Importance of AI in Medical Billing and Coding

 Republic of American Hospitals in medical billing and coding.

Reducing profit margins, lowering reimbursement rates and uncertainties of healthcare reform have made the hospital a very risky business. 

At Genesis RCS, consolidating small, rural hospitals into a larger system has helped save many struggling hospitals, but others have continued to lose money despite their best efforts to cut costs. In fact, between 2010 and 2018, 83 rural hospitals stopped providing inconsistent care - a much-needed service to most communities.

With an aging population, how can we save our struggling health system to ensure that patients have access to the services they need to stay healthy? Experts have spent the last decade looking for answers without much promise. While many believe that we need a miracle to solve this mess, which is to make us work smart for healthcare organizations, not hard.

AI to be liberated in medical billing and coding?

To accomplish this, many organizations have turned to artificial intelligence (AI) to tap into machine learning, powerful algorithms, and unseen truths that are buried deep in mounds of data generated by hospitals. AI has shown genuine promise to healthcare providers in using the incredible amount of data available to improve healthcare delivery and efficiency. From supportive diagnosis to predicting the imminent patient decline and mortality, AI is already having a major impact on the quality and value of health care.

And this effect will not slow down any time soon. Reports indicate that Artificial Intelligence (AI) in healthcare is here to stay - and may just hold secrets to help hospitals improve their margins. Experts estimate that AI could actually save up to $ 150 billion over the next 7 years. 

The top opportunities for AI to change and reduce healthcare expenditure include the following unique applications: robot-assisted surgery (Valued at $ 40B.) Virtual Nursing Assistant. ($ 20B.) Automatic Image Diagnosis. ($ 3 b.) Fraud detection. ($ 17B.) Dose error reduction. ($ 16B.) And administrative workflow support. ($ 18B.) among many others.

AI for a healthy revenue cycle management.

For hospitals, the "administrative workflow assistance" opportunity mentioned in Accenture's report could possibly be found on the ground floor where dozens of coders and billing specialists could sit - perhaps in an area that held rows of first rows and paper medical files. . in many cases, These mission-critical medical coders and billing specialists still use highly manual procedures to ensure that records are properly documented, Coded and presented as payers' claims - providing a promising opportunity for AI to increase efficiency. We are not talking about eliminating these valuable billing and coding team members - AI will enhance the work they are doing to improve efficiency and accuracy.

When it comes to supporting medical coding and billing, So artificial intelligence is not only being used to help detect key data points from EMR, Which leads to precise HCC coding, It is also being used in new ways to support a healthy revenue cycle management for hospitals. Predictive analytics paired with AI through massive amounts of data to identify whether patients or accounts are most likely to pay their bills. 

With historical and current data coming to the weeds, these innovative technologies can brighten important information, which the human eye will never detect. Healthcare organizations adopting such technologies can improve efficiency, improve their revenue cycle, and even improve the patient experience without investing in new staff, training, or infrastructure.

GenesisRCS Healthcare Solutions was recently named an innovator in Artificial Intelligence by the 2019 Business Intelligence and Artificial Intelligence Conclave for its intelligent auto coding tool, Which medical coding and auditing workflows as well as sophisticated data analytics, Adds suspicious / probabilities and data visualization. 

GenesisRCS' auto Intelligent code (iCode) AI-infused CAC uses machine learning algorithms such as association rules mining and decision tree induction to search for classification rules for specific goals. To learn more about how GenesisRCS's technology-enabled solutions can support your revenue cycle management operations, click here to request a consultation.