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Learn how AI can help to promote early identification of patients likely to experience a cost bloom, reduce the incidence and progression of chronic conditions, and avoid traditional risk scoring methods that fail to explicitly account for cost bloomers. Discover how AI can help to predict the low-cost patients most likely become cost bloomers, identify gaps in chronic condition management, and assess a wide variety of distinct and often overlooked patient data to identify rising risk.

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.

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.

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ClosedLoop generates explainable predictions using

thousands of auto-generated, clinically relevant contributing factors

Mark Wilson
48-Year-Old Male
Risk of costs rising at least 50% in the next 12 months
Risk Score Percentile
Impact on Risk  |  Contributing Factor
+21% | # of Distinct Providers (12M)
+16% | Pct Abnormal Lab Results
+11% | Resting Heart Rate
+8% | Total # of Lab Tests (3M)

What Are Cost Bloomers?

It is well documented that a small percentage of patients account for most of the healthcare expenditures in the United States; the costliest 10% of patients account for more than two-thirds of the healthcare expenditures in the United States.¹ What is less well known is that, while some high-cost patients have consecutively high-cost years, the majority experienced what has been termed a ‘cost bloom’—a dramatic year-over-year cost increase that moved them from lower expenditure deciles into the upper decile of spending. These previously low-cost patients account for roughly 68% of all high-cost patients annually.² 

Why It Matters

Proactively identifying such rising risk patients is especially important, as they may disproportionately benefit from interventions designed to mitigate future costs. Such efforts can be an effective way to simultaneously improve quality and reduce costs, and are distinct from programs that address the needs of existing high-cost patients.

Unfortunately, accurately identifying these rising risk patients can prove challenging for healthcare organizations (HCOs). Nearly 50% of cost bloomers are likely to have no inpatient hospital costs, and when compared to persistently high-cost patients, are diagnosed with four times fewer chronic conditions.²  

In addition, and to the extent that standard prediction tools and reimbursement formulas have failed to accurately predict or account for ‘cost bloomers,’ HCOs pay the price. Such models leave HCOs that are held accountable for the total cost of care vulnerable to inadequate funding, financial penalties, and unfair performance assessments, which can also ultimately affect the health outcomes for patients.² 

AI Presents an Opportunity

As HCOs invest to improve patient outcomes and avoid unnecessary costs, it is paramount they have the ability to anticipate and address the needs of rising risk patients. AI can help by accurately identifying patients likely to be cost bloomers. Using AI-based models can also avoid the drawbacks of traditional risk scoring by allowing providers to easily integrate a wide range of distinct and dynamic patient data that would otherwise have been excluded. By leveraging AI, HCOs can launch proactive interventions designed to address the needs of rising risk patients in ways that drive a disproportionate benefit in quality, costs and future health outcomes. 

Did You Know…

  • 25% of patients identified as low-cost move to a higher echelon of expenditure each year³ 
  • More than half of high-cost patients in a three-year analysis did not have high costs in the prior two years²
  • $28,468 is the annual mean expenditure for the top 10% of high-cost patients¹
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. 

2. Tamang, Susan, Milstein, Arnold, et al. “Predicting patient ‘cost blooms’ in Denmark: a longitudinal population-based study.” BMJ open, vol. 7, no. 1, Jan. 2017. DOI: 10.1136/bmjopen-2016-011580.

3. Cohen, Steven B. “Statistical Brief #481 The Concentration and Persistence in the level of Health Expenditures over Time: Estimates for the U.S. Population, 2012-2013.” Agency for HealthCare Research and Quality, Sep. 2015. PMID: 28783282. Accessed on 12/11/2020.

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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