Believe it or not, many Americans distrust the healthcare industry even more than Wall Street.
This is hard to do, considering that big banks tanked the economy in 2008, and economic recovery has been uneven and painful ever since. Yet even as Americans dislike the healthcare industry on the whole, they report significant trust in individuals working within that system, such as their doctors. If anything, this seems to suggest that the problem lies less with providers themselves than it does with the associated expenses and paperwork.
So how can the healthcare sector rehabilitate its reputation? It can start by reducing sky-high expenses and inefficiencies, specifically through the careful use of AI. Automating menial, mindless, and mentally grueling tasks will allow administrators streamline their sector in a number of ways, from cracking down on runaway costs to trimming bloated departments.
Why AI is so suitable
If you look closely, you’ll see that AI’s strengths are the perfect foil for the healthcare industry’s shortcomings, which center around excessive paperwork, bureaucracy, and an inability to address mistakes. The strengths of AI are in tracking and crunching reams of data and presenting such information in a simple, easy-to-digest form. Tasks that would take an army of human analysts working nonstop for days could be completed by AI in a matter of hours.
Rather than replacing administrators outright, AI can simply make their jobs easier. The algorithms in use today are narrow AIs: they specialize in a clearly defined niche, and are limited in their functionality. For now, such AIs can gather and interpret data–but when it comes to complex problem solving (like negotiating contracts between insurers and providers or troubleshooting excess financial waste), human intervention is required. Even if computer-generated data inform strategies and action plans, on its own, most AIs can’t easily adapt to complex environments.
AI Can Reduce Wasted Supplies
And there’s little doubt that healthcare is particularly complex, and as a result, very wasteful, especially at facilities such as hospitals or inpatient clinics. In such complicated operations with so many moving parts, perhaps some inefficiency is inevitable.
What is preventable, however, is how much perfectly good equipment and medicine is discarded. In 2017, ProPublica revealed just how many medical supplies were discarded. Reporters visited large warehouses overflowing with unexpired, never-used medical devices and drugs in mint condition. One nonprofit that collects these supplies estimates that they have $20 million worth of supplies in four warehouses.
Others have reached similar conclusions. In 2012, the Institute of Medicine estimated that the US healthcare system squandered some $750 billion annually–that’s 30 cents to every dollar spent. A more recent study by the Journal of Neurosurgery reported that the average cost of unused supplies per surgery at UC San Francisco was $653, or $2.9 million annually, for the neurosurgery department alone.
Therein lies the promise of AI: rather than leaving overworked staff to rummage through supply cabinets and throw out expired and unused stuff alike, AI can track, organize, and maintain stores of medicines and supplies. After all, there are already similar devices for the consumer market, such as smart refrigerators, which can help households order new groceries when they run low, keep tabs on what’s expired and what’s fresh, and so forth.
Obviously, hospitals or inpatient clinics require something more powerful than a residential fridge. But the foundation for this smart technology, known as the Internet of Things (IoT), may hold the key to solving the issue of medical waste. The IoT is basically a network of advanced devices that can collect data through their sensors, share information across the cloud, and finally, apply advanced analytics to glean insights. For example, a human administrator could forget that there is an existing cache of antipsychotics located in a certain storage room (and thus order more). However, a well-maintained IoT interface could help staff track down this extra medicine, saving the money that would have otherwise been wasted in buying redundant supplies.
AI can correct billing mistakes
If we break down the $750 billion annual waste in the American healthcare system, a significant portion comes from back-end problems. A graphic by The Atlanticputs this into perspective: inflated prices, excessive administrative costs, and outright fraud account for a total of 48.3 percent of all unnecessary expenditures.
At times, it can be hard to separate fraud from clerical error. The American Medical Association found that nearly one in ten medical bills submitted to insurers have mistakes. For consumers, that number is can get even higher, with some estimates as high as 75 to 85 percent. An audit by Equifax found that hospital bills of over $10,000 often contained an average error of $1,300.
In fact, my company, Zealie, was born out of my own frustration with the medical billing process. For me, the heart of the problem was inefficiency and confusion: there were countless errors caused either by humans, poor procedures, or lack of adequate technology adoption by all the parties involved. To catch and correct all the errors caused by these issues it took up hours of my staff’s time, and oftentimes they would simply give up on the process due to frustration.
AI can help mitigate many of the most common medical billing errors, such as duplicate charges for a single service, unbundling (improperly listing a series of related tests separately), and upcoding (such as charging a patient brand-name prices for a generic prescription). In particular, medical billing mistakes persist for several reasons, from a lack of incentive to a lack of resources to thoroughly audit bills. With AI, hospitals can no longer say that detailed medical bills are too complicated for patients to sort through on their own–nor can they refuse to audit or check mistaken bills.
This is especially true if an AI is overseeing a network of IoT devices at a hospital. In this capacity, an AI could track treatment, codes, drugs, dosages, and tests, cross referencing line items with both paper and digital records–often in real time. After all, connected devices constantly submit data to their network; this could take the form of bar codes, or smart cabinets that track the exact type and quantity of each prescription filled. Best of all, unlike humans, an AI biller is less likely to upcode, upcharge, or double bill, simply because it doesn’t make mistakes–nor can it engage in fraud.
In 2015, healthcare spending exceeded Social Security expenditures for the first time. And yet, if we are to overcome our collective health challenges (one of which is declining life expectancy), then the healthcare sector has to rein in runaway costs and reduce inefficiency. Thankfully, AI can help, enabling providers, insurers, and administrators to work smarter, not harder. Should this technology be adopted by the healthcare industry, it can streamline its processes and operations significantly–and perhaps even rehabilitate its tattered reputation.
Ali Beheshti is the founder and CEO of Zealie, the premier behavioral health Revenue Cycle Management. Ali is passionate about transforming behavioral health by creating influential businesses that uses data, automation, AI, and other emerging technologies to bring innovation and efficiency to the sector.