Exigencies of Artificial Intelligence in Healthcare

The term ‘Artificial Intelligence’ or ‘AI’ may sound appalling to some, but it has been used for decades and its use cases are more prevalent than we might imagine. Today, AI assists us in every sphere of our lives. For Example, Google uses AI to offer smart replies in Gmail or filter spam from getting into your inbox. Siri or Alexa, are smart voice assistants that use AI to play music for you, get directions or even make a phone call. Amazon uses AI to suggest products to personalize your shopping experience. These examples are galore and AI is weaving itself into the fabric of our society and daily lives and healthcare is also in the league.

The rise of the Healthcare AI market is unparalleled, with the AI industry’s fastest growing sub-sectors, it is forecasted to grow at a 39.4% CAGR. According to Mckinsey’s study, “AI might create more value in the business of healthcare systems and services. It estimated these potential savings at $269.4 billion annually”. With almost 80% of healthcare data unstructured such as text or images, the appeal of AI in healthcare is its capability to gather, analyse and make sense of massive amounts of unstructured data get streamlined quickly, efficiently and often more accurately than humans.

However, Healthcare organizations are still struggling to transition to value-based care. And it is not because of a lack of patient data, but because:

  1. The current generation of commercial AI technology is rule-based and doesn’t understand the context and semantics required to capture patient risks accurately.
  2. They are built using legacy stack, so they are rigid and unable to meet organizations’ need for modular workflows.

AI for healthcare revenue:

Applications in this segment leverage AI to automate manual processes, optimize the efficiencies, eliminate waste and remove administrative burden in a revenue cycle. The impact of AI investments in healthcare is realized first in the operational & administrative side of the healthcare system rather than the clinical side. Reason being, clearly defined opportunities exist, as the value of AI’s impact and ROI can be precisely tracked due to improved staff productivity, reimbursement and cash flow of the healthcare organizations

Risks and Barriers to AI Adoption in Risk Adjustment

Everything is not rosy with AI adoption in Risk Adjustment. There lie significant challenges ahead of us before we reap the benefits of AI. Healthcare Industry has been sluggish to adopt AI as compared to other industries because of the following major reasons:

  • Challenges with Privacy and Data Security 
  • Lack of data standards results in interoperability and integration issues of AI tools within the hospital’s workflows 
  • Reluctance to trust AI-based algorithms by medical practitioners due to it’s “black box” output
  • Inadequate access to a quality large data sets as it continues to be locked down in silos
  • The legal risk for AI applications as regulatory guidelines are ambiguous & evolving for licensing its appropriate use 
  • Shortage of talent with specific knowledge and skills needed to succeed with AI

Conclusion

Today, healthcare is harnessing the power of AI in bits and pieces. It is already being touted as a revolutionary force that is going to transform the industry. Healthcare leaders need to start getting their feet wet in the AI wave or else they might be left behind and will be forced to play a catch-up role. That is a scarier proposition for any healthcare leader due to various forces already crippling the healthcare organizations such as rising costs, razor-thin operating margins, declining reimbursement and value-based future.

Healthcare leaders need to ensure that clinical and senior leadership understands the use cases, the challenges, and then cut through AI hype to identify, adopt and deploy realistic use cases for their organizations. The success would largely depend on the vendor that works hand in hand like a partner with healthcare organizations.

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Disclaimer: All the information, views, and opinions expressed in this blog are those of the authors and their respective web sources and in no way reflect the principles, views, or objectives of RAAPID.