Impact of Automated Insulin Delivery on Type 2 Diabetes Measures

ABSTRACT

Objective: To evaluate the effect of using the t:slim X2 insulin pump with Control-IQ technology (hereafter, Control-IQ; Tandem Diabetes Care, Inc) on glycemic status in individuals with type 2 diabetes (T2D) who switched from multiple daily insulin injections.

Study Design: This 12-month retrospective analysis utilized data from the Tandem t:connect web application and customer relationship management database, encompassing individuals covered by commercial insurers, Medicaid, and Medicare.

Methods: We assessed changes in glucose management indicator (GMI) values from baseline hemoglobin A_1c (HbA_1c) at 12 months relative to Healthcare Effectiveness Data and Information Set (HEDIS) quality measures (HbA_1c or GMI: < 8.0% [in control] and > 9.0% [poor control]). The primary outcome was the proportion of individuals who met the recommended HEDIS measure. Achievement of a GMI of less than 7.0% was included as a secondary outcome. Changes in glycemic outcomes and the proportion of individuals meeting the national threshold were analyzed.

Results: The analysis included 1737 individuals with T2D who had baseline HbA_1c data. Following initiation of Control-IQ, the number of individuals who met the HEDIS (GMI < 8.0%) measure increased from 755 (43.5%) at baseline to 1545 (88.9%) at 12 months (∆ = 104.6%). The number of individuals who achieved a GMI of less than 7% increased from 282 (16.2%) to 678 (39.0%) (∆ = 140.4%). The number of individuals with HbA_1c greater than 9.0% at baseline decreased from 514 (29.6%) at baseline to 10 (0.6%) (∆ = –98.1%) at 12 months.

Conclusions: A greater proportion of individuals who transitioned to Control-IQ therapy achieved the HEDIS goal for in-control diabetes across all payer types, as measured by GMI.

Am J Manag Care. 2026;32(7):e294-e299

doi:10.37765/ajmc.2026.89986

Takeaway Points

• The glucose management indicator (GMI) is a core metric used to assess glycemic control as measured by continuous glucose monitoring.
• The National Committee for Quality Assurance recently included the GMI as a surrogate for hemoglobin A_1c in its Healthcare Effectiveness Data and Information Set (HEDIS) diabetes-related measures.
• The study assessed the impact of Control-IQ technology—an automated insulin delivery (AID) therapy—on the HEDIS-recommended target for individuals with type 2 diabetes covered by commercial insurance, Medicaid, or Medicare who transitioned from multiple daily insulin injection treatment.
• A greater proportion of individuals met the HEDIS-recommended target across all payer types when using AID as measured by GMI.

The number of people with type 2 diabetes (T2D) is expected to increase from the current estimate of 483 million to more than 704 million worldwide over the next 20 years.¹ It is estimated that more than 30 million individuals are treated with insulin therapy.²

Although the complications and associated costs of diabetes are well recognized, the overall prevalence of optimal glycemic management among people with insulin-treated T2D is low. Fewer than 24% of individuals meet the American Diabetes Association (ADA)–recommended target of a hemoglobin A_1c (HbA_1c) level of less than 7%, which applies to many nonpregnant adults without severe or frequent hypoglycemia affecting health or quality of life.³ In the US, the total direct and indirect annual costs of diabetes now exceeds $412.9 billion.⁴ More than $129 billion (31%) of the direct costs are attributed to increased inpatient hospitalizations, emergency department visits, and outpatient hospital visits, resulting from suboptimal glycemic control and other diabetes-related conditions.⁴

Although continuous glucose monitoring (CGM) was introduced more than 20 years ago, the establishment of standardized targets for CGM-based glucose metrics in 2019 significantly enhanced the utility of this technology for retrospective analysis of clinical practice data.⁵ The CGM metrics used for assessing glycemic status in nonpregnant individuals with diabetes include time in range (%TIR; 70-180 mg/dL), time below range (%TBR; level 1 hypoglycemia [< 70 mg/dL] and level 2 hypoglycemia [< 54 mg/dL]), time above range (%TAR; 181-250 mg/dL and > 250 mg/dL), and glucose management indicator (GMI).⁵ A recently established secondary end point is the time spent in tight range (%TITR; 70-140 mg/dL).⁶

The Healthcare Effectiveness Data and Information Set (HEDIS) is a set of diverse measures that assess health care effectiveness, access, and enrollee experience; it is used by CMS and more than 90% of health plans.⁷ The HEDIS Glycemic Status Assessment for Patients With Diabetes assesses HbA_1c or GMI against 2 thresholds of less than 8.0% and greater than 9.0%.⁸ The metric uses the most recent result, with no preference for GMI or HbA_1c.^9,10 Historically, HEDIS has referred to an HbA_1c level less than 8.0% as in control and an HbA_1c level greater than 9.0% as poor control.¹¹

Ongoing advancements in glucose monitoring and insulin dosing technologies have led to the development of CGM sensors and automated insulin delivery (AID) systems. These systems utilize sophisticated control algorithms that automatically increase or decrease basal insulin delivery in response to current and predicted CGM glucose values and other factors such as carbohydrate intake and physical activity.¹² The t:slim X2 insulin pump with its integrated CGM sensor and Control-IQ technology (hereafter, Control-IQ; Tandem Diabetes Care, Inc) is an advanced AID system that reduces the incidence and severity of hypoglycemia and hyperglycemia, and maximizes users’ %TIR.

Although the results of numerous randomized controlled trials and retrospective studies have demonstrated that AID improves glycemic control and reduces risk of hypoglycemia in adults, adolescents, and pediatric patients with type 1 diabetes,^13,14 certain AID systems, including the latest update to Control-IQ (Control-IQ+ technology), have been cleared for use by individuals with T2D.¹⁵ This real-world retrospective analysis evaluated glycemic outcomes in patients with T2D using Control-IQ among those covered by commercial health plans, Medicaid, or Medicare.

METHODS

Study Design and Population

This US-based, retrospective analysis evaluated changes in glycemic status over 12 months among children/adolescents and adults with T2D who initiated therapy with the Control-IQ system following therapy with multiple daily insulin injections (MDI). Uploaded glycemic information between January 15, 2020, and March 14, 2024, was obtained from the Tandem t:connect web application; this included data automatically uploaded via the t:connect app, as well as user-initiated at-home and clinic downloads. Baseline patient characteristics were obtained from the Tandem customer relationship management database. Patient consent to the use of their data for research purposes was provided as part of their onboarding to Tandem when initiating their t:connect account. Institutional review board approval was not sought for this retrospective analysis. Inclusion criteria were having T2D; prior treatment with MDI; Control-IQ initiation at least 1 year prior to the study end date; a recorded baseline HbA_1c measurement within 6 months prior to Control-IQ initiation; commercial, Medicare, or Medicaid insurance coverage; and at least 70% CGM use during the first 2 weeks following Control-IQ initiation and at each 3-month interval (quarter [Q] 1, Q2, Q3, Q4) during the 12-month postinitiation period.⁶ There were no exclusion criteria for this analysis. Results were stratified by key payer type and baseline HbA_1c level greater than 9.0%. GMI was calculated for each 3-month interval in the 12-month postinitiation period and compared with baseline HbA_1c levels. Figure 1 presents the overall study patient flowchart.

Outcome Measures

The primary outcomes were (1) change in GMI from baseline HbA_1c over the 12-month observation period, (2) change in the number and proportion of individuals who achieved the HEDIS quality performance metric for GMI (< 8.0%)¹⁶ within the full cohort and according to payer type, and (3) change in number and proportion of individuals with a GMI measure of greater than 9.0% at study end.¹⁶ Secondary outcomes were changes in aforementioned CGM metrics from the first 2 weeks post Control-IQ initiation and the number and proportion of individuals who achieved a GMI of less than 7%. The goals for the CGM metrics among otherwise healthy adults with diabetes are greater than 70% for %TIR, greater than 50% for %TITR, less than 4% for %TBR less than 70 mg/dL, less than 1% for %TBR less than 54 mg/dL, and less than 25% for %TAR greater than 180 mg/dL.^5,17 Although the threshold of GMI of less than 7% aligns with the current ADA target of HbA_1c of less than 7%, the ADA does not recommend a GMI target.³ However, the Ambulatory Glucose Profile (AGP), which includes GMI of less than 7% as a goal, is presented in the 2023 ADA clinical recommendations.¹⁸

Statistical Analysis

The equation used to compute the GMI (%) is 3.31 + 0.02392 × mean glucose in mg/dL, or GMI (mmol/mol) = 12.71 + 4.70587 × mean glucose in mmol/L.¹⁹ Changes in glycemic outcomes were analyzed using paired t tests. The proportions of patients meeting national thresholds were analyzed using McNemar χ² tests.

RESULTS

Study Population Characteristics

The analysis included 1737 individuals with T2D. Baseline demographic characteristics are presented in the Table. Most participants (55%) were women, and aged 45 to 64 years (53%). Most individuals were covered by commercial insurance, followed by Medicare and Medicaid. Within this cohort, 514 (29.6%) patients had a baseline HbA_1c greater than 9%.

Changes in GMI per Quarter From Baseline to Q4

A significant reduction in GMI from baseline HbA_1c was observed in the full cohort during the first 2 weeks of Control-IQ use (Figure 2). This improvement was sustained throughout the observation period (P < .0001 at all increments). The greatest ∆ was observed in the first 2 weeks of Q1. The largest reductions in GMI at Q4 were observed among individuals with an HbA_1c greater than 9.0% at baseline, from a mean (SD) HbA_1c of 10.5% (1.3%) to a mean (SD) GMI of 7.4% (0.6%) (∆ = –3.1% [1.4%]).

Achievement of Glycemic Quality Standard

At baseline, 56.5% of individuals were not meeting the HEDIS quality standard for glycemic control and 83.8% were not meeting the glycemic target of less than 7%. By Q4, significant improvements were seen in the percentage of individuals meeting the HEDIS quality standard. The largest increase in the proportion of individuals meeting the threshold of less than 8% was observed among patients covered by Medicaid, followed by those covered by commercial insurers and Medicare (Figure 3 [A]). The largest increase in the proportion of individuals meeting the secondary outcome of GMI of less than 7% was observed in those with Medicaid coverage (Figure 3 [B]).

Reduction in Percentage of Patients With HbA_1c Greater Than 9.0% at Baseline

A total of 514 patients with baseline HbA_1c greater than 9.0% were identified (commercial, n = 345; Medicaid, n = 68; Medicare, n = 101). GMI values at Q4 showed greater than 95% reductions in the percentage of individuals with baseline HbA_1c greater than 9.0% in all payer groups (Figure 4). The largest percentage reduction was observed among patients covered by Medicare plans.

Changes in CGM Metrics

Within the full cohort, slight changes were observed in all mean (SD) CGM metrics from the first 2 weeks of Q1 to Q4: %TIR (from 69.3% [18.7%] to 67.5% [18.5%]), %TITR (from 40.5% [19.2%] to 39.0% [18.6%]), %TAR greater than 180 mg/dL (from 29.7% [18.2%] to 31.4% [18.0%]), %TBR less than 70 mg/dL (from 1.0% [2.2%] to 1.1% [1.9%]), and %TBR less than 54 mg/dL (from 0.5% [2.0%] to 0.6% [1.6%]).

DISCUSSION

In this retrospective analysis of individuals with suboptimally controlled T2D, the transition from MDI to Control-IQ resulted in notable reductions in GMI from baseline HbA_1c that were sustained over the 12-month observation period across all payer groups, with statistically significant increases in the percentage of patients who achieved the HEDIS standards for acceptable glycemic control. Although the percentage of patients with T2D who had an HbA_1c greater than 9.0% at baseline (29.6%) was notably higher than that reported in the US population (13%),²⁰ this does not limit the generalizability of our findings. Rather, the significant reductions observed in all payer groups are generalizable to this population with poorly controlled T2D and demonstrate the effectiveness of Control-IQ use.

In an earlier study by Forlenza et al that included 500 patients with T2D with a baseline GMI of 7.3%, use of the Control-IQ system was associated with a 0.2% decrease in GMI.²¹ Although the magnitude of improvement was lower than observed in the current study, it is important to note that patients in the Forlenza et al study had prior AID experience and at least 30 days of CGM data before Control-IQ initiation. This likely resulted in their low GMI at baseline, whereas the current study only included individuals switching from MDI therapy.

From a payer perspective, the advent of CGM has created opportunities to more accurately assess health care quality and performance using newly established glycemic metrics.⁵ For example, the National Committee for Quality Assurance (NCQA) included GMI for measurement year 2024 as a surrogate for HbA_1c in its HEDIS diabetes-related measures.²²

The decision to include the GMI as a surrogate for HbA_1c in the HEDIS measures greatly expands payer access to more timely information that may help support improvement in quality scores, which are essential to health plans in maintaining NCQA health plan ratings and CMS star ratings. Moreover, it is an important step in NCQA recognition of the value of other CGM metrics in assessing diabetes status and health plan performance.

The utility of CGM data extends to clinicians as well. Although the real-time data enable patients to be more effective in their daily self-management, the full value of CGM is realized through retrospective analysis of the data using the AGP.¹² The AGP is a 1-page report that aggregates the glucose data transmitted from patients’ smartphones via cloud-based transmission or downloaded from the CGM reader. The data are displayed in an easy-to-interpret report based on the established CGM metrics.⁵ Retrospective analysis of the data enables clinicians to quickly identify problematic glucose patterns and make appropriate adjustments in therapy when needed. It also facilitates more collaborative clinician-patient discussion and provides patients with a better understanding of their diabetes and the impact of medications, food, and other health behaviors on glycemic control.

To further streamline clinician workflows and improve efficiency, the Diabetes Technology Society initiated the Integration of Continuous Glucose Monitoring Data into the Electronic Health Record (iCoDE) project to facilitate automated uploading and integration of CGM data into the electronic health record (EHR).²³ The AGP also helps researchers by giving them the ability to obtain accurate data on clinical outcomes in diabetes by using the GMI surrogate.

Strengths of this study include a large sample size and real-world longitudinal data. This study is unique in that it measured the performance of an AID system among users switching from MDI therapy, whereas previous studies compared the effects of AID use in individuals with prior pump experience. Additionally, this study focused on performance on HEDIS standards on a population level across different payer types. The relatively large sample size and long duration of the study allowed for assessment of the sustainability of glycemic improvement associated with the use of an AID system.

Findings from this study offer valuable insights for payers and providers from quality-improvement and population-health perspectives, aiding understanding of the real-world effectiveness of AID systems, their relative impact across different lines of business, risk stratification, and the prioritization of strategies to optimize outcomes in diabetes care. Our findings also demonstrated how rapidly glycemic control improves when AID treatment is initiated. Rapid improvement in glycemic control assessed by %TIR has been reported in recent studies.^24-26 Evaluating performance by payer type provides meaningful context for payers on quality improvement, value, and population health.

Limitations

This study has notable limitations. For example, changes in the GMI and other CGM metrics reflect only postimplementation period observations. They do not indicate the magnitude of change from baseline because CGM data prior to Control-IQ initiation were unavailable for comparisons. Another limitation was the inability to measure performance for individuals for whom sufficient data were unavailable according to the minimum necessary CGM data (≥ 70% of 14 days of continuous use) per international consensus guidelines.⁶ It is also recognized that HbA_1c and GMI measure 2 different glycemic characteristics. Whereas HbA_1c measures the amount of circulating glucose attached to hemoglobin over the past 2 to 3 months,²⁷ the GMI is calculated using the glucose values captured during at least 14 days of CGM use and provides only an estimate of the HbA_1c level.¹⁹ When comparing HbA_1c and GMI, there is frequent discordance between the two.²⁴ At an individual level, there can be higher discordance between these measures, whereas at the population level, the relationship has been established with multiple studies, resulting in an equation to translate and compare the 2 measures.¹⁷ Importantly, use of the GMI has notable advantages over the HbA_1c measurement, which can be affected by long-term salicylate and opioid ingestion and other conditions that can falsely raise or lower HbA_1c results.^25,26 These factors do not influence the GMI. Additionally, because the GMI is automatically calculated by CGM downloaded software, laboratory costs are avoided, and the results are immediately available to clinicians and health plans. Moreover, individuals are not burdened by the pain and inconvenience of presenting at the laboratory or clinic for blood draws.

Inclusion of pediatric patients in the study is a potential limitation. Although several factors can influence the effectiveness and interpretation of Control-IQ AID outcomes in pediatric patients, we believe the small number of patients aged 6 to 17 years (n = 18) included in our analysis had minimal impact on the generalizability of our findings. A final limitation was the lack of more comprehensive information about patient characteristics, such as duration of diabetes, CGM use, comorbidities, medication history, and specific payer details (eg, managed care vs fee-for-service Medicare), which were unavailable and may be potential confounders.

CONCLUSIONS

In this retrospective database analysis of children/adolescents and adults with T2D previously treated with MDI therapy, use of Control-IQ was associated with a significantly higher percentage of individuals achieving the HEDIS goals for in-control diabetes across all payer types. As discussed, the iCoDE project is currently working to facilitate automated transfer and incorporation of CGM data into EHRs.²³ Immediate and broader access to these data would enable other health care professionals to leverage near real-time information for identification and risk stratification of clinical improvement opportunities across services, including pharmacist-led medication therapy management and nurse-led care management. The challenge of addressing the increasing prevalence of diabetes and the clinical and economic consequences of suboptimal glycemic control can be improved by increased awareness of and access to AID technology.

Acknowledgments

The authors thank Christopher Parkin, MS, CGParkin Communications, Inc, for providing editorial support in the development of this manuscript.

Author Affiliations: International Diabetes Center, HealthPartners (TWM), Minneapolis, MN; Tandem Diabetes Care (BVP, SMW, LHM, JEP, MRP), San Diego, CA; University of Utah (DCM, DIB), Salt Lake City, UT; Cleveland Clinic Endocrinology and Metabolism Institute (DI), Cleveland, OH; American Diabetes Association (OE), Alexandria, VA.

Source of Funding: Tandem Diabetes Care provided funding for this analysis.

Author Disclosures: Dr Martens reports consultancies or paid advisory boards for Abbott, Dexcom, Lilly, and Sanofi; grants received from Abbott, Amgen, Dexcom, Lilly, Novo Nordisk, Sanofi, Sequel, Tandem Diabetes Care, and Zealand; honoraria from Dexcom; lecture fees from Abbott and Dexcom; meeting attendance in conjunction with Dexcom and Sanofi; and employment with HealthPartners Institute, which contracts for his services. Drs Patel, Wang, Messer, and Pinsker and Ms Polin report employment with Tandem Diabetes Care. Dr Brixner reports consultancies or paid advisory boards for Embecta, Sanofi, and Tandem Diabetes Care. The remaining authors report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

Authorship Information: Concept and design (BVP, SMW); acquisition of data (MRP); analysis and interpretation of data (TWM, BVP, SMW, LHM, JEP, DCM, DIB, DI, OE); drafting of the manuscript (TWM, BVP, SMW, LHM, JEP, DCM, DIB, DI, OE); critical revision of the manuscript for important intellectual content (TWM, BVP, SMW, LHM, JEP, DCM, MRP, DIB, DI, OE); statistical analysis (MRP); obtaining funding (BVP, SMW); administrative, technical, or logistic support (BVP, SMW); and supervision (BVP, SMW).

Address Correspondence to: Sharon M. Wang, PharmD, MS, Tandem Diabetes Care, 12400 High Bluff Dr, San Diego, CA 92130. Email: swang@tandemdiabetes.com.

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