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With more than one-third of Americans reporting that an unexpected medical bill greater than $100 would push them into debt, healthcare organizations are grappling with increasing payment collection difficulties, major margin reductions, and a significant loss of revenue. In 2018, $12 million was the average amount of uncompensated care reported by hospitals, and this figure is expected to grow.
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.
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.
Geo-centric data with details about the social and environmental influences on people’s health and outcomes.
Data from health plans, clinics and service providers that capture details about service interactions, billing and payment, technical issues, and complaints.
ClosedLoop generates explainable predictions using
thousands of auto-generated, clinically relevant contributing factors
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.
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.
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.
