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Three ways clinical analytics are becoming healthcare’s vital intelligence tool

By Carolyn Scott

Healthcare continues to undergo a major transition and while the shift from fee-for-service to value-based care is predominantly the key driver, an influx of data and technology innovations designed to create greater value out of this data are also driving this monumental shift. Systems and solutions driven by artificial intelligence, as well as predictive and analytic technologies, are not only improving the efficiency and productivity of health organizations and clinicians, but are enhancing patient outcomes, workflow and care management at all levels.

Due to the growing aging population, a number of which have chronic conditions, healthcare organizations are tasked with not only increasing the quality of care to improve outcomes, but decreasing costs and ensuring the sustainably of overall healthcare costs as well. Data is helping provide solutions to these challenges, meaning today’s clinicians and providers are expected to do more with clinical data to achieve these imperatives.

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However, care teams frequently find themselves equipped with outdated tools that aren’t effectively harnessing this information, and don’t support the industry’s new quality and cost initiatives – leaving room for clinical analytics to become an increasingly critical tool. The availability of data in electronic form is a prime factor for this technology’s growing role and, with data elements being stored electronically and available in real time, patient information can be collected, aggregated and analyzed to predict health outcomes and better manage patient conditions.

With value-based goals to work towards and a continuous flow of patient data, real-time clinical analytics are poised to give clinicians the actionable insights they need to significantly impact their workflows and patient’s outcomes. Particularly in three key areas:

• Enabling proactive care – Clinical analysis of all health data available can help detect subtle signs of patient deterioration, enabling clinicians – particularly nurses – to treat patients more proactively. Real-time and predictive clinical surveillance tools that synthesize available, but siloed clinical data, can paint a detailed picture of a patient’s condition, signaling subtle signs of deterioration before they’re otherwise noticeable. Not only do these early warning signs of change enable nurses to proactively intervene before further progression occurs and situations become urgent, but they also enable a transformation in care team workflow.

At a higher level, clinical surveillance tools that utilize analytics will enable clinicians to prioritize the order in which they see patients – specifically, which patients require attention sooner than others. If nurses and physicians are able to detect and treat signs of patient deterioration before a patient’s condition becomes critical, they effectively reduce the need for a rapid response or code blue down the line, which can lead to improved patient outcomes and overall satisfaction, as well as reduced lengths of stay and costs.

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