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AI Drives Improvement in Healthcare, But Gaps Curb Full Adoption

AI will transform the way doctors, hospitals, and healthcare systems collect and manage their revenue cycle, but financial, security, and privacy concerns hinder AI adoption.

AI, Artificial Intelligence

Source: Getty Images

By Samantha McGrail

- A recent Change Healthcare study found that artificial intelligence (AI) is driving a wide range of improvements in healthcare, but the approach is tactical and not end-to-end. 

In the study, Poised to Transform: AI in the Revenue Cycle, researchers measured healthcare executives’ familiarity with AI, discovered areas for improvement, and learned how the technology is used now and will be used in the future.

Specifically, they found that AI will transform the way doctors, hospitals, and healthcare systems collect and manage their revenue cycles,with 98 percent of healthcare leaders anticipating using AI in revenue cycle management (RCM) and 65 percent reporting that they currently use AI for RCM.

Additionally, 81 percent of executives are already conducting a tech evaluation reviewing AI technology providers, solutions, or software systems to improve RCM process. 

And 35 percent of respondents said their implementation may be “early mainstream/fully mature” by 2023, while only 12 percent answered that their implementation is mature today.

Overall, familiarity with AI and its impact varies among executives, IT, and revenue cycle leadership. But gaps in opinion are impeding healthcare from taking full advantage of the transformative power of AI, researchers stressed. 

Specifically, financial, security, and privacy concerns hinder AI adoption and decrease success factors.

Budget concerns are the leading cause of delay of initiating AI in RCM and full AI integration, researchers noted, while 75 percent of non-technical executives said that budgetary concerns are the primary obstacle. 

On the other hand, 56 percent of providers reported liability, risk, and privacy concerns, while staffing, trust in information, and infrastructure challenges were other top reasons. 

A 2019 IDC FutureScape report predicted that AI-driven interfaces will transform the future in one of three health systems and hospitals by 2023. And the worldwide spend on AI for diagnosis and treatment functions is growing at a CAGR of 24 percent and could reach $4.9 billion by 2023. 

Additionally, nearly 50 percent of pharmaceutical and biotech manufacturers will employ prescriptive analytics or AI using IoT data to optimize their supply chains by 2021.

In an October KLAS and Center for Connected Medicine (CCM) report, researchers found that artificial intelligence (AI) will be one of the most promising emerging technologies in the next two years.

Currently, clinical decision support is the most common use area for AI (61 percent), followed by dictation assistant or transcription (50 percent) and diagnostic medical imaging (48 percent). 

Although implementing AI is beneficial and transformative for health systems, it is crucial to proceed with caution and balance healthcare AI in a thoughtful and effective way, a National Academy of Medicine report stated.  

First, there should be complete transparency on the composition, semantics, provenance, and quality of data used to develop AI tools, researchers said. There should also be definitive separation of data, algorithmic, and performance reporting in AI dialogue. 

Human-centered tools should be at the forefront of AI implementation, and these tools should support humans rather than replace it with full automation.

Furthermore, the AI community must develop a framework for implementation and maintenance by incorporating existing best practices of ethical inclusivity, software development, implementation science, and human–computer interaction.

“AI is primed to transform revenue cycle management for those providers who understand how to use it strategically,” Luyuan Fang, PhD, chief AI officer at Change Healthcare, said in the press release detailing the study.

“Providers that close the gaps revealed by this research will be well-positioned to reap financial, operational, and clinical gains from the technology—including improving the end-to-end revenue cycle, claims accuracy, denial reduction, clinical insights, level-of-care prediction, and more,” Fang continued.