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How much revenue is your healthcare practice losing due to inefficient revenue cycle processes?

Insight Article - May 14, 2021

Billing & Collections

Reimbursement

Teri Gatchel-Schmidt MBA, CPC
Inefficient processes across the revenue cycle collectively cost healthcare practices billions of dollars each year. In the United States alone, the industry could have saved $16.3 billion in 2020, or 42% of the $39 billion spent on administrative transactions tracked by the 2020 CAQH Index.

Administrators have probably heard statements like this before. In years past, they may have brushed off such concerns while continuing to stay afloat by assigning manual workarounds or other time-consuming tasks to their staff without seeking more efficient, electronic alternatives.

But after a year in which the pandemic choked revenues at many practices by reducing opportunities to perform elective procedures or even provide routine care, organizations are re-examining their administrative procedures with renewed interest. Many have already explored initial steps to automate mundane tasks to gain the greatest efficiency and improvements to the bottom line.

To assist healthcare leaders in deciding whether further modernization will benefit their practices, it is helpful to gauge where they can expect to see the return on their investment and the timing to achieve specific improvements. To illustrate the cumulative impact of automating a portion or the entire revenue cycle management (RCM) system to a digital platform, here are improvements some organizations have made and the results they achieved.

Counting costs

An efficient RCM process minimizes costs largely by freeing administrative staff to focus on less routine activities. An organization can measure these savings by extrapolating the average time spent on manual tasks to a payroll cost. Time saved with the assistance of technology frees employees to focus their time on tasks that require critical thinking.

Shifting from manual tasks to an automated process workflow will reduce time spent per task from minutes to fractions of a second. Case studies show that manually posting payments and denials takes 2.10 minutes per claim, compared to a rate of 2 seconds per claim with robotic process automation (RPA). Included in this 2 second interval is also the time it takes robotic billing software to process a claim, compared to 2 minutes to process manually.

Systems that dramatically ramp up the number of claims sent to payers will drive up revenues through increased volume and eliminate the amount of rework in handling rejections and rebilling. The return on investment in technology adds up quickly, particularly for a practice that files tens or hundreds of thousands of claims monthly. Similar results also come from automating eligibility and coverage discovery, appeals and denial management.

Platforms that incorporate machine learning or artificial intelligence can provide predictive analysis to further reduce costs over time. By identifying patterns in rejected claims, staff can avoid wasting time inquiring into claims that are unlikely to be paid and can fix corrections while learning on the fly to reduce future rejections.

Case Study 1: Device supplier

Problem: A supplier of cardiac monitoring devices to more than 1,000 clinics struggled with its billing system. Claims lingered in A/R for 137 days on average, and the firm was losing revenue due to delays in securing patient consent letters and medical records.

Solution: The firm adopted billing software to address its difficulties, with enhancements for collection follow-ups, patient-friendly billing and payment options, and weekly monitoring of key performance indicators (KPIs).

Results: The new platform slashed days in A/R nearly in half — to an average of 70 days per claim. The percentage of clean claims increased from 55% to 62% before the conversion. More uniform patient statements and a direct payment process helped patient collections increase by 154%. Improvements reduced delays, with more than 98% of claims reviewed within 48 hours.

Case Study 2: Genetic testing laboratory

Problem: A genetic testing company servicing more than 2,000 clinics and providers realized it needed to automate its costly, manual RCM processes. The organization wanted a more successful and systematic appeal process. The lab also needed to reduce revenue losses due to lengthy turnaround times, as well as lost revenue attributable to claim holds.

Solution: The firm adopted automation solutions to replace manual processes throughout its RCM. Improvements included an appeal process solution; electronic conversion of billing, remittances, and fund processing; custom solutions to improve billing data entry turnaround times; and billing system enhancements.

Results: Revenue increased by 24% after streamlining processes through automation. The organization increased its appeals from about 2,500 per week to more than 8,000 and increased its average appeals turnover rate from 30% to 41%. Turnaround on claims improved by 75%. With a higher clean claim rate, the firm was soon billing more claims and receiving more payments on time.

As these examples illustrate, the industry already has access to technology capable of boosting efficiency throughout the revenue cycle. Combining innovations such as robotic process automation and machine learning makes it possible to automate even the most complex end-to-end processes and functions. This allows organizations to improve the bottom line while scaling resources to focus on complex, value-added activities.

Healthcare administrators will find it worth their effort to revisit each step in their revenue cycle to identify where they may be leaving money on the table and investigate the solutions available to maximize revenues.
 

About the Author

Teri Gatchel-Schmidt
Teri Gatchel-Schmidt MBA, CPC
Vice President, Business Development SYNERGEN Health

Teri Gatchel-Schmidt has more than 28 years of experience in the health care industry as a practice administrator, operational leader and revenue cycle optimization consultant to large health systems and physician organizations. Over the past 18 years, she has worked extensively with health systems to enhance their financial performance in academic and physician ambulatory-based settings. 

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