The application of artificial intelligence (AI) in healthcare holds the potential to enhance patient safety, alleviate physician burnout, and streamline healthcare operations. However, the healthcare industry has been slow to adopt AI due to technical challenges and difficulties in measuring its financial benefits, according to a new review published in The New England Journal of Medicine. Compared to other sectors, healthcare faces unique challenges in interpreting qualitative information like clinical notes and handling multifactorial outcomes in clinical decision-making.
“In our experience, the environment in which some health care organizations operate often leads these organizations to focus on near-term financial results at the cost of investment in longer-term, innovative forms of technology such as AI,” study authors wrote. “Healthcare organizations that prioritize innovation link investment decisions to ‘total mission value,’ which includes both financial and non-financial factors such as quality improvement, patient safety, patient experience, clinician satisfaction, and increased access to care.”
To accelerate the adoption of AI in healthcare, decision-makers should consider nonfinancial factors, such as patient safety and clinician well-being, alongside financial considerations when evaluating AI investments. This broader perspective can help AI realize its potential in domains where its impact is primarily nonfinancial, such as quality and safety improvement.
Financial incentives play a significant role in the adoption of AI in healthcare organizations. Some prioritize short-term financial gains over long-term technological innovation like AI. Those who prioritize innovation consider both financial and non-financial factors, including quality improvement, patient safety, patient experience, clinician satisfaction, and increased access to care.
One notable application of AI in healthcare is in processing reimbursements, where AI can help reduce claim denials and improve the patient experience. AI can also optimize clinical operations, such as operating room capacity, although widespread implementation remains uncertain.
AI shows promise in improving quality and safety in healthcare by addressing problems like adverse drug events and diagnostic errors. However, a significant portion of the value of AI in this domain is nonfinancial, which has limited its current adoption.
The slow adoption of AI in healthcare is attributed to practical challenges, including the complexity of healthcare compared to simpler AI applications like movie recommendations on Netflix. Additionally, the fee-for-service payment model in the US healthcare system discourages investment in AI, which is more aligned with a value-based payment model that prioritizes improving care and safety.
To accelerate the adoption of AI in healthcare, decision-makers should consider nonfinancial factors, such as patient safety and clinician well-being, alongside financial considerations when evaluating AI investments. This broader perspective can help AI realize its potential in domains where its impact is primarily nonfinancial, such as quality and safety improvement.