A featured contribution from Leadership Perspectives, a curated forum for enterprise technology leaders, nominated by our subscribers and vetted by the CIOApplications Editorial Board.

University of Illinois at Chicago

Biomedical and Health Information Sciences University of Illinois at Chicago

AI has become mission-critical for competitive advantage in industries such as retail, travel, and banking. In healthcare, we are beginning to hear about new applications for AI every week. With all of this activity, it is important to take a strategic look at the possibilities for AI innovations in healthcare.

We need to return to the question Clayton Christensen posed in his book The Innovators Prescription and ask, “What job(s) do we want AI to do for us?” The goals everyone in healthcare is working to address are to improve quality and patient experience and reduce costs. It has also been acknowledged that improving the providers' experience is important to address burnout. So how can AI support these efforts?

Some of the jobs we need AI to do in healthcare:

• Automate administrative tasks
• Manage risk
• More accurate diagnosis (radiology)
• Increase patient engagement – communications between consumers and healthcare providers
• Improve patient health outcomes
• patient engagement – communications between consumers and healthcare providers
• medication management

How can we get some immediate impact? If AI can do the job of reducing costs, it would check an important box for all stakeholders. Costs are an overriding concern to consumers and our concerns and complaints have reached the ears of Congress. Several congressional hearings this spring have focused on understanding the cost issue.
If we can automate (and speed up) manual processes, reduce diagnostic errors, and improve patient health outcomes by deploying AI solutions, studies have estimated potential savings of $269.4 billion annually through solutions such as:

• robot-assisted surgery
• virtual nursing assistants
• automation of administrative tasks
• fraud detection
• reduction of medication errors
• improved accuracy of diagnoses
 

AI has become mission-critical for competitive advantage in industries such as retail, travel and banking


Leadership and Workforce needed for AI

Healthcare executives are fully aware of this potential and are investing in these AI solutions and others. Beyond CIO roles, healthcare leadership at the C-level is expanding into roles such as Chief Transformation Officer and Chief Innovation Officer. These roles are seen as having increasing influence as the move to AI advances.

To actually deploy AI, a workforce is needed with the ability to develop AI solutions implement them throughout the organization. This deployment will not only take place in a hospital and acute settings, but also in non-acute areas of the care continuum such as ambulatory clinics, provider offices nursing and long-term care facilities and consumer homes. Employers will need informaticists and data scientists with expertise in how technology can be used in healthcare.

AI is likely to follow a predictable path, similar to the one we saw for the EMR and other health information technology, the technology turns out to be the easy part. Leading the organization through a change that requires a paradigm shift to adopt the new technology can be an even greater challenge. Human factors and change leadership requires informatics skills in addition to the technical skills for AI development and design.

• Will providers trust the data and algorithms?
• Will AI be deployed seamlessly within the provider workflows?
• Will providers, many of whom are already experiencing burnout, be asked to do more or will AI really make their job easier?
• Will consumers trust AI innovation?

What will the role of clinicians and providers be in AI?

Much like clinical decision support, the AI solutions of today will help to further progress on evidence-based practice and standardization of clinical practice across the organization. While standardization is sometimes seen as a negative or limiting in practice of medicine, it also is a positive from a quality perspective due to the focus on reducing variation. If standardization reduces variation, it will support improved quality and patient outcomes. AI solutions will go beyond pushing out alerts to prescribers to actually using algorithms and models that develop machine learning which can support and even automate parts of the clinical workflow. To do this, a deeper understanding of the data behind the models and how the algorithms actually work is needed. Physicians will need some knowledge of how AI works to see how it will impact their practice of medicine. Many physicians, nurses, pharmacists and others will find themselves as the “super users” of AI providing content expertise to those on the AI team with technical skills and expertise. Healthcare expertise will be needed to interpret the results of AI algorithms, raise questions and challenge the data to build trust and confidence in the outcomes.

What’s Next: Strategy and Prioritization

The healthcare industry is currently at a tipping point. There is momentum to move forward with change and the technology is available. Now is the time for IT leaders to prioritize AI projects according to their strategic goals. Will the focus for implementation be on reducing costs since this is a pain point for most of the stakeholders? An analysis of the benefits of specific AI use cases and the immediate impact on stakeholders must be understood to move ahead.

Ultimately, if AI can deliver on improving patient outcomes and reducing costs, healthcare will be changed significantly.

The articles from these contributors are based on their personal expertise and viewpoints, and do not necessarily reflect the opinions of their employers or affiliated organizations.