By Sanjeev Agrawal, President & COO at LeanTaaS, finalist of Best SaaS Product for Healthcare at the 2022 SaaS Awards

To say that the healthcare industry faces intensifying challenges is an understatement. Mounting financial pressures, an ongoing nursing shortage, increasing barriers to patient access, and staff burnout are just a few of them.

Over the next 20 years, the demand for services is likely to grow unabated, driven by a growing population of aging individuals with increasing healthcare needs and increased life expectancy due to improvements in clinical care. All of this points to rising issues with patient access and declining patient experiences – unless providers figure out how to do more with less, similar to the way other asset-intensive industries have created greater access to services at lower unit delivery costs.

How other asset-intensive industries have successfully used AI to generate ROI

  • Airlines have put a huge amount of computing horsepower into predicting passenger demand, optimizing travel routes, and forecasting trip schedules. Airlines must also predict supply and demand, cancellations, and missed plans and deliberately overbook to ensure yield maximization while still providing a good customer experience. The ability to create accurate models, and determine the right pricing and route planning, requires a powerful level of predictive and prescriptive support.
  • The package delivery industry must predict the volume as well as the origins and destinations of millions of packages that are shipped daily. At the same time, they must optimally load packages of varying shapes and sizes into static-sized trucks and determine the most efficient routes to deliver them.
  • Consumer services companies, like Amazon, use advanced data on shoppers’ habits to recommend additional products they may like or guide them to repurchase regularly needed items like toiletries or supplements. Companies that make revenue through advertisements, such as Google, also reference user history to display ads that the user is most likely to click.

The daily operations and results from these companies – nearly three million passengers fly safely and efficiently through US airports every day, 6.1 million packages arrive daily at their destinations via FedEx, and Google’s revenue from advertising amounted to over 200 billion dollars in 2021 – are clear indicators of a consistent return on investment from AI and analytics in other industries. These proof points show that with the proper tools, healthcare too can achieve safe and accurate predictive practices through AI.

What can healthcare gain from predictive and prescriptive solutions?

For healthcare organizations, implementing tools that use AI to predict upcoming capacity needs and offer prescriptive solutions can help unlock capacity, improve patient access and experience, and reduce costs for health systems.

  • AI can help operating rooms direct optimal staffing and equipment use just as it helps airlines determine optimal plane routes. It supports daily clinical schedules being packed more efficiently, like delivery trucks, to make the best use of available staff and open resources. Machine learning is a powerful field of AI that has been applied successfully in a variety of sectors.
    With the increased availability of electronically stored clinical information in recent years, the healthcare industry has evolved into an excellent environment for the development and deployment of these new technologies.
    Artificial intelligence has the potential to improve health-care systems by creating prognostic, screening, and diagnostic models based on the study of millions of clinical data points. Despite the fact that the application of algorithmic approaches can improve the quality of health systems and the lives of patients, an acceptable validation process is still required before these technologies can be implemented.
  • Recommendation engines, similar to those underlying consumer platforms, can be used to serve the best appointment or procedure times to surgeons and other physicians.
  • Driving more efficient schedules and use of resources in these ways decreases the number of commonly underutilized assets like operating rooms, inpatient beds, and infusion chairs.

This more efficient use of hospital resources leads to improved patient care and experience, ultimately leading to better outcomes, as well as producing cost savings and new revenue. Healthcare is positioned now to adopt the technology other industries are applying, and the results will be well worthwhile.

Why healthcare has lagged in the adoption of AI – some “Sacred Cows”

Hospitals have simply not leveraged AI to optimize resources to the same extent other industries have. Reasons why include:

  • A perception that existing technology like electronic health records (EHRs) are enough or that hospitals can build any further technology themselves. EHRs are excellent at what they were built for – gathering clinical and financial information from each patient encounter into a single, secure database – but they only display capacity problems, without predicting potential issues and offering solutions. Further, the sheer analytics power needed to do this requires deep expertise and high capacity in operations, data science, building scalable software, and enabling process changes on the front line, which are frequently beyond the scope of busy hospital IT teams and analytics groups. As a data point, the best funded predictive analytics companies have raised and deployed hundreds of millions of dollars to build capabilities they can scale across hundreds of hospitals cost effectively. No single health system is likely to be able to afford such an investment and maintain it year after year.
  • Healthcare’s special concern for safety and accuracy has created an aversion to risk taking and implementing new technologies. This need for safety and accuracy is not unique to healthcare, however. Airlines require extreme accuracy in order to keep millions of travelers safe daily, and use AI to help do so.
  • The pressures of limited resources, meaning healthcare organizations cannot risk making an investment without a guaranteed reward. But the return on the investment from deploying AI is clearly visible from its success in other industries, as outlined above.

Becoming operationally efficient through the use of modern tools is no longer a luxury for health systems. It is an imperative that every other asset-intensive industry has gone through – retail, consumer goods, financial services, manufacturing, transportation, airlines and more.

The time is now for healthcare and the first movers will have a big advantage.