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Your Guide to Augmented Intelligence in Healthcare: Three Hows for AI Success
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Success Story
AI-Enabled Care Management Reduces Healthcare Spending by $32.2M
Almost four trillion dollars is spent on healthcare each year in the U.S., with most expenditures going to patients with chronic health conditions due to unnecessary visits to the emergency department (ED) and unplanned hospital admissions. UnityPoint Health sought to reduce costs by identifying patients with chronic health conditions at a higher risk for overutilizing healthcare services. By leveraging its analytics platform and augmented intelligence (AI), UnityPoint Health has been able to efficiently identify patients that could benefit from enrollment in its care management program.
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Five Ways Healthcare AI Gives You Superpowers
As healthcare decisions, data points, and options increase, time, resources, and margin of error decrease. To succeed in this environment, leaders and analysts must know where to focus and how to allocate resources and set accountability targets. With Healthcare.AI™, five super-powered assistive augmented intelligence capabilities help healthcare leaders and analysts determine values, understand context, and provide data-driven motivation to transform healthcare:
Insights
Your AI Journey Starts Here: A Four-Step Framework for Predictive Analytic Success
COVID-19 has highlighted the imperative for health systems to proactively prepare for future scenarios. One way organizations can ready themselves is by using artificial intelligence (AI), such as predictive analytics, to forecast clinical, operational, and financial needs. While many health systems have the historical and current data they need for predictive modeling, they often lack the requisite analytics foundation and knowledge to begin any AI project, let alone predictive analytics journey. Data and analytics technology lay the foundation to support a health system for a successful AI pursuit, including predictive analytics. With the right tools in place, health systems are ready to follow the four-step framework.
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Predicting Denials to Improve the Healthcare Revenue Cycle and Maximize Operating Margins
Healthcare financial leaders are constantly brainstorming ways to increase operating margins through better revenue cycle performance. These efforts often lead revenue cycle leaders to denied claims—when a payer doesn’t reimburse a health system for a service rendered. Although denials are a common reason for lost revenue, experts deem nearly 90 percent avoidable. Effective denials management starts with prevention. Organizations can use revenue cycle performance data, combined with artificial intelligence, to predict areas within each claim’s lifecycle that are likely to result in a denial. With denial insight, health systems can optimize revenue cycle processes to prevent denials and increase operating margins.
Success Story
AI-Powered Benchmarking Transforms Data into Insight, Improving Organizational Effectiveness
Healthcare organizations have a vast amount of data available. Data need to be converted into better decisions regarding organizational focus, resource allocation, and setting and driving toward appropriate targets to optimize performance. Beyond significant data integration and transformation, optimal leadership decisions require a broad perspective and cutting-edge analytics. Previously, INTEGRIS Health had high volumes of data but lacked the insight it wanted to drive performance. By leveraging a robust analytics platform, INTEGRIS Health now has more comprehensive and better-integrated data coupled with the cutting-edge analytics it needs to make critical leadership decisions and drive daily performance.
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