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How AI is making an impact in healthcare
There’s a lot of hype surrounding artificial intelligence, with expectations among healthcare providers rising higher for the use of the technology in a number of areas. While questions remain about the practical value of AI in today’s healthcare world, AI is beginning to leave behind its sci-fi legacy to become a transformative technology in a modern digital age, contends Optum, which offers technological, operational and advisory services to a broad range of healthcare stakeholders.
How best to define artificial intelligence
A recent analysis by Optum notes that artificial intelligence is not a singular technology but an umbrella term that includes using deep learning, machine learning and natural language process (NLP), among other methods, to perform “smart” tasks often associated with the human mind, such as learning and reasoning.
Here are 11 ways that these capabilities will be brought to bear in healthcare.
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Natural language processing can identify indicators across a patient’s electronic medical record. NLP unlocks the unstructured data to understand each case and identify where information may be missing. This enables more complete and accurate documentation to support coding and quality measures.
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NLP within artificial intelligence can understand clinical documentation to capture comprehensive diagnosis and procedure codes, eliminating manual case reviews. This means coders don’t have to start from scratch to provide more accurate codes that lead to more appropriate revenue capture.
This is all about ensuring payments made from one organization to another are done correctly and appropriately. Data analysis and predictive modeling finds problem areas and improper claims faster and more accurately.
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Pharmacists have all relevant prior authorization information along with insurance coverage, formulary rules and potential drug-to-drug interactions in real time. This enables patients to get their medications quicker and with less uncertainty when they get to the counter.
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Early disease identification
Artificial intelligence predictive models using regression techniques and newer machine learning approaches help in early identification and preventive treatment of specific conditions. For example, current models can identify signals of dementia five to eight years earlier than the first diagnosis.
Prescription benefit management
Predictive models are used to identify the right intervention at the right time. This is done with algorithms using pharmacy data, lab results and other electronic health data to get to the right outcome.
Natural language processing and artificial intelligence are fueling prospective risk adjustment analytics that identify undiagnosed conditions and support correct interventions that lead to enhanced quality of care.
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Simplified population analytics
Advanced analytics can be presented in a graphical or pictorial format, helping to quickly define and profile populations, simplifying and speeding time to analysis and evidence generation to lower risk and improve outcomes.
Artificial intelligence helps call center agents make actionable and data-driven decisions at an individual member level. Interventions are prioritized according to clinical value and an individual’s propensity to take action.
Artificial intelligence offers reliable benefits data that is integrated and easy to understand to help translate health care data into actionable information.
With intuitive and configurable workflow designs, healthcare providers get the important information crucial to coordinating care. These include key patient identifiers, gaps in care and complications with chronic and complex populations.