Real Time Clinical Decision Support System

Lin, Hsueh-Chun

doi:10.5772/38141

Cited by 4 publications

(3 citation statements)

References 45 publications

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“…The qualitative results provide two potential mechanisms by which this positive effect might be produced. One is improved communication (Lin, 2012), with patients feeling that their physicians had a better sense of their cases and were more prepared for appointments. The other is an improved sense of patient ability to self-advocate, which corresponded to physician comments about more active patient involvement in care, having the same questionnaire results and graphs available to both parties may have helped to improve this sense of patient self-advocacy (Walker et al, 2011).…”

Section: Impact On Physician-patient Relationshipmentioning

confidence: 99%

A Mixed-Methods Feasibility Study of a Novel AI-Enabled, Web-Based, Clinical Decision Support System for the Treatment of Major Depression in Adults

Qassim

Golden

Slowey

et al. 2022

Preprint

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The objective of this paper is to discuss perceived clinical utility and impact on physician-patient relationship of a novel, artificial-intelligence (AI) enabled clinical decision support system (CDSS) for use in the treatment of adults with major depression. Patients had a baseline appointment, followed by a minimum of two appointments with the CDSS. For both physicians and patients, study exit questionnaires and interviews were conducted to assess perceived clinical utility, impact on patient-physician relationship, and understanding and trust in the CDSS. 17 patients consented to participate in the study, of which 14 completed. 86% of physicians (6/7) felt the information provided by the CDSS provided a more comprehensive understanding of patient situations and 71% (5/7) felt the information was helpful. 86% of physicians (6/7) reported the AI/predictive model was useful when making treatment decisions. 62% of patients (8/13) reported improvement in their care as a result of the tool. 46% of patients (6/13) felt the app significantly or somewhat improved their relationship with their physicians; 54% felt it did not change. 71% of physicians (5/7) and 62% of patients (8/13) rated they trusted the tool. Qualitative results are analyzed and presented. Findings suggest physicians perceived the tool as useful in conducting appointments and used it while making treatment decisions. Physicians and patients generally found the tool trustworthy, and it may have positive effects on physician-patient relationships.

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Section: Impact On Physician-patient Relationshipmentioning

confidence: 99%

A Mixed-Methods Feasibility Study of a Novel AI-Enabled, Web-Based, Clinical Decision Support System for the Treatment of Major Depression in Adults

Qassim

Golden

Slowey

et al. 2022

Preprint

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show abstract

“…In 2012, Lin in his book chapter [11] discussed the state of the art and modern research trends of rt-CDSS; specifically he proposed a web-based rt-CDSS with a full architecture showing all the model-view-controller components. In-depth discussions are reported from process scheduling, system integration, to a full networked infrastructure.…”

Section: Related Workmentioning

confidence: 99%

“…In our previous paper, we proposed a framework of rt-CDSS [10]; Very Fast Decision Tree (VFDT) was adopted as a candidate of a real-time classifier in the system design, because VFDT is classical and the most original type of stream-based classifiers [11]. Successively there are other variants modified from VFDT.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Stream Mining Classifiers for Real-Time Clinical Decision Support System: A Case Study of Blood Glucose Prediction in Diabetes Therapy

Fong¹,

Zhang²,

Fiaidhi

et al. 2013

BioMed Research International

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Earlier on, a conceptual design on the real-time clinical decision support system (rt-CDSS) with data stream mining was proposed and published. The new system is introduced that can analyze medical data streams and can make real-time prediction. This system is based on a stream mining algorithm called VFDT. The VFDT is extended with the capability of using pointers to allow the decision tree to remember the mapping relationship between leaf nodes and the history records. In this paper, which is a sequel to the rt-CDSS design, several popular machine learning algorithms are investigated for their suitability to be a candidate in the implementation of classifier at the rt-CDSS. A classifier essentially needs to accurately map the events inputted to the system into one of the several predefined classes of assessments, such that the rt-CDSS can follow up with the prescribed remedies being recommended to the clinicians. For a real-time system like rt-CDSS, the major technological challenges lie in the capability of the classifier to process, analyze and classify the dynamic input data, quickly and upmost reliably. An experimental comparison is conducted. This paper contributes to the insight of choosing and embedding a stream mining classifier into rt-CDSS with a case study of diabetes therapy.

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Development of a Knowledge-based Clinical Decision Support System for Multiple Sclerosis Diagnosis

2020

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The diagnosis of multiple sclerosis (MS) is difficult considering its complexity, variety in signs and symptoms, and its similarity to the signs and symptoms of other neurological diseases. The purpose of this study is to design and develop a clinical decision support system (CDSS) to help physicians diagnose MS with a relapsing-remitting phenotype. The CDSS software was developed in four stages: requirement analysis, system design, system development, and system evaluation. The Rational Rose and SQL Server were used to design the object-oriented conceptual model and develop the database. The C sharp programming language and the Visual Studio programming environment were used to develop the software. To evaluate the efficiency and applicability of the software, the data of 130 medical records of patients aged 20 to 40 between 2017 and 2019 were used along with the Nilsson standard questionnaire. SPSS Statistics was also used to analyze the data. For MS diagnosis, CDSS had a sensitivity, specificity and accuracy of 1, 0.97 and 0.99, respectively, and the area under the ROC curve was 0.98. The agreement rate of kappa coefficient (κ) between software diagnosis and physician’s diagnosis was 0.98. The average score of software users was 98.33%, 96.65%, and 96.9% regarding the ease of learning, memorability, and satisfaction, respectively. Therefore, the applicability of the CDSS for MS diagnosis was confirmed by the neurologists. The evaluation findings show that CDSS can help physicians in the accurate and timely diagnosis of MS by using the rule-based method.

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Real Time Clinical Decision Support System

Cited by 4 publications

References 45 publications

A Mixed-Methods Feasibility Study of a Novel AI-Enabled, Web-Based, Clinical Decision Support System for the Treatment of Major Depression in Adults

A Mixed-Methods Feasibility Study of a Novel AI-Enabled, Web-Based, Clinical Decision Support System for the Treatment of Major Depression in Adults

Evaluation of Stream Mining Classifiers for Real-Time Clinical Decision Support System: A Case Study of Blood Glucose Prediction in Diabetes Therapy

Development of a Knowledge-based Clinical Decision Support System for Multiple Sclerosis Diagnosis

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