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Learn how AI can help to reduce avoidable ED utilization, improve chronic care management, and promote care continuity. Discover how AI can help to predict declining health due to chronic conditions, identify barriers to care that may arise from social determinants of health, and pinpoint disconnects in the care continuum.

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.

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.

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

Jack Stricker
42-Year-Old Male
Risk of an avoidable ED visit in the next six months
Risk Score Percentile
Impact on Risk  |  Contributing Factor
+25% | Diagnosis of Asthma (12M)
+17% | Access to Primary Care
+14% | Self-rated Health
+10% | # of ER Visits (6M)

What Is Avoidable ED Utilization?

In 2018, there were 130 million emergency department (ED) visits in the United States, the equivalent of 40.4 ED visits per 100 people.¹  According to a recently revised algorithm to assess ED visit severity, less than half were clinically emergent.  The majority either did not require ED resources or could have been avoided with better primary care.  An estimated 33% of visits were non-emergent, 20% were primary care treatable, and another 4% needed the ED but were potentially avoidable. These visits cost an average of more than $2000 individually, and the total cost exceeded $150 billion.²   

Why It Matters

Such high levels of avoidable ED visits occur for multiple reasons, including poor chronic disease management, insufficient care site alternatives, lack of adequate primary care access, or other significant barriers to care.³  More than half of frequent ED utilizers have at least one chronic condition, and patients living in the lowest income communities represent roughly one-third of aggregate ED visits and costs.⁴⁻⁵  Preventing ED visits in older adults is particularly noteworthy, since a trip to the ED for an older adult can often be a tipping point; ED utilization by older adults increases risk of developing a potentially permanent disability by roughly 15%.⁶⁻⁸ 

AI Presents an Opportunity

Predictive analytics present an opportunity to proactively identify individual patients at high risk for avoidable ED visits. This insight can enable care teams to identify and address potential issues and support initiatives to improve chronic care management, promote care continuity, and address barriers to care. Armed with AI, providers will be in a position to reduce avoidable ED visits, lower costs, and improve health outcomes. 

Did You Know…

  • 33% of ED visits are non-emergent² 
  • Medicaid beneficiaries are more likely to use the ED for non emergency care²
  • 5% of ED visits are for mental health and substance abuse² 
  • 20% of ED visits are primary care treatable²
Citations & Footnotes

1. National Center for Health Statistics. Emergency Department Visits. Emergency Department Visits. Published 2021. Accessed March 22, 2021. https://www.cdc.gov/nchs/fastats/emergency-department.htm

2. Lemke KW, Pham K, Ravert DM, Weiner JP. A Revised Classification Algorithm for Assessing Emergency Department Visit Severity of Populations. The American Journal of Managed Care. 2020;26(3). https://www.ajmc.com/view/a-revised-classification-algorithm-for-assessing-emergency-department-visit-severity-of-populations

3. Dowd B, Karmarker M, Swenson T, et al. Emergency Department Utilization as a Measure of Physician Performance. American Journal of Medical Quality. 2013;29(2):135-143. doi:10.1177/1062860613487196

4. Capp, Roberta, et al. “Coordination Program Reduced Acute Care Use and Increased Primary Care Visits Among Frequent Emergency Care Users.” Health Affairs, vol. 36, no. 10, Oct. 2017, doi.org/10.1377/hlthaff.2017.0612. Accessed 4 Mar. 2021. 

5. Moore, Brian J., Liang, Lan. “Statistical Brief #268: Costs of Emergency Department Visits in the United States, 2017.” Agency for Healthcare Research and Quality, Dec. 2020. https://www.hcup-us.ahrq.gov/reports/statbriefs/sb268-ED-Costs-2017.pdf. Accessed 4 Mar. 2021. 

6. NCOA. “Healthy Aging Facts.” National Council on Aging, 10 Jul. 2018, https://www.ncoa.org/resources/fact-sheet-healthy-aging/. Accessed 4 Mar. 2021. 

7. Graham, Judith. “For Elder Health, Trips To The ER Are Often A Tipping Point.” Kaiser Health News, 11 Jan. 2018, khn.org/news/for-elder-health-trips-to-the-er-are-often-a-tipping-point/. Accessed 4 Mar. 2021.

8. Nagurney, Justine M., et al. “Emergency Department Visits Without Hospitalization Are Associated With Functional Decline in Older Persons.” Annals of Emergency Medicine, vol. 69, no. 4, Apr. 2017, pp. 426–433, 10.1016/j.annemergmed.2016.09.018. 

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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