Will AI Hinder Digital Transformation in Healthcare?

Will AI Hinder Digital Transformation in Healthcare?

Improving healthcare delivery remains a major priority for hospitals and health systems as issues like chronic disease management and health equity continue to present challenges for providers. Significant efforts to enhance care revolve around gathering and effectively utilizing the data points generated across patients' journeys.This requires healthcare organizations to pursue digital transformation through the integration of technologies such as EHRs, wearable devices and AI.While these tools play a vital role in helping organizations navigate the healthcare data cycle, some have raised concerns that such technologies could also throw a wrench in health systems' digitalization initiatives.The healthcare digitalization landscape is unique in many ways, but Viswanathan emphasized that the industry's slow adoption of new technologies is a critical aspect of the conversation around digital transformation.

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The potential for tools like AI to improve productivity and reduce burnout by serving in an assistive role for clinicians is one of the positive impacts of digital transformation efforts, Viswanathan continued. Despite the hype, healthcare organizations looking to adopt AI to bolster digital transformation and interoperability face various challenges.One of the biggest challenges is that these efforts are data-driven, which creates data quality pitfalls such as the garbage in, garbage out (GIGO) problem. GIGO is used to demonstrate that the quality of a data output is determined by the quality of a model's data inputs.In healthcare, this means that using low-quality clinical data in AI and other technologies will result in low-quality -- and, therefore, unusable -- model outputs.


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