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Learn how AI can help to reduce uncompensated care, improve utilization to reduce costs, and optimize payment collection. Discover how AI can help to predict patients likely to delay or defer payment, identify acute care utilization for treatment that could be provided in a non-acute setting, and predict patients that are unable to pay but qualify for benefits.

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.

Social Determinants of Health (SDoH)

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

Operations & Services

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

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ClosedLoop generates explainable predictions using

thousands of auto-generated, clinically relevant contributing factors

Victoria Washburn
37-Year-Old Female
Likelihood that patient bill will not be paid in the next six months
Risk Score Percentile
Impact on Risk  |  Contributing Factor
+24% | Uninsured Children %age Measure
+18% | # of Avoidable ER Visits (12M)
+12% | # of Missed / Rescheduled Appts (12M)
+10% | Reported Barriers to Care

Why It Matters

Recent national economic hardships are placing an incredible financial strain on Americans.  As a result, healthcare organizations (HCOs) are grappling with increasing payment collection difficulties, major margin reductions, and a significant loss of revenue. With more than one-third of Americans polled in 2017 reporting that an unexpected medical bill greater than $100 would push them into debt, patients who were already struggling to pay for necessary care are now entirely unable to pay.¹  This leads to patients deferring treatment and delaying payment, forcing organizations to contend with a loss of revenue, the increased burden of uncompensated care, and costly ED utilization and avoidable admissions when chronic conditions progress due to lack of care continuity. 

AI Presents an Opportunity

While the current economic challenges are daunting, AI-based models are perfectly positioned to help healthcare organizations predict propensity to pay. AI can enable organizations to identify the likelihood of payment for care through analysis of socioeconomic variables, EMRs, clinical notes, credit histories, and individual payment histories. Armed with this insight, organizations can optimize their collection methods on an individual basis. This may be achieved by supporting patients with financial literature, identifying patients likely to qualify for health benefits, and predicting what percentage amounts will be recoverable from debtors to reduce write-offs. Critically, this enables HCOs to increase revenue, reduce the financial burden of medical debt on patients, and work to mitigate any adverse impact on patient outcomes. 

Did You Know…

  • 56% of patients report delaying medical bill payments and cite high deductibles and confusion about benefits as major obstacles to payment² 
  • $12 million was the average amount of uncompensated care reported by hospitals in 2018—up nearly two million from 2015³
  • More than one-third of Americans could not afford an unexpected medical bill for more than $100 without going into debt¹
  • $660 billion has cumulatively been reported since 2000 as uncompensated costs of care⁴
Citations & Footnotes

1. Ipsos. “Ipsos/Amino Poll: 63% of Americans Think a Large Medical Bill That They Can’t Afford is Worse Than or Equal to a Serious Illness.” GlobalNewswire, 21 Mar. 2017, Press Release. 

2. Intrado. “Optimizing Revenue: Solving Healthcare’s Revenue Cycle Challenges Using Technology-enabled Communications.” West Corporation, Aug. 2017, Accessed 27 Nov. 2020.

3. Shinkman, Ron.  “Uncompensated Care Up Significantly at US Hospitals, Led by Southeast.” HealthcareDive Nov. 2019, https://www.healthcaredive.com/news/uncompensated-care-up-significantly-at-us-hospitals-led-by-southeast/567882/. Accessed 1 Dec. 2020. 

4 AHA “Fact Sheet: Uncompensated Hospital Care Costs.” American Hospital Association, Jan. 2020. https://www.aha.org/fact-sheets/2020-01-06-fact-sheet-uncompensated-hospital-care-cost. Accessed 27 Nov. 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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