Maximizing financial performance with intelligent clinical decision support

(Editor's Note: This article is part two of a three part series. Part one is published here. Part three is published here.)

Wasteful spending attributed to overtreatment amounts to a staggering $158-to-$226 billion annually.[1] With numbers like these, it’s not surprising that minimizing clinical care variation and controlling avoidable utilization are among the top five concerns for hospital and health system executives.[2] 

When a patient’s health is not well managed or it takes longer to heal from an illness or injury, not only does the patient suffer, but the costs shared by the patient, providers, employers and payers increase as well. In a value-based care environment, the more responsibility providers assume for the quality and cost of care, the more critical it becomes to be able to manage financial risk while improving health outcomes.

Following practice guidelines supported by medical evidence is widely proven as an effective way to achieve these goals – and could reduce U.S. healthcare costs by $90-to-$110 billion annually.[3] By leveraging intelligent clinical decision support tools at the point of care, providers can consistently employ the most effective treatments to return patients to health safely and efficiently. Once implemented, healthcare organizations can gain substantial benefits from more effective resource utilization coupled with better clinical outcomes.

Yet, many providers lack reliable, evidence-based tools at the point of care to support consistent and effective treatment. When providers don’t have access to the information and tools they need during the patient encounter, waste such as redundant or unnecessary tests and treatments, extra visits or overprescribing of medications, is often the result. By some estimates, 30 percent of all Medicare clinical care spending is actually unnecessary or even harmful and could be avoided altogether.[4]

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accountable care organizations, ACOs, clinical decision support, duration tables, predictive modeling, Reed Group, Triple Aim


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