An AI software, for example, can assemble data from various documents into a logical timeline to make sense of unrelated incidents, diagnoses, and procedures, reducing coding and reporting errors.
FREMONT, CA: In the medical billing and coding industry, artificial intelligence will help healthcare organizations streamline their billing processes while reducing costly errors. Machine learning and Natural Language Processing (NLP), for example, are AI-driven technologies that can rapidly and accurately interpret and organize large amounts of data. As a result, they are ideal for locating and extracting data from EHRs, then matching the data to applicable medical codes.
Furthermore, by analyzing data from different sources and connecting the dots, these technologies can contextualize unstructured data. An AI software, for example, can assemble data from various documents into a logical timeline to make sense of unrelated incidents, diagnoses, and procedures, reducing coding and reporting errors.
The Advantages of Using AI in Medical Billing and Coding
Because of their numerous benefits, AI-driven technologies have become commonly used by healthcare organizations to increase the cost-efficiency of their billing and coding processes:
Automating the coding process to provide a modular solution that is less reliant on qualified billing professionals' availability.
Improving cash flow by ensuring that timely and reliable patient statements are released.
Reducing administrative staff's regular work hours will help them improve their work efficiency in other fields.
Eliminating expensive human errors by matching EHR data with the appropriate medical codes and integrating systems such that only a small amount of data could be manually entered into various applications.
Doing real-time audits to avoid the issue of finding a mistake too late in the process to fix it without incurring high costs.
Professional coders may address complex problems requiring their knowledge and experience by automating repetitive and repeated menial tasks.
Connecting unstructured data and extracting relevant information from different EHRs within a healthcare organization to produce integrated patient statements that streamline the payment process and enable patients to pay their bills sooner.
Reducing the operational costs of performing routine analyses and billing procedures by hand.
Lowering administrative costs by reducing the number of customer service requests or conflicts relevant to patient statements.