The Value of Machine Learning in Value-based Care

The following is a guest blog post by Mary Hardy, Vice President of Healthcare for Ayasdi. Variation is a natural element in most healthcare delivery. After all, every patient is unique. But unwarranted clinical variation—the kind that results from a lack of systems and collaboration or the inappropriate use of care and services—is another issue altogether. Healthcare industry thought leaders have called for the reduction of such unwarranted variation as the key to improving the quality and decreasing the cost of care. They have declared, quite rightly, that the quality of care an individual receives should not depend on geography. In response, hospitals throughout the United States are taking on the significant challenge of understanding and managing this variation. Most hospitals recognize that the ability to distill the right insights from patient data is the catalyst for eliminating unwarranted clinical variation and is essential to implementing care models based on value. However, the complexity of patient data—a complexity that will only increase with the impending onslaught of data from biometric and personal fitness devices—can be overwhelming to even the most advanced organizations. There aren’t enough data scientists or analysts to make sense of the exponentially growing data sets within each organization. Enter machine learning. Machine learning applications combine algorithms from computational biology and other disciplines to find patterns within billio...
Source: EMR and HIPAA - Category: Information Technology Authors: Tags: Healthcare Analytics Healthcare Business Intelligence HealthCare IT Ayasdi Machine Learning Mary Hardy Mercy Source Type: blogs