Healthcare Artificial Intelligence Shouldn't Stop At A Diagnosis: What To Look For In 2018

There’s a lot of buzz around the impact of artificial intelligence (AI) in healthcare this year, both excitement and fear. Will AI detect disease that is otherwise hidden to the naked eye? Will it replace jobs? What is known for certain is the tremendous potential for AI to complement the abilities of medical professionals to improve both clinical AND financial outcomes. Here’s a look into what’s on the horizon for AI in healthcare in 2018 and a peek into how ImagineSoftware is using AI today to improve healthcare revenue cycle management.

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AI in Healthcare for Detecting Diseases

Researchers around the world are training AI to identify disease based on X-rays and MRI scans in efforts to improve early detection for diseases like Alzheimer’s and cancer. Using algorithms that scour databases for information from clinical trials and medical journals, AI systems can search for a patient’s symptoms while also considering the patient’s medical history to come up with an accurate diagnosis. This could give patients the power to seek treatment to slow down the condition’s efforts, prepare themselves legally and financially if necessary, or halt the disease altogether.



Machine Learning in Healthcare to Improve Clinical Documentation

With growing challenges for providers to consistently meet the improvements necessary for value-based care, some organizations are building AI into electronic health records (EHR) for improved documentation. Using machine learning, it can detect missing information or something that needs clarification and alerts the provider. This leads to better quality scores and higher rates of reimbursement. 



Enhanced Customer Service

One of the newer areas of healthcare artificial intelligence beginning to gain traction within the healthcare industry is the bot. A bot is an AI application that patients can interact with through a chat window, website or via telephone that helps receive patient requests. Imagine calling into your physician’s office and having the ability to schedule your next appointment or get answers about your medication in a matter of minutes, without being put on hold or handed off to various members of the administrative staff. This is a two for one – lowering administrative costs for providers and increasing patient satisfaction.



Better Medical Coding

Imagine a coder making a mistake as he quickly codes a chart. His AI assistant flags the mistake, informs the coder how it could affect the organization monetarily, and provides a quick solution. So much promise lies in this process. AI isn’t meant to replace the coder, but rather help the coder become more efficient and allow them to focus on more complex tasks. This could lead to a more automated and streamlined submission of claims and a lower rate of denials for the organization.



More Efficient Patient Collections

Consider this scenario: A patient comes in for a chest X-ray. Within minutes, the billing staff discovers the right coverage for the patient, evaluates the patient’s ability to pay, determines customized billing messaging based on that information, and provides alternative payment methods including the potential for financial aid if necessary. This technology is real and can be accomplished through ImagineAI™ -- the artificial intelligence propensity to pay model by ImagineSoftware. “The manually-intensive process of verifying coverage and working to obtain patient payments is no longer efficient,” said ImagineSoftware President and CEO Sam Khashman. “We have pioneered the technology behind ImagineAI™ to create a truly unique, market-leading product that produces results in a fraction of the time and cost.  This is a powerful, innovative approach to digital automation in healthcare, and we are extremely excited to unveil ImagineAI™ as the new industry standard for revenue cycle management.”


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Sources:
http://www.healthcareitnews.com/slideshow/how-ai-transforming-healthcare-and-solving-problems-2017?page=1

http://www.auntminnie.com/index.aspx?sec=sup&sub=aic&pag=dis&ItemID=117460https://www.imagineteam.com/blog/machine-learning-the-future-of-radiology

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