Natural language processing can identify clinical indicators across a patient’s entire electronic medical record, including lab results, notes and prescriptions. NLP unlocks this unstructured data to understand each case and identify those where information may be missing. The result is more complete and accurate documentation to support better coding and appropriate quality measures.
Predictive models using regression techniques and newer machine learning approaches are helping in the early identification and preventative treatment of specific conditions. As an example, current models can identify signals of dementia five to eight years earlier than the first diagnosis. Predictive models are used to identify the right intervention at the right time. Algorithms use pharmacy data, lab results and other electronic health data to get to the right outcome.
AI helps call center agents make actionable, data-driven decisions at an individual member level. Interventions are prioritized according to clinical value and an individual's propensity to take action. AI provides reliable benefits data that is integrated and easy to understand to help translate health care data into actionable information.
Predictive accuracy of a system increases from below 1% to over 90% accuracy by including health care information from a variety of sources. – Artificial Intelligence and Advanced Analytics in Health Care, Frost and Sullivan, 2017Delivering on the promise of AI, we infuse our products and services with OptumIQ™, a unique combination of data, analytics and applied expertise.
You’ll need data scientists to translate and expedite results. You’ll also need people in the field who can and will apply them. Data from disparate sources needs to be standardized and organized so it can uncover patterns and validate repeatable solutions. Identify key benchmarks or milestones and be sure to document and share the results.
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