Wide application of artificial intelligence (AI) in health care could greatly improve patient safety, reduce physician burnout, and improve the overall efficiency of health care from scheduling and billing to making surgeries safer. However the healthcare industry lags in AI adoption due to inherent technical challenges and because the financial benefits of AI adoption are often difficult to measure, according to a new review published in The New England Journal of Medicine.
The study authors detailed the AI challenges confronting health care compared with other industries.
“Early AI took root in business sectors in which large amounts of structured, quantitative data were available and the computer algorithms, which are the heart of AI, could be trained on discrete outcomes—for example, a customer looked at a product and bought it or did not buy it,” they wrote. “Qualitative information, such as clinical notes and patients’ reports, are generally harder to interpret, and multifactorial outcomes associated with clinical decision-making make algorithm training more difficult.”
Another major factor slowing the utilization of AI is the financial incentives, or lack thereof, that influence decisions at different healthcare organizations.
“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,” they 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.”
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