Much of the buzz you hear about artificial intelligence (AI) or machine learning (ML) in healthcare is about its clinical applications, whether assisting doctors with diagnoses or customizing cancer treatment plans. But healthcare is a big industry, and the financial and administrative sides are immense and complex. The reality is AI and ML in healthcare are powerful tools revolutionizing many of the healthcare processes that, although not medical in nature, directly affect provider efficiency and productivity — as well as the patient experience.
Providing Insight Into Claim Denials
Providers are always striving to keep denials and rejections to a minimum while processing them at a speed that keeps the practice running. But with the complexity of coding and billing rules, this can be one of the most difficult parts of revenue cycle management (RCM). Improvements here can make a big difference for both healthcare providers and insurance payers.
That’s where AI and ML in healthcare come in. These technologies can predict denials with a high degree of accuracy and precision. Building that intelligence into billing office workflows before claims are submitted helps organizations significantly reduce denials and appeals — and the time and cost associated with them. What’s more, by detecting patterns and noting the probability that claims may be denied, these tools can help teams know where to focus their efforts to maximize how much payment the provider ultimately receives.
When a claim is submitted and denied, AI/ML can examine past overturn rates and assign a probability to the chance of overturning a denial — that is, if a claim can be adjusted or corrected and eventually paid. With this information, billing teams can prioritize based on what will be most effective and what will ultimately be paid out.
Making Claim Settlement More Efficient
Turnaround time on claims presents a challenge for any hospital system. The speed and efficiency with which claims are handled directly affects an organization’s bottom line.
Suppose a major payer consistently processes and pays for hip replacement procedures, but it takes 25 days. If RCM teams don’t know this, they are playing a guessing game on follow-up. How long should they know to wait for payment? How much effort should they be putting into follow-up? Without the historical context, working to get these payments becomes an inefficient use of time.
AI can tell you how long a payer takes to settle a claim. It also can show when a claim has taken an unusually long time to get paid. This is not about AI replacing RCM jobs, but rather about AI finding the point in the process where human intervention is needed.
Automating And Increasing Accuracy In Data Entry
According to Medical Billing Advocates of America, three-fourths of medical bills have errors. Data entry typos and mis-entries are common in healthcare, causing errors from diagnosis to treatment, billing and beyond. Administrative costs are expected to reach almost $500 billion in 2019, and it’s estimated that 20% to 25% of all medical spending is wasted.
And it is in these routine parts of healthcare that AI/ML can make a huge difference. AI tools can detect where data entry mistakes may happen and work to either correct them or alert humans. Machines also have a perfect memory — meaning as records are moved or copied, AI tools can constantly be checking them for consistency and accuracy.
Overcoming Challenges In AI And ML Adoption
As with any new technology, there are barriers to clear and missteps to avoid in adopting AI and ML. The first challenge is awareness and education. Decision-makers may not know what tools exist, so it is up to forward-thinkers in each organization to stay informed and bring new technologies to the table for discussion.
Even if organizations are aware of emerging technologies, there are some common misconceptions and even apprehension surrounding AI. Some may worry that AI will replace human intelligence, wresting control and judgment calls from human teams. The truth is AI augments human ability, handling monotonous and time-consuming tasks with less error. This frees up human resources for more elevated work, such as complex cases that require human discernment or conscience and overarching strategy. Human oversight and intelligence will always be necessary — AI simply allows for more time and energy spent on higher priorities.
Once an organization is open to integrating AI, it can be challenging to identify where and how to do so — and it’s tough to automate legacy processes. For that reason, the most successful organizations implement specific AI initiatives as part of broader transformation programs. This helps determine which processes can be automated or improved by machine learning, robotic process automation and AI, and lays the right foundation for the success of those technologies.
Lastly, setting expectations and criteria for success is a crucial part of AI adoption. AI takes time to learn and grow, so results might not be immediate, but they will grow and improve with time and proper reporting. Organizations should set goals and benchmarks upfront and measure along the way, providing results and feedback to their AI technology provider to optimize algorithms and improve performance.
Toward A Brighter Future
AI and ML in healthcare are already making a big difference, bringing order, predictability and efficiency to a revenue system that has historically been difficult to wrangle. These gains, enabled by AI and ML, will continue to be drivers for hospital revenue and a more transparent, improved patient financial experience. When healthcare providers of all sizes and specialties can control and understand their finances better, the ripple effect produces positive consequences for everyone.