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Artificial Intelligence in Medical Billing: Will It Take Over Humans?

TechnologyArtificial Intelligence in Medical Billing: Will It Take Over...

Artificial intelligence comprises machine learning, language recognition, and robotic automation. In the medical field, those technologies can assist surgeons with simple manipulations, doctors in recording patient histories, and administrative staff in the document workflow. In this post, experts from the Belitsoft software development company share their insights about implementing AI technologies in medical billing.

Medical billing implies coding, which is the process of turning any medical service into a universal code. Then, this code is sent to the insurance company for reimbursement. The document usually looks like a file with numbers that mean diagnoses, surgical operations, medications, and any other manipulations, received at a hospital or medical center. Traditionally, medical staff perform the coding manually. However, today, artificial intelligence has appeared and facilitated the processes.

Challenges of human medical billing

  • Time costs. Manual coding takes much time as coders explore medical records, diagnoses, equipment used, etc., to charge a patient accurately.
  • Human errors. People make mistakes. As for medical coding mistakes, they can lead to significant financial losses. A false diagnosis may result in flaws in claims and the necessity to rebill.

AI in the revenue cycle management systems (RCM)

To be capable of providing high-quality care to patients, medical institutions should keep their financial operations streamlined and safe. RCM systems enable hospitals and medical centers to manage their revenue cycles, which include billing, payments, and claims. AI makes those systems multipurpose in terms of the functions they perform.

Dmitry Baraishuk, a partner and Chief Innovation Officer at software development company Belitsoft with 19 years of HealthTech expertise, says that AI in healthcare can significantly reduce costs spent on administrative and revenue cycle functions and relieve the workload burden off medical professionals.

Machine learning and natural language processing technologies allow for a vast examination of data related to medical manipulations. That data is usually stored in numerous relational databases, so extraction and analysis require a sufficient amount of time. When a coder faces the task of detecting the correct code for the medical service, the program offers only a limited set of data extracted automatically. It shortens the time spent on examination and releases about 30% of coders’ working time.

Will AI replace medical billing and coding?

Automated procedures and the application of programs can speed up medical billing and eliminate human mistakes. Systems examine loads of medical information, starting a moment a patient first addresses a medical institution to receive any kind of help. The data includes the patient’s name, the name of the insurance provider, coverage terms, existing health conditions, lab tests, and much more. After the treatment, billers or coders classify the services according to the International Classification of Diseases. How does AI simplify handling those activities for people?

  • Routine validation

Artificial intelligence can help with routine tasks. AI software validates the data faster and does repetitive tasks more efficiently. In this way, AI assists humans and enhances their performance.

  • Data analysis

AI technologies with machine learning abilities can analyze large data sets. Therefore, a program examines medical records, histories, and X-ray or MRI images in a shorter time than a human. As a result, people and programs work as a team. The software swiftly scans the data, provides relevant cases and records, and a human accurately codes the information. In this way, coders and programs complement each other and productively work in synergy.

  • Process automation

Less sophisticated programs can perform duties like filling out forms, updating records, or extracting required information from medical history. At their core, those systems follow a set of predefined rules. With additional AI functionality, such programs know how to take the information from images (computer vision), identify missing information and make requests to add it.

  • Text and word recognition

Natural language processing technology helps AI read medical data, analyze it, and make decisions based on the analysis. Further AI development will allow medical staff to give oral orders to virtual assistants, asking them for help.

The future of AI in medical billing

The rapidly growing and aging population leads to increased numbers of patients in hospitals, medical centers, and other medical institutions. The exploitation of AI technologies allows for automating parts of tasks and releasing medical professionals for other issues. As a result, clinics can do more with the same amount of staff and save money.

Another advantage is the development of technical skills among medical coders. They learn to use AI in their work, which increases their expertise. Besides, splitting the tasks with a machine decreases burnout conditions.

On the flip side

Artificial intelligence is an idea that is supposed to make life easier. However, every concept has its reverse. Machine learning implies the idea of examining historical data. That is why this data should be of the highest quality and should not contain contradicting facts. Otherwise, the software will deliver false results. Proper analysis of the existing coding medical data needs to be carried out. Besides, predictive analytical models may fail to provide true patterns, as the original input data contains mistakes. Insufficient data may be another bottleneck, as the system needs much information to be able to analyze, extract, and make conclusions.

A deep audit of the existing information should become a compulsory first step before passing it to the AI training engine. And it should be a human who performs this audit. Such a preparation phase will eliminate future errors and result in productive cooperation between the team and AI.

Conclusion

Autonomic medical billing and coding are becoming a necessity for any medical institution. AI cannot replace humans entirely in the medical billing process. It is not the goal, as cooperation and teamwork bring better results. Coders spend less time on repetitive tasks due to AI software. Consequently, medical institutions benefit from AI applications in terms of costs and resources. Medical experts have more time to devote to vital issues. However, a deep data audit is necessary to implement the AI applications effectively. It will help to avoid possible mistakes in the automated workflows.

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