Generating new knowledge. One of the earliest uses of big data to generate new insights has been around predictive analytics. In addition to the typical administrative and clinical data, integrating additional data about the patient and his or her environment might provide better predictions and help target interventions to the right patients. These predictions may help identify areas to improve both quality and efficiency in health care in areas such as readmissions, adverse events, treatment optimization, and early identification of worsening health states or highest-need populations. Equally critical is the focus on new methods. One of the reasons health care is lagging behind other industries is it has relied for too long on standard regression-based methods that have their limits. Many other industries, notably retail, have long been leveraging newer methods such as machine learning and graph analytics to gain new insights. But health care is catching up. For example, hospitals are starting to use graph analytics to evaluate the relationship across many complex variables such as laboratory results, nursing notes, patient family history, diagnoses, medications, and patient surveys to identify patients who may be at risk of an adverse outcome. Better knowledge and efficient assessment of disparate facts about patients at risk could mean the difference between timely intervention and a missed window for treatment. Natural language processing and other artificial intelligence methods have also become more mainstream, though they are mostly useful in harvesting unstructured text data that are found in medical records, physician notes, and social media. Mayo Clinic teamed up with the IBM cognitive computer known as Watson, which is being trained to analyze clinical-trial criteria in order to determine appropriate matches for patients. As the artificial-intelligence computer gets more information and learns about matching patients to studies, Watson may also help locate patients for hard-to-fill trials such as those involving rare diseases.
Posted on: Mon, 15 Dec 2014 04:56:21 +0000
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