scholarly journals Technical Requirement of Clinical Decision Support system for Diabetic Patients

2020 ◽  
Vol 9 (1) ◽  
pp. 31
Author(s):  
Shamim Kiyani ◽  
Sanaz Abasi ◽  
Zahra Koohjani ◽  
Azam Aslani
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Support System ◽  
Support Systems ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Diabetic Patients ◽  
Design Metrics

Introduction: Diabetes is a public health problem which is originating an increment in the demand for health services. There is an obvious gap exists between actual clinical practice and optimal patient care, Clinical decision support systems (CDSSs) have been promoted as a promising approach that targets safe and effective diabetes management. The purpose of this article is reviewing diabetes decision support systems based on system design metrics, type and purpose of decision support systems. Materials and Methods: The literature search was performed in peer reviewed journals indexed in PubMed by keywords such as medical decision making, clinical decision support systems, Reminder systems, diabetes, interface, interaction, information to 2019. This article review the diabetes decision support systems based on system design metrics (interface, interaction, and information), type and purpose of decision support system. Results: 32 of the 35 articles were decision support systems that provided specific warnings, reminders, a set of physician guidelines, or other recommendations for direct action. The most important decisions of the systems were support for blood glucose control and insulin dose adjustment, as well as 13 warning and reminder articles. Of the 35 articles, there were 21 user interface items (such as simplicity, readability, font sizes and ect), 23 interaction items (such as Fit, use selection tools, facilitate ease of use and ect. ) and 31 item information items (such as Content guidance, diagnostic support and concise and ect ).Discussion: This study identified important aspects of designing decision support system, It can be applied not only to diabetic patients but also to other decision support systems.Conclusion: Most decision support systems take into account a number of design criteria; system designers can look at design aspects to improve the efficiency of these systems. Decision support system evaluation models can also be added to the factors under consideration.

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.

Methodologies of Legacy Clinical Decision Support System

2020 ◽  
pp. 553-568
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.

Clinical Decision Support Systems

2021 ◽  
pp. 64-73
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Medical Informatics ◽  
Clinical Decision Support ◽  
Support System ◽  
Support Systems ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Health Decision ◽  
Software Development Methodologies

The BACIS program is an example of an e-health decision support system, and therefore a chapter focusing on the topic of decision support systems is needed as part of the background and context to the BACIS program study. The chapter begins with a discussion of the design of decision support systems. In this discussion, the software development methodologies used in their development is explained. Then various architectures for their design are considered. This is followed by a section on implementation of decision support systems in developing country contexts. The chapter closes with a discussion of the Clinical Decision Support (CDS) roadmap of the International Medical Informatics Association.

scholarly journals Determining attitude and readiness of Medical students in Kerman University of Medical Sciences concerning acceptance of Decision Support Systems for Electronic Prescription in 2017

2017 ◽  
Vol 1 (4) ◽  
pp. 113-114
Author(s):  
Reza Khajouei Khajouei ◽  
Maryam Eslami Jahromi ◽  
Fatemeh Tabatabaei
Keyword(s):  
Decision Support ◽  
Medical Students ◽  
Decision Support System ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Support System ◽  
Support Systems ◽  
Clinical Decision ◽  
Medical Sciences ◽  
Electronic Prescription

Introduction: Clinical Decision Support Systems (CDSS) are any computer program designed to assist health care professionals in making clinical decisions, especially at the moment of action. These information systems are flexible and interactive, helping people by making use of models and rules of a comprehensive database, or on the basis of clinical guidelines, to make applicable decisions, especially solving difficult problems with common scientific methods. Therefore, these systems support complex medical decisions, increase their effectiveness, and can lead to a reduction in the types of medical errors, such as medication errors. However, knowledge and attitude of the future physicians in the country about these systems is unclear. The purpose of this study was Determining attitude and readiness of Medical students concerning acceptance of Decision Support Systems for Electronic Prescription. Methods: This research is descriptive-analytic. The research population included general medical students of Kerman University of Medical Sciences in 2017. Out of these students, a sample of 226 people was selected by proportional sampling. Data collection was done using a standard questionnaire. The questionnaire was first translated into Persian and then its validity was confirmed by three medical informatics specialists. Data analysis was done descriptively-analytically using SPSS 19. Results: The findings of this study showed that 17% of students (n=36) knew about this system and how to use it, and 83% of them (n=177) were willing to learn and use this system to practice their profession in the future. More than half of them (n=121) believed that using this system would Reduce errors in prescribing. 80% of them (n=170) lacked a strategic plan to implement a decision support system for electronic prescription and a lack of standard and acceptable software, and about 60% (n=128) lack of financial incentives and lack of sufficient evidence Concerned about the usefulness of using it as a potential obstacle to the implementation of this system. There was no significant difference between the age, sex and term of the students with their familiarity with this system (P>0.05). Conclusion: The results of this study showed that while many students lack sufficient knowledge about the clinical decision support system, they are interested in learning and using it, especially in the field of prescribing drugs. This result indicates that the system is likely to be admitted if it is implemented and educated to medical students and doctors.

scholarly journals The Effects of Computerized Clinical Decision Support Systems on Laboratory Test Ordering: A Systematic Review

2017 ◽  
Vol 141 (4) ◽  
pp. 585-595 ◽  
Author(s):  
Nicolas Delvaux ◽  
Katrien Van Thienen ◽  
Annemie Heselmans ◽  
Stijn Van de Velde ◽  
Dirk Ramaekers ◽  
...  
Keyword(s):  
Decision Support ◽  
Laboratory Test ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Support System ◽  
Clinical Decision Support System ◽  
Support Systems ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Test Ordering

Context.— Inappropriate laboratory test ordering has been shown to be as high as 30%. This can have an important impact on quality of care and costs because of downstream consequences such as additional diagnostics, repeat testing, imaging, prescriptions, surgeries, or hospital stays. Objective.— To evaluate the effect of computerized clinical decision support systems on appropriateness of laboratory test ordering. Data Sources.— We used MEDLINE, Embase, CINAHL, MEDLINE In-Process and Other Non-Indexed Citations, Clinicaltrials.gov, Cochrane Library, and Inspec through December 2015. Investigators independently screened articles to identify randomized trials that assessed a computerized clinical decision support system aimed at improving laboratory test ordering by providing patient-specific information, delivered in the form of an on-screen management option, reminder, or suggestion through a computerized physician order entry using a rule-based or algorithm-based system relying on an evidence-based knowledge resource. Investigators extracted data from 30 papers about study design, various study characteristics, study setting, various intervention characteristics, involvement of the software developers in the evaluation of the computerized clinical decision support system, outcome types, and various outcome characteristics. Conclusions.— Because of heterogeneity of systems and settings, pooled estimates of effect could not be made. Data showed that computerized clinical decision support systems had little or no effect on clinical outcomes but some effect on compliance. Computerized clinical decision support systems targeted at laboratory test ordering for multiple conditions appear to be more effective than those targeted at a single condition.

scholarly journals Effect of clinical decision support systems on clinical outcome for acute kidney injury: a systematic review and meta-analysis

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Youlu Zhao ◽  
Xizi Zheng ◽  
Jinwei Wang ◽  
Damin Xu ◽  
Shuangling Li ◽  
...  
Keyword(s):  
Acute Kidney Injury ◽  
Decision Support ◽  
Decision Support System ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Support System ◽  
Clinical Decision Support System ◽  
Kidney Injury ◽  
Clinical Decision Support Systems ◽  
Clinical Decision

Abstract Background Clinical decision support systems including both electronic alerts and care bundles have been developed for hospitalized patients with acute kidney injury. Methods Electronic databases were searched for randomized, before-after and cohort studies that implemented a clinical decision support system for hospitalized patients with acute kidney injury between 1990 and 2019. The studies must describe their impact on care processes, patient-related outcomes, or hospital length of stay. The clinical decision support system included both electronic alerts and care bundles. Results We identified seven studies involving 32,846 participants. Clinical decision support system implementation significantly reduced mortality (OR 0.86; 95 % CI, 0.75–0.99; p = 0.040, I2 = 65.3 %; n = 5 studies; N = 30,791 participants) and increased the proportion of acute kidney injury recognition (OR 3.12; 95 % CI, 2.37–4.10; p < 0.001, I2 = 77.1 %; n = 2 studies; N = 25,121 participants), and investigations (OR 3.07; 95 % CI, 2.91–3.24; p < 0.001, I2 = 0.0 %; n = 2 studies; N = 25,121 participants). Conclusions Nonrandomized controlled trials of clinical decision support systems for acute kidney injury have yielded evidence of improved patient-centered outcomes and care processes. This review is limited by the low number of randomized trials and the relatively short follow-up period.

scholarly journals Towards a Knowledge Graph-Based Explainable Decision Support System in Healthcare

2021 ◽  
Author(s):  
Enayat Rajabi ◽  
Kobra Etminani
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Support System ◽  
Support Systems ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Knowledge Graph ◽  
Explainable Ai ◽  
Knowledge Graphs

The decisions derived from AI-based clinical decision support systems should be explainable and transparent so that the healthcare professionals can understand the rationale behind the predictions. To improve the explanations, knowledge graphs are a well-suited choice to be integrated into eXplainable AI. In this paper, we introduce a knowledge graph-based explainable framework for AI-based clinical decision support systems to increase their level of explainability.

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.

A Clinical Decision Support System for Assessing the Risk of Cardiovascular Diseases in Diabetic Hemodialysis Patients

2020 ◽  
Vol 16 (3) ◽  
pp. 262-269
Author(s):  
Tahere Talebi Azad Boni ◽  
Haleh Ayatollahi ◽  
Mostafa Langarizadeh
Keyword(s):  
Decision Support ◽  
Cardiovascular Diseases ◽  
Decision Support System ◽  
Clinical Decision Support ◽  
Support System ◽  
Clinical Decision Support System ◽  
Clinical Decision ◽  
Diabetic Patients ◽  
K Value ◽  
Care Plans

Background: One of the greatest challenges in the field of medicine is the increasing burden of chronic diseases, such as diabetes. Diabetes may cause several complications, such as kidney failure which is followed by hemodialysis and an increasing risk of cardiovascular diseases. Objective: The purpose of this research was to develop a clinical decision support system for assessing the risk of cardiovascular diseases in diabetic patients undergoing hemodialysis by using a fuzzy logic approach. Methods: This study was conducted in 2018. Initially, the views of physicians on the importance of assessment parameters were determined by using a questionnaire. The face and content validity of the questionnaire was approved by the experts in the field of medicine. The reliability of the questionnaire was calculated by using the test-retest method (r = 0.89). This system was designed and implemented by using MATLAB software. Then, it was evaluated by using the medical records of diabetic patients undergoing hemodialysis (n=208). Results: According to the physicians' point of view, the most important parameters for assessing the risk of cardiovascular diseases were glomerular filtration, duration of diabetes, age, blood pressure, type of diabetes, body mass index, smoking, and C reactive protein. The system was designed and the evaluation results showed that the values of sensitivity, accuracy, and validity were 85%, 92% and 90%, respectively. The K-value was 0.62. Conclusion: The results of the system were largely similar to the patients’ records and showed that the designed system can be used to help physicians to assess the risk of cardiovascular diseases and to improve the quality of care services for diabetic patients undergoing hemodialysis. By predicting the risk of the disease and classifying patients in different risk groups, it is possible to provide them with better care plans.

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