Complex artificial intelligence models require enormous amounts of data to learn, and health care data make up nearly a third of all existing data. Nevertheless, this information could spur the development of algorithms capable of detecting and diagnosing disease, prescribing preventive treatment, and performing other functions with far greater speed and accuracy than even the most highly trained medical team. With such prospects, this power certainly deserves to be harnessed.
Applications of AI in healthcare
In some cases, the potential of AI has already been realized. For example, radiologists equipped with AI-powered instruments have been able to view and translate mammograms 30 times faster than they could, with 99% accuracy. Naturally, these results caused a considerable stir among stakeholders in the healthcare ecosystem.
However, the reality is that most healthcare organizations do not have the infrastructure or data management capabilities necessary to sufficiently train algorithms that can be reliably applied in clinical settings.
While there are still significant barriers to adopting AI in clinical practice, these barriers are not as substantial in other applications. However, as the amount of data in healthcare continues to grow exponentially, medical coders will continue to be in high demand and short supply.
Fortunately, artificial intelligence tools with natural language processing features can help alleviate some coding burdens, allowing medical organizations to overcome growing staffing shortages while improving coding speed and accuracy.
Many organizations relying solely on human teams (often understaffed) think speed and accuracy are inversely proportional. However, a suitable AI/NLP mechanism will not require them to sacrifice one for another.
Using artificial intelligence to speed up the medical coding process gives coders more time to correct costly errors, improve accuracy, and potentially allow institutions to put more resources needed most.
Over the past five years, NLP has made significant advances. And coding tools that use this technology improve the accuracy of codes and solve the overcoding problem by removing codes that shouldn't be there.
At first glance, this type of application may seem relatively mundane, but its implications could be revolutionary for healthcare providers, suppliers, and patients.
AI/NLP systems turn the medical coder experience into a more helpful asset for health plans, allowing these professionals to focus on more meaningful work (e.g., identifying unusual trends and patterns in data).
As coders master AI-based tools, their coding speed will increase over time, reducing the cost of coding a card and making these employees increasingly valuable to their organizations.
No human being can remember all the hierarchical codes of state categories (which have expanded rapidly over the past few decades). Nevertheless, human teams can quickly match the code to the appropriate condition with artificial intelligence.
The price of complacency
Companies that keep relying on traditional coding tools will face the same obstacles plagued the sector for years.
Furthermore, because traditional technologies often have minimal reporting capabilities, teams will continue to struggle with project monitoring and performance management.
Using advanced algorithms
Advanced algorithms, machine learning, and natural language processing (NLP) techniques can assist teams in gathering valuable ideas and concepts from previously considered material buried in text form.
By articulating unstructured data, these tools enable a better understanding of data quality, continuous process improvement, risk adjustment accuracy, and patient outcomes.
In addition, most NLP tools still have a user interface that is not optimized to meet coders' needs. This is no small problem, as a poorly designed user interface can cause coders to lose the context of clinical records and make other errors.
The effectiveness of these tools also relies heavily on workflows designed around them.
NLP coding tools designed with a one-size-fits-all approach typically do not support workflows that are not solely related to risk adjustment. For example, most of them do not provide features that enable auditing of vendor performance.
When well designed and properly implemented, AI/NLP engines can improve the speed and accuracy of medical coding. They allow coders to significantly speed up the coding process while reducing human error and the many risks that go along with it.
By extracting information from physician records and assigning medical codes much more efficiently, these tools speed up the billing process and ensure that organizations that use them can continue to keep patients healthy into the future.
In the future, it is expected that NLP solutions will become more widespread. Healthcare providers and pharmaceutical companies realize how much time they can save on data management. In addition, some barriers, such as the lack of knowledge about AI tools, can be overcome by several new initiatives in this area by various stakeholders, including governments and industry leaders.