken into account during lithium therapy. We translated this information into clinical rules for the CDSS system Gaston®# # Gaston®: P.A.de Clerq, MEDECS b.v. http://www.medecs.nl (plan). These rules were retrospectively tested on patients admitted to the Catharina-hospital Eindhoven in 2004 and 2005 using lithium (do). Signals were checked in the electronic patients record on: (1) appropriateness and (2) the action undertaken by the physician compared to the action warranted by the CDSS. For every rule the Positive Predictive Value (PPV) was determined: number of appropriate signals produced /total number of signals. When the action undertaken was not right according to the clinical rules we scored this as a Potential Adverse Drug Event (PADE) (check). Results were discussed with an expert panel and the clinical rules adjusted according their consensus (act). Results: The PDCA strategy with an expert panel resulted in a package of clinical rules for patients starting with, or using lithium therapy. To design the definite clinical rules the input of the expert team was essential, because literature left to much room for interpretation in clinical practice. The clinical rules were tested on 38 patients starting with and 93 patients using lithium in 2004 or 2005. The PPV’s of these clinical rules varied from 0.2 to 1.0 (mean 0.8). We identified 81 PADE’s in 71 patients, that could have been prevented when the CDSS would have been active. Most important PADE’s were: 1) omitted determination of lithium plasma levels after reduction of creatinin clearance < 50 ml/min (4 times) and starting or stopping of an interacting drug (17 times); 2) determination of lithium, Thyroid Stimulating Hormone (TSH), leucocyte and calcium plasma levels every 3 or 6 months (resp. 17, 6, 2 and 4 times). An important limitation for the creation of higher PPV’s and for the identification of PADE’s was the lack of outpatient data. Further research is needed to show that this system is clinically applicable. Conclusion: A strategy of PDCA combined with an expert panel is a prerequisite for the development of clinical rules for drugs in a CDSS. We demonstrated that a CDSS is able to identify patients with potential adverse drug events. Therefore we conclude that application of CDSS can have a relevant impact on medication safety in clinical practice.