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The costliest 5% of patients incur roughly 50% of all healthcare spending, and the cost of care for the most expensive 1% exceeds 20% of total spending. Patients with chronic conditions are consistently among the most expensive. On average, people with five or more chronic conditions—an astonishing 12% of U.S. adults—incur 14 times the cost of patients without chronic conditions.
Learn how AI can help HCOs to reduce utilization and costs of care for patients with chronic conditions, slow the progression of chronic conditions, and potentially prevent adverse outcomes and serious complications. Discover how AI can help to predict patients at high risk for acute events, identify the potential impact of improving patient-specific risk factors, and distinguish opportunities for long-term treatment in non-acute settings.
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.
Data extracted from health insurance pharmacy claims with details about each medication and its type, fill dates, days supply, pharmacy location, and prescribing clinician.
Self-reported data from health questionnaires that assess a person’s individual medical history, health risks, lifestyle, health behaviors, and quality of life.
ClosedLoop generates explainable predictions using
thousands of auto-generated, clinically relevant contributing factors
A small percent of patients utilize a disproportionately large portion of total healthcare resources. The costliest 5% of patients incur 50% of all healthcare expenditures, and costs for the top 1% exceed 20% of total healthcare spending.¹ Annual healthcare expenditures in the U.S. totaled $3.8 trillion in 2019 and are projected to reach $6.2 trillion by 2028.² With such concentrated resource utilization, targeting the most expensive patients with care management efforts holds the potential to improve outcomes and curb costs.
The specific individuals that comprise the costliest cohort vary annually, but patients with chronic conditions consistently make up a significant portion. People with five or more chronic conditions—an astonishing 12% of U.S. adults—incur 14 times the cost of patients without chronic conditions, and one-third of these patients visit the ED at least once a year.³ Moreover, chronic conditions are exceedingly prevalent; 60% of adults have at least one chronic condition and 81% of adults aged 65 and over have multiple.³ In total, treatment for people with chronic conditions accounts for 90% of annual healthcare costs, and seven of the top ten leading causes of death are chronic diseases.⁴𝄒⁵
Fortunately, AI-based models can accurately predict which patients are likely to have high utilization, costly adverse events, or a functional decline that may lead to a sharp increase in resource utilization. With the insights surfaced by AI, care teams can facilitate care management programs to promote continuity of care, monitor treatment effectiveness, and support patient self-management and education. Ultimately, intervention efforts specifically tailored to the individual patients likely to require the greatest utilization can significantly reduce adverse events, improve outcomes, and reduce healthcare costs.
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. Accessed on 12/09/2020.
2. “National Health Expenditures 2019 Fact Sheet.” Centers for Medicare and Medicaid Services, Mar. 2020. Accessed 14 Dec. 2020.
3. Buttorff, Christine, et al. “Multiple Chronic Conditions in the United States.” RAND Corporation, 2017. https://www.rand.org/pubs/tools/TL221.html. Accessed 14 Dec. 2020.
4. “Health and Economic Costs of Chronic Diseases.” Centers for Disease Control and Prevention, Nov. 2020, https://www.cdc.gov/chronicdisease/about/costs/index.htm#ref1. Accessed 9 Dec. 2020.
5. Raghupathi, Wullianallur, and Viju Raghupathi. “An Empirical Study of Chronic Diseases in the United States: A Visual Analytics Approach.” International journal of environmental research and public health vol. 15, no. 3, Mar. 2018, doi:10.3390/ijerph15030431.
