Learn How

Learn how AI can help HCOs to promote early diagnosis of chronic disease, mitigate the effects and progression of chronic conditions, and proactively avoid adverse events and complications. Discover how AI can help to identify undiagnosed patients, predict individuals at high-risk of disease progression, determine opportunities to improve chronic care management, and predict avoidable acute care utilization and other adverse events.

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

Vital Signs

Data indicating the status of the body’s vital and life-sustaining functions, with core vital signs including blood pressure, pulse, respiration rate, and body temperature.

EHR Problem Lists

Data capturing the most important problems facing a patient, when it occurred and when it was resolved, and lists other illnesses, injuries and factors that affect their health.

Remote Monitoring Data

Remote monitoring data capture key vital signs and health behaviors (e.g. blood pressure, heart rate, blood glucose, activity levels, etc.).

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ClosedLoop generates explainable predictions using

thousands of auto-generated, clinically relevant contributing factors

Robert Grisher
52-Year-Old Male
Risk patient will develop sleep apnea
Risk Score Percentile
Impact on Risk  |  Contributing Factor
+22% | High Blood Pressure (mmHg)
+17% | Decline in Sleep Quality (episodes / week)
2 to 5
+14% | Decline in Average Oxygen Saturation (Spo2 Pct)
95% to 90%
+11% | History of Asthma

What Are Chronic Diseases?

The United States spends $3.8 trillion annually on healthcare expenditures, 90% of which is spent for people with chronic and mental health conditions.¹ Chronic diseases, such as heart disease, cancer, and diabetes, are conditions that last one year or longer and require ongoing medical care, limit a person’s activities of daily living, or both. 

Why It Matters

A staggering 60% of adults have at least one chronic disease and nearly 30% have three or more.¹ Managing chronic conditions can be challenging. These conditions do not exist in isolation and frequently occur with other comorbidities. This is especially true for older adults—81% of older adults have at least two chronic conditions and 25% have five or more.²  

The presence of multiple conditions magnifies utilization, costs, and the vulnerability to complications.  Adults with three or more conditions are 3.6 times more likely to have an ED visit, 5.3 times more likely to be admitted, have 10 times higher healthcare costs, and fill 36 times more prescriptions than an adult without any conditions.²  They are also 15–18 times more likely to experience physical and social limitations which can lead to functional decline, particularly for older adults, and significantly decrease their quality of life.³ As the population continues to age, the prevalence and impact of chronic diseases is expected to continue to grow. 

AI Presents an Opportunity

Care management programs can mitigate the effects of chronic diseases, prevent adverse events, improve health outcomes, and reduce healthcare costs. Healthcare organizations (HCOs) can use AI to amplify the success of their programs.  Predictive analytics enable HCOs to identify high-risk individuals and predict adverse events and potential complications.  AI-driven insights also surface specific modifiable risk factors that may have otherwise gone undetected. With these insights, care teams can personalize care plans and improve patient outcomes and quality of life. 

Did You Know...

  • $3.4 trillion is the total U.S. spending on chronic disease treatment annually¹ 
  • 10x higher total healthcare costs are incurred by adults with three or more conditions compared to patients with no chronic conditions²
  • 30% of all adults in the U.S. have three or more chronic diseases²
Citations & Footnotes

1. “About Chronic Diseases.” Centers for Disease Control and Prevention, 2021, www.cdc.gov/chronicdisease/about/index.htm. Accessed 21 Mar. 2021.

2. Buttorff, Christine, et al. “Multiple Chronic Conditions in the United States.” Rand Corporation, 2017, doi: https://doi.org/10.7249/TL221. Accessed 21 Mar. 2021.

3. Fong, Joelle H. “Disability Incidence and Functional Decline among Older Adults with Major Chronic Diseases.” BMC Geriatrics, vol. 19, no. 1, 21 Nov. 2019, doi: 10.1186/s12877-019-1348-z. Accessed 21 Mar. 2021.

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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