Navigating AI Adoption in Healthcare: Overcoming Key Challenges for Sustainable Growth
The following is a guest article by BreAnne Buehl, Head of Healthcare & Life Sciences at VMware Cloud Foundation by Broadcom
Healthcare is one of our most complex, heavily regulated industries, so it’s no surprise that its adoption of AI is complicated and wildly variable. Despite significant obstacles, including data security, data governance, and the high costs of scaling AI in a compliant way, healthcare’s adoption of AI is outpacing other industries. Most healthcare leaders understand the importance of implementing AI, and tech leaders are willing to fund and explore the various new options around Generative AI, with the largest organizations leading the way in evaluating use cases.
AI isn’t new to healthcare. The industry has been an early adopter since the 1970s when AI was widely used to identify blood infection treatments. However, with GenAI exploding onto the scene creating a seemingly endless number of ways to harness this technology, healthcare leadership is grappling with the pressures to evaluate, adopt, and deploy solutions at pace to keep up with the market.
Now that we’ve seen how quickly the technology environment can change, healthcare organizations need a roadmap for secure, scalable AI adoption that is both cost-effective and aligned with strategic goals. This structured approach ensures sustainable growth, builds resilience against emerging risks, and lays the foundation for a future-proof AI strategy. By focusing on robust data governance, continuous model optimization, and clear ROI evaluation, healthcare organizations can begin to fully realize AI’s potential–in whatever form it takes.
Three Practical Strategies for Effective and Sustainable AI Adoption
Craft a Comprehensive AI and Data Strategy
For healthcare organizations, creating a trusted, reliable AI framework starts with defining clear objectives that align with real-world needs. Leaders should identify specific goals, such as improving diagnostic accuracy, streamlining administrative tasks, or enhancing risk prediction for patients. Assessing data readiness is another critical step—evaluating data quality, accessibility, and any integration or security gaps. This includes setting robust data governance policies to meet privacy regulations set forth by HIPAA and GDPR. Additionally, testing new strategies through small-scale pilot programs can provide essential feedback before scaling up, making sure that AI solutions integrate smoothly with existing systems, such as EHRs and clinical workflows, to minimize data silos. Deciding on a cloud strategy, whether private, public, or hybrid, will have a significant impact on the cost, scalability, and security of those programs.
Finally, a data-driven culture that prioritizes data literacy and staff training is key. It’s not uncommon to see new leadership roles emerge like Chief Artificial Intelligence Officer (CAIO) at innovative healthcare systems, but regardless of the presence of such leadership roles, cross-functional teams will ultimately drive success for any AI strategy. Bringing experts in data science, AI, and IT together with healthcare practitioners, lines of business, and legal experts will result in solutions that solve the actual workflow, diagnostic, operational, and public health goals of a given healthcare system.
Adopt Continuous Model Optimization for Efficiency
Continuous model optimization is essential for keeping AI models accurate and adaptable to the fast-changing demands of healthcare. A solid monitoring framework that tracks model accuracy, reliability, and potential biases through alerts and dashboards is foundational. Regular data feedback loops allow AI models to stay up-to-date by incorporating fresh data, while automated retraining pipelines help minimize manual oversight and ensure consistent performance. Bias and drift monitoring is also crucial, as models must be fine-tuned to meet clinical goals fairly and reliably as conditions evolve.
Evaluate and Communicate ROI Clearly
Clearly demonstrating the return on investment (ROI) of AI initiatives is critical for healthcare organizations. It’s notoriously difficult to trace ROI back to many healthcare IT initiatives, but it ensures ongoing support from stakeholders and helps prioritize precious future funding. Leaders can define metrics such as cost savings, operational efficiency, and patient outcomes that align with the organization’s strategic priorities, and having that cross-functional team from Item 1 above will be a critical element in measuring the ROI against those priorities. Establishing pre-implementation baselines allows for accurate comparisons, while a structured approach to tracking performance data ensures meaningful insights. Using data visualization tools to communicate these results keeps stakeholders with varying levels of technical literacy engaged and invested.
Building a Future-Proof AI Strategy
In short, effective AI adoption requires a balance between initial investment and long-term gains. By beginning with pilot projects to demonstrate value and building a robust data infrastructure that enables interoperability, healthcare organizations can create a foundation for sustained AI growth. Emphasizing transparency, ethical considerations, and continuous improvement enables healthcare leaders to drive impactful, secure AI adoption that enhances both operational efficiencies and patient outcomes.
BreAnne Buehl is the Head of Healthcare and Life Sciences for Broadcom’s VMware Cloud Foundation division, where she champions the industry’s digital transformation and modernization for the betterment of population health outcomes. She was previously the UK’s Vice Consul for Healthcare and Life Sciences in the Pacific Northwest, a commercial diplomacy role that focused on bringing together the best of US and UK health technology, research, therapeutics, and care innovations. She held a leadership role in the rapid response program between the US and UK governments during the COVID-19 pandemic, supporting several major hospital systems with their sudden need for virtual care, rapid diagnostics, and equipment. She is based in Silicon Valley.
The following is a guest article by BreAnne Buehl, Head of Healthcare & Life Sciences at VMware Cloud Foundation by Broadcom
Healthcare is one of our most complex, heavily regulated industries, so it’s no surprise that its adoption of AI is complicated and wildly variable. Despite significant obstacles, including data security, data governance, and the high costs of scaling AI in a compliant way, healthcare’s adoption of AI is outpacing other industries. Most healthcare leaders understand the importance of implementing AI, and tech leaders are willing to fund and explore the various new options around Generative AI, with the largest organizations leading the way in evaluating use cases.
AI isn’t new to healthcare. The industry has been an early adopter since the 1970s when AI was widely used to identify blood infection treatments. However, with GenAI exploding onto the scene creating a seemingly endless number of ways to harness this technology, healthcare leadership is grappling with the pressures to evaluate, adopt, and deploy solutions at pace to keep up with the market.
Now that we’ve seen how quickly the technology environment can change, healthcare organizations need a roadmap for secure, scalable AI adoption that is both cost-effective and aligned with strategic goals. This structured approach ensures sustainable growth, builds resilience against emerging risks, and lays the foundation for a future-proof AI strategy. By focusing on robust data governance, continuous model optimization, and clear ROI evaluation, healthcare organizations can begin to fully realize AI’s potential–in whatever form it takes.
Three Practical Strategies for Effective and Sustainable AI Adoption
Craft a Comprehensive AI and Data Strategy
For healthcare organizations, creating a trusted, reliable AI framework starts with defining clear objectives that align with real-world needs. Leaders should identify specific goals, such as improving diagnostic accuracy, streamlining administrative tasks, or enhancing risk prediction for patients. Assessing data readiness is another critical step—evaluating data quality, accessibility, and any integration or security gaps. This includes setting robust data governance policies to meet privacy regulations set forth by HIPAA and GDPR. Additionally, testing new strategies through small-scale pilot programs can provide essential feedback before scaling up, making sure that AI solutions integrate smoothly with existing systems, such as EHRs and clinical workflows, to minimize data silos. Deciding on a cloud strategy, whether private, public, or hybrid, will have a significant impact on the cost, scalability, and security of those programs.
Finally, a data-driven culture that prioritizes data literacy and staff training is key. It’s not uncommon to see new leadership roles emerge like Chief Artificial Intelligence Officer (CAIO) at innovative healthcare systems, but regardless of the presence of such leadership roles, cross-functional teams will ultimately drive success for any AI strategy. Bringing experts in data science, AI, and IT together with healthcare practitioners, lines of business, and legal experts will result in solutions that solve the actual workflow, diagnostic, operational, and public health goals of a given healthcare system.
Adopt Continuous Model Optimization for Efficiency
Continuous model optimization is essential for keeping AI models accurate and adaptable to the fast-changing demands of healthcare. A solid monitoring framework that tracks model accuracy, reliability, and potential biases through alerts and dashboards is foundational. Regular data feedback loops allow AI models to stay up-to-date by incorporating fresh data, while automated retraining pipelines help minimize manual oversight and ensure consistent performance. Bias and drift monitoring is also crucial, as models must be fine-tuned to meet clinical goals fairly and reliably as conditions evolve.
Evaluate and Communicate ROI Clearly
Clearly demonstrating the return on investment (ROI) of AI initiatives is critical for healthcare organizations. It’s notoriously difficult to trace ROI back to many healthcare IT initiatives, but it ensures ongoing support from stakeholders and helps prioritize precious future funding. Leaders can define metrics such as cost savings, operational efficiency, and patient outcomes that align with the organization’s strategic priorities, and having that cross-functional team from Item 1 above will be a critical element in measuring the ROI against those priorities. Establishing pre-implementation baselines allows for accurate comparisons, while a structured approach to tracking performance data ensures meaningful insights. Using data visualization tools to communicate these results keeps stakeholders with varying levels of technical literacy engaged and invested.
Building a Future-Proof AI Strategy
In short, effective AI adoption requires a balance between initial investment and long-term gains. By beginning with pilot projects to demonstrate value and building a robust data infrastructure that enables interoperability, healthcare organizations can create a foundation for sustained AI growth. Emphasizing transparency, ethical considerations, and continuous improvement enables healthcare leaders to drive impactful, secure AI adoption that enhances both operational efficiencies and patient outcomes.
BreAnne Buehl is the Head of Healthcare and Life Sciences for Broadcom’s VMware Cloud Foundation division, where she champions the industry’s digital transformation and modernization for the betterment of population health outcomes. She was previously the UK’s Vice Consul for Healthcare and Life Sciences in the Pacific Northwest, a commercial diplomacy role that focused on bringing together the best of US and UK health technology, research, therapeutics, and care innovations. She held a leadership role in the rapid response program between the US and UK governments during the COVID-19 pandemic, supporting several major hospital systems with their sudden need for virtual care, rapid diagnostics, and equipment. She is based in Silicon Valley.
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2024-12-05 20:30
Except for the headline, this story has not been edited by Healthcare Central is published from a syndicated feed.
