Multi-national research finds healthcare AI maturing, despite staffing and data challenges
Healthcare organizations seeking to implement and extend their artificial intelligence (AI) capabilities struggle with finding skilled personnel and sufficient, high quality data, according to a new IDC, sponsored by InterSystems, AI In Healthcare: Early Stage with Steady March to Maturity.
“With a heightened focus on quality data and models that can be used by both developers and data scientists, healthcare organizations can accelerate and leverage the true power of AI”
The study amassed insights from more than 200 hospitals in the U.S., UK, and Germany. The survey explores the delay in AI adoption in the healthcare industry compared to other industries, the breadth of use cases of interest to healthcare providers, as well as the role data quality plays as a primary barrier for adoption.
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Key findings from the study include:
- Half (50%) of respondents have an AI framework in place; the remaining respondents will be online within 24 months.
- 46 percent of respondents reported that data volume and confidence in the data are critical success factors in AI adoption.
- Applying AI to improve data quality is among the top three reasons 35 percent of organizations plan to adopt it.
- Only 21 percent of organizations intend to build AI algorithms in-house in 2020; declining to 18 percent by 2023.
- AI deemed more important to healthcare providers: 38 percent of healthcare organizations indicated that AI is a corporate priority, compared to 33 percent of respondents in a global, multisector survey.
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“With a heightened focus on quality data and models that can be used by both developers and data scientists, healthcare organizations can accelerate and leverage the true power of AI,” said Cynthia Burghard, IDC Research Director for Value-based Healthcare IT Transformation Strategies. “Healthcare leaders must invest now in their data quality and push for clean, harmonized data in order to implement and get the most out of their AI tools.”
Current use cases in the healthcare industry range from improving data quality (35 percent), reading images to assist in diagnosis (30 percent), and early identification of hospital acquired infections (30 percent).
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