Artificial intelligence (AI) and machine learning (ML) are transforming many areas of the healthcare industry, ranging from patient-facing to patient service activities. This is made possible by the sheer quantity of data produced by healthcare providers and the opportunity to identify patterns and augment the capabilities of doctors and staff.

Most medical professionals know the impact that AI is having from a clinical perspective such as AI-assisted robotic surgery. However, an important way AI is changing healthcare is through its ability to automate administrative tasks such as coding and billing. This article reviews current billing processes and gives an answer to the question,

”How can AI improve a medical practice’s billing process?”

Hurdles with current medical billing processes

The traditional billing processes involve a lot of manual documentation and paperwork. Medical billing staff are tasked with analysing records to match treatments and charges with the correct codes. This then needs to be manually entered for billing purposes.

However, with more than 70,000 recognised billing codes in the medical industry, coding can be a logistical nightmare. It’s a detailed, time-consuming process, often subject to human error.

Overview of the current billing process

• Patient visits the doctor’s office
• Patient and billing data captured
• Patient checks-in and gets treatment
• Doctor or assistant writes billing instructions
• Medical billing staff adds treatment codes
• Paper forms with coding are sent or captured electronically to medical billers

It is easy to see how  ‘broken telephone’ translations from this method can lead to claim rejections. The doctor diagnosis and may not capture all thoughts. Billing staff then try to interpret and match to one of the over 70 000 possibilities. This often leads to incorrect claims and whether done intentionally or by mistake, inaccuracies in billing and coding cost everyone.

How can AI boost medical billing processes?

Machine Learning (ML) and Natural Language Processing (NLP) are at the forefront of billing and coding technology. Machine learning algorithms look at historical claims data to recognise tariffs and diagnosis codes. It then identifies the main procedure and uses this together with medical aid and patient data to automatically optimise invoices for the practice. In other words, it predicts what the medical practice might want to bill for, optimises the tariff code combinations and automates invoice creation. As a result, medical professionals are able to reduce work hours, streamline workflows, improve coding accuracy and potentially increase revenue.

AI Case study:
Medical practice group overcomes admin burden and an immediate increase in revenue by 20.4% within the 1st year with AI technology

 

When a busy medical practice calculated the impact of paper-based billing systems on their revenue, they implemented iHealth with MxNode.

MxNode harnesses machine learning and Natural Language Processing to analyse the data captured at the patient consultation. This enables practices to automatically generate patient quotes and billing instructions for instant invoicing. Combining MxNode with iHealth, Anaesthetists now have a solution that genuinely alleviates their administration burden and allows them to optimise revenue.

Specialist anaesthetist, Dr Daniel Muller co-led the development of MxNode. He conducted a study of a group practice, Allwright & Partners, to measure the impact of the clinician-led AI solutions from a clinical and financial perspective.

Click the link to download the case study: AI solution, iHealth with MxNode, helps medical practices increase revenue by 63%