Clinical decision support systems with team-based care on type 2 diabetes improvement for Medicaid patients: A quality improvement project

Zhang, Xiaoni; Svec, Michelle; Tracy, Robert J.; Ozanich, Gary

doi:10.1016/j.ijmedinf.2021.104626

Cited by 8 publications

(11 citation statements)

References 22 publications

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“…Glucose Path, an AI-enabled CDSs for diabetes, effectively reduces the glucose level of patients with poorly controlled diabetes in the Medicaid population. These CDSs facilitate team-based care allowing a cost-effective solution to be produced for patients [13]. GlycASSIST, another diabetes CDS, facilitated treatment intensification and was acceptable to patients with diabetes and general practitioners [14].…”

Section: Discussionmentioning

confidence: 99%

Clinical Decision Support Systems for Diabetes Care: Evidence and Development between 2017 and Present

Zhang¹,

Jiang²,

Ozanich³

2023

Biomedical Engineering

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The clinical decision support systems (CDSs) for diabetes have improved significantly over the years. Multiple factors serve as driving forces for the uptake of CDSs. Newer technologies, initiatives, government mandates, and a competitive environment collectively facilitate advancement in diabetes care. This book chapter summarizes global CDSs development in recent years. Our review of the past few years’ publications on CDSs for diabetes shows that the United States is leading the world in technology development and clinical evidence generation. Developing countries worldwide are catching up in CDSs development and standards of patient care. Though most CDSs and published studies are on diabetes diagnosis, treatment, and management, a small portion of the research is devoted to prediabetes and type I diabetes. Increased efforts worldwide have been devoted to artificial intelligence and machine learning in diabetes care.

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Section: Discussionmentioning

confidence: 99%

Clinical Decision Support Systems for Diabetes Care: Evidence and Development between 2017 and Present

Zhang¹,

Jiang²,

Ozanich³

2023

Biomedical Engineering

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“…As healthcare transitions towards a more patient-centric model, CDSS can significantly improve patient engagement by providing them access to easy-to-understand information, allowing them to be active participants in their care journey. 76 …”

Section: Current State Of Cdssmentioning

confidence: 99%

Harnessing the power of clinical decision support systems: challenges and opportunities

Chen,

Liang,

Zhang

et al. 2023

Open Heart

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Clinical decision support systems (CDSSs) are increasingly integrated into healthcare settings to improve patient outcomes, reduce medical errors and enhance clinical efficiency by providing clinicians with evidence-based recommendations at the point of care. However, the adoption and optimisation of these systems remain a challenge. This review aims to provide an overview of the current state of CDSS, discussing their development, implementation, benefits, limitations and future directions. We also explore the potential for enhancing their effectiveness and provide an outlook for future developments in this field. There are several challenges in CDSS implementation, including data privacy concerns, system integration and clinician acceptance. While CDSS have demonstrated significant potential, their adoption and optimisation remain a challenge.

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“…After removing duplicated publications, 9607 (83%) studies were available for title and abstract screening. Finally, 237 (2.5%) studies were evaluated based on their full text, and 85 (35.9%) studies [23, (including 13/18, 15%, conference abstracts) were selected for analysis. The PRISMA-ScR flow diagram is shown in Figure 1 and the PRISMA-ScR checklist in Multimedia Appendix 2.…”

Section: Search and Selectionmentioning

confidence: 99%

“…Valid studies (n=75, 88%) c [28,30,32,40,43,48,52,59,63,64,69,71,77,80,83,86,97,99,104,109] 20 (27) <6 months [31, 33, 36-39, 41, 45-47, 49-51, 55-57, 61, 62, 65, 68, 74-76,78, 81, 84, 85, 87, 88, 92, 95, 96, 100-102, 107, 108, 111] 38 (51) 6-11 months [23,29,34,35,42,44,53,79,93,98 Unapplicable studies d a In 3 (4%) studies, the study design was a randomized controlled trial (RCT) cross-over; the follow-up period was defined as the overall study duration including the washout period for cross-over studies. b Number of studies and percentages were presented.…”

Section: References Studies N (%) B Characteristic and Categories For...mentioning

confidence: 99%

“…Although CDSSs are a rapidly adopted and emerging technology in the field of diabetes care, some HCPs are still relatively unfamiliar with them in terms of applications in managing and treating diabetes [23]. CDSSs can promote diabetes care by facilitating patient self-management [24] and improving the process of medication management [19].…”

Section: Introductionmentioning

confidence: 99%

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Applications of Clinical Decision Support Systems in Diabetes Care: Scoping Review

Huang,

Liang,

et al. 2023

J Med Internet Res

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Background Providing comprehensive and individualized diabetes care remains a significant challenge in the face of the increasing complexity of diabetes management and a lack of specialized endocrinologists to support diabetes care. Clinical decision support systems (CDSSs) are progressively being used to improve diabetes care, while many health care providers lack awareness and knowledge about CDSSs in diabetes care. A comprehensive analysis of the applications of CDSSs in diabetes care is still lacking. Objective This review aimed to summarize the research landscape, clinical applications, and impact on both patients and physicians of CDSSs in diabetes care. Methods We conducted a scoping review following the Arksey and O’Malley framework. A search was conducted in 7 electronic databases to identify the clinical applications of CDSSs in diabetes care up to June 30, 2022. Additional searches were conducted for conference abstracts from the period of 2021-2022. Two researchers independently performed the screening and data charting processes. Results Of 11,569 retrieved studies, 85 (0.7%) were included for analysis. Research interest is growing in this field, with 45 (53%) of the 85 studies published in the past 5 years. Among the 58 (68%) out of 85 studies disclosing the underlying decision-making mechanism, most CDSSs (44/58, 76%) were knowledge based, while the number of non-knowledge-based systems has been increasing in recent years. Among the 81 (95%) out of 85 studies disclosing application scenarios, the majority of CDSSs were used for treatment recommendation (63/81, 78%). Among the 39 (46%) out of 85 studies disclosing physician user types, primary care physicians (20/39, 51%) were the most common, followed by endocrinologists (15/39, 39%) and nonendocrinology specialists (8/39, 21%). CDSSs significantly improved patients’ blood glucose, blood pressure, and lipid profiles in 71% (45/63), 67% (12/18), and 38% (8/21) of the studies, respectively, with no increase in the risk of hypoglycemia. Conclusions CDSSs are both effective and safe in improving diabetes care, implying that they could be a potentially reliable assistant in diabetes care, especially for physicians with limited experience and patients with limited access to medical resources. International Registered Report Identifier (IRRID) RR2-10.37766/inplasy2022.9.0061

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Clinical decision support systems with team-based care on type 2 diabetes improvement for Medicaid patients: A quality improvement project

Cited by 8 publications

References 22 publications

Clinical Decision Support Systems for Diabetes Care: Evidence and Development between 2017 and Present

Clinical Decision Support Systems for Diabetes Care: Evidence and Development between 2017 and Present

Harnessing the power of clinical decision support systems: challenges and opportunities

Applications of Clinical Decision Support Systems in Diabetes Care: Scoping Review

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