What You Should Know:
– KLAS, a leading research and insights firm focusing on healthcare IT, recently released a report exploring the growing adoption and future plans of generative AI in healthcare organizations.
– The report, “Generative AI 2023: What Are Organizations’ Current Adoption & Future Plans?,” is based on the perspectives of 66 healthcare executives, provides valuable insights into the current state and future potential of generative AI in healthcare.
What is Generative AI?
Generative AI refers to artificial intelligence that can create new content, like text, images, or other media, based on existing data. It utilizes machine learning models to understand patterns and structures in the data, allowing it to generate novel and relevant outputs.
Generative AI Solutions Are Being Adopted, Mostly by Larger Organizations
As interest in generative AI surges across industries, healthcare organizations are taking a cautious yet optimistic approach. While adoption remains relatively low, with only 25% of surveyed executives reporting current implementation, a significant 58% anticipate acquiring solutions within the next year. This trend is particularly pronounced among larger organizations with greater resources and data availability.
Future Plans: Widespread Generative AI Investment Anticipated
While only 25% of respondents currently use generative AI solutions, a significant 58% plan to implement or purchase them within the next year. This widespread interest is driven by the desire to enhance operational efficiency, a critical concern for organizations facing staffing and financial constraints.
Generative AI Hopes for Automation and Improved Patient Engagement
Respondents see generative AI as a powerful tool for automating tasks like generating reports, facilitating personalized patient communication, and streamlining workflows. This could free up valuable time for healthcare professionals and improve patient engagement through timely information and personalized care.
Generative AI Challenges: Accuracy, Cost/ROI, and Security
While optimism abounds, there are also concerns. Accuracy and reliability are paramount in healthcare, and respondents worry about potential biases, errors, and “AI hallucinations” that could negatively impact patients and decisions. Additionally, the cost of implementing and maintaining AI infrastructure can be significant, with potential delays in realizing return on investment. Finally, ensuring patient privacy and data security in the context of AI workflows presents a significant challenge.
Addressing Generative AI Concerns and Proving Value
As healthcare organizations explore generative AI, addressing accuracy concerns and proving its value will be crucial for long-term adoption. This means implementing robust testing and validation procedures, demonstrating clear return on investment, and fostering trust through transparency and ethical considerations.
KLAS intends to publish further research on generative AI in collaboration with the Center for Connected Medicine (CCM) in early 2024. This further research will provide deeper insights into the specific applications and impact of generative AI across different healthcare areas.
