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Learn how AI can help HCOs to reduce avoidable ER visits and observation stays, enhance continuity of care, and avert adverse events to improve utilization and outcomes. Discover how AI can help to identify patients at the highest risk for ER visits and observation stays, identify gaps in the care continuum, and enable proactive screening and patient assessment.

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.

Operations & Services

Data from health plans, clinics and service providers that capture details about service interactions, billing and payment, technical issues, and complaints.

Care Quality

Data with details from CMS Hospital Compare and other quality measures related to timely and effective care, complications, and readmissions and deaths.

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ClosedLoop generates explainable predictions using

thousands of auto-generated, clinically relevant contributing factors

Ryan Logger
71-Year-Old Male
Risk of an ER visit or observation stay in the next six months
Risk Score Percentile
Impact on Risk  |  Contributing Factor
+26% | Number of ER Visits (12M)
+19% | Increase in Delirium Risk Index
4 to 6
+14% | # of Missed / Rescheduled Appts (12M)
+9% | 30-day Hospital Readmit Rate Measure

Why It Matters

Patients in the U.S. have more than 144 million ER visits and observation stays each year.¹ Experts have concluded that some ER visits are avoidable (i.e. that they don’t require the intense resources of the ER or could have been avoided with better primary care).  But other ER visits are clinically emergent and cannot be avoided. Even when a patient is not admitted to the hospital, emergent visits can be signals of critical health challenges—particularly for older adults. 

Older adults often experience a period of heightened vulnerability to adverse outcomes following an ER visit or observation stay, and an initial ER visit can lead to repeat visits and increased risk of morbidity and mortality.²  Roughly two-thirds of older adults return home after an ER visit, but in the six months following, they are 20–25% more likely to experience functional disabilities and a persistent decline in their community mobility.³𝄒⁴  

Older patients that required minimal assistance prior to a visit may enter a downward spiral, either from an acute injury or the exacerbation of a chronic condition.  For example, patients who sustain a serious fall-related injury (a leading cause of ER visits for older adults) often begin to limit their daily activities, which can lead to a decline in their health. Alternatively, underlying risk factors and complications, such as depression or delirium, can intensify following an ER visit and result in adverse health outcomes.

AI Presents an Opportunity

ER visits and observation stays are becoming increasingly common, and as the number of elderly Americans grows, addressing the needs of patients at the greatest risk for visits is imperative.⁵𝄒⁶  AI-based models can help healthcare organizations (HCOs) identify patients at risk for ER visits and observation stays, surface modifiable risk factors, and streamline patient outreach.  Care teams can leverage AI-enhanced insights to promote continuity of care, proactively assess patients and screen for functional decline, reduce potentially avoidable adverse events, and improve health outcomes.⁷ 

Did You Know…

  • 30 million older adults account for nearly 30 million ER visits annually¹
  • 20–25% of older patients experience functional decline in the six months after an ER visit²
  • $20 billion is the annual cost associated with ER visits from older adults¹
Citations & Footnotes

1. Moore, Brian J., Liang Lan. “Statistical Brief #268: Costs of Emergency Department Visits in the United States 2017.” Agency for Healthcare Research and Quality, Healthcare Cost and Utilization Project, Dec. 2020. https://www.hcup-us.ahrq.gov/reports/statbriefs/sb268-ED-Costs-2017.pdf. Accessed 16 Mar. 2021. 

2. 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, doi:10.1016/j.annemergmed.2016.09.018.

3. 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 16 Mar. 2021. 

4. Brown, Cynthia J., et al. “Impact of Emergency Department Visits and Hospitalization on Mobility Among Community-Dwelling Older Adults.” The American Journal of Medicine, vol. 129, no. 10, Oct. 2016, pp. 1124.e9-1124.e15, doi:10.1016/j.amjmed.2016.05.016.

5. Figueroa, Jose F., et al. “Trends in Hospitalization vs Observation Stay for Ambulatory Care–Sensitive Conditions.” JAMA Internal Medicine, vol. 179, no. 12, 1 Dec. 2019, p. 1714, doi:10.1001/jamainternmed.2019.3177.

6. Sheehy, Ann M, et al. “Identifying Observation Stays in Medicare Data: Policy Implications of a Definition.” Journal of Hospital Medicine, vol. 14, no. 2, 1 Feb. 2019, pp. 96–100, doi: 10.12788/jhm.3038. Accessed 16 Mar. 2021.

7. Fleischman, William. “Study: Older Patients Vulnerable to Functional Decline Following ED Visit.” ED Management : The Monthly Update on Emergency Department Management, vol. 29, no. 5, 2017, pp. 58–59.

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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