Revenue Cycle Sets Sights on Autonomous Coding, Claims Automation

Revenue cycle leaders at top healthcare organizations are prioritizing automation, with autonomous coding and claims management at the top of their wish lists, a new KLAS report reveals.

KLAS surveyed a small sample of revenue cycle leaders attending the market research firm’s first Revenue Cycle Management Summit this past August. The findings underscored a commitment to revenue cycle automation despite various obstacles to automation.

Revenue cycle leaders said their organizations are automating across the revenue cycle, but most are focusing automation efforts on claims.

KLAS reported that claims processing was the most frequently highlighted use case for automation in the revenue cycle. About a quarter of respondents to the survey (26 percent) also stated that claims, including secondary billing, claims attachments, and split bills, were being automated at their organization, the most cited response in the survey followed by claims statusing (14 percent) and denials (14 percent).

About 11 percent of respondents also said their organizations had coding automation, while 9 percent were automating patient estimates and prior authorizations, respectively. Respondents could choose more than one option.

Healthcare organizations are seeing the most return on investment (ROI) from automation of claims management tasks, including secondary billing and claims attachments. Approximately 17 percent of respondents said they have seen ROI from automating claims, while 14 percent realized ROI for claims statusing.

Automation of prior authorization also generated ROI for 11 percent of respondents, while 9 percent of respondents said denials and registration, respectively.

ROI came in the form of returned staff time, especially for when organizations automated claims statusing, and shorter wait times on the phone using bot technology. Revenue cycle leaders also reported a reduction in manual workflows, which combined with other metrics of ROI, allowed them to allocate staff to new or more challenging work that required human oversight.

Demonstrating ROI is key for revenue cycle leaders to continue down the path of revenue cycle automation, KLAS highlighted in the report. A top barrier to the adoption of revenue cycle automation, cited by 23 percent of respondents, was unknown ROI when implementing automation. The only other obstacles ranking higher were cost with 43 percent of respondents and the prioritization of other projects with 34 percent.

Other obstacles to revenue cycle automation identified in the survey included organizational resistance to change (14 percent), IT resources (9 percent), staffing (9 percent), and technology limitations (9 percent). Eleven percent of respondents said they encounter other barriers to adoption.

Revenue cycle leaders must work to overcome the top obstacles to revenue cycle automation considering their plans to invest in technology over the next two years. Most respondents (40 percent) said they plan to invest in autonomous coding over the next 12 to 24 months.

About a third of the revenue cycle leaders (34 percent) also expect to invest in automation for claims management and denials management, respectively. About 31 percent also said insurance discovery and prior authorizations were up next for automation.

Attendees at KLAS’ Revenue Cycle Management Summit told researchers that autonomous coding reduces administrative burden and increases coding efficiency, both of which are key as organizations face staffing shortages. This type of technology is key to bridging staffing gaps, attendees said.

However, cost is a critical consideration factor when investing in autonomous coding, specifically. Revenue cycle leaders said their organizations do not want to adopt a solution if it will cost more than using human coders, KLAS reported.

Overall, attendees underscored the need for optimized organizational processes prior to revenue cycle automation implementations. Healthcare organizations also need to address data assimilation and implementation challenges in order to realize better outcomes and more meaningful automation.