Learn How

Learn how AI can help HCOs to reduce utilization and costs of care for patients with chronic conditions, slow the progression of chronic conditions, and potentially prevent adverse outcomes and serious complications. Discover how AI can help to predict patients at high risk for acute events, identify the potential impact of improving patient-specific risk factors, and distinguish opportunities for long-term treatment in non-acute settings.

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

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.

Rx Claims

Data extracted from health insurance pharmacy claims with details about each medication and its type, fill dates, days supply, pharmacy location, and prescribing clinician.

Health Risk Assessments

Self-reported data from health questionnaires that assess a person’s individual medical history, health risks, lifestyle, health behaviors, and quality of life.

Closedloop icon with a line

ClosedLoop generates explainable predictions using

thousands of auto-generated, clinically relevant contributing factors

Bridget Faulkner
62-Year-Old Female
Risk that total costs in the next 12 months will be in top 5%
Risk Score Percentile
Impact on Risk  |  Contributing Factor
+31% | # of Unique Medications
+21% | Increase in Charlson Comorbidity Index
5 to 8
+14% | Adherence to Loop Diuretics
+8%  |  Self-rated Health (1-5)

What Contributes to Total Cost and Utilization?

A small percent of patients utilize a disproportionately large portion of total healthcare resources.  The costliest 5% of patients incur 50% of all healthcare expenditures, and costs for the top 1% exceed 20% of total healthcare spending.¹ Annual healthcare expenditures in the U.S. totaled $3.8 trillion in 2019 and are projected to reach $6.2 trillion by 2028.² With such concentrated resource utilization, targeting the most expensive patients with care management efforts holds the potential to improve outcomes and curb costs. 

Why It Matters

The specific individuals that comprise the costliest cohort vary annually, but patients with chronic conditions consistently make up a significant portion. People with five or more chronic conditions—an astonishing 12% of U.S. adults—incur 14 times the cost of patients without chronic conditions, and one-third of these patients visit the ED at least once a year.³  Moreover, chronic conditions are exceedingly prevalent; 60% of adults have at least one chronic condition and 81% of adults aged 65 and over have multiple.³ In total, treatment for people with chronic conditions accounts for 90% of annual healthcare costs, and seven of the top ten leading causes of death are chronic diseases.⁴𝄒⁵

AI Presents an Opportunity

Fortunately, AI-based models can accurately predict which patients are likely to have high utilization, costly adverse events, or a functional decline that may lead to a sharp increase in resource utilization. With the insights surfaced by AI, care teams can facilitate care management programs to promote continuity of care, monitor treatment effectiveness, and support patient self-management and education. Ultimately, intervention efforts specifically tailored to the individual patients likely to require the greatest utilization can significantly reduce adverse events, improve outcomes, and reduce healthcare costs. 

Did You Know…

  • 24% of all healthcare expenditures comes from the top 1% costliest patients.¹
  • 90% of healthcare costs are tied to chronic disease treatment⁴
  • 5% of all patients account for more than 50% of healthcare spending¹
Citations & Footnotes

1. Wammes, Joost Johan Godert, et al. “Systematic review of high-cost patients' characteristics and healthcare utilisation.” BMJ open vol. 8, no. 9, Sep. 2018, doi:10.1136/bmjopen-2018-023113. Accessed on 12/09/2020.

2. “National Health Expenditures 2019 Fact Sheet.” Centers for Medicare and Medicaid Services, Mar. 2020. Accessed 14 Dec. 2020. 

3. Buttorff, Christine, et al. “Multiple Chronic Conditions in the United States.” RAND Corporation, 2017. https://www.rand.org/pubs/tools/TL221.html. Accessed 14 Dec. 2020. 

4. “Health and Economic Costs of Chronic Diseases.” Centers for Disease Control and Prevention, Nov. 2020, https://www.cdc.gov/chronicdisease/about/costs/index.htm#ref1. Accessed 9 Dec. 2020. 

5. Raghupathi, Wullianallur, and Viju Raghupathi. “An Empirical Study of Chronic Diseases in the United States: A Visual Analytics Approach.” International journal of environmental research and public health vol. 15, no. 3, Mar. 2018, doi:10.3390/ijerph15030431.

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

Learn What ClosedLoop Can Do for Your Organization

The industry’s best collection of customizable predictive models for common healthcare use cases.

Talk To An Expert