The future of healthcare: Why enterprises must embrace AI innovation

The intersection of AI, software, and data management is set to revolutionize healthcare and will serve as a critical driver of medical innovation and improved patient outcomes. But successfully adopting this mix of emerging and advanced technologies can be daunting and complex. As healthcare leaders consider how to best embrace AI innovation, there are several steps to take that will ensure their organizations are positioned to address today’s most pressing challenges and pave the way to a healthier future.

The pivotal role of AI in healthcare

From clinical applications to operational efficiencies, AI is already having a significant impact on the healthcare industry. Radiology, for instance, stands out as a pioneering field where AI is making significant strides. Advanced diagnostic procedures such as MRIs, CAT scans, and X-rays are now benefiting from AI’s ability to assist radiologists by highlighting potential issues that may be overlooked during manual reviews. This not only scales human effort but also enhances diagnostic accuracy, enabling radiologists to focus on more complex cases and significantly reducing the risk of oversight.

These applications also extend into drug research. By analyzing vast datasets, AI can identify new chemical combinations and potential treatments for diseases like ALS and Alzheimer’s. This capability accelerates the discovery process and opens new avenues for medical research that were previously unimaginable.

Beyond improved patient outcomes, AI integrated into site reliability engineering can help improve the scalability of software systems. By analyzing problem reports and test failures, AI can identify patterns and underlying issues that human operators might miss. This improves system reliability and ensures that healthcare infrastructure remains robust and efficient.

At the other end of the spectrum, AI is also deeply influencing more traditional operational and regulatory elements of healthcare. In revenue management, for example, AI is streamlining processes like prior authorizations. Traditionally, these tasks involved significant manual effort and were prone to errors. AI systems can now automate much of this work, reducing paperwork errors and allowing healthcare professionals to focus more on patient care.

Although AI is primarily seen as an assistive technology, ensuring that it is used ethically and safely is paramount. Enterprises should use ethical frameworks to ensure that AI applications undergo rigorous testing and validation before being deployed in order to safeguard patient safety and data privacy.

How to embrace the digital health revolution

The integration of AI into healthcare is a revolution that promises to transform every facet of the industry. With the right frameworks in place, healthcare providers can not only improve patient outcomes but also ensure that the industry remains resilient and adaptive to future challenges.

First, it will be key to identify clear objectives for AI’s adoption. Determine specific areas where AI can add value, such as diagnostics, predictive analytics, patient management, drug discovery, and operational efficiencies. Leaders should also set measurable goals for what the AI implementation aims to achieve to better understand its outcomes.

Choosing the right AI technologies and platforms – specifically, those that are tailored to the healthcare industry – will also be an important fundamental step. Enterprises should look for tools and applications that comply with all relevant healthcare regulations and standards, such as HIPAA in the United States, and ensure that the AI tools integrate seamlessly into existing clinical workflows to avoid disruption. This includes interoperability with Electronic Health Records and other healthcare systems.

Once the right platforms and solutions are in place, start with pilot projects to test AI applications on a smaller scale. This allows for the identification and resolution of potential issues before a full-scale rollout, and feedback from these projects will help refine and improve future models. In parallel, teams should ensure regular monitoring and performance evaluations are happening to track progress against the implementation’s objectives and metrics such as accuracy, efficiency, and patient outcomes. From there, all that’s left to do is scale these applications up across the organization for greater efficiency and patient benefits.

VMware Tanzu can play a pivotal role in this transformation. The Tanzu Platform provides pre-built tools and workflows for enterprise AI adoption, reducing the need for custom development, while also delivering the data governance and security capabilities organizations depend on to address ethical and regulatory concerns around AI. Learn more about how VMware Tanzu can help with the application of AI across industries, including healthcare, at https://tanzu.vmware.com/platform