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AI-Led Medical Data Labeling For Coding and Billing

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The Healthcare sector is among the largest and most critical service sectors, globally. Recent events like the Covid-19 pandemic have furthered the challenge to handle medical emergencies with contemplative capacity and infrastructure. Within the healthcare domain, healthcare equipment supply and usage have come under sharp focus during the pandemic. The sector continues to grow at a fast pace and will record a 20.1% CAGR of surge; plus, it is estimated to surpass $662 billion by 2026. Countries like the US spend a major chunk of their GDP on healthcare.


How Machine Learning Can Improve Patient Eligibility Verification

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The current state of the consumer healthcare experience has significant room for improvement. According to the Accolade Consumer Healthcare Experience Index, nearly a third of Americans say that navigating a healthcare system makes them more uncomfortable than does buying a home, a car, or some expensive technology. Sarah Buhr at TechCrunch describes medical billing as "murky" since "most of the time it's not clear how much something will cost and sometimes you don't even get the (possibly whopping) bill until months down the road." Clearly, medical billing and electronic claims processing can be improved to be more transparent and intuitive. A report by Meritalk highlights how data integration issues with healthcare benefits verification led to a staggering $343 billion in economic costs annually, which are borne by government health and human services agencies.


R1 RCM to buy artificial intelligence software firm Cloudmed

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Revenue-cycle management company R1 RCM on Monday said it plans to acquire Cloudmed in an all-stock transaction that values Cloudmed at roughly $4.1 billion. Cloudmed uses artificial intelligence and automation to analyze medical records, payment data and medical insurance models for revenue-cycle management. The company has more than 3,100 healthcare provider customers. The acquisition fits into R1's vision of creating an end-to-end platform for managing revenue cycle for providers and engaging patients around payment. "We have been very deliberate and very consistent in terms of our excitement around the long-term automation potential that exists in this industry," said Joe Flanagan, R1's president and chief executive officer, Monday at a conference. "This transaction significantly increases our data footprint and we are positioned very well for meaningful innovation in and around data." Cloudmed's data will accelerate R1's work in machine learning, which requires data to create accurate models, Flanagan said at J.P. Morgan's annual healthcare conference--which is virtual for the second year due to the COVID-19 pandemic.


How AI is Revolutionizing Healthcare Billing and Collections (Infographic) - MailMyStatements

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Artificial Intelligence (AI) is the latest technology development gaining significant traction in various areas of the healthcare industry, including diagnostics, patient outreach, and revenue cycle management (RCM) activities. Because revenue cycle management functions require substantial time, financial, and personnel resources, this area is particularly suited to benefit from the adaption of AI. In fact, payers and providers spend $496 billion on billing and insurance-related (BIR) costs each year. Manual and redundant tasks like coding, billing, collections, and denials become instantly simplified with appropriate artificial intelligence. AI imitates human intelligence through algorithms that identify patterns and plan for future outcomes, unlike machine learning or other robotic processes that only focus on accuracy.


How AI is Revolutionizing Healthcare Billing and Collections (Infographic)

#artificialintelligence

Artificial Intelligence (AI) is the latest technology development gaining significant traction in various areas of the healthcare industry, including diagnostics, patient outreach, and revenue cycle management (RCM) activities. Because revenue cycle management functions require substantial time, financial, and personnel resources, this area is particularly suited to benefit from the adaption of AI. In fact, payers and providers spend $496 billion on billing and insurance-related (BIR) costs each year. Manual and redundant tasks like coding, billing, collections, and denials become instantly simplified with appropriate artificial intelligence. AI imitates human intelligence through algorithms that identify patterns and plan for future outcomes, unlike machine learning or other robotic processes that only focus on accuracy.