From the March 2017 issue of HealthCare Business News magazine
By Prashanth Kini
Health care is a data-gathering powerhouse.
But that means nothing if the industry can’t effectively harness this fire hose of information into discrete, actionable insights to improve outcomes, eliminate waste and uncover new revenue streams. Emerging value-based models of care, and upcoming regulatory mandates, demand that health care organizations quickly transform from data-gathering caterpillars into agile analytics butterflies. Millions of lives and billions of dollars are at stake.
People deliver most of the value of health care. Clinician expertise, intuition, diligence and warmth are invaluable to the patient experience and health care outcomes. But the big data problem in health care is beyond the scope of the work of the human mind. Many health care organizations employ a patchwork of data and analytics technology solutions to bridge the gap between what clinicians do today and what they should do tomorrow to achieve better results for patients and the bottom line. This patchwork is marked by breakpoints, handoffs, widespread administrative burden and limited visibility into the systemic health of the organization. This approach is also marked by its long lag times in identifying areas for improvement and operationalizing those insights.
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By contrast, machine intelligence applications offer an alternative that can simultaneously take on data analytics challenges across the continuum of care. Machine learning applications can examine complex and continuously growing data elements far faster and capture insights more comprehensively than traditional or homegrown analytics tools. The ability to meaningfully track clinical practice adherence to clinical pathways, predictively manage population health and optimize revenue cycle management will prove to be a model for innovative providers and integrated health care systems to follow. Applications of machine intelligence for home health and telemedicine also are promising.
The answer inside
Health systems need to transition quickly to value-based care. A lever for accelerating change already exists within every health care organization — and that lever is the organization’s own data. However, analytics involves a different set of skills, a different organizational mind-set and a different suite of technologies. The meaningful use of data requires more than light-lift SQL queries, dashboard software or marginal enhancements to flow charts. Most health care organizations are overwhelmed by the complexities of their data. There are simply not enough data scientists or analysts to make sense of the exponentially growing data sets within each organization.
Machine intelligence distills complexity, finds patterns within billions of data points and gives organizations data-driven insight into the best opportunities for improving the quality and cost of health care. Here are four examples of the power of machine intelligence to transform health care data into better, more efficient care:
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