On September 3, 1980, The New York Times published an article titled “A Robot Is After Your Job.” Personal computers (PC) had arrived—and along with them came “computerphobia,” a fear that mass unemployment would ensue after most jobs were taken by these PCs. We know now that the world didn’t end, but we also know that leaders who embraced new technology fared better than those who buried their head in the sand.
Today, the conversation is about artificial intelligence (AI), and revenue cycle – especially medical coding – has faced rising pressure to automate jobs. While this may be possible for certain straightforward use cases (for example diagnostic radiology), transformative coding leaders should look beyond automation and identify opportunities where AI can empower coders.
As an example, inpatient ICD-10 and HCPCS coding is significantly more complex, nuanced and higher-stakes (for revenue capture) than most outpatient varieties. There are many grey areas that require human judgment, including selecting a principal diagnosis and making a judgment call for quality measures such as Patient Safety Indicators (PSIs) and Hospital Acquired Conditions (HACs).
In this setting, rather than direct automation, advanced AI can act as a diligent aid by meticulously searching through every progress note and identifying diagnoses that may have been overlooked in the initial coder review; by identifying PSI and HAC exclusionary criteria that allow medical coders to optimize quality in addition to revenue; and by reviewing clinical indicators to identify areas where physician documentation has gaps and to suggest queries.
The integration of AI further helps human coders enhance their craft by learning from areas where AI has flagged opportunities, much like a diligent student can use a spellchecker to improve their spelling accuracy. As a result, this technology makes it easier for coding managers to upskill their teams around new guidelines and requirements, ensuring that their staff stays at the forefront of the latest coding practices.
The application of AI as an augmenting tool can lead to financial gains without resulting in job losses. This alignment between the coders’ enhanced capabilities and hospital fiscal interests also creates stability. Interestingly, by using AI to augment people rather than replace them, the additional revenue can be greater than the cost of a hospital’s coding workforce, making the AI-augmented approach very attractive from a financial standpoint.
In 1984, a 29-year-old businessman was interviewed by Newsweek for his perspective on computers and automation. He called the PC “a bicycle for the mind,” something that would ultimately help people do more with less rather than something that would replace them (this man was Steve Jobs!). Similarly, healthcare leaders must recognize AI’s possibilities to multiply coder success instead of replacing them – helping hospitals and staff prosper together now and for the long term.
Michael Gao, MD
Dr. Michael Gao is CEO and co-founder of SmarterDX.
