Artificial intelligence (AI) is moving from hypothetical to business critical in healthcare. The global healthcare AI market is expected to reach $6.16 billion by 2022. Experts have estimated that AI applications can potentially create $150 billion in annual savings for the U.S. healthcare economy by 2026, and AI can address an estimated 20 percent of unmet clinical demand. As the statistics pile up, implementing AI may seem like a cure-all to every hospital woe, from data entry to population health to imaging, and it would be easy to get overwhelmed.
Keeping all of this in mind, let’s take a step back and examine AI for what it is: a powerful technology that can play a role in improving individual and population health when implemented judiciously. In the wrong hands, it’s clear that AI tools could be misused, but with the right strategies and careful use of AI aligned with an organization’s goals, AI can be used to generate insights based on data and analytics that may have been otherwise missed. AI has the potential to improve the quality of care and reduce cost by preventing unnecessary tests and procedures, while accelerating diagnoses and improving access by better utilizing resources. In the current healthcare climate, adding value while improving patient outcomes and access is not only a stated goal but also an imperative for survival of health systems in the emerging value-based integrated care environment.
Data-Centric Architecture Makes Real World AI Possible
So how do health systems get started with AI? Many start with small projects, using infrastructure that is on hand, but quickly identify limitations and outgrow this approach.
We’ve all heard that embracing data-centric architecture will help providers create a platform on which AI will thrive. But what does this mean? Building AI models requires data, a lot of data, on a scale most health systems have not previously explored in analytics environments. In many cases, the data exists in the health system or the community, but needs to be aggregated, cleansed and organized to support AI projects.
For timely results, health systems may need to invest in a data hub that can be used to stage data for AI models, as well as GPU-based compute environment that allows researchers to train and optimize AI systems. AI requires a departure from traditional architectures due to its large scale and computational intensity, but also requires agility and scalability as programs and use grow. Forward-looking health systems who recognize the potential of AI will invest in agile, high-performing, and cost-effective AI platforms that allow researchers to thrive. By upgrading an organization’s physical architecture and infrastructure to support AI, teams will be able to better leverage AI and accelerate the pace of innovation. But technology isn’t the only variable needed to succeed in AI.
Optimize Your Implementation: AI as a Culture Change
IT leaders know that technology cannot change in isolation; people and organizational processes also need to be brought along to support the change. Putting the appropriate AI infrastructure in place for researchers is a first step to getting started, but clinical staff and teams also need to be trained, so that they understand the models in use and are comfortable with them.
Most clinicians will initially at least express distrust with the “black box” aspects of an AI implementation. Training should cover how to use the new technology, how it works, when it does and does not replace current processes and procedures, how to discuss AI with patients, and when AI should be trusted and when it should be questioned. In addition, adding AI to treatment decision making makes IT a partner in delivering care, and clinicians will need to work more closely with IT as a result. IT should be prepared for this change to the working relationship.
Data is also an issue; the sources of the data used in AI, as well as data handling, needs to be transparent so that the clinical community is comfortable trusting the findings from AI. Beyond the technical aspects, healthcare organizations should develop their processes to support the use of AI in a tangible way. From visualization, which presents information to clinicians clearly and succinctly, to integration of AI information with workflows, all the way to automated decisions, which act on ever-evolving algorithms, analytics and AI are key to a practical and effective architecture.
A large concern from healthcare leaders around establishing an AI architecture is cost—all that data can come at a hefty price for organizations of all sizes, not to mention the costs associated with hiring the proper experts and training team members. However, AI can cut costs by automating tasks that would previously be done by clinicians or staff, freeing up their time for more crucial work. And, despite the associated costs, AI is no longer just a flashy option for healthcare organizations—it can also provide a distinct advantage in both quality of care and business performance, as AI leaders have begun to see evidence of in their organizations.
Optimize for AI: Making an Impact
We’ve understood the need for quality data and integrating that data into a secure data-centric architecture to get it ready for AI. Now, it’s time to ensure AI makes a lasting impact, both within your organization and for the patients you serve. This requires a shift in mindset from all employees, and it starts from the top.
C-suite leaders should work to create a community around AI, sharing inspiration and forming multi-disciplinary teams that elevate the AI narrative and boost economies of scale. This connection with others, from all parts of the organization, is key to reshaping the current landscape in the short-term, and lays the foundation for AI as the new normal. AI is neither the poison pill nor the ultimate cure-all, but powerful medicine in the quest for better care and lower costs.
Josh Gluck is an adjunct professor of health policy and management at NYU’s Wagner School of Public Service and the Vice President of Healthcare Technology Strategy at Pure Storage. Gluck has over 20 years of experience directing information technology initiatives, managing complex IT projects, leading technical and professional teams, and providing critical business strategy support. His previous roles include Deputy CIO for Weill Cornell Medicine and Director of Information Technology at New York Presbyterian Hospital.