Since the Affordable Health Care Act was passed in 2010, the number of adults with health insurance has increased by more than 20 million, according to a survey by the National Center for Health Statistics. This improved access to healthcare, along with an aging baby boomer population requiring more medical care, has vastly increased the amount of patient data that systems need to capture and the number of insurance claims to process. Many hospitals are struggling to keep up. Robotic process automation in healthcare is a partial solution.
According to Deloitte, between 51 and 60% of hospitals could see negative margins by 2025 if they are unable to achieve productivity growth.
Providers can greatly increase productivity by enhancing their legacy systems with robotic process automation (RPA) — essentially smart software robots that act as a digital workforce, taking over basic tasks that humans do with greater speed and accuracy. Without having to upgrade or replace existing systems, providers can automate repetitive clerical tasks, allowing workers more time to focus on higher-level responsibilities such as resolving complex insurance issues, scaling workloads, and improving the quality of patient care.
Hospitals and doctors’ offices are increasingly using automated business processes to handle time-consuming administrative tasks such as billing and claims processing. RPA bots can also be put in place to ensure that healthcare providers have all the authorisations and paperwork necessary to treat patients without encountering interruptions due to miscommunication or clerical errors. And software bots can automate much of the data entry involved with electronic health record (EHR) systems, making it faster and easier for physicians to update patients’ medical histories.
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Robotic process automation in healthcare: Billing and claims
A recent HIMSS Analytics survey found that 31% of healthcare providers still use a manual process to manage claim denials. And denials are on the rise, according to the AHA, costing hospitals more time and money each year. A denied claim costs about $118 in administrative fees per item, collectively costing hospitals about $262 billion per year.
Although some codified billing and coding structures exist, these tasks are still, for the most part, being completed by humans. These manual processes are time-consuming and prone to human error — the cause of many initial claim denials.
RPA helps reduce human error and ensures that each claim includes the information it needs to move forward, helping providers get paid in a timely manner. According to Ernst & Young, automation can reduce turnaround times for insurance claims by as much as 75 to 85 percent, and it can eliminate 50 to 70 percent of repetitive tasks entirely.
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Robotic process automation in healthcare: Unobstructed Patient Care (It’s better than a fax)
Depending on the insurance plan, patients often need to get referrals from their primary care physicians before seeing specialists. Communication breakdowns between physicians and specialists can lead to inconsistent treatment and delayed diagnoses, both of which diminish the quality of care. However, communication about referrals is currently done manually — often via fax (what’s a fax – Ed? Is it one of those things they used in olden times?) — and, as a result, the process can be tedious and riddled with clerical errors. Software bots can automatically scan documents and check them for accuracy and consistency, saving both offices time and effort.
In many cases, specialists need to obtain prior authorisation from insurance companies before treating a patient to guarantee that they will get paid expeditiously. According to a survey by the American Medical Association, 30% of providers reported having to wait at least three business days for responses from insurance companies, and 92% of providers reported that these delays caused patients to wait longer for care. Perhaps more troubling, 61% of providers said they feel these delays negatively affect patient outcomes in significant ways.
RPA can dramatically reduce the back-and-forth between providers and insurance companies by ensuring that all the necessary components, including referrals from primary care physicians, are in order before providers submit prior authorization forms. Software bots can also flag authorizations that are about to expire, preventing unnecessary gaps in patient care.
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Robotic process automation in healthcare: Automated EHR Management
A 2018 Medscape survey found that 42% of doctors said they have experienced burnout at some point in their careers. One major contributor to burnout is the widespread adoption of electronic health records.
EHRs have helped modernise the healthcare system and have created a treasure trove of valuable patient data. However, they require doctors to do more clerical work than ever before, in some cases spending more time in front of computers each day than interacting with patients. RPA bots can fill in many of the fields automatically and duplicate information across relevant systems. This not only reduces human error but also allows doctors to spend more time with patients and, ultimately, to see more patients each day.
Outdated systems that push healthcare workers to perform needlessly repetitive tasks are being modernized to help providers adequately treat the swelling numbers of patients under their care. RPA will be invaluable when it comes to inputting, organising, and analysing the massive amounts of the resulting medical claims and patient information — all without requiring providers to overhaul their current systems or retrain their staff. Software bots represent a powerful digital workforce that can work tirelessly in the background so that doctors and administrators can focus on improving patient outcomes for the growing number of people gaining access to care.
Henry Xie, is the CEO and founder at Simple Fractal, a software consultancy that focuses on robotic process automation (RPA) and production innovation. Henry graduated with honors from Harvard, where he studied Applied Mathematics, Computer Science and Economics.
