Patient engagement with digital therapeutic leads to reduction of A1C and costs in T2DM patients: Cost savings are correlated to both A1C drops as well as patients’ starting A1C levels Kate Higgins, MPH1, Brian Griffin, MBA1, Malinda Peeples, MS, RN, CDE2, Mansur Shomali, MD, CM2, and Anand Iyer, MBA, PhD2 1IBM Watson Health. Bethesda, MD, 2WellDoc, Inc. Columbia, MD Background A Type 2 diabetes is the 7th leading cause of death in the US, affecting approximately 1 in 7 Americans.1 The cost of diabetes in the US is steadily rising, increasing 26% from 2012 to 2017, to $327 billion annually.2 Despite advances in pharmaceutical therapeutics and glucose sensing technologies, many patients’ continue to have high A1C values, putting them at a higher risk for complications and increased treatment costs.3 BlueStar® is an FDA 510K-cleared digital therapeutic designed (1) to coach adults with type 2 diabetes to self-manage their condition and (2) to enhance patient-provider communication, which has been shown to shift A1C levels in populations with diabetes.4-6 However, the current literature lacks established methods for quantifying reductions in A1C level into accurate cost savings estimates. The use of real-world, laboratory-result linked claims data allows for more accurate and up-to-date estimates of cost-savings, specific to starting A1C range. Figure 2. Total healthcare costs by A1C band pre-data cleansing (A) and post-data cleansing (B) Methods Conclusions The MarketScan® Commercial and Medicare Supplemental Databases7 were utilized to capture patients aged 40 or older with at least one inpatient or outpatient claim with a diagnosis of type 2 diabetes (International Classification of Diseases, Ninth Revision, Clinical Modification [ICD-9-CM] or ICD-10-CM). Patients with continuous enrollment with medical and pharmacy benefits from January 2014 to December 2015 were initially selected, to capture healthcare services and prescription experiences. Data cleansing was performed to create a clean cohort of stable diabetic patients (Figure 1). Total healthcare costs were assessed for each group of patients in each of the four A1C bands. Using actual patient data from BlueStar® studies4-6, a matrix of starting and ending A1C values was established to predict the shift in test values in this population and the savings associated with those shifts (Table 1) by calculating the weighted average starting and ending costs. The data cleansing procedures were critical in building a usable cost model for this analysis The use of a comprehensive and contemporary claims database allows more accurate cost estimates than previously published models This claims database-derived model can then be used to estimate cost savings based on interventions, such as BlueStar, where the baseline A1C as well as the A1C improvement are available Regardless of starting A1C, all BlueStar user groups demonstrated potential reductions of total healthcare costs, highlighting a significant opportunity for cost-savings Further analysis can help elucidate the categories of cost savings impacted by interventions at various A1C levels; i.e. pharmacy costs vs. utilization of hospital services, etc. as well as impacts on payments from programs such as STARS and HEDIS Figure 1. Data Cleansing References Results A1C Range Commercial Cost Savings (per patient, per month) Medicare Cost Savings (per patient, per month) A1C ≥7 $152 $116 A1C ≥8 $271 $254 A1C ≥9 $437 $306 Centers for Disease Control and Prevention. National Diabetes Statistics Report, 2017. Atlanta, GA: US Department of Health and Human Services, 2017. Economic Costs of Diabetes in the U.S. in 2017. Diabetes Care. 2018;41(5):917-28. Herman WH, Braffett BH, Kuo S, et al. The 30-Year Cost-Effectiveness of Alternative Strategies to Achieve Excellent Glycemic Control in Type 1 Diabetes: An Economic Simulation Informed by the Results of the Diabetes Control and Complications Trial/Epidemiology of Diabetes Interventions and Complications (DCCT/EDIC). J Diabetes Complications. 2018. Quinn CC, Clough SS, Minor JM, et al. WellDoc™ Mobile Diabetes Management Randomized Controlled Trial: Change in Clinical and Behavioral Outcomes and Patient and Physician Satisfaction. Diabetes Technol Ther. 2008;10(3):160-8. Quinn CC, Shardell MD, Terrin ML, et al. Cluster-Randomized Trial of a Mobile Phone Personalized Behavioral Intervention for Blood Glucose Control. Diabetes Care. 2011. Quinn CC, Sareh PL, Shardell ML, et al. Mobile Diabetes Intervention for Glycemic Control: Impact on Physician Prescribing. J Diabetes Sci Technol. 2014;8(2):362-70. IBM Watson Health. IBM MarketScan Research Databases for Health Services Researchers, Cambridge, MA. 2018. Patients with diabetes captured in our analysis had average annual costs ranging from $10,601 to $19,980, per patient (Figure 2). Total healthcare costs were lowest for the “in control” A1C patients (A1C 6-6.99) and progressively increased as A1C ranges increased. Drop in A1C levels in patients with diabetes was shown to not be linear; starting A1C level impacted the potential cost reduction. Patients with a higher starting A1C value (8-8.99, ≥9) have a larger financial impact associated with the same 1-point A1C reduction, when compared to patients with a lower starting A1C (7-7.99). Table 1. Estimated cost saving from BlueStar per patient, per month, by A1C range 18th Annual Meeting Bethesda, MD – Nov 8-10, 2018