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Drive Innovation February 27, 2019

Featured in HBR: Can AI Address Healthcare’s Red Tape Problem?

Partner, Health & Life Sciences, Oliver Wyman
Principal, Health & Life Sciences, Oliver Wyman
Key Takeaway
When it comes to cutting healthcare’s administrative burden through AI, we are only beginning to scratch the surface. But the industry’s ability to amplify that impact will be constrained unless it moves to remove certain impediments. #OWHealth @HarvardBiz

Editor's Note: This article first appeared in Harvard Business Review on November 5, 2018.

Productivity in the United States’ healthcare industry is declining — and has been ever since World War II. As the cost of treating patients continues to rise, life expectancy in America is beginning to fall. But there is mounting evidence that artificial intelligence can reverse the downward spiral in productivity by automating the system’s labyrinth of labor-intensive, inefficient administrative tasks, many of which have little to do with treating patients.

Administrative and operational inefficiencies account for nearly one-third of the US healthcare system’s $3 trillion in annual costs. Labor is the industry’s single largest operating expense, with six out of every 10 people who work in healthcare never interacting with patients. Even those who do can spend as little as 27 percent of their time working directly with patients. The rest is spent in front of computers, performing administrative tasks.

"Administrative and operational inefficiencies account for nearly one-third of the US healthcare system’s $3 trillion in annual costs."

Using artificial intelligence-powered tools capable of processing vast amounts of data and making real-time recommendations, some hospitals and insurers are discovering that they can reduce administrative hours, especially in the areas of regulatory documentation and fraudulent claims. This allows healthcare employees to devote more of their time to patients and focus on meeting their needs more efficiently.

To be sure, as we’ve seen with the adoption of electronic health records, the healthcare industry has a track record of dragging its feet when it comes to adopting new technologies — and for failing to maximize efficiency gains from new technologies. It was among the last industries to accept the need to digitize, and by and large has designed digital systems that doctors and medical staff dislike, contributing to warnings about burnout in the industry.

Adopting artificial intelligence, however, doesn’t require the Herculean effort electronic health records did. Where electronic health records required billions of dollars in investment and multi-year commitments from health systems, artificial intelligence is more about targeted solutions. It involves productivity improvements made in increments by individual organizations without the prerequisite collaboration and standardization across healthcare players required with electronic health record adoption.

Indeed, artificial intelligence solutions dealing with cost-cutting and reducing bureaucracy — where artificial intelligence could have the biggest impact on productivity — are already producing the kind of internal gains that suggest much more is possible in healthcare players’ back offices. In most cases, these are experiments launched by individual hospitals or insurers.

Here, we analyze three ways artificial intelligence is chipping away at mundane, administrative tasks at various healthcare providers and achieving new efficiencies.

"Artificial intelligence solutions are already producing the kind of internal gains that suggest much more is possible in healthcare players’ back offices."

Faster Hospital Bed Assignments

Quickly assigning patients to beds is critical to both the patients’ recovery and the financial health of hospitals. Large hospitals typically employ teams of 50 or more bed managers who spend the bulk of their day making calls and sending faxes to various departments vying for their share of the beds available. This job is made more complex by the unique requirements of each patient and the timing of incoming bed requests, so it’s not always a case of not enough beds but rather not enough of the right type at the right time.

Enter artificial intelligence with the capability to help hospitals more accurately anticipate demand for beds and assign them more efficiently. For instance, by combining bed availability data and patient clinical data with projected future bed requests, an artificial intelligence-powered control center at Johns Hopkins Hospital has been able to foresee bottlenecks and suggest corrective actions to avoid them, sometimes days in advance.

As a result, since the hospital introduced its new system two years ago, Johns Hopkins can assign beds 30 percent faster. This has reduced the need to keep surgery patients in recovery rooms longer than necessary by 80 percent and cut the wait time for beds for incoming emergency room patients by 20 percent. The new efficiencies also permitted Hopkins to accept 60 percent more transfer patients from other hospitals.

All of these improvements mean more hospital revenue. Hopkins’s success has prompted Humber River Hospital in Toronto and Tampa General Hospital in Florida to create their own artificial intelligence-powered control centers as well.

Easier And Improved Documentation

Rapid collection, analysis and validation of health records is another place where artificial intelligence has begun to make a difference. Healthcare providers typically spend nearly $39 billion every year to ensure that their electronic health records comply with about 600 federal guidelines. Hospitals assign about 60 people to this task on average, one quarter of whom are doctors and nurses.

This calculus changes when providers use an artificial intelligence-powered tool developed in cooperation with electronic health record vendor Cerner Corporation. Embedded in physicians’ workflow, the artificial intelligence tool created by Nuance Communications offers real-time suggestions to doctors on how to comply with federal guidelines by analyzing both patient clinical data and administrative data.

By following the artificial intelligence tool’s recommendations, some healthcare providers have cut the time spent on documentation by up to 45 percent while simultaneously making their records 36 percent more compliant.

Automated Fraud Detection

Fraud, waste, and abuse also continues to be a consistent drain. Despite an army of claims investigators, it annually costs the industry as much as $200 billion.

While artificial intelligence won’t eliminate those problems, it does help insurers better identify the claims that investigators should review — in many cases, even before they are paid — to more efficiently reduce the number of suspect claims making it through the system. For example, startup Fraudscope has already saved insurers more than $1 billion by using machine learning algorithms to identify potentially fraudulent claims and alert investigators prior to payment. Its artificial intelligence system also prioritizes the claims that will yield the most savings, ensuring that time and resources are used where they will have the greatest impact.

Getting Ready For Artificial Intelligence

When it comes to cutting healthcare’s administrative burden through artificial intelligence, we are only beginning to scratch the surface. But the industry’s ability to amplify that impact will be constrained unless it moves to remove certain impediments.

First, healthcare organizations must simplify and standardize data and processes before artificial intelligence algorithms can work with them. For example, efficiently finding available hospital beds can’t happen unless all departments define bed space in the same terms.

Second, healthcare providers will have to break down the barriers that usually exist between customized and conflicting information technology systems in different departments. AI can only automate the transfer of patients from operating rooms to intensive care units if both departments’ Information Technology systems are able to communicate with each other.

Finally, the industry’s productivity will not improve as long as too many healthcare personnel continue in jobs that don’t add value to the business by improving outcomes. Healthcare players need to begin reducing their workforces by taking advantage of the industry’s 20 percent attrition rate and automating tasks, rather than filling positions on autopilot.

The task of improving productivity in healthcare by automating administrative tasks with artificial intelligence will not be completed quickly or easily. But the progress already achieved by artificial intelligence solutions is encouraging enough for some to wonder whether re-investing savings from it might also ultimately cut the overall cost of healthcare as well as improve its quality. For an industry known for its glacial approach to change, artificial intelligence offers more than a little light at the end of a long tunnel.

This article is posted with permission of Harvard Business Publishing. Any further copying, distribution, or use is prohibited without written consent from HBP - permissions@harvardbusiness.org

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