Learn How

Learn how AI can help to reduce unplanned hospital admissions, avoid unforeseen readmissions, and promote effective care transitions. Discover how AI can identify at-risk patients with critical gaps in care continuity, distinguish patients in need of enhanced functional supports, and improve rehabilitation and transition planning.

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.

Lab Test Results

Data about important risk markers from tests used for diagnosis, monitoring therapy, or screening, with details about specific results and abnormal indicators.

Social Determinants of Health (SDoH)

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

Closedloop icon with a line

ClosedLoop generates explainable predictions using

thousands of auto-generated, clinically relevant contributing factors

Kaya Ahuja
75-Year-Old Female
Risk of unplanned hospital admission in the next six months
Risk Score Percentile
Impact on Risk  |  Contributing Factor
+25% | Increase in # of HCCs (12M)
4 to 7
+16% | Increase in Delirium Risk Index
3 to 5
+11% | # of Days Since Last PCP Visit
+5% | Pct Flu Vaccination Measure

What Are UHAs?

Unplanned hospital admissions (UHAs) are acute clinical events that require an urgent hospital admission. For older adults, UHAs often presage a critical turning point in their health and frequently lead to disability in activities of daily living (ADL), significantly diminishing their ability to maintain functional independence. UHAs are also associated with increased morbidity, mortality, and dramatically increased long-term care needs and costs. 

Why It Matters

Annually, an estimated 8.5 million* Medicare beneficiaries experience at least one unplanned hospital admission, and hospitalizations among older adults account for over one-third of all hospitalizations.¹ 30% of older adults hospitalized for acute events experience decline in their ability to perform ADL as a result of their hospitalization, and for many, it is permanent.² Patients discharged with new or additional ADLs are also approximately 20% more likely to die within one year.³ 

More than 25% of patients discharged with a new ADL or additional disability fail to recover their baseline functioning after one year, and UHAs reduces the likelihood of recovery from existing disabilities.³ For non-surgical hospitalizations, patients can also experience a decline in community mobility, and many show little evidence of recovery even after two years. Although functional decline following UHAs may be attributable to the initial cause of admission, aspects of the hospitalization experience itself, such as prolonged immobility and disorientation, can also contribute significantly.⁴ 

AI Presents an Opportunity

Predictive analytics present an opportunity to proactively identify individual patients at high risk for an UHA. This insight can support comprehensive care programs centered on reducing UHAs and may facilitate key intervention efforts. Leveraging AI can enable care teams to identify and address potential disconnects in the care continuum on an individual basis, improve transition planning, and provide additional support following planned procedures for high-risk patients. Armed with AI, providers are ultimately able to reduce UHAs, lower costs, and improve health outcomes. 

Did You Know…

  • 30% of older adults that are hospitalized experience a decline in their ability to perform activities of daily living²
  • More than 25% of patients discharged with a new ADL disability fail to recover their baseline functioning within a year³
  • $13,600 is the average cost of hospital stays for patients with an expected payer of Medicare¹
Citations & Footnotes

* This figure was calculated by taking the percentage of beneficiaries with UHAs in the final stage of the CMS Artificial Intelligence Health Outcomes Challenge and applying that to the total number of all Medicare beneficiaries.

1. AHRQ “Statistical Brief #261 - National Inpatient Hospital Costs: The Most Expensive Conditions by Payer, 2017.” Healthcare Cost and Utilization Project, Agency for Healthcare Research and Quality, Jul. 2020. Accessed Mar. 9, 2021. 

2. Wells, Elina U. et al. "Factors That Contribute To Recovery Of Community Mobility After Hospitalization Among Community-Dwelling Older Adults". Journal Of Applied Gerontology, vol 39, no. 4, 2018, pp. 435-441. SAGE Publications, doi:10.1177/0733464818770788.

3. Boyd, Cynthia M et al. “Recovery of activities of daily living in older adults after hospitalization for acute medical illness.” Journal of the American Geriatrics Society vol. 56, no. 12, Dec. 2008 pp. 2171-9. doi:10.1111/j.1532-5415.2008.02023.x

4. Zisberg, Anna et al. “Hospital-associated functional decline: the role of hospitalization processes beyond individual risk factors.” Journal of the American Geriatrics Society vol. 63, no. 1 17 Jan. 2015, pp. 55-62. doi:10.1111/jgs.13193.

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