Under more pressure than ever before to deliver better care with fewer resources, healthcare organizations need augmented intelligence (AI) to achieve the Quadruple Aim. According to our chief analytics and data science officer, Jason Jones, PhD, AI in healthcare falls short because organizations fail to realize three key opportunities AI offers.
Augmented intelligence doesn’t replace humans but enhances human decision making. AI can quickly scour millions of data sets, then deliver the most relevant insight to decision makers when they need it. With access to more information than users would ever have the time to review manually, team members can make the most informed decision every time. In other words, AI helps every member of the organization make better decisions, but it won’t replace them.
Predictive models are probably the most common type of AI in healthcare, yet many providers forget to prioritize a critical driver in predictive model successful: change management. Change management is the perspective that ensures the model supports the users’ needs and solves the problem it was intended to solve. Too often, end users start using predictive models for purposes other than for what they were intended or continue using models that don’t solve the intentional problem. With effective change management, team members constantly evaluate the model’s effectiveness, ensuring the model delivers according to its design and effectively supports team member needs.
Healthcare disparities have a significant impact on a person’s health, and AI can help clinicians overcome those disparities. Healthcare providers can use AI to identify health disparities that put a patient at risk for a worse outcome, then use their expertise to decide which patients to focus on and how to best address their health needs.
Although healthcare can feel fractured at times due to so many data sources, organizations can find ways to use AI in healthcare to overcome those fractures and treat people in the best way possible.
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