The Importance of Generative AI in Healthcare and the Future of Data Connectivity

Ben H.

Hatched by Ben H.

Nov 26, 2023

3 min read

0

The Importance of Generative AI in Healthcare and the Future of Data Connectivity

In a recent report by Bain, it was found that only 6% of health systems have a comprehensive generative AI strategy. This is a concerning statistic considering the potential that generative AI has to reshape the healthcare industry. Despite this, there is still considerable excitement surrounding the use of generative AI applications like ChatGPT to reduce administrative headaches.

Generative AI has reached a turning point in its ability to reshape the healthcare industry, according to 75% of health system executives surveyed. However, the majority of these executives have not identified a clear pathway to implement the technology. This lack of strategy can be attributed to the uncertainty surrounding how generative AI works, whether to build their own applications or buy solutions from a vendor, and how to deploy it safely.

In the absence of robust regulation from policymakers, larger health systems at the forefront of deploying generative AI are taking matters into their own hands by creating their own safety and efficacy standards. This is a proactive approach to ensure that the technology is implemented in a way that is safe and effective for patients. However, for most health systems, the lack of technical expertise and resource constraints are the biggest barriers to implementing generative AI.

To overcome these challenges, organizations should focus on established and accepted initial use cases for generative AI. One such use case is the ability of generative AI to compose clinical notes after a patient visit. Companies like Nuance, Abridge, and Augmedix offer solutions that input draft clinical notes directly into electronic health records for clinicians to review following a patient visit. This streamlines the documentation process and allows healthcare providers to focus more on patient care.

In addition to the challenges surrounding generative AI, the issue of data fragmentation in healthcare is also a pressing concern. Data fragmentation refers to the lack of interoperability and connectivity between different healthcare institutions. This can hinder the exchange of health data, which is crucial for improving patient care, expediting medical research, and reducing the cost of healthcare.

To address this problem, Datavant, a leader in health data connectivity, has recently closed its merger with Ciox Health and launched an expanded product suite called Datavant Switchboard. This product suite provides a neutral, trusted, and ubiquitous infrastructure for the secure exchange of health data across tens of thousands of healthcare institutions. By solving the issue of data fragmentation, Datavant aims to unlock the power of health data and drive advancements in the healthcare industry.

The merger between Datavant and Ciox Health is a significant step towards creating a more connected and interoperable healthcare system. With the combined resources and expertise of both companies, Datavant is well-positioned to tackle the challenge of data fragmentation at scale. This will ultimately benefit providers, payers, health data analytics companies, patient-facing applications, government agencies, research institutions, and life science companies.

In conclusion, the implementation of generative AI and the improvement of data connectivity are two critical areas that need attention in the healthcare industry. To successfully implement generative AI, organizations should develop clear strategies and focus on established use cases. Additionally, addressing the issue of data fragmentation through initiatives like the Datavant-Ciox merger is essential for unlocking the full potential of health data. By prioritizing these areas, healthcare can benefit from the transformative power of technology and drive advancements in patient care, research, and cost reduction.

Actionable Advice:

  • 1. Develop a comprehensive generative AI strategy by identifying clear use cases and pathways for implementation.
  • 2. Invest in technical expertise and overcome resource constraints to effectively implement generative AI.
  • 3. Collaborate with data connectivity solutions like Datavant to address the issue of data fragmentation and improve interoperability across healthcare institutions.

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