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        Your Health System Needs AI to Bridge the Staffing Shortage Gap

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        The Great Resignation has hit the healthcare industry from all angles. The loss of clinical staff has hurt the frontline of many health systems.

        However, what many do not realize is that revenue cycle teams are inundated with claims, denials and requests that pose a challenge to all aspects of the healthcare journey. Healthcare finance leaders report revenue cycle workforce shortages, with one in four saying they need to hire more than 20 employees to fully staff their department, according to a recent HFMA survey.

        Revenue cycle teams struggling in the claims management process and efficiency, productivity and revenue capture are at risk. It’s time to reinvent these processes, leveraging RPA to operate more efficiently now and in the future.

        Three factors affect revenue cycle staffing shortages:

        • Volume fluctuations challenge workflow and risk overloading staff
        • Remote work is an expectation for many job seekers
        • Staff have been reassigned to other tasks, doing more with less

        Many healthcare and revenue cycle workers are filling staffing gaps, taking on large amounts of claims and working accounts as quickly as they can to keep the revenue cycle flowing. RPA is an efficient, effective and accurate tool that relieves burden off revenue cycle staff, allowing them to allocate resources to high-priority accounts while bots take care of repetitive tasks. Reinventing workflows and adding automation to help staff both compensate for change and keep up with the claims management process reallocates time and energy spent while reducing human error through automation.

        Leverage RPA to do more with less

        Healthcare specialists need to work smarter to keep up with demand while ensuring high levels of patient satisfaction by shifting low-value-added tasks to software bots to replicate repetitive work. Instead of a person logging into software to get work done, RPA can do it. For example, a bot can enter a username and password, access an account, trigger application programming interfaces and operate directly on the objects within the system to parse information.

        Common bot applications include:

        • Payer request for medical records
        • Denials management lock boxes to address and/or route correspondence
        • Handling eligibility processes
        • Submitting notifications on admissions
        • Adding claims attachments
        • Executing medical billing edits
        • Translating Medicare codes to standard codes
        • Automating authorization submission and tracking

        Efficiency improvements from RPA are dramatic, as bots can do in a matter of seconds what takes minutes for a human specialist. Lightening this load for staff frees them to work on more complex, human decision-making requirements.

        While RPA addresses basic cleanup items such as eligibility mistakes or rejections, specialists can talk with patients about their itemized statements. Most patients still prefer human interaction for that purpose.

        Pairing artificial intelligence with RPA

        According to Becker’s Hospital Review, “57% of healthcare staff are worried they will burn out due to the number of repetitive tasks they have to handle.” Leveraging RPA reduces the repetitive tasks healthcare workers must undertake, reducing the potential for burn out and freeing them to address high-priority tasks and those that require human intervention.

        For organizations that want to take RPA to the next level, it is possible to build in custom artificial intelligence layers to extract data and act on it. For instance, RPA can be used as a powerful health information technology solution to capture data from insurance cards and match it with the correct payer plan code selection, a common source of human error. This is the same technology that enables Facebook or Google to recognize an individual using facial recognition AI. It simply overlays on top of insurance cards to map the insurance card information directly to the right payer code. In addition to defining machine learning rules that are specific, this is more sophisticated technology that solves some of the root-cause problems we discover in our RPA engagements.

        Reaping the rewards

        Ultimately, for healthcare organizations that embark on an RPA implementation, the average labor savings can be significant. For instance, one large, 50+ hospital that deployed bots for adjustment claims (XX7 type bill), eligibility denials and root cause (XX7 claims), eliminated two to four FTEs per application, resulting in ROIs of up to 583%. Smaller facilities can also greatly benefit, often eliminating two or more FTEs.

        To learn how RPA solutions can help your hospital or healthcare organization and lead to increased revenue cycle success, visit our RPA solution page or contact FinThrive.

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