Evaluation of clinical decision support systems for diabetes care: An overview of current evidence

Jia, Pengli; Zhao, Pujing; Chen, Jingjing; Zhang, Mingming

doi:10.1111/jep.12968

Cited by 35 publications

(19 citation statements)

References 36 publications

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“…Both commercially and locally developed CDSSs have been found to be effective at improving healthcare process measures across diverse settings. [ 784 785 ]…”

Section: R Ationale and E Videncementioning

confidence: 99%

RSSDI-ESI clinical practice recommendations for the management of type 2 diabetes mellitus 2020

Chawla

Madhu

Makkar

et al. 2020

Indian J Endocr Metab

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“…Both commercially and locally developed CDSSs have been found to be effective at improving healthcare process measures across diverse settings. [ 784 785 ]…”

Section: R Ationale and E Videncementioning

confidence: 99%

RSSDI-ESI clinical practice recommendations for the management of type 2 diabetes mellitus 2020

Chawla

Madhu

Makkar

et al. 2020

Indian J Endocr Metab

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“…By means of this new technology, data can be automatically collected, transmitted, aggregated with other physiological data, analyzed, stored, and presented to the patient. By integrating e-health and tele-monitoring systems, DSSs for T1DM have the potential to improve glycemic outcomes thanks to prevention of hypo- or hyperglycemic events, reducing uncertainty when making critical self-management decisions [59]. A DSS for diabetes treatment provides an alternative to the closed-loop system, the so-called artificial pancreas (AP).…”

Section: Advanced Applications Based On Real-time Cgmmentioning

confidence: 99%

Continuous Glucose Monitoring Sensors for Diabetes Management: A Review of Technologies and Applications

et al. 2019

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By providing blood glucose (BG) concentration measurements in an almost continuous-time fashion for several consecutive days, wearable minimally-invasive continuous glucose monitoring (CGM) sensors are revolutionizing diabetes management, and are becoming an increasingly adopted technology especially for diabetic individuals requiring insulin administrations. Indeed, by providing glucose real-time insights of BG dynamics and trend, and being equipped with visual and acoustic alarms for hypo- and hyperglycemia, CGM devices have been proved to improve safety and effectiveness of diabetes therapy, reduce hypoglycemia incidence and duration, and decrease glycemic variability. Furthermore, the real-time availability of BG values has been stimulating the realization of new tools to provide patients with decision support to improve insulin dosage tuning and infusion. The aim of this paper is to offer an overview of current literature and future possible developments regarding CGM technologies and applications. In particular, first, we outline the technological evolution of CGM devices through the last 20 years. Then, we discuss about the current use of CGM sensors from patients affected by diabetes, and, we report some works proving the beneficial impact provided by the adoption of CGM. Finally, we review some recent advanced applications for diabetes treatment based on CGM sensors.

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“…This can be attributed to the fact that T2D symptoms may be less marked than other types of diabetes (e.g., Type 1) [1]. Worldwide, approximately 415 million adults suffer from diabetes, and the numbers are projected to rise to 552 million by 2040 [5]. The growth of diabetes prevalence is causing an increasing demand in health care services which affects the clinicians' workload as medical resources do not grow at the same rate as the diabetic population [2].…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, an obvious gap exists between actual clinical practice and optimal patient care. The need for cost-effective improvements in managing and treating diabetes is evidently important [5]. Clinical decision support systems (CDSSs) have been promoted as a promising approach that targets safe and effective diabetes management [5].…”

Section: Introductionmentioning

confidence: 99%

“…The need for cost-effective improvements in managing and treating diabetes is evidently important [5]. Clinical decision support systems (CDSSs) have been promoted as a promising approach that targets safe and effective diabetes management [5]. Decision support tools can help clinicians with the inspection of monitoring data, providing a preliminary analysis to ease their interpretation and reduce the evaluation time per patient [2].…”

Section: Introductionmentioning

confidence: 99%

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Technical Requirement of Clinical Decision Support system for Diabetic Patients

Kiyani

Abasi

Koohjani

et al. 2020

Front Health Inform

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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.

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Evaluation of clinical decision support systems for diabetes care: An overview of current evidence

Cited by 35 publications

References 36 publications

RSSDI-ESI clinical practice recommendations for the management of type 2 diabetes mellitus 2020

RSSDI-ESI clinical practice recommendations for the management of type 2 diabetes mellitus 2020

Continuous Glucose Monitoring Sensors for Diabetes Management: A Review of Technologies and Applications

Technical Requirement of Clinical Decision Support system for Diabetic Patients

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