The costliest 10% of patients account for more than two-thirds of healthcare spending. While some of these high-cost patients have consecutively high-cost years, the majority experienced a ‘cost bloom’—a dramatic year-over-year cost increase that catapulted them into the upper decile of spending. These previously low-cost patients account for roughly 68% of all high-cost patients annually.
Learn how AI can help to promote early identification of patients likely to experience a cost bloom, reduce the incidence and progression of chronic conditions, and avoid traditional risk scoring methods that fail to explicitly account for cost bloomers. Discover how AI can help to predict the low-cost patients most likely become cost bloomers, identify gaps in chronic condition management, and assess a wide variety of distinct and often overlooked patient data to identify rising risk.
Data indicating the status of the body’s vital and life-sustaining functions, with core vital signs including blood pressure, pulse, respiration rate, and body temperature.
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 about important risk markers from tests used for diagnosis, monitoring therapy, or screening, with details about specific results and abnormal indicators.
ClosedLoop generates explainable predictions using
thousands of auto-generated, clinically relevant contributing factors
It is well documented that a small percentage of patients account for most of the healthcare expenditures in the United States; the costliest 10% of patients account for more than two-thirds of the healthcare expenditures in the United States.¹ What is less well known is that, while some high-cost patients have consecutively high-cost years, the majority experienced what has been termed a ‘cost bloom’—a dramatic year-over-year cost increase that moved them from lower expenditure deciles into the upper decile of spending. These previously low-cost patients account for roughly 68% of all high-cost patients annually.²
Proactively identifying such rising risk patients is especially important, as they may disproportionately beneﬁt from interventions designed to mitigate future costs. Such efforts can be an effective way to simultaneously improve quality and reduce costs, and are distinct from programs that address the needs of existing high-cost patients.
Unfortunately, accurately identifying these rising risk patients can prove challenging for healthcare organizations (HCOs). Nearly 50% of cost bloomers are likely to have no inpatient hospital costs, and when compared to persistently high-cost patients, are diagnosed with four times fewer chronic conditions.²
In addition, and to the extent that standard prediction tools and reimbursement formulas have failed to accurately predict or account for ‘cost bloomers,’ HCOs pay the price. Such models leave HCOs that are held accountable for the total cost of care vulnerable to inadequate funding, ﬁnancial penalties, and unfair performance assessments, which can also ultimately affect the health outcomes for patients.²
As HCOs invest to improve patient outcomes and avoid unnecessary costs, it is paramount they have the ability to anticipate and address the needs of rising risk patients. AI can help by accurately identifying patients likely to be cost bloomers. Using AI-based models can also avoid the drawbacks of traditional risk scoring by allowing providers to easily integrate a wide range of distinct and dynamic patient data that would otherwise have been excluded. By leveraging AI, HCOs can launch proactive interventions designed to address the needs of rising risk patients in ways that drive a disproportionate benefit in quality, costs and future health outcomes.
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.
2. Tamang, Susan, Milstein, Arnold, et al. “Predicting patient ‘cost blooms’ in Denmark: a longitudinal population-based study.” BMJ open, vol. 7, no. 1, Jan. 2017. DOI: 10.1136/bmjopen-2016-011580.
3. Cohen, Steven B. “Statistical Brief #481 The Concentration and Persistence in the level of Health Expenditures over Time: Estimates for the U.S. Population, 2012-2013.” Agency for HealthCare Research and Quality, Sep. 2015. PMID: 28783282. Accessed on 12/11/2020.