A Definitive Guide to Using Augmented Intelligence to Predict Revenue and Optimize RCM

Key Takeaways:

Bringing technology and humans together to create augmented intelligence services has elevated healthcare in a myriad of ways. From robotic surgery assistance to outcomes management to predicting revenue, augmented intelligence services improve the patient experience and allow clinicians to focus more on providing care and less on administrative burdens. Zeroing in on prior authorization, AR optimization, and insurance discovery, we look deep at how artificial intelligence (AI) and machine learning complements rather than replaces human intelligence.

Augmented Intelligence to Predict Revenue

In business today, emerging augmented intelligence services bring advanced technology together with human intelligence to create supercharged processes capable of far more than before. In the healthcare field, specifically, entities are finding creative and effective ways to leverage innovative technology with human intellect to create a robust and integrated ecosystem ready to meet today’s challenges

Rapidly advancing technology is at the point where many see completely automated solutions as the next logical step. However, computers are unable to do it all. Decision making, as it applies to the healthcare industry, still requires human intellect through augmented intelligence services to meet the demands required in this quickly changing field.

According to a recent survey conducted by MIT Technology and GE Healthcare, 79% of healthcare professionals have indicated that AI-supported changes have helped mitigate provider burnout, with over a third of respondents seeing a significant reduction in administrative burdens such as optimizing ARs and predicting potential RCM workflow.1The AI Effect: How artificial intelligence is making healthcare more human. MIT Technology Review partnering with GE Healthcare. 2019. https://www.technologyreview.com/hub/ai-effect/. And while we know that only the tip of the iceberg has been mainstreamed, the opportunities for augmented intelligence automation are vast.

Optimizing Reimbursement

Healthcare is in a transitional phase with massive changes to the payment structure underway, as well as further tightening of reimbursement for all specialties and services. The timing is perfect for implementing augmented intelligence services as a way to capture more revenue by optimizing and prioritizing both front and back-end processes within the reimbursement workflow.

Improvements to Workflow and Employee Engagement

While there has been abundant speculation on the impact AI will have on the human workforce in terms of task replacement exposure, many now agree that there will be a natural settling point that merges digital augmentation and human intelligence. With the healthcare industry rapidly adopting AI-enhanced solutions, one particular challenge going forward will be to minimize disruption and guide impacted employees towards higher-value and more rewarding initiatives and opportunities.

Three Areas Where Augmented Intelligence Solutions Work Exceedingly Well

When melding the high technology of AI and machine learning with highly qualified specialists, you arrive at solutions that predict and capture revenue while reducing the organization’s overall administrative burden. Let’s take a look at the impact on prior authorizations, ARs and denials management, and insurance discovery.

Capture Revenue with Augmented Intelligence-Driven Prior Authorizations

While the original intention set by utilization review and prior authorizations was a good one, the mechanics of administering them has created a cumbersome and unnecessarily awkward process with frequent denials and patient care issues. A recent report from the AMA notes that 94% of physicians report that patient care is negatively affected and often results in delays in care up to and including care abandonment.22020 AMA Prior Authorization Physician Survey. American Medical Association. November, 2020.. https://www.ama-assn.org/system/files/2021-04/prior-authorization-survey.pdf. Accessed May 23, 2021.

A Targeted Approach

According to the 2020 CAQH Index on Closing the Gap as much as 79% of medical practices use a manual system to manage prior authorizations at the cost of $14.24 per pre-authorization. Add to that the unworked claim denials experienced from missing or rejected prior auths, and the costs are astronomical.32020 CAQH Index – Closing the Gap: The Industry Continues to Improve, But Opportunities for Automation Remain. 2020. https://www.caqh.org/sites/default/files/explorations/index/2020-caqh-index.pdf. Accessed January 2, 2021.

By utilizing an augmented intelligence-enabled system, the cost per prior authorization would average $1.93 each, bringing a savings of $12.31 per occurrence.4Ibid, iii. With an augmented intelligence solution that leverages AI, predictive analysis, and machine learning, supported by experienced prior authorization specialists, the entire process can be managed in real-time for treatment, surgical procedures, advanced testing, medications, rehabilitation, etc., including:

  • Determining necessity based on patient’s referring diagnosis and insurance requirements and guidelines,
  • Collecting necessary information on patient demographics, verifying insurance benefits, and confirming allowable care,
  • Submitting completed prior authorizations to the appropriate insurance payer for review,
  • Following up by monitoring payer portals 24/7 and retrieving case status updates,
  • Generating and resubmitting appeals, if necessary,
  • Supporting emergent or complex prior authorizations through a team of highly-training specialists available to complete and finalize outlier situations,
  • Providing full transparency into prior authorization workflow using analytics and status reporting to track every claim.

By automating prior authorizations using AI-driven software, preauths can be determined in real-time to allow patient scheduling to commence immediately, lessening the chance of patient abandonment and revenue loss.

Optimized AR to Predict Revenue

It seems that often priorities in healthcare revolve around patient care and treatment, but equally important is effectively billing and collecting the revenue associated with that care. Unfortunately, the system often breaks down once the claims have left the office, and readily forthcoming money is accepted.

Revenue that is a little more challenging to acquire is tied up in the AR, and collecting it often requires hands-on follow-up that is sometimes postponed or ignored in favor of more urgent administrative tasks.

As an example, the Centers for Medicare and Medicaid Services (CMS) denies, on average, 26 percent of all claims, and of those, 40 percent are never resubmitted even though over 2/3’s are recoverable, and 90% are preventable.5Brown B. Hospital Revenue Cycle Management: 5 Ways to Improve. Health Catalyst, Explore Health Catalyst Insights. May 7, 2014. https://www.healthcatalyst.com/hospital-revenue-cycle-opportunities. Accessed June 10, 2020.

By focusing on an augmented intelligence solution to tackle ARs and reduce days outstanding, revenue that is owed can be collected and claims resolved. The impact is three-fold:

1. Increased Cash Flow and Overall Cash Position—it’s almost a certainty that the bottom-line will improve when money is collected quickly and efficiently. With the additional revenue, day-to-day operations can be funded, expansion plans can be initiated, or capital equipment can be pursued.

2. Reduced Administrative Costs—by automating the billing and AR functions, the burdensome follow-up would be eliminated, and billing personnel could be redeployed to higher-level functions, including improving the patient experience.

3. Analytics-Derived Operational Improvements—With knowledge comes the ability to make overall improvements in the operations functioning, including enhancing patient flow, improving patient portion collections, advancing cash controls, and preventing future errors in coding and billing.

The Groundbreaking Impact of AI-Driven AR Optimization

By leveraging AI, automation, and machine learning through an augmented intelligence-driven solution, outstanding third-party aging AR activities can be turned into actionable insights optimizing recovery and decreasing write-offs.

With AI and machine learning, a curated knowledge base using predictive rules can determine the “next best course of action” and prioritize the resolution effort to maximize dollar recovery. This concentrates the efforts of the highly specialized AR recovery team on best use functions.

With a concentrated approach, denials management can be tracked, and each denial can be automatically appealed, if necessary so that revenue isn’t lost in the system or abandoned prematurely. Denials that used to sit untouched can now be addressed and processed using state-of-the-art automated systems that give up-to-date progress updates 24/7 and detailed analytics reporting – overall, especially useful when negotiating third-party payer contracts.

Using Insurance Discovery Effectively

Every healthcare provider or organizational representative has experienced the feeling of defeat when reviewing their uncollectible bad debt. Even with the best business practices, ironclad policies, and strong intake personnel training, there are inevitably insurance claims that are rejected or denied with balances owed transferring to patient responsibility.

We know for sure—that throughout the healthcare spectrum, patients frequently present for care without understanding their insurance coverage or benefits. Phrases like “annual maximums,” “remaining deductible,” and “explanation of benefits” may be overwhelming to patients that are unfamiliar with insurance terms and how they’re treated within the industry. The fact that an organization has ended up with an outstanding amount is often not from misrepresentation, but merely misunderstanding.

Couple that with the growth of patient consumerism and High Deductible Health Plans (HDHP) in recent years, we see a cascading problem that can only worsen with time. The key may be early intervention using a cloud-based AI-driven Insurance Discovery solution.

The Insurance Discovery Process Defined

Once the organization’s patient information is downloaded into a proprietary insurance discovery system, AI-driven software using machine learning capabilities, deep data mining and probabilistic analytics gleans, checks, and double-checks information. Access to this information comes from a sophisticated network of insurance payer clearinghouses and direct payer connections and is supplemented through a network of public and private databases.

Collectively, this information is then used to identify undisclosed coverage and socio-demographic identifiers to correct and update claims so that they can be processed by a team of highly qualified billing experts and plans can begin to pay. Part of understanding insurance discovery is recognizing one issue that plays a big part—timing!

Do Augmented Intelligence Systems Meet or Exceed Compliance Standards?

With a program driven by augmented intelligence, all information is integrated seamlessly from the EHR/EMR system by utilizing an HL7 or API-based bi-directional integration. It offers a comprehensive dashboard that allows instantaneous status checks. This exceeds all standards expected, including those set by HIPAA.


Moving into the future, AI-driven technology will bring improvements to not only clinical care but also patient access and revenue cycle management, further improving operational efficiency. Patients are already demanding low touch, high technology solutions that bring easy access and convenience. Even now, patients are far less willing to tolerate the inefficiencies brought with manual processes; they want and demand expert resolution.

Today, we are seeing solutions move from descriptive analytics toward the more sophisticated predictive analytics that estimates the likelihood of a future outcome based on patterns in the historical data. Timely actionable insights through optimized ARs leverages robotic process automation making revenue more readily collectible.

For more information on how your organization can predict revenue by using an augmented intelligence-driven patient access or optimized AR solution, schedule a demo with Infinx Healthcare.

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      1. Loria K. Putting the AI in Radiology. Radiology Today, Vol. 19, No. 1, P. 10.
        Accessed on June 7, 2020.
      2. Carlson B. Molecular Diagnostics Market Now Larger than the Economies of 50 Nations, per New Report. Kalorama Information Website. October 30, 2019.
        Accessed on January 20, 2020.
      3. Atella V, Mortari A, et.al. Trends in are-related disease burden and healthcare utilization. Aging Cell, 2019 Feb; 19(1): e12681. Published online November 29, 2018. doi: 10.1111/acel.12861.
        Accessed June 1, 2020.
      4. Cohen R, Zammitti E. High-deductible Health Plan Enrollment Among Adults Aged 18-64 with Employment-based Insurance Coverage. Centres for Disease Control and Prevention NCHS Data Brief No. 317. August 2018. https://www.cdc.gov/nchs/products/databriefs/db317.htm.
        Accessed on January 24, 2020.
      5. PAMA Regulations, Important Update. CMS.gov, Centers for Medicare and Medicaid Services.
        Accessed on January 28, 2020.
      6. 2018 AMA Prior Authorization (PA) Physician Survey. American Medical Association, Prior Authorization Research & Reports. 2019. file:///C:/Users/katem/Downloads/priorauth-2018%20(1).pdf.
        Accessed on November 23, 2019.
      7. Yu Y, MD. Transforming the prior authorization process to improve patient care and the financial bottom line. MGMA, Knowledge Expansion Insight Article, Reimbrusement.
        Accessed June 10, 2020.
      8. Finnegan J. MGMA19: No progress to fix prior authorization, as practice leaders say it’s gotten worse. Fierce Healthcare. October 16, 2019. https://www.fiercehealthcare.com/practices/mgma19-no-progress-to-fix-prior-authorization-as-practice-leaders-say-it-s-gotten-worse.
        Accessed June 8, 2020.
      9. 2019 CAQH Conducting Electronic Business Transactions: Why Greater Harmonization Across the Industry is Needed, p. 2. 2020.
        Accessed on January 30, 2020.
      10. Joint Authorship. Consensus Statement on Improving the Prior Authorization Process. American Medical Association. 2018. https://www.ama-assn.org/sites/ama-assn.org/files/corp/media-browser/public/arc-public/prior-authorization-consensus-statement.pdf.
        Accessed on January 22, 2020.
      11. Letter to the House of Representatives in support of Improving Seniors’ Timely Access to Care Act 2019 (H.R. 3107) from 370 Associations. September 9, 2019.
        Accessed on January 30, 2020.
      12. Livingston S, Luthi S. House Committee Throws Spotlight on Prior Authorization Burden, Modern Healthcare. September 11, 2019. https://www.modernhealthcare.com/politics-policy/house-committee-throws-spotlight-prior-authorization-burden.
        Accessed on February 3, 2020.
      13. Ibid. 9.
      14. Industry Checkup: Measuring Progress in Improving Prior Authorization. American Medical Association. 2019.
        Accessed on February 2, 2020.
      15. Artificial Intelligence – What it is and Why it Matters. SAS Insights. 2020.
        Accessed on February 1, 2020.
      16. Siwicki B. At RadNet, AI-fueled Prior Authorization Tech Shows Promise, Healthcare IT News, Global Edition. May 6, 2019. https://www.healthcareitnews.com/news/radnet-aifueled-prior-authorization-tech-99-accurate.
        Accessed on January 6, 2020.
      17. Napco’s iBridge Technology Named Top Innovation of 2014 by Security Sales & Integration Magazine – Recurring Revenue Model Makes iBridge a Top Choice for Security Dealers. January 13, 2015.
        Accessed on February 3, 2020.
      18. Integrating Prior Authorization Solution with Epic PMS While Protecting PHI at a Pennsylvania Hospital Group. Infinx Case Study. 2018. https://www.infinx.com/resourcecasestudy/integrating-preauthorization-solution-with-epic-pms-lt/.
        Accessed on February 2, 2020.
      19.  Implemented a Complete Overhaul of Revenue Cycle Management Program for Large Hospital-Owned Cardiology Clinic. Infinx/Enhanced Revenue Solutions Case Study. 2018.
        Accessed January 12, 2020.
      20. Ibid. 9.
      21. *******Chhaltralia V. What Does the Future Hold for Artificial Intelligence? Industry Analysis and Graphic, AI Business. March 22, 2018. https://aibusiness.com/industrygraphic-artificial-intelligence/.
        Accessed on February 2, 2020.
      22. Maximize Hospital Revenue with a Holistic Insurance Discovery Strategy. January 7, 2019.
        Accessed on January 20, 2020.
      23. How Using Insurance Discovery Can Significantly Improve A/R, Infinx Blob. January 23, 2020.
        Accessed on January 23, 2020.
      24. 2018 Survey of America’s Physicians: Practice Patterns and Perspectives. The Physicians Foundation, Empowering Physicians/Improving Healthcare. 2019.
        Accessed on January 30, 2020.
      25.  New Findings Confirm Predictions on Physician Shortage. Association for American Medical Colleges. April 23, 2019. https://www.aamc.org/news-insights/press-releases/new-findings-confirm-predictions-physician-shortage.
        Accessed on February 1, 2020.
      26. Reporting appropriate use criteria in claims for Medicare Patients. American Medical Association. August 17, 2020.
        Accessed on August 19, 2020.

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