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Learn how AI can help to enhance care documentation, improve HCC capture rates, and accurately reflect the profiles of complex patients. Discover how AI can help to identify patients with undocumented-yet-suspected HCCs, surface actionable opportunities to clinical teams, estimate economic impacts, and use previous HCC documentation decisions to automatically improve.

Automatically ingest data from dozens of health data sources including...

Unstructured Clinical Notes

Data extracted from EHR clinical notes for conditions being diagnosed, monitored, or treated about important clinical concepts related to symptoms, test results, diagnoses and treatments.

e-Prescribing Data

Data from electronic prescriptions detailing key information about medications, dosage, patient instructions for frequency and timing, and available refills.

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.

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ClosedLoop generates explainable predictions using

thousands of auto-generated, clinically relevant contributing factors

Jeff Lee
71-Year-Old Male
Likely undocumented condition for CHF
Risk Score Percentile
Impact on Risk  |  Contributing Factor
+29% | Decline in LV Ejection Fraction
.45 to .35
+14% | Dyspnea on Exertion
June 14, 2020
+10% | Procedure for Electrocardiogram
June 14, 2020
+10% | e-Rx for Beta Blockers
June 14, 2020

What Are HCCs?

Risk adjustment is quietly becoming an economic cornerstone in healthcare. It determines payments to Medicare Advantage plans—accounting for more than 24 million Medicare beneficiaries—and is increasingly implemented in value-based contracts.¹ At risk adjustment’s core is the Hierarchical Condition Category (HCC) model, which is based on diagnosis codes captured during clinical encounters. Because HCCs determine payments, they influence an organization’s economic viability, available resources, and care delivery capacity, which means accurate and complete diagnosis coding is becoming an economic and clinical imperative.

Why It Matters

Despite this growing importance, the diagnostic codes that are foundational to the HCC model can be inaccurate or incomplete, particularly for new patients. Inaccurate and incomplete coding not only reduces payments for healthcare organizations, it impacts the resources available for addressing the full spectrum of patient needs, especially for complex patients.

AI Presents an Opportunity

ClosedLoop’s Suspect HCC models identify patients with undocumented-yet-suspected HCCs and surface the contributing factors that best explain why each HCC is suspected. This insight enables organizations to accurately and completely reflect their complex patient profiles, identify and prioritize actionable opportunities for clinical teams, and produce expected RAF scores and economic impact. Moreover, ClosedLoop’s models assess documentation determinations to automatically improve overall accuracy, continuously refining diagnosis identification while minimizing “false positive” suspects. This allows organizations to remain agile and adapt as CMS continues to modify their risk adjustment processes and reimbursement models.

Did You Know...

  • Only 45% of chronic disease is reconfirmed in Medicare year-over-year²
  • 55% of HCOs reported accurately coding based on patient data as their biggest challenge²
  • #1 HCO Need was an easy way to prioritize patients with missing diagnosis codes²
Citations & Footnotes

1. Freed, Meredith, et al. “A Dozen Facts about Medicare Advantage in 2020.” Kaiser Family Foundation, 13 Jan. 2021, https://www.kff.org/medicare/issue-brief/a-dozen-facts-about-medicare-advantage-in-2020/. Accessed 11 Mar. 2021.

2. Smith D. “Case Study: Hierarchical condition categories - Get documentation and coding right - 3M Inside Angle.” 3M Inside Angle. Published 2017. Accessed March 25, 2021. https://www.3mhisinsideangle.com/klab-post/case-study-hierarchical-condition-categories-get-documentation-coding-right/

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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