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Increasing healthcare ROI through AI to unlock capacity: Q&A with Sanjeev Agrawal, President and COO of LeanTaaS

A version of this piece was originally published on Becker’s Hospital Review

As part of Becker’s 12th Annual Meeting in April 2022, LeanTaaS gave a half-day summit to explore the role of data in expanding capacity for care. During the event, LeanTaaS President & COO Sanjeev Agrawal discussed in depth how AI is already driving optimized capacity and revenue in other asset-driven industries and the potential to map those successes on to better return on investment (ROI) in healthcare. 

Q1: What are some other industries that healthcare can look to where AI has been used to achieve optimized use of assets and return on investment? 

We see instances of the use of AI throughout our daily lives:

  • Airlines: Every time we fly, and we are always on a plane that is reasonably full, the airline has done a lot of work to manage the yield – predict passenger demand by route and optimize each route. To achieve this, airlines use historical data to forecast on which given day one of any 300 million Americans might fly from which of ~20,000 airports to which of 19,999 others. They must also predict how many people will buy tickets and not fly in order to accurately “overbook”. Drawing accurate predictions requires a powerful level of prediction and prescription, including for pricing and route planning.
  • Package Delivery: UPS and other delivery services must both predict the volume and origins / destinations of millions of packages that will be shipped each day, while also optimally loading packages of varied shapes and sizes into static-sized trucks. In addition, they must determine the most efficient routes to deliver them.
  • Navigation Systems: Apps like Waze predict the precise patterns of traffic based on both historic data along routes, as well as update them in real time based on immediate traffic events.
  • Consumer Services: Amazon uses buyers’ habits to recommend further products that will likely be of interest to them, and Google, whose revenue comes purely from ads, can display those most likely to ensure user clicks.

In all these instances, AI drives key conclusions from pools of data far too large or complex for manual calculations, and thus drives the ROI that keeps these industries running.

Q2. What lessons can healthcare providers learn from these other industries? 

Like airlines and package delivery systems, maximizing access, patient care, and profits requires that healthcare providers use their capacity well. Directing patients and appointment schedules accurately relies on the same level of prediction, based on historic patterns and real time updates, these other industries use. Leaving room for unexpected occurrences is also critical to ensuring smooth day-to-day operations.

Operating rooms for instance are very like airplanes, in that they only create revenue when they are full and “in flight”, and so they are most lucrative when fully booked. Outpatient appointments, such as those for infusion treatments, consist of different sizes and shapes being “packed” into a static space, a clinic’s schedule, and available rooms and chairs, not unlike packages being loaded onto a UPS truck. Directing the flow of inpatients through different bed units and levels, to ensure “traffic” runs without bottlenecks, requires the same precision and accuracy used by a platform like Waze to predict the fastest possible route at a given moment.

All of these functions can ensure the fullest possible use of the assets a hospital or clinic already has and drive better ROI. The good news is that the corpus of historical data required to produce these predictions and prescribed actions has already been captured in most hospital and health system EHRs. From the examples of other industries, we can also see that the technology to drive these functions already exists. But we tend not to leverage this in healthcare, instead relying in many cases on manual scheduling and decisions based on intuition and tribal rules. There is still a massive opportunity to use AI to capture our capacity and drive revenue.

Q3. What are the reasons healthcare does not leverage AI to the extent of other industries?  

There are several reasons healthcare leaders tend not to adopt analytics at the same level as other industries. There is a perception that existing technology like EHRs can already do everything we need; that in this industry we have a special obligation to safety and accuracy that we cannot trust to newer technologies; and finally, that as financial resources are precious, we cannot risk making an investment without a guaranteed reward.

In fact, while EHRs and data dashboards are good at performing what they were designed for – gathering clinical and financial information from each patient encounter into a single database – they only display capacity problems. AI-level predictive analytics that would make informed recommendations are beyond what EHRs can do and also beyond what most in-house healthcare teams can build.

Of the 125+ health systems who partner with LeanTaaS for AI analytics, many have already tried to innovate themselves before seeking an outside source. To perform at the level of other industries, these analytics require deep expertise in operations, data science, building scalable software and enabling process changes on the front line. These capabilities require significant investment and are not easy for most providers to assemble, independent of size.

Healthcare is also not unique in its concern for safety and accuracy. To return to the airline industry, every morning 3 million Americans board a 200,000 pound steel tube, and are shot across the sky. If even as few as .001% of these passengers did not land safely, the industry would risk 30 deaths each day. But this is not the case, and to a less dire but no less crucial point, very rarely are these passengers’ bags lost. Healthcare is no different in its concerns, and should be no different in its leveraging of analytics to master the science of increasing access and lowering cost.

At LeanTaaS we recognize the very real financial concerns of healthcare systems. To this end, we offer an unconditional satisfaction guarantee to customers who don’t achieve their desired results when they use our healthcare AI solutions. No one has taken us up on this yet.

Q4. What ROI can health systems achieve by deploying AI? 

Our iQueue analytics solutions focus on operating rooms, infusion appointments, and inpatient flow in particular. We have calculated that 57 customers who were live with iQueue for Operating Rooms for over one year made an average $500,000 worth of OR time available annually for more surgeries. An iQueue for Infusion Centers customer used AI to more efficiently “pack” daily schedules and thus increase patient volumes by 25%. On the inpatient side, an iQueue for Inpatient Bed customer’s improved predictability yielded an 8% decrease in opportunity days, equal to $8 million in value. In each of these cases, by deploying AI to optimize asset utilization as other industries already do, healthcare providers unlocked new revenue. All healthcare organizations have the opportunity to do the same.

Explore more examples and real-life success stories of deploying AI to increase healthcare capacity here.

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