Using Cluster Analysis to Understand Clinician Readiness to Promote Continuous Glucose Monitoring Adoption

Tanenbaum, Molly L.; Adams, Rebecca N.; Lanning, Monica S.; Hanes, Sarah; Agustin, Bianca; Naranjo, Diana; Hood, Korey K.

doi:10.1177/1932296818786486

Cited by 39 publications

(36 citation statements)

References 32 publications

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“…Notably, there is increasing evidence suggesting that health care professionals’ attitude toward CGM constitutes a barrier to CGM use expansion. 45 In addition, Tanenbaum et al 46 have reported a readiness chart for clinicians (MDs and CDEs) who are likely to prescribe diabetes technology tools. Three “clinician personas” were described, based on their readiness to promote CGM technology and comfort in keeping up with technology advances.…”

Section: Resultsmentioning

confidence: 99%

Continuous Glucose Monitoring Integration in Clinical Practice: A Stepped Guide to Data Review and Interpretation

Webb

2018

J Diabetes Sci Technol

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Background: The advent of continuous glucose monitoring (CGM) technology has transformed the approach to diabetes care. Multiple CGM systems are commercially available and increased accuracy has allowed development of hybrid and automated insulin delivery systems. Evidence of CGM clinical benefits has also increased exponentially in the last decade. Methods: Literature search, review of professional guidelines, and consensus statements were used to guide the preparation of this article. The clinical benefits of both professional and personal CGM in clinical practice as well as barriers to wider adotpion were explored. A stepped approach to review and interpretation of CGM data is suggested for use in the clinician’s office regardless of the software used. Results: Although increasing, the use of CGM in patients with diabetes is still not widespread; multiple barriers are still in place, despite the approval of CGM systems for patients above the age of 2 years old, the extension of coverage for Medicare beneficiaries and the integration of CGM with multiple insulin pump systems. Integration of CGM technology in clinical practice presents various challenges, from concerns relative to time constraints during office visits to lack of systematic approach to interpretation of the data. Conclusions: Understanding the usefulness of personal and professional CGM, appropriate patient selection as well as patient and provider training are crucial for the expansion of CGM therapy use in clinical practice. Utilizing the proposed stepped approach to CGM review and interpretation may allow wider adoption of CGM with more effective and efficient office visits.

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Section: Resultsmentioning

confidence: 99%

Continuous Glucose Monitoring Integration in Clinical Practice: A Stepped Guide to Data Review and Interpretation

Webb

2018

J Diabetes Sci Technol

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“…Educators were overall satisfied with the program as well and reported low difficulty with the different components overall. Device uploads were reported as the highest difficulty, which is an ongoing frustration in clinical practice as well 26,27 . Several months after the evaluation of this program, the device manufacturer released a cloudconnected app for Control-IQ, which will reduce burden with obtaining device downloads in the future.…”

Section: Discussionmentioning

confidence: 99%

“…The largest limitation is the time spent by diabetes educators and administrative staff. Time and workflow are ongoing concerns in our clinical practice, and are identified in the literature as a common barrier to clinicians' ability to embrace advanced diabetes technologies 18,26. It is not realistic that our Clinic could indefinitely support nurses spending 45 minutes of time following up on new HCL users when the task is not currently reimbursable by insurance.…”

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confidence: 99%

Initiating hybrid closed loop: A program evaluation of an educator‐led Control‐IQ follow‐up at a large pediatric clinic

et al. 2021

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Background: Control-IQ (Tandem Diabetes) is a hybrid closed-loop (HCL) system that users self-initiate after completing online training. Best practices for clinical followup are not known. Our quality improvement objective was to evaluate the usefulness of an educator-led follow-up program for new HCL users in a type 1 diabetes pediatric clinic.Methods: We implemented an ''HCLCheck-in'' program, first determining when users started HCL, then having diabetes educators contact them for a follow-up call 2-weeks after start. Educators used a Clinical Tool to inform insulin dose and behavior recommendations, and used four benchmarks to determine need for further follow-up: ≥71% HCL use, ≥71% CGM use, ≥60% Time-in-Range (TIR, 70-180 mg/ dL), <5% below 70 mg/dL. Family and educator satisfaction were surveyed.Results: One-hundred-twenty-three youth [mean age 13.6 ± 3.7 y, 53.7% female, mean HbA1c 7.6 ± 1.4% (60 mmol/mol)] completed an HCLCheck-in call a median (IQR) of 18(15, 21) days post-HCL start. 74 users (60%) surpassed benchmarks with 94% HCL use and 71% TIR. Of the 49 who did not, 16 completed a second call, and improved median TIR 12.5% (p = 0.03). HCL users reported high satisfaction with the program overall [median 10 (9, 10) out of 10]. Educators spent a median of 45 (32,70) minutes per user and rated satisfaction with the program as 8 (7,9.5) and the Tool as 9 (9, 10). Conclusion:Our HCLCheck-in program received high satisfaction ratings and resulted in improved TIR for those initially not meeting benchmarks, suggesting users may benefit from early follow-up. Similar programs may be beneficial for other new technologies. K E Y W O R D S hybrid closed loop, pediatrics, program evaluation, quality improvement, type 1 diabetes 1 | INTRODUCTION Diabetes technology options have greatly increased for persons with type 1 diabetes (PWD) in the past decade, with the commercialization of multiple advanced insulin pumps, including hybrid closed-loop (HCL) systems. 1 HCL systems consist of an insulin pump, a connected continuous glucose monitor (CGM), and an algorithm to increase or decrease insulin delivery in response to glucose levels. The most recently available hybrid closed loop system, the t:slim X2 with Control-IQ technology (''Control-IQ'', Tandem Diabetes), was commercially approved in the United States in January 2020 for PWD ages 14 and over and has since been approved for PWD ages 6 and

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“…The use of cluster analyses in healthcare decision making is still relatively uncommon but appears to be gaining acceptance [17,18,19,20,21]. Our work builds upon a few previously published cluster analyses in diabetes, which focused on readiness of CGM and other diabetesrelated devices, self-management patterns in a pediatric population, and factors in uencing people with diabetes who have poorly controlled conditions [22,23,24,25,26]. These previous studies involved smaller numbers of participants from relatively homogeneous populations (e.g., T1DM registry) and/or more controlled conditions (e.g., clinical trial).…”

Section: Discussionmentioning

confidence: 99%

Evaluating the relationship between clinical and demographic characteristics of insulin-using people with diabetes and their health outcomes: a cluster analysis application

Eby

Edwards

Meadows

et al. 2021

Preprint

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Background: The aim of this study was to determine how clusters or subgroups of insulin-treated people with diabetes, based upon healthcare resource utilization, select social demographic and clinical characteristics, and diabetes management parameters, are related to health outcomes including acute care visits and hospital admissions.Methods: This was a non-experimental, retrospective cluster analysis. We utilized Aetna administrative claims data to identify insulin-using people with diabetes with service dates from 01 January 2015 to 30 June 2018. The study included adults over the age of 18 years who had a diagnosis of type 1 (T1DM) or type 2 diabetes mellitus (T2DM) on insulin therapy and had Aetna medical and pharmacy coverage for at least 18 months (6 months prior and 12 months after their index date, defined as either their first insulin prescription fill date or their earliest date allowing for 6 months’ prior coverage). We used K-means clustering methods to identify relevant subgroups of people with diabetes based on 13 primary outcome variables.Results: A total of 100,650 insulin-using people with diabetes were identified in the Aetna administrative claims database and met study criteria, including 11,826 (11.7%) with T1DM and 88,824 (88.3%) with T2DM. Of these 79,053 (78.5%) people were existing insulin users. Seven distinct clusters were identified with different characteristics and potential risks of diabetes complications. Overall, clusters were significantly associated with differences in healthcare utilization (emergency room visits, inpatient admissions, and total inpatient days) after multivariable adjustment.Conclusions: This novel analysis of healthcare claims data using clustering methodologies identified meaningful subgroups of patients with diabetes using insulin. The subgroups differed in comorbidity burden, healthcare utilization, and demographic factors which could be used to identify higher risk patients and/or guide the management and treatment of diabetes.

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Using Cluster Analysis to Understand Clinician Readiness to Promote Continuous Glucose Monitoring Adoption

Abstract: Some diabetes clinicians may benefit from tailored interventions and additional time and resources to empower them to help facilitate increased uptake of CGM technology.

Cited by 39 publications

References 32 publications

Continuous Glucose Monitoring Integration in Clinical Practice: A Stepped Guide to Data Review and Interpretation

Continuous Glucose Monitoring Integration in Clinical Practice: A Stepped Guide to Data Review and Interpretation

Initiating hybrid closed loop: A program evaluation of an educator‐led Control‐IQ follow‐up at a large pediatric clinic

Evaluating the relationship between clinical and demographic characteristics of insulin-using people with diabetes and their health outcomes: a cluster analysis application

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