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Learn how AI can help to promote early diagnosis of diabetes, decrease patient risk for serious complications, and reduce the incidence of potentially preventable diabetes. Discover how AI can help to identify patients with undiagnosed diabetes, predict adverse complications (e.g., foot amputation), and determine patients at high risk for developing diabetes.

Automatically ingest data from dozens of health data sources including...

Electronic Health Records

EHR data with comprehensive patient histories of vital signs and symptoms, problem lists and chief complaints, tests results, diagnoses and procedures, and prescriptions.

Medical Claims

Data extracted from health insurance medical claims with details about dates and place of service, diagnosis codes, key procedures, use of medical equipment, and provider specialties.

Social Determinants of Health (SDoH)

Geo-centric data with details about the social and environmental influences on people’s health and outcomes.

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ClosedLoop generates explainable predictions using

thousands of auto-generated, clinically relevant contributing factors

Haley Norton
59-Year-Old Female
Risk of potentially avoidable hospitalization for diabetes in the next 12 months
Risk Score Percentile
Impact on Risk  |  Contributing Factor
+21% | Medicare Disability Status
+18% | Increase in # of HCCs (12M)
4 to 6
+11% | # of Days Since Last PCP Visit
+9% | Pct Mammography Screening

Why It Matters

Diabetes affects approximately 34 million adults—more than 10% of Americans—and is the seventh leading cause of death in the United States.¹ Uncontrolled diabetes can lead to biochemical imbalances that cause acute life-threatening events and hospitalization.² Potential complications include significantly increased risk of heart attacks, strokes, kidney failure, lower-limb amputations, and adult blindness. Diabetes is also steadily becoming more common; in the last 20 years, the number of adults diagnosed with diabetes has more than doubled.¹ But despite its increasing prevalence, more than one in five people with diabetes are estimated to be undiagnosed and unaware of their condition.¹

Diabetes is also responsible for exorbitant expenditures with a total estimated cost of $327 billion.³ On average, people with diagnosed diabetes incur medical expenditures of $16,752 per year, approximately $9,601 of which is attributed to diabetes.³ 

AI Presents an Opportunity

To improve health outcomes and combat costs, providers can leverage predictive analytics to proactively identify patients at high risk for diabetes and patients likely to experience severe complications. This insight can enable cost-effective, proven interventions. Enrollment in comprehensive prevention programs can reduce risk of type 2 diabetes by more than 50%, and interventions based on diabetes self-management education are extremely cost-effective ($5,047/QALY)* compared to routine care.⁴𝄒⁵

Diabetes interventions based on self-management can empower people to dramatically impact their own health.⁶ Self-monitoring of blood sugar to achieve glycemic control can reduce the risk of eye disease, kidney disease, and nerve disease by 40%.⁴ Other self-management interventions include adherence to healthy dietary practices and engaging in regular exercise. Additionally, strengthening primary care continuity is critical. Health care services that include regular foot exams can prevent up to 85% of diabetes-related amputations, and regular eye exams can prevent up to 90% of diabetes-related blindness.⁷𝄒⁸ 

Did You Know…

  • More than 4000 Americans are diagnosed with diabetes every day¹ 
  • 88 million Americans have prediabetes¹
  • 2.3x higher average medical expenditures for people with diabetes³
Citations & Footnotes

* Public health interventions that cost less than $50,000 per QALY are widely considered cost-effective.

1. CDC. “National Diabetes Statistics Report, 2020.” U.S. Department of Health and Human Services, 18 Feb. 2020. 

2. Kim, Sunny. “Burden of Hospitalizations Primarily Due to Uncontrolled Diabetes.” American Diabetes Association, vol. 30, no. 5, May 2007, pp. 1281-1282, doi.org/10.2337/dc06-2070. 

3. American Diabetes Association. “Economic Costs of Diabetes in the U.S. in 2017.” Diabetes Care, vol. 41, no. 5, 22 Mar. 2018, pp. 917-928. doi.org/10.2337/dci18-0007.

4. CDC “Cost-Effectiveness of Diabetes Interventions.” Centers for Disease Control and Prevention, 29 Sep. 2020, https://www.cdc.gov/chronicdisease/programs-impact/pop/diabetes.htm. Accessed 12 Feb. 2021. 

5. Zhao, Xilin, et al. “ Cost-effectiveness of Diabetes Prevention Interventions Targeting High-risk Individuals and Whole Populations: A Systematic Review.” American Diabetes Association: Diabetes Care, vol. 43, no. 7,  Jul. 2020, pp. 1593-1616. doi.org/10.2337/dci20-0018.

6. Shrivastava, Saurabh, et al. “Role of Self-Care in Management of Diabetes Mellitus.” Journal of Diabetes & Metabolic Disorders, vol. 12, no. 1, 2013, p. 14, 10.1186/2251-6581-12-14.

7. Geiss, Linda, et al. “Resurgence of diabetes-related nontraumatic lower-extremity amputation in the young and middle-aged adult US population.” Diabetes Care, vol. 42, no. 1, Jan. 2019, pp. 50–54.  DOI: 10.2337/dc18-1380

8. Mitchison, Ann P., et al. “Resurgence of diabetes-related nontraumatic lower-extremity amputation in the young and middle-aged adult US population.” British Medical Journal: Open Diabetes Research & Care, vol. 5, 31 Jul. 2017, no. 1, doi.org/10.1136/bmjdrc-2016-000333. 

ClosedLoop is an exciting and important partner in our strategy to develop timely predictive insights through operationalized AI solutions that will drive member engagement and better health outcomes.

Pat Wang
President & CEO, Healthfirst

Our partnership with ClosedLoop adds the depth of AI to more accurately identify those patients who are most likely to benefit from our primary care interventions as well to evaluate these interventions and make more relevant and timely modifications to improve their effectiveness.

David Klebonis
Chief Operating Officer, PBACO

We are extremely impressed with the predictive modeling capabilities the ClosedLoop platform has delivered. The ClosedLoop team has exceeded all defined goals and benchmarks set to date and we anticipate a substantial return on our investment as these predictions are operationally deployed.

Cheryl Lulias
President & Executive Director, MHN

We’re able to store and operationalize analytics directly from ClosedLoop. That’s driving real value—it’s accelerated the implementation of key insights into clinical workflows and it allows us to more easily account for all of the different factors that influence intervention decisions.

Christer Johnson
Chief Analytics Officer, Healthfirst

At SWHR, our patient-centered network is committed to innovative care models that are value-based, high-quality, and data-driven. Because of our partnership with ClosedLoop, we’re better able to identify and act on the most impactful opportunities for our physician partners to improve patient results and reduce unnecessary costs.

Dr. Jason Fish
Chief Medical Officer & SVP, Southwestern Health Resources

We compared the ClosedLoop predicted spend to the spend predicted by a leading rules-based risk prediction engine and found that 75% of the time ClosedLoop was closer, and in most cases significantly closer, to the actual spend.

Phillip Bruns
Chief Technology Officer, CareATC

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