Outpatient diabetes clinical decision support: current status and future directions

Clinical decision support systems for diabetic foot ulcers: a scoping review

OBJECTIVE

Map the scientific evidence on the use of clinical decision support systems in diabetic foot care.

METHOD

A scoping review based on the JBI Manual for Evidence Synthesis and registered on the Open Science Framework platform. Searches were carried out in primary and secondary sources on prototypes and computerized tools aimed at assisting patients with diabetic foot or at risk of having it, published in any language or period, in eleven databases and grey literature.

RESULTS

A total of 710 studies were identified and, following the eligibility criteria, 23 were selected, which portrayed the use of decision support systems in diabetic foot screening, predicting the risk of ulcers and amputations, classifying the stage of severity, deciding on the treatment plan, and evaluating the effectiveness of interventions, by processing data relating to clinical and sociodemographic information.

CONCLUSION

Expert systems stand out for their satisfactory results, with high precision and sensitivity when it comes to guiding and qualifying the decision-making process in diabetic foot prevention and care.

Diabetes and artificial intelligence beyond the closed loop: a review of the landscape, promise and challenges

The discourse amongst diabetes specialists and academics regarding technology and artificial intelligence (AI) typically centres around the 10% of people with diabetes who have type 1 diabetes, focusing on glucose sensors, insulin pumps and, increasingly, closed-loop systems. This focus is reflected in conference topics, strategy documents, technology appraisals and funding streams. What is often overlooked is the wider application of data and AI, as demonstrated through published literature and emerging marketplace products, that offers promising avenues for enhanced clinical care, health-service efficiency and cost-effectiveness. This review provides an overview of AI techniques and explores the use and potential of AI and data-driven systems in a broad context, covering all diabetes types, encompassing: (1) patient education and self-management; (2) clinical decision support systems and predictive analytics, including diagnostic support, treatment and screening advice, complications prediction; and (3) the use of multimodal data, such as imaging or genetic data. The review provides a perspective on how data- and AI-driven systems could transform diabetes care in the coming years and how they could be integrated into daily clinical practice. We discuss evidence for benefits and potential harms, and consider existing barriers to scalable adoption, including challenges related to data availability and exchange, health inequality, clinician hesitancy and regulation. Stakeholders, including clinicians, academics, commissioners, policymakers and those with lived experience, must proactively collaborate to realise the potential benefits that AI-supported diabetes care could bring, whilst mitigating risk and navigating the challenges along the way.

1. Improving Care and Promoting Health in Populations: Standards of Care in Diabetes-2024

The American Diabetes Association (ADA) "Standards of Care in Diabetes" includes the ADA's current clinical practice recommendations and is intended to provide the components of diabetes care, general treatment goals and guidelines, and tools to evaluate quality of care. Members of the ADA Professional Practice Committee, a interprofessional expert committee, are responsible for updating the Standards of Care annually, or more frequently as warranted. For a detailed description of ADA standards, statements, and reports, as well as the evidence-grading system for ADA's clinical practice recommendations and a full list of Professional Practice Committee members, please refer to Introduction and Methodology. Readers who wish to comment on the Standards of Care are invited to do so at https://professional.diabetes.org/SOC.

Individualized Models for Glucose Prediction in Type 1 Diabetes: Comparing Black-Box Approaches to a Physiological White-Box One

OBJECTIVE

Accurate blood glucose (BG) prediction are key in next-generation tools for type 1 diabetes (T1D) management, such as improved decision support systems and advanced closed-loop control. Glucose prediction algorithms commonly rely on black-box models. Large physiological models, successfully adopted for simulation, were little explored for glucose prediction, mostly because their parameters are hard to individualize. In this work, we develop a BG prediction algorithm based on a personalized physiological model inspired by the UVA/Padova T1D Simulator. Then we compare white-box and advanced black-box personalized prediction techniques.

METHODS

A personalized nonlinear physiological model is identified from patient data through a Bayesian approach based on Markov Chain Monte Carlo technique. The individualized model was integrated within a particle filter (PF) to predict future BG concentrations. The black-box methodologies considered are non-parametric models estimated via gaussian regression (NP), three deep learning methods: long-short-term-memory (LSTM), gated recurrent unit (GRU), temporal convolutional networks (TCN), and a recursive autoregressive with exogenous input model (rARX). BG forecasting performances are assessed for several prediction horizons (PH) on 12 individuals with T1D, monitored in free-living conditions under open-loop therapy for 10 weeks.

RESULTS

NP models provide the most effective BG predictions by achieving a root mean square error (RMSE), RMSE = 18.99 mg/dL, RMSE = 25.72 mg/dL and RMSE = 31.60 mg/dL, significantly outperforming: LSTM, GRU (for PH = 30 minutes), TCN, rARX, and the proposed physiological model for PH=30, 45 and 60 minutes.

CONCLUSIONS

Black-box strategies remain preferable for glucose prediction even when compared to a white-box model with sound physiological structure and individualized parameters.

The Diabetes-Heart Disease Connection: Recent Discoveries and Implications

Diabetes and heart disease are 2 prevalent and interconnected conditions with a significant global burden. Understanding the relationship between diabetes and heart disease is crucial for effective management and prevention strategies. This article provides an overview of the 2 conditions, highlighting their types, risk factors, and global prevalence. Recent research findings establish a strong correlation between diabetes and various aspects of cardiovascular health, including coronary artery disease, heart failure, and stroke. Mechanisms such as insulin resistance, inflammation, and oxidative stress contribute to the interplay between diabetes and heart disease. The implications for clinical practice underscore the importance of early detection, risk assessment, and comprehensive management of both conditions. Lifestyle modifications, such as diet, exercise, and weight management, are essential interventions. Pharmacological interventions, including antidiabetic drugs and cardiovascular medications, play a crucial role in treatment. Managing diabetes and heart disease simultaneously poses challenges that require interdisciplinary collaboration among endocrinologists, cardiologists, and primary care physicians. Ongoing research explores personalized medicine and targeted therapies as potential future directions. Continued research and awareness are paramount to mitigate the impact of the diabetes-heart disease connection and improve patient outcomes.

Evaluating the costs and consequences of computerized clinical decision support systems in hospitals: a scoping review and recommendations for future practice

OBJECTIVE

Sustainable investment in computerized decision support systems (CDSS) requires robust evaluation of their economic impacts compared with current clinical workflows. We reviewed current approaches used to evaluate the costs and consequences of CDSS in hospital settings and presented recommendations to improve the generalizability of future evaluations.

MATERIALS AND METHODS

A scoping review of peer-reviewed research articles published since 2010. Searches were completed in the PubMed, Ovid Medline, Embase, and Scopus databases (last searched February 14, 2023). All studies reported the costs and consequences of a CDSS-based intervention compared with current hospital workflows. Findings were summarized using narrative synthesis. Individual studies were further appraised against the Consolidated Health Economic Evaluation and Reporting (CHEERS) 2022 checklist.

RESULTS

Twenty-nine studies published since 2010 were included. Studies evaluated CDSS for adverse event surveillance (5 studies), antimicrobial stewardship (4 studies), blood product management (8 studies), laboratory testing (7 studies), and medication safety (5 studies). All studies evaluated costs from a hospital perspective but varied based on the valuation of resources affected by CDSS implementation, and the measurement of consequences. We recommend future studies follow guidance from the CHEERS checklist; use study designs that adjust for confounders; consider both the costs of CDSS implementation and adherence; evaluate consequences that are directly or indirectly affected by CDSS-initiated behavior change; examine the impacts of uncertainty and differences in outcomes across patient subgroups.

DISCUSSION AND CONCLUSION

Improving consistency in the conduct and reporting of evaluations will enable detailed comparisons between promising initiatives, and their subsequent uptake by decision-makers.

Effect of an Automated Advice Algorithm (CloudConnect) on Adolescent-Parent Diabetes-Specific Communication and Glycemic Management: A Randomized Trial

INTRODUCTION

Because adolescence is a time of difficult management of Type 1 diabetes (T1D) in part from adolescent-parent shared responsibility of T1D management, our objective was to assess the effects of a decision support system (DSS) CloudConnect on T1D-related communication between adolescents and their parents and on glycemic management.

METHODS

We followed 86 participants including 43 adolescents with T1D (not on automated insulin delivery systems, AID) and their parents/care-giver for a 12-week intervention of UsualCare + CGM or CloudConnect, which included a Weekly Report of automated T1D advice, including insulin dose adjustments, based on data from continuous glucose monitors (CGM), Fitbit and insulin use. Primary outcome was T1D-specific communication and secondary outcomes were hemoglobin A1c, time-in-target range (TIR) 70-180 mg/dl, and additional psychosocial scales.

RESULTS

Adolescents and parents reported a similar amount of T1D-related communication in both the UsualCare + CGM or CloudConnect groups and had similar levels of final HbA1c. Overall blood glucose time in range 70-180 mg/dl and time below 70 mg/dl were not different between groups. Parents but not children in the CloudConnect group reported less T1D-related conflict; however, compared to the UsualCare + CGM group, adolescents and parents in the CloudConnect reported a more negative tone of T1D-related communication. Adolescent-parent pairs in the CloudConnect group reported more frequent changes in insulin dose. There were no differences in T1D quality of life between groups.

CONCLUSIONS

While feasible, the CloudConnect DSS system did not increase T1D communication or provide improvements in glycemic management. Further efforts are needed to improve T1D management in adolescents with T1D not on AID systems.

1. Improving Care and Promoting Health in Populations: Standards of Care in Diabetes-2023

The American Diabetes Association (ADA) "Standards of Care in Diabetes" includes the ADA's current clinical practice recommendations and is intended to provide the components of diabetes care, general treatment goals and guidelines, and tools to evaluate quality of care. Members of the ADA Professional Practice Committee, a multidisciplinary expert committee, are responsible for updating the Standards of Care annually, or more frequently as warranted. For a detailed description of ADA standards, statements, and reports, as well as the evidence-grading system for ADA's clinical practice recommendations and a full list of Professional Practice Committee members, please refer to Introduction and Methodology. Readers who wish to comment on the Standards of Care are invited to do so at professional.diabetes.org/SOC.

Development of a novel clinical decision support tool for diabetes prevention and feasibility of its implementation in primary care

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Clinical decision support may represent a strategy for promoting diabetes prevention in primary care.

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We developed a novel clinical decision support tool with input from primary care providers.

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This clinician-facing tool was associated with improvements in processes of prediabetes care.

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Exploratory analyses found small, but nonsignificant weight loss associated with its use.

Prediabetes impacts 88 million U.S. adults, yet uptake of evidence-based treatment with intensive lifestyle interventions and metformin remains exceedingly low. After incorporating feedback from 15 primary care providers collected during semi-structured interviews, we developed a novel Prediabetes Clinical Decision Support (PreDM CDS) from August 2019 to February 2020. This tool included order options enabling prediabetes management in a single location within the electronic health record. We conducted a retrospective observational study examining the feasibility of implementing this tool at Erie Family Health Centers, a large community health center, examining its use and related outcomes among patients for whom it was used vs not. Overall, 7,424 eligible patients were seen during the implementation period (February 2020 to August 2021), and the PreDM CDS was used for 108 (1.5 %). Using the PreDM CDS was associated with higher rates of hemoglobin A1c orders (70.4 % vs 22.2 %; p < 0.001), lifestyle counseling (38.0 % vs 7.8 %; p < 0.001), and metformin prescription orders (5.6 % vs 2.6 %; p = 0.06). Exploratory analyses revealed small, nonsignificant weight loss among patients for whom the PreDM CDS was used. This study demonstrates the feasibility of developing and implementing the PreDM CDS in primary care. Its low use was likely related to not imposing an interruptive ‘pop-up’ alert, as well as major changes in workflows and clinical priorities during the Covid-19 pandemic. Use of the tool was associated with improved process outcomes. Future efforts with the PreDM CDS should follow standard CDS implementation processes that were not possible due to the Covid-19 pandemic.

Patient-Tailored Decision Support System Improves Short- and Long-Term Glycemic Control in Type 2 Diabetes

BACKGROUND

The increasing prevalence of type 2 diabetes mellitus (T2D) and specialist shortage has caused a healthcare gap that can be bridged by a decision support system (DSS). We investigated whether a diabetes DSS can improve long- and/or short-term glycemic control.

METHODS

This is a retrospective observational cohort study of the Diabetiva program, which offered a patient-tailored DSS using Karlsburger Diabetes-Management System (KADIS) once a year. Glycemic control was analyzed at baseline and after 12 months in 452 individuals with T2D. Time in range (TIR; glucose 3.9-10 mmol/L) and Q-Score, a composite metric developed for analysis of continuous glucose profiles, were short-term and HbA1c long-term measures of glycemic control. Glucose variability (GV) was also measured.

RESULTS

At baseline, one-third of patients had good short- and long-term glycemic control. Q-Score identified insufficient short-term glycemic control in 17.9% of patients with HbA1c <6.5%, mainly due to hypoglycemia. GV and hyperglycemia were responsible in patients with HbA1c >7.5% and >8%, respectively. Application of DSS at baseline improved short- and long-term glycemic control, as shown by the reduced Q-Score, GV, and HbA1c after 12 months. Multiple regression demonstrated that the total effect on GV resulted from the single effects of all influential parameters.

CONCLUSIONS

DSS can improve short- and long-term glycemic control in individuals with T2D without increasing hypoglycemia. The Q-Score allows identification of individuals with insufficient glycemic control. An effective strategy for therapy optimization could be the selection of individuals with T2D most at need using the Q-Score, followed by offering patient-tailored DSS.

Are Clinicians Aggressive Enough in Treating Diabetes-Related Hyperlipidemia in Youth?

PURPOSE OF REVIEW

Cardiovascular disease is the leading cause of death in patients with type 1 diabetes (T1D) and type 2 diabetes (T2D). Subclinical atherosclerotic changes are noted in youth with diabetes; therefore, timely identification and management of modifiable cardiovascular risk factors including hyperlipidemia is crucial. We review the current guidelines for hyperlipidemia screening and treatment in youth with T1D and T2D. We discuss the efficacy of non-pharmacological strategies including dietary modifications, exercise, and glycemic control and pharmacological therapy. We summarize reported rates of treatment of diabetes-related hyperlipidemia in youth.

RECENT FINDINGS

Hyperlipidemia is prevalent among youth with T1D and T2D. Vast majority of youth with diabetes-related hyperlipidemia do not receive lipid-lowering treatments. There are several factors that contribute to suboptimal management of hyperlipidemia in youth with diabetes including limited data on efficacy and safety of statins in youth with diabetes. We propose strategies to improve hyperlipidemia management including education of providers and patients, quality improvement methods, and electronic health record alerts. Additionally, further studies are warranted to examine the safety of statins in youth with diabetes, cost-benefit analysis to aggressive screening and treatment, and long-term effect for improving cardiovascular morbidity and mortality.

Developing a Data Quality Standard Primer for Cardiovascular Risk Assessment from Electronic Health Record Data Using the DataGauge Process

The learning health systems aim to support the needs of patients with chronic diseases, which require methods that account for electronic health recorded (EHR) data limitations. EHR data is often used to calculate cardiovascular risk scores. However, it is unclear whether EHR data presents high enough quality to provide accurate estimates. Still, there is currently no open standard available to assess data quality for such applications. We applied the DataGauge process to develop a data quality standard based on expert clinical, analytical and informatics knowledge by conducting four interviews and one focus group that produced 61 individual data quality requirements. These requirements covered all standard data quality dimensions and uncovered 705 quality issues in EHR data for 456 patients. These requirements will be expanded and further validated in future work. Our work initiates the development of open and explicit data quality standards for specific secondary uses of clinical data.

1. Improving Care and Promoting Health in Populations: Standards of Medical Care in Diabetes-2022

The American Diabetes Association (ADA) "Standards of Medical Care in Diabetes" includes the ADA's current clinical practice recommendations and is intended to provide the components of diabetes care, general treatment goals and guidelines, and tools to evaluate quality of care. Members of the ADA Professional Practice Committee, a multidisciplinary expert committee (https://doi.org/10.2337/dc22-SPPC), are responsible for updating the Standards of Care annually, or more frequently as warranted. For a detailed description of ADA standards, statements, and reports, as well as the evidence-grading system for ADA's clinical practice recommendations, please refer to the Standards of Care Introduction (https://doi.org/10.2337/dc22-SINT). Readers who wish to comment on the Standards of Care are invited to do so at professional.diabetes.org/SOC.

Impact of a Novel Diabetes Support System on a Cohort of Individuals With Type 1 Diabetes Treated With Multiple Daily Injections: A Multicenter Randomized Study

OBJECTIVE

Achieving optimal glycemic control for many individuals with type 1 diabetes (T1D) remains challenging, even with the advent of newer management tools, including continuous glucose monitoring (CGM). Modern management of T1D generates a wealth of data; however, use of these data to optimize glycemic control remains limited. We evaluated the impact of a CGM-based decision support system (DSS) in patients with T1D using multiple daily injections (MDI).

RESEARCH DESIGN AND METHODS

The studied DSS included real-time dosing advice and retrospective therapy optimization. Adults and adolescents (age >15 years) with T1D using MDI were enrolled at three sites in a 14-week randomized controlled trial of MDI + CGM + DSS versus MDI + CGM. All participants (N = 80) used degludec basal insulin and Dexcom G5 CGM. CGM-based and patient-reported outcomes were analyzed. Within the DSS group, ad hoc analysis further contrasted active versus nonactive DSS users.

RESULTS

No significant differences were detected between experimental and control groups (e.g., time in range [TIR] +3.3% with CGM vs. +4.4% with DSS). Participants in both groups reported lower HbA1c (-0.3%; P = 0.001) with respect to baseline. While TIR may have improved in both groups, it was statistically significant only for DSS; the same was apparent for time spent <60 mg/dL. Active versus nonactive DSS users showed lower risk of and exposure to hypoglycemia with system use.

CONCLUSIONS

Our DSS seems to be a feasible option for individuals using MDI, although the glycemic benefits associated with use need to be further investigated. System design, therapy requirements, and target population should be further refined prior to use in clinical care.

Implementation of App-Based Diabetes Medication Management: Outpatient and Perioperative Clinical Decision Support

PURPOSE OF REVIEW

Outpatient and perioperative therapeutic decision making for patients with diabetes involves increasingly complex medical-decision making due to rapid advances in knowledge and treatment modalities. We sought to review mobile decision support tools available to clinicians for this essential and increasingly difficult task, and to highlight the development and implementation of novel mobile applications for these purposes.

RECENT FINDINGS

We found 211 mobile applications related to diabetes from the search, but only five were found to provide clinical decision support for outpatient diabetes management and none for perioperative decision support. We found a dearth of tools for clinicians to navigate these tasks. We highlight key aspects for effective development of future diabetes decision support. These include just-in-time availability, respect for the five rights of clinical decision support, and integration with clinical workflows including the electronic medical record.

Comprehensive Survey of Using Machine Learning in the COVID-19 Pandemic

Since December 2019, the global health population has faced the rapid spreading of coronavirus disease (COVID-19). With the incremental acceleration of the number of infected cases, the World Health Organization (WHO) has reported COVID-19 as an epidemic that puts a heavy burden on healthcare sectors in almost every country. The potential of artificial intelligence (AI) in this context is difficult to ignore. AI companies have been racing to develop innovative tools that contribute to arm the world against this pandemic and minimize the disruption that it may cause. The main objective of this study is to survey the decisive role of AI as a technology used to fight against the COVID-19 pandemic. Five significant applications of AI for COVID-19 were found, including (1) COVID-19 diagnosis using various data types (e.g., images, sound, and text); (2) estimation of the possible future spread of the disease based on the current confirmed cases; (3) association between COVID-19 infection and patient characteristics; (4) vaccine development and drug interaction; and (5) development of supporting applications. This study also introduces a comparison between current COVID-19 datasets. Based on the limitations of the current literature, this review highlights the open research challenges that could inspire the future application of AI in COVID-19.

Leveraging Artificial Intelligence to Improve Chronic Disease Care: Methods and Application to Pharmacotherapy Decision Support for Type-2 Diabetes Mellitus

OBJECTIVES

Artificial intelligence (AI), including predictive analytics, has great potential to improve the care of common chronic conditions with high morbidity and mortality. However, there are still many challenges to achieving this vision. The goal of this project was to develop and apply methods for enhancing chronic disease care using AI.

METHODS

Using a dataset of 27,904 patients with diabetes, an analytical method was developed and validated for generating a treatment pathway graph which consists of models that predict the likelihood of alternate treatment strategies achieving care goals. An AI-driven clinical decision support system (CDSS) integrated with the electronic health record (EHR) was developed by encapsulating the prediction models in an OpenCDS Web service module and delivering the model outputs through a SMART on FHIR (Substitutable Medical Applications and Reusable Technologies on Fast Healthcare Interoperability Resources) web-based dashboard. This CDSS enables clinicians and patients to review relevant patient parameters, select treatment goals, and review alternate treatment strategies based on prediction results.

RESULTS

The proposed analytical method outperformed previous machine-learning algorithms on prediction accuracy. The CDSS was successfully integrated with the Epic EHR at the University of Utah.

CONCLUSION

A predictive analytics-based CDSS was developed and successfully integrated with the EHR through standards-based interoperability frameworks. The approach used could potentially be applied to many other chronic conditions to bring AI-driven CDSS to the point of care.

Development and Implementation of a Decision Support System to Improve Control of Hypertension and Diabetes in a Resource-Constrained Area in Brazil: Mixed Methods Study

Background

The low levels of control of hypertension and diabetes mellitus are a challenge that requires innovative strategies to surpass barriers of low sources, distance, and quality of health care.

Objective

The aim of this study is to develop a clinical decision support system (CDSS) for diabetes and hypertension management in primary care, to implement it in a resource-constrained region, and to evaluate its usability and health care practitioner satisfaction.

Methods

This mixed methods study is a substudy of HealthRise Brazil Project, a multinational study designed to implement pilot programs to improve screening, diagnosis, management, and control of hypertension and diabetes among underserved communities. Following the identification of gaps in usual care, a team of clinicians established the software functional requirements. Recommendations from evidence-based guidelines were reviewed and organized into a decision algorithm, which bases the CDSS reminders and suggestions. Following pretesting and expert panel assessment, pilot testing was conducted in a quasi-experimental study, which included 34 primary care units of 10 municipalities in a resource-constrained area in Brazil. A Likert-scale questionnaire evaluating perceived feasibility, usability, and utility of the application and professionals’ satisfaction was applied after 6 months. In the end-line assessment, 2 focus groups with primary care physicians and nurses were performed.

Results

A total of 159 reminders and suggestions were created and implemented for the CDSS. At the 6-month assessment, there were 1939 patients registered in the application database and 2160 consultations were performed by primary care teams. Of the 96 health care professionals who were invited for the usability assessment, 26% (25/96) were physicians, 46% (44/96) were nurses, and 28% (27/96) were other health professionals. The questionnaire included 24 items on impressions of feasibility, usability, utility, and satisfaction, and presented global Cronbach α of .93. As for feasibility, all professionals agreed (median scores of 4 or 5) that the application could be used in primary care settings and it could be easily incorporated in work routines, but physicians claimed that the application might have caused significant delays in daily routines. As for usability, overall evaluation was good and it was claimed that the application was easy to understand and use. All professionals agreed that the application was useful (score 4 or 5) to promote prevention, assist treatment, and might improve patient care, and they were overall satisfied with the application (median scores between 4 and 5). In the end-line assessment, there were 4211 patients (94.82% [3993/4211] with hypertension and 24.41% [1028/4211] with diabetes) registered in the application’s database and 7960 consultations were performed by primary health care teams. The 17 participants of the focus groups were consistent to affirm they were very satisfied with the CDSS.

Conclusions

The CDSS was applicable in the context of primary health care settings in low-income regions, with good user satisfaction and potential to improve adherence to evidence-based practices.

Improving Care and Promoting Health in Populations: Standards of Medical Care in Diabetes-2021

The American Diabetes Association (ADA) "Standards of Medical Care in Diabetes" includes the ADA's current clinical practice recommendations and is intended to provide the components of diabetes care, general treatment goals and guidelines, and tools to evaluate quality of care. Members of the ADA Professional Practice Committee, a multidisciplinary expert committee (https://doi.org/10.2337/dc21-SPPC), are responsible for updating the Standards of Care annually, or more frequently as warranted. For a detailed description of ADA standards, statements, and reports, as well as the evidence-grading system for ADA's clinical practice recommendations, please refer to the Standards of Care Introduction (https://doi.org/10.2337/dc21-SINT). Readers who wish to comment on the Standards of Care are invited to do so at professional.diabetes.org/SOC.

Improving Referrals to Diabetes Self-Management Education in Medically Underserved Adults

OBJECTIVE

Electronic health records (EHRs) and clinical decision-support algorithms improve diabetes care. This quality improvement (QI) project aimed to determine whether an electronic diabetes education referral protocol using the Diabetes Self-Management Education and Support for Adults With Type 2 Diabetes: Algorithm of Care (DSMES Algorithm) and protocol training would increase the proportion of adult patients with type 2 diabetes at a federally qualified health center electronically referred for diabetes self-management education and support (DSMES).

DESIGN AND METHODS

The EHR was modified to include the DSMES Algorithm and questions regarding prior participation in diabetes education. Protocol trainings were conducted. Data were obtained via retrospective chart review. A one-sample t test was used to evaluate the statistical difference between the electronic referral (e-referral) rates of the pre-intervention and intervention groups.

RESULTS

Completion of the DSMES Algorithm was positively associated with e-referrals to diabetes education (P <0.001). The intervention group had a higher rate of e-referral for DSMES than the pre-intervention group (31 vs. 0%, P <0.001).

CONCLUSION

E-referral protocols using the DSMES Algorithm and protocol training may aid in the identification and documentation of self-care needs of medically underserved patients with type 2 diabetes and improve e-referrals to DSMES. Of clinical importance, these findings translate into active patient engagement, team-based care, and information-sharing. Additional work is needed to determine whether the e-referral rate is sustained or increases over time. Further investigations should also be explored to evaluate the impact of e-referral protocols and algorithms on participation in DSMES.

Guiding Glucose Management Discussions Among Adults With Type 2 Diabetes in General Practice: Development and Pretesting of a Clinical Decision Support Tool Prototype Embedded in an Electronic Medical Record

BACKGROUND

Managing type 2 diabetes (T2D) requires progressive lifestyle changes and, sometimes, pharmacological treatment intensification. General practitioners (GPs) are integral to this process but can find pharmacological treatment intensification challenging because of the complexity of continually emerging treatment options.

OBJECTIVE

This study aimed to use a co-design method to develop and pretest a clinical decision support (CDS) tool prototype (GlycASSIST) embedded within an electronic medical record, which uses evidence-based guidelines to provide GPs and people with T2D with recommendations for setting glycated hemoglobin (HbA1c) targets and intensifying treatment together in real time in consultations.

METHODS

The literature on T2D-related CDS tools informed the initial GlycASSIST design. A two-part co-design method was then used. Initial feedback was sought via interviews and focus groups with clinicians (4 GPs, 5 endocrinologists, and 3 diabetes educators) and 6 people with T2D. Following refinements, 8 GPs participated in mock consultations in which they had access to GlycASSIST. Six people with T2D viewed a similar mock consultation. Participants provided feedback on the functionality of GlycASSIST and its role in supporting shared decision making (SDM) and treatment intensification.

RESULTS

Clinicians and people with T2D believed that GlycASSIST could support SDM (although this was not always observed in the mock consultations) and individualized treatment intensification. They recommended that GlycASSIST includes less information while maintaining relevance and credibility and using graphs and colors to enhance visual appeal. Maintaining clinical autonomy was important to GPs, as they wanted the capacity to override GlycASSIST's recommendations when appropriate. Clinicians requested easier screen navigation and greater prescribing guidance and capabilities.

CONCLUSIONS

GlycASSIST was perceived to achieve its purpose of facilitating treatment intensification and was acceptable to people with T2D and GPs. The GlycASSIST prototype is being refined based on these findings to prepare for quantitative evaluation.

Effect of a novel pharmacist-led reporting system on appropriate use of direct-acting oral anticoagulants (DOACs) in a patient-centered medical home

Recent analyses demonstrate roughly 16-24% of patients taking direct-acting oral anticoagulants (DOACs) are prescribed an inappropriate dose, exposing patients to increased risk of thrombosis or bleeding. The use of reporting systems in the outpatient setting can efficiently identify potential medication errors and safety concerns. The purpose of this study was to evaluate the effect of a novel pharmacist-driven reporting system on appropriate prescribing of DOACs in the outpatient setting. This single-center qualitative study was conducted within a patient-centered medical home (PCMH). Reports were generated monthly to include all new DOAC prescriptions. Branching logic and filters were utilized within a secure web application to make the reporting process more efficient and identify regimens needing an intervention. Pharmacists reviewed the regimens populated by filters and made recommendations to prescribers as appropriate. The number of interventions proposed was captured as the primary outcome. Secondary outcomes include the nature of drug therapy problems identified and number of interventions accepted by prescribers. A total of 107 patients were analyzed for appropriateness from November 2017 to February 2019. Of the regimens included for review, 15 regimens were identified as potentially inappropriate. The nature of drug therapy problems identified include under dosing (4.25%), overdosing (2.13%), correction of documentation (2.13%), clarification of indication (3.19%), and ordering laboratory studies (3.19%). Of the interventions recommended, fourteen (93%) were accepted. Pharmacists integrated in a PCMH are well positioned to monitor and resolve DOAC drug therapy problems using local clinical reports.

Clinician Perceptions of a Computerized Decision Support System for Pediatric Type 2 Diabetes Screening

OBJECTIVE

With the increasing prevalence of type 2 diabetes (T2D) in youth, primary care providers must identify patients at high risk and implement evidence-based screening promptly. Clinical decision support systems (CDSSs) provide clinicians with personalized reminders according to best evidence. One example is the Child Health Improvement through Computer Automation (CHICA) system, which, as we have previously shown, significantly improves screening for T2D. Given that the long-term success of any CDSS depends on its acceptability and its users' perceptions, we examined what clinicians think of the CHICA diabetes module.

METHODS

CHICA users completed an annual quality improvement and satisfaction questionnaire. Between May and August of 2015 and 2016, the survey included two statements related to the T2D-module: (1) "CHICA improves my ability to identify patients who might benefit from screening for T2D" and (2) "CHICA makes it easier to get the lab tests necessary to identify patients who have diabetes or prediabetes." Answers were scored using a 5-point Likert scale and were later converted to a 2-point scale: agree and disagree. The Pearson chi-square test was used to assess the relationship between responses and the respondents. Answers per cohort were compared using the Mann-Whitney U-test.

RESULTS

The majority of respondents (N = 60) agreed that CHICA improved their ability to identify patients who might benefit from screening but disagreed as to whether it helped them get the necessary laboratories. Scores were comparable across both years.

CONCLUSION

CHICA was endorsed as being effective for T2D screening. Research is needed to improve satisfaction for getting laboratories with CHICA.

Computerized clinical decision support system for diabetes in primary care does not improve quality of care: a cluster-randomized controlled trial

Background

The EBMeDS system is the computerized clinical decision support (CCDS) system of EBPNet, a national computerized point-of-care information service in Belgium. There is no clear evidence of more complex CCDS systems to manage chronic diseases in primary care practices (PCPs). The objective of this study was to assess the effectiveness of EBMeDS use in improving diabetes care.

Methods

A cluster-randomized trial with before-and-after measurements was performed in Belgian PCPs over 1 year, from May 2017 to May 2018. We randomly assigned 51 practices to either the intervention group (IG), to receive the EBMeDS system, or to the control group (CG), to receive usual care. Primary and secondary outcomes were the 1-year pre- to post-implementation change in HbA1c, LDL cholesterol, and systolic and diastolic blood pressure. Composite patient and process scores were calculated. A process evaluation was added to the analysis. Results were analyzed at 6 and 12 months. Linear mixed models and logistic regression models based on generalized estimating equations were used where appropriate.

Results

Of the 51 PCPs that were enrolled and randomly assigned (26 PCPs in the CG and 25 in the IG), 29 practices (3815 patients) were analyzed in the study: 2464 patients in the CG and 1351 patients in the IG. No change differences existed between groups in primary or secondary outcomes. Change difference between CG and IG after 1-year follow-up was − 0.09 (95% CI − 0.18; 0.01, p-value = 0.06) for HbA1c; 1.76 (95% CI − 0.46; 3.98, p-value = 0.12) for LDL cholesterol; and 0.13 (95% CI − 0.91; 1.16, p-value = 0.81) and 0.12 (95% CI − 1.25;1.49, p-value = 0.86) for systolic and diastolic blood pressure respectively. The odds ratio of the IG versus the CG for the probability of no worsening and improvement was 1.09 (95% CI 0.73; 1.63, p-value = 0.67) for the process composite score and 0.74 (95% CI 0.49; 1.12, p-value = 0.16) for the composite patient score. All but one physician was satisfied with the EBMeDS system.

Conclusions

The CCDS system EBMeDS did not improve diabetes care in Belgian primary care. The lack of improvement was mainly caused by imperfections in the organizational context of Belgian primary care for chronic disease management and shortcomings in the system requirements for the correct use of the EBMeDS system (e.g., complete structured records). These shortcomings probably caused low-use rates of the system.

Trial registration

ClinicalTrials.gov, NCT01830569, Registered 12 April 2013.

Improving Care and Promoting Health in Populations: Standards of Medical Care in Diabetes-2020

The American Diabetes Association (ADA) “Standards of Medical Care in Diabetes” includes the ADA’s current clinical practice recommendations and is intended to provide the components of diabetes care, general treatment goals and guidelines, and tools to evaluate quality of care. Members of the ADA Professional Practice Committee, a multidisciplinary expert committee (https://doi.org/10.2337/dc20-SPPC), are responsible for updating the Standards of Care annually, or more frequently as warranted. For a detailed description of ADA standards, statements, and reports, as well as the evidence-grading system for ADA’s clinical practice recommendations, please refer to the Standards of Care Introduction (https://doi.org/10.2337/dc20-SINT). Readers who wish to comment on the Standards of Care are invited to do so at professional.diabetes.org/SOC.

Clinical decision support directed to primary care patients and providers reduces cardiovascular risk: a randomized trial

Objective

To test the hypothesis that use of a clinical decision support (CDS) system in a primary care setting can reduce cardiovascular (CV) risk in patients.

Materials and Methods

Twenty primary care clinics were randomly assigned to usual care (UC) or CDS. For CDS clinic patients identified algorithmically with high CV risk, rooming staff were prompted by the electronic health record (EHR) to print CDS that identified evidence-based treatment options for lipid, blood pressure, weight, tobacco, or aspirin management and prioritized them based on potential benefit to the patient. The intention-to-treat analysis included 7914 adults who met high CV risk criteria at an index clinic visit and had at least one post-index visit, accounted for clustering, and assessed impact on predicted annual rate of change in 10-year CV risk over a 14-month period.

Results

The CDS was printed at 75% of targeted visits, and providers reported 85% to 98% satisfaction with various aspects of the intervention. Predicted annual rate of change in absolute 10-year CV risk was significantly better in CDS clinics than in UC clinics (-0.59% vs. +1.66%, -2.24%; P < .001), with difference in 10-year CV risk at 12 months post-index favoring the CDS group (UC 24.4%, CDS 22.5%, P < .03).

Discussion

Deploying to both patients and providers within primary care visit workflow and limiting CDS display and print burden to two mouse clicks by rooming staff contributed to high CDS use rates and high provider satisfaction.

Conclusion

This EHR-integrated, web-based outpatient CDS system significantly improved 10-year CV risk trajectory in targeted adults.

CM-SHARE: Development, Integration, and Adoption of an Electronic Health Record-Linked Digital Health Solution to Support Care for Diabetes in Primary Care

IN BRIEF Chronic conditions such as diabetes are largely managed by primary care providers (PCPs), with significant patient self-management. This article describes the development, pilot testing, and fine-tuning of a Web-based digital health solution to help PCPs manage patients with cardiometabolic diseases during routine office encounters. It shows that such products can be successfully integrated into primary care settings when they address important unmet needs and are developed with input from end-users.

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

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.

Current Status and Future Directions for Electronic Point-of-Care Clinical Decision Support to Improve Diabetes Management in Primary Care

In the past decade there have been major improvements in the design, use, and effectiveness of point-of-care clinical decision support (CDS) systems to improve quality of care for patients with diabetes and related conditions. Advances in data exchange, data security, and human factors research have driven these improvements. Current diabetes CDS systems have high use rates, high clinician/user satisfaction rates, and have measurably improved glucose control, blood pressure control, and cardiovascular risk trajectories in adults with diabetes. As diabetes care increasingly relies on complex biomarker-driven risk prediction methods to optimize care goals and prioritize treatment options based on potential benefit to an individual patient, CDS systems will become indispensable tools to guide clinician and patient decision-making. In this study we describe specific challenges that must be addressed further to improve the design, implementation, and effectiveness of primary care diabetes CDS systems in coming years.

Improving Care and Promoting Health in Populations: Standards of Medical Care in Diabetes-2019

The American Diabetes Association (ADA) "Standards of Medical Care in Diabetes" includes ADA's current clinical practice recommendations and is intended to provide the components of diabetes care, general treatment goals and guidelines, and tools to evaluate quality of care. Members of the ADA Professional Practice Committee, a multidisciplinary expert committee, are responsible for updating the Standards of Care annually, or more frequently as warranted. For a detailed description of ADA standards, statements, and reports, as well as the evidence-grading system for ADA's clinical practice recommendations, please refer to the Standards of Care Introduction Readers who wish to comment on the Standards of Care are invited to do so at professional.diabetes.org/SOC.

Understanding Primary Care Provider Attitudes and Behaviors Regarding Cardiovascular Disease Risk and Diabetes Prevention in the Northern Midwest

IN BRIEF We sought to fill critical gaps in understanding primary care providers' (PCPs') beliefs regarding diabetes prevention and cardiovascular disease risk in the prediabetes population, including through comparison of attitudes between rural and non-rural PCPs. We used data from a 2016 cross-sectional survey sent to 299 PCPs practicing in 36 primary clinics that are part of a randomized control trial in a predominately rural northern Midwestern integrated health care system. Results showed a few significant, but clinically marginal, differences between rural and non-rural PCPs. Generally, PCPs agreed with the importance of screening for prediabetes and thoroughly and clearly discussing CV risk with high-risk patients.

Smart Diabetic Screening and Managing Software, A Novel Decision Support System

BACKGROUND

Diabetes is a serious chronic disease, and its increasing prevalence is a global concern. If diabetes mellitus is left untreated, poor control of blood glucose may cause long-term complications. A big challenge encountered by clinicians is the clinical management of diabetes. Many IT-based interventions such ad CDSS have been made to improve the adherence to the standard care for chronic diseases.

OBJECTIVE

The aim of this study is to establish a decision support system of diabetes management based on diabetes care guidelines in order to reduce medical errors and increase adherence to guidelines.

MATERIALS AND METHODS

To start the process, at first the existing guidelines in the field of diabetes mellitus such as ADA 2017 and AACE guideline 2017 were reviewed, and accordingly, flowcharts and algorithms for screening and managing of diabetes were designed. Then, it was passed on to the information technology team to design software.

RESULTS

The most significant outcome of this research was to establish a smart diabetic screening and managing software, which is an important stride to promote patients' health status, control diabetes and save patients' information as an important and reliable source.

CONCLUSION

Health care technologies have the potential to improve the quality of diabetes care through IT-based intervention, such as clinical decision support systems. In a chronic disease like diabetes, the critical component is the disease management. The advantages of this web-based system are on-time registration, reports of diabetic prevalence, uncontrolled diabetes, diabetic complications and reducing the rate of mismanagement of diabetes, so that it helps the physicians in order to manage the patients in a better way.

An Informatics Approach to Implement Support for Shared Decision Making for Primary Prevention Statin Therapy

Background. Shared decision making (SDM) is recommended prior to initiation of statin therapy for primary prevention but is underutilized. We designed an informatics decision-support tool to facilitate use of the Mayo Clinic Statin Choice decision aid at the point-of-care and evaluated its impact. Methods. Using an iterative approach, we designed and implemented a single-click decision-support tool embedded within the electronic health records (EHRs) to automate the calculation of 10-year atherosclerotic cardiovascular disease (ASCVD) risk and populate the Statin Choice decision aid. We surveyed primary care providers at two clinics regarding their attitudes about SDM before and after deployment of intervention, as well as their usage of and perceived competence regarding SDM for primary prevention statin therapy. Three-month web traffic to the Statin Choice website was calculated before and after deployment of the intervention. Results. Pre-post surveys were completed by 60 primary care providers (24 [40%] attending physicians and 36 [60%] housestaff physicians). After deployment of the EHR tool, respondents were more aware of the Statin Choice decision aid (P < 0.001), reported being more competent regarding SDM (P = 0.047), and reported using decision aids more often when considering statin initiation (P = 0.043). There was no significant change in attitudes about SDM as measured through the Patient Provider Orientation Scale (pre 4.23 ± 0.40 v. post 4.16 ± 0.38, P = 0.11) and the SDM belief scale (pre 21.4 ± 2.1 v. post 21.1 ± 2.0, P = 0.35). Web-based usage rates for the Statin Choice decision aid increased from 3.4 to 5.2 per 1,000 outpatient clinic visits (P = 0.002). Conclusions. Implementation of a point-of-care decision-support tool increased the usage of decision aids for primary prevention statin therapy. This effect does not appear to be mediated by any concomitant changes in physician attitude toward SDM.

Improving Care and Promoting Health in Populations: Standards of Medical Care in Diabetes-2018

The American Diabetes Association (ADA) "Standards of Medical Care in Diabetes" includes ADA's current clinical practice recommendations and is intended to provide the components of diabetes care, general treatment goals and guidelines, and tools to evaluate quality of care. Members of the ADA Professional Practice Committee, a multi-disciplinary expert committee, are responsible for updating the Standards of Care annually, or more frequently as warranted. For a detailed description of ADA standards, statements, and reports, as well as the evidence-grading system for ADA's clinical practice recommendations, please refer to the Standards of Care Introduction Readers who wish to comment on the Standards of Care are invited to do so at professional.diabetes.org/content/clinical-practice-recommendations.

Digital health technology and diabetes management

Diabetes care is largely dependent on patient self-management and empowerment, given that patients with diabetes must make numerous daily decisions as to what to eat, when to exercise, and determine their insulin dose and timing if required. In addition, patients and providers are generating vast amounts of data from many sources, including electronic medical records, insulin pumps, sensors, glucometers, and other wearables, as well as evolving genomic, proteomic, metabolomics, and microbiomic data. Multiple digital tools and apps have been developed to assist patients to choose wisely, and to enhance their compliance by using motivational tools and incorporating incentives from social media and gaming techniques. Healthcare teams (HCTs) and health administrators benefit from digital developments that sift through the enormous amounts of patient-generated data. Data are acquired, integrated, analyzed, and presented in a self-explanatory manner, highlighting important trends and items that require attention. The use of decision support systems may propose data-driven actions that, for the most, require final approval by the patient or physician before execution and, once implemented, may improve patient outcomes. The digital diabetes clinic aims to incorporate all digital patient data and provide individually tailored virtual or face-to-face visits to those persons who need them most. Digital diabetes care has demonstrated only modest HbA1c reduction in multiple studies and borderline cost-effectiveness, although patient satisfaction appears to be increased. Better understanding of the barriers to digital diabetes care and identification of unmet needs may yield improved utilization of this evolving technology in a safe, effective, and cost-saving manner.

Advances in Clinical Decision Support: Highlights of Practice and the Literature 2015-2016

Introduction: Advances in clinical decision support (CDS) continue to evolve to support the goals of clinicians, policymakers, patients and professional organizations to improve clinical practice, patient safety, and the quality of care. Objectives: Identify key thematic areas or foci in research and practice involving clinical decision support during the 2015-2016 time period. Methods: Thematic analysis consistent with a grounded theory approach was applied in a targeted review of journal publications, the proceedings of key scientific conferences as well as activities in standards development organizations in order to identify the key themes underlying work related to CDS. Results: Ten key thematic areas were identified, including: 1) an emphasis on knowledge representation, with a focus on clinical practice guidelines; 2) various aspects of precision medicine, including the use of sensor and genomic data as well as big data; 3) efforts in quality improvement; 4) innovative uses of computer-based provider order entry (CPOE) systems, including relevant data displays; 5) expansion of CDS in various clinical settings; 6) patient-directed CDS; 7) understanding the potential negative impact of CDS; 8) obtaining structured data to drive CDS interventions; 9) the use of diagnostic decision support; and 10) the development and use of standards for CDS. Conclusions: Active research and practice in 2015-2016 continue to underscore the importance and broad utility of CDS for effecting change and improving the quality and outcome of clinical care.

Development of a clinical decision support system for diabetes care: A pilot study

Management of complex chronic diseases such as diabetes requires the assimilation and interpretation of multiple laboratory test results. Traditional electronic health records tend to display laboratory results in a piecemeal and segregated fashion. This makes the assembly and interpretation of results related to diabetes care challenging. We developed a diabetes-specific clinical decision support system (Diabetes Dashboard) interface for displaying glycemic, lipid and renal function results, in an integrated form with decision support capabilities, based on local clinical practice guidelines. The clinical decision support system included a dashboard feature that graphically summarized all relevant laboratory results and displayed them in a color-coded system that allowed quick interpretation of the metabolic control of the patients. An alert module informs the user of tests that are due for repeat testing. An interactive graph module was also developed for better visual appreciation of the trends of the laboratory results of the patient. In a pilot study involving case scenarios administered via an electronic questionnaire, the Diabetes Dashboard, compared to the existing laboratory reporting interface, significantly improved the identification of abnormal laboratory results, of the long-term trend of the laboratory tests and of tests due for repeat testing. However, the Diabetes Dashboard did not significantly improve the identification of patients requiring treatment adjustment or the amount of time spent on each case scenario. In conclusion, we have developed and shown that the use of the Diabetes Dashboard, which incorporates several decision support features, can improve the management of diabetes. It is anticipated that this dashboard will be most helpful when deployed in an outpatient setting, where physicians can quickly make clinical decisions based on summarized information and be alerted to pertinent areas of care that require additional attention.

If DSME Were a Pill, Would You Prescribe It?

EDITOR'S NOTE: This address was delivered by Margaret A. Powers, PhD, RD, CDE, President, Health Care & Education, of the American Diabetes Association (ADA), at the ADA's 76th Scientific Sessions in New Orleans, La., on 11 June 2016. Dr. Powers conducts research and has a clinical practice as a registered dietitian and diabetes educator at the International Diabetes Center at Park Nicollet in Minneapolis, Minn. Her research focuses on improving diabetes outcomes, including factors that affect the clinical, psychosocial, and behavioral aspects of diabetes. Dr. Powers has been an ADA volunteer for more than 25 years, including serving as a founding editor of Diabetes Spectrum. She is the lead author of the 2015 joint Position Statement on Diabetes Self-management Education and Support published by the ADA, American Association of Diabetes Educators, and Academy of Nutrition and Dietetics. She is the recipient of the ADA's Outstanding Educator in Diabetes Award and has published research, authored numerous articles and chapters, published five books, and is an international presenter. Dr. Powers holds a doctorate in education with a focus on performance improvement from Capella University. She received her Master of Science from the University of Illinois at Chicago and her Bachelor of Science from Michigan State University. She completed her dietetic internship at Cook County Hospital in Chicago.

2016 Health Care & Education Presidential Address: If DSME Were a Pill, Would You Prescribe It?

This address was delivered by Margaret A. Powers, PhD, RD, CDE, President, Health Care & Education, of the American Diabetes Association (ADA), at the ADA's 76th Scientific Sessions in New Orleans, LA, on 11 June 2016. Dr. Powers conducts research and has a clinical practice as a registered dietitian and diabetes educator at the International Diabetes Center at Park Nicollet in Minneapolis. Her research focuses on improving diabetes outcomes including factors that affect the clinical, psychosocial, and behavioral aspects of diabetes. Dr. Powers has been an ADA volunteer for more than 25 years, including serving as a founding editor of Diabetes Spectrum She is the lead author of the 2015 joint Position Statement on Diabetes Self-management Education and Support published by the ADA, American Association of Diabetes Educators, and Academy of Nutrition and Dietetics. She is the recipient of the ADA's Outstanding Educator in Diabetes Award and has published research, authored numerous articles and chapters, published five books, and is an international presenter. Dr. Powers holds a doctorate in education with a focus on performance improvement from Capella University. She received her Master of Science from the University of Illinois at Chicago and her Bachelor of Science from Michigan State University. She completed her dietetic internship at Cook County Hospital in Chicago.