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AI4Ops: A Data-Driven Solution to Burnout and Administrative Waste

by Jody Ranck | December 16, 2021

Key Takeaways

The pandemic offered an opportunity for a number of AI in Operations (AI4Ops) vendors to demonstrate a measurable ROI during the crisis. While much of the attention in health IT over the past year has focused on the rise of virtual care, we found that vendors in the AIOps space proved that their offerings are more mature and robust than many analysts had thought.

Some vendors in our report are confident enough in solutions to take on risk in contracts with users. Vendors such as Olive are offering ROI-based fees for their services and others are providing guarantees for ROI.

Many healthcare organizations have been using point solutions for operations as they initially engage with AI, but the leading vendors are building multi-functional, end-to-end platforms across operations and RCM functional areas.  This domain is experiencing a number of acquisitions as investors pour substantial funding into the space and existing vendors such as Olive and Waystar, for example, acquire startups to build more complete platforms across the spectrum of operations’ use cases.

Market Forecast for AI4Ops through 2026

Introduction

AI/ML applications have been developed in recent years to address the complexities of the administrative costs and operations from revenue cycle management (RCM) to hospital operations (e.g., OR scheduling) and supply chains. While most of the attention in the AI space has focused on clinical applications, AI for operations – AI4Ops – is where the real action is today, despite receiving far less attention in the media and analyst reports in 2020-21.. Today, Chilmark Research is releasing a new report that takes a closer look at several leading solutions in this space and provides an overview of the trends and dynamics of the market as it evolves post-pandemic.

Our research focused on the largest segments of the operations spectrum from supply chains to hospital operations. We have also included a small number of supply chain and RPA vendors that we believe compose an important, but smaller segment than RCM and hospital operations.

Setting the Context: COVID and Hospital Operations

Many health systems and hospitals suffered a severe financial shock with the onset of the COVID-19 pandemic in March 2020. Lockdowns and overburdened health systems treating an influx of COVID-19 patients saw dramatic decreases in elective procedures and ER visits that impacted their bottom lines. Hospital operating margins are already so slim that a shock that pushes margins into negative territory can impact the survival of smaller hospitals.

Source: American Hospital Association

The dire financial circumstances created by the pandemic created an environment where CFOs and CIOs had to respond quickly, and this opened the door to a number of AI and operations vendors that had solutions mature enough to meet their needs across hospital operations, revenue cycle management, and supply chains.

Some of the vendors we interviewed for our report were already present in health systems and were able to provide better analytics on hospital beds, supply chains, and facilitate revenue cycle management as we moved out of lock downs and elective procedures slowly returned to normal volumes.

Use Cases in AI4Ops

Our report covers a number of operational use cases including the following:

  • Discharge Planning: ER management, Discharge barriers, Perioperative
  • Hospital Operations: Staffing, Hospital beds, Surgery, Asset optimization
  • Revenue Cycle Management (RCM): Intelligent claims management/denial prevention, Prior authorizations/eligibility, Patient ability to pay, Fraud detection
  • Supply Chains: Predictive caseloads, Supply chain forecasting, Resource prioritization

AI has the advantage over traditional algorithms used for operational use cases due to the mathematical complexity of some issues such as operational excellence and asset optimization where the combinatorial possibilities exceed the computing power of many non-ML approaches. A major focus of many vendors is automating burdensome, back-office administrative associated with claims management and eligibility validation.

Figure 2: Advantages of AIML over traditional approaches to AI

Evolution towards end-to-end platforms in RCM

Investors have obviously caught on to the business that can be garnered from attacking inefficiencies and waste in the administrative aspects of healthcare. Substantial investments in this sector happening over the past two years have facilitated growth in capabilities of platforms such as Waystar and Olive. Olive has even begun investing in startups themselves (via an in-house studio) that may become standalone companies that can partner with Olive in their quest to automate administrative processes. Change Healthcare processes nearly half of all US commercial claims through its clearinghouse services amounting to nearly $1.5T. Change has built one of the leading platforms in AI4Ops and as part of an AI-driven digital transformation approach.

LeanTaaS, whose focus is on operational excellence and asset optimization, has a slightly different take where the initial focus has been on moving beyond grid-based block scheduling to smarter capacity management. The initial focus of their iQueue platform was on infusion chairs, inpatient hospital beds, and operating rooms, and they will begin expanding into other assets such as labs, imaging, and clinics. Their experience to date has shown a 7-20x ROI and ability to recover faster from the impact of COVID.

Conclusion

While it is still the early days for AI in healthcare we are beginning to see some dividends from investments in the operations space. This space is one of the most critical in overall digital transformation and where automation is most needed.

Given the burnout we have seen in the healthcare sector over the past year, AI4Ops has a role to play in ameliorating some of the issues associated with overwork and too many tasks. There are still risks to trusting code too much, though they pale in comparison to the clinical side of healthcare. For example, organizations will need to audit for bias in algorithms for eligibility and ability to pay.

Our report provides clear examples of how healthcare can learn from other industries such as airlines and airport capacity management to improve operations in hospitals. We also highlight vendors who excel at the change management component of AI4Ops, which is critical to successful digital transformation. Many of the lessons learned from the pandemic will carry through to the future for both mundane operational needs as well as natural disasters from wildfires to hurricanes as healthcare organizations must mitigate an ever wider range of risks.

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