Policy Implications of Artificial Intelligence and Machine Learning in Diabetes Management

Broome, David T.; Hilton, Cameron Beau; Mehta, Neil

doi:10.1007/s11892-020-1287-2

Cited by 23 publications

(13 citation statements)

References 16 publications

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“…Machine learning has been used to study precision medicine in diabetes care and complications, variables to predict the development of diabetes, and individual characteristics related to diabetes outcomes [ 20 - 22 ]. However, there is a lack of evidence around practical solutions for real-world implementation, specifically around diabetes self-management behaviors, which are the foundation of successfully living with the chronic condition [ 23 ]. It has been well-established that diabetes management programs are effective in helping participants obtain glycemic control, improve HbA 1c values, and increase self-efficacy with the support of diabetes coaches and structured SMBG [ 2 , 5 , 7 - 9 ].…”

Section: Discussionmentioning

confidence: 99%

Improving Outcomes Through Personalized Recommendations in a Remote Diabetes Monitoring Program: Observational Study

Kamath¹,

Kappaganthu²,

Painter³

et al. 2022

JMIR Form Res

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Background Diabetes management is complex, and program personalization has been identified to enhance engagement and clinical outcomes in diabetes management programs. However, 50% of individuals living with diabetes are unable to achieve glycemic control, presenting a gap in the delivery of self-management education and behavior change. Machine learning and recommender systems, which have been used within the health care setting, could be a feasible application for diabetes management programs to provide a personalized user experience and improve user engagement and outcomes. Objective This study aims to evaluate machine learning models using member-level engagements to predict improvement in estimated A1c and develop personalized action recommendations within a remote diabetes monitoring program to improve clinical outcomes. Methods A retrospective study of Livongo for Diabetes member engagement data was analyzed within five action categories (interacting with a coach, reading education content, self-monitoring blood glucose level, tracking physical activity, and monitoring nutrition) to build a member-level model to predict if a specific type and level of engagement could lead to improved estimated A1c for members with type 2 diabetes. Engagement and improvement in estimated A1c can be correlated; therefore, the doubly robust learning method was used to model the heterogeneous treatment effect of action engagement on improvements in estimated A1c. Results The treatment effect was successfully computed within the five action categories on estimated A1c reduction for each member. Results show interaction with coaches and self-monitoring blood glucose levels were the actions that resulted in the highest average decrease in estimated A1c (1.7% and 1.4%, respectively) and were the most recommended actions for 54% of the population. However, these were found to not be the optimal interventions for all members; 46% of members were predicted to have better outcomes with one of the other three interventions. Members who engaged with their recommended actions had on average a 0.8% larger reduction in estimated A1c than those who did not engage in recommended actions within the first 3 months of the program. Conclusions Personalized action recommendations using heterogeneous treatment effects to compute the impact of member actions can reduce estimated A1c and be a valuable tool for diabetes management programs in encouraging members toward actions to improve clinical outcomes.

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Section: Discussionmentioning

confidence: 99%

Improving Outcomes Through Personalized Recommendations in a Remote Diabetes Monitoring Program: Observational Study

Kamath¹,

Kappaganthu²,

Painter³

et al. 2022

JMIR Form Res

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“…Aspects that have not yet been clarified, such as changes in ML-related software over time due to changing datasets, should be of particular interest. In the literature, suggestions are increasingly being submitted and discussed [ 30 , 72 ], both on general regulatory aspects [ 29 , 73 , 74 ] and on device- or subject-specific features, e.g., in view of medical imaging [ 75 , 76 ].…”

Section: Discussionmentioning

confidence: 99%

Rise of Clinical Studies in the Field of Machine Learning: A Review of Data Registered in ClinicalTrials.gov

Zippel

Bohnet-Joschko

2021

IJERPH

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Although advances in machine-learning healthcare applications promise great potential for innovative medical care, few data are available on the translational status of these new technologies. We aimed to provide a comprehensive characterization of the development and status quo of clinical studies in the field of machine learning. For this purpose, we performed a registry-based analysis of machine-learning-related studies that were published and first available in the ClinicalTrials.gov database until 2020, using the database’s study classification. In total, n = 358 eligible studies could be included in the analysis. Of these, 82% were initiated by academic institutions/university (hospitals) and 18% by industry sponsors. A total of 96% were national and 4% international. About half of the studies (47%) had at least one recruiting location in a country in North America, followed by Europe (37%) and Asia (15%). Most of the studies reported were initiated in the medical field of imaging (12%), followed by cardiology, psychiatry, anesthesia/intensive care medicine (all 11%) and neurology (10%). Although the majority of the clinical studies were still initiated in an academic research context, the first industry-financed projects on machine-learning-based algorithms are becoming visible. The number of clinical studies with machine-learning-related applications and the variety of medical challenges addressed serve to indicate their increasing importance in future clinical care. Finally, they also set a time frame for the adjustment of medical device-related regulation and governance.

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“…Within the field of diabetes, machine learning has been implemented broadly to improve detection of diabetes diagnosis [ 15 ] and complications [ 16 ], cardiovascular risk stratification [ 17 ], and insulin dosing [ 18 ]. An early application of machine learning in diabetes was a neural network model that achieved a sensitivity and specificity of 0.76 in predicting the onset of diabetes [ 19 ].…”

Section: Machine Learning Overviewmentioning

confidence: 99%

Machine Learning Models for Inpatient Glucose Prediction

Zale

Mathioudakis

2022

Curr Diab Rep

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Purpose of Review Glucose management in the hospital is difficult due to non-static factors such as antihyperglycemic and steroid doses, renal function, infection, surgical status, and diet. Given these complex and dynamic factors, machine learning approaches can be leveraged for prediction of glucose trends in the hospital to mitigate and prevent suboptimal hypoglycemic and hyperglycemic outcomes. Our aim was to review the clinical evidence for the role of machine learning–based models in predicting hospitalized patients’ glucose trajectory. Recent Findings The published literature on machine learning algorithms has varied in terms of population studied, outcomes of interest, and validation methods. There have been tools developed that utilize data from both continuous glucose monitors and large electronic health records (EHRs). With increasing sample sizes, inclusion of a greater number of predictor variables, and use of more advanced machine learning algorithms, there has been a trend in recent years towards increasing predictive accuracy for glycemic outcomes in the hospital setting. While current models predicting glucose trajectory offer promising results, they have not been tested prospectively in the clinical setting. Summary Accurate machine learning algorithms have been developed and validated for prediction of hypoglycemia and hyperglycemia in the hospital. Further work is needed in implementation/integration of machine learning models into EHR systems, with prospective studies to evaluate effectiveness and safety of such clinical decision support on glycemic and other clinical outcomes.

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Policy Implications of Artificial Intelligence and Machine Learning in Diabetes Management

Cited by 23 publications

References 16 publications

Improving Outcomes Through Personalized Recommendations in a Remote Diabetes Monitoring Program: Observational Study

Improving Outcomes Through Personalized Recommendations in a Remote Diabetes Monitoring Program: Observational Study

Rise of Clinical Studies in the Field of Machine Learning: A Review of Data Registered in ClinicalTrials.gov

Machine Learning Models for Inpatient Glucose Prediction

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