Learn How

Learn how AI can help to promote early diagnosis of HF, reduce HF readmissions, and address avoidable adverse events. Discover how AI can help to identify patients at high risk of HF, predict preventable hospitalization due to HF, and identify complications tied to modifiable risk factors (e.g., poor medication adherence).

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.

Rx Claims

Data extracted from health insurance pharmacy claims with details about each medication and its type, fill dates, days supply, pharmacy location, and prescribing clinician.

Electronic Health Records

EHR data with comprehensive patient histories of vital signs and symptoms, problem lists and chief complaints, tests results, diagnoses and procedures, and prescriptions.

nodes
Closedloop icon with a line

ClosedLoop generates explainable predictions using

thousands of auto-generated, clinically relevant contributing factors

Camille Phillips
57-Year-Old Female
Risk of admission for acute heart failure in the next 12 months
Risk Score Percentile
98
Impact on Risk  |  Contributing Factor
Value
+27% | # of Acute Decompensated HF Admissions (12M)
2
+19% | Decline in LV Ejection Fraction
0.45 to 0.35
+14% | Adherence to Loop Diuretics
60%
+11% | Hemoglobin (g/dL)
11.5

Why It Matters

Today, approximately 6.5 million adult Americans are living with heart failure (HF).¹ By 2030, this is estimated to rise to 8 million people with total economic costs reaching $70 billion at which point 2.97% of U.S. adults will have HF and 71% of them will be age 65 or older.² With more than one million hospitalizations each year, HF is one of the most common causes of admissions and readmissions and a leading cause of mortality; after a diagnosis of HF, survival estimates are 50% and 10% at five and ten years, respectively.³

Beneficiaries with HF constitute 10.5% of all FFS Medicare beneficiaries and their costs (excluding medications) make up 33.2% of all Medicare costs.⁴  HF is a chronic disease characterized by acute exacerbation, and a major cost driver is treatment for worsening HF and fluid overload, 80% of which occurs in inpatient settings.²𝄒³  

Many instances of hospitalization for HF patients are considered preventable, yet HF remains the leading cause of hospitalization for patients over age 65.²𝄒⁵ HF admissions also generate the highest number and highest rate of 30-day readmissions among Medicare beneficiaries.⁶𝄒⁷  

AI Presents an Opportunity

Organizations can employ predictive analytics to identify high risk HF patients and use insights from AI to enroll patients in care management programs.  Proactively identifying high-risk HF patients and intervening to prevent significant exacerbations that cause hospitalization is essential to improving quality of life and reducing avoidable costs. For example, interventions centered on patient self-management have been shown to reduce the odds of readmission after one year by 40%.⁸ Such programs prevent hospitalizations by strengthening care continuity, improving adherence to complex medication regimens, and ultimately identifying early warning signs more readily. 

Did You Know…

  • 33% of all Medicare costs are for patients with heart failure⁴
  • 10% of HF patients survive 10 years after being diagnosed with HF³ 
  • $70 billion is the projected economic cost in 2030 for patients with HF¹
  • 6.5 million adult Americans are living with heart failure²
expand_more
Citations & Footnotes

1. Benjamin, Emelia J., et al. “Heart Disease and Stroke Statistics—2019 Update: A Report From the American Heart Association.” Circulation, vol. 139, no.10, Jan. 2019, https://www.ahajournals.org/doi/10.1161/CIR.0000000000000659 

2. Fitch K, Lau J, Engel T, Medicis JJ, Mohr JF, Weintraub WS. The cost impact to Medicare of shifting treatment of worsening heart failure from inpatient to outpatient management settings. ClinicoEconomics and Outcomes Research. 2018;Volume 10:855-863. doi:10.2147/ceor.s184048

3. Roger VL. Epidemiology of Heart Failure. Circulation Research. 2013;113(6):646-659. doi:10.1161/circresaha.113.300268

4. Fitch K, Engel T, Lau J. The Cost Burden of Worsening Heart Failure in the Medicare Fee for Service Population: An Actuarial Analysis. Milliman, Inc; 2017. 

5. Michalsen A, König G, Thimme W. “Preventable causative factors leading to hospital admission with decompensated heart failure.” BJM Journals, Heart, vol. 80, no. 5, Nov. 1998, pp. 437–441. 

6. Jencks, Steven F., et al. “Rehospitalizations among patients in the Medicare fee-for-service program.”  The New England Journal of Medicine, vol. 360, no. 14, Apr. 2009, pp. 418–1428. DOI:10.1056/NEJMsa0803563 

7. Reddy, Yogesh, et al. “Readmissions in Heart Failure: It’s More Than Just the Medicine.” Mayo Clinic Proceedings, vol. 94, no. 10, Oct. 2019, pp. 1919–1921. DOI: https://doi.org/10.1016/j.mayocp.2019.08.015

8. Jovicic, A., et al. “Effects of Self-Management Intervention on Health Outcomes of Patients with Heart Failure: A Systematic Review of Randomized Controlled Trials.” BMC Cardiovasc Disorders, vol. 6, no. 43, 2 Nov. 2006, https://doi.org/10.1186/1471-2261-6-43.

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
Cheryl

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 amount to the Johns Hopkins ACG predicted spend amount and found that 75% of the time ClosedLoop was closer, and in most cases significantly closer, to the actual spend amount.

Vickie Rice
Senior Vice President of Strategic Analytics, 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