Generative AI Will Transform Health Care Sooner Than You Think
Leaders need to understand the growing range of AI applications—and how to capitalize on their potential while avoiding the risks.
Basic AI is becoming table stakes in health care, and leading hospitals and health systems are taking advantage of the latest iteration—generative AI—to improve performance, reduce costs, and provide a better experience for patients and staff. (See “What Is Generative AI?”) Some organizations are just starting to experiment with initial use cases and are focusing primarily on administrative functions, while others have 100-plus use cases in place, including clinical applications at scale.
There’s no benefit to waiting to apply GenAI. In fact, leading organizations can capture an advantage by accelerating their adoption of the technology. But that’s a significant challenge, given that less than 30% of digital leaders in the health care industry feel prepared to adopt AI, and given that GenAI is evolving faster than most complex health care organizations are designed to adapt.
Implementing the technology does not need to be overwhelming, however. Based on our experience working with leading providers, we have developed a simple, five-step approach that helps leaders systematically apply GenAI. It allows organizations to identify the most powerful applications for their unique needs, begin capturing value, and build the internal capabilities needed to manage risk and generate greater value in the future.
Like any new technology, GenAI is sometimes treated as a solution in search of a problem. That’s a recipe for failure. Instead, organizations can start by identifying key unmet needs or challenges that the technology is suited to address.
Exhibit 1 shows some of the more than 1,000 GenAI applications that can lead to better performance in the following four main areas:
Technology is advancing faster than most health systems are designed to respond—in part because new solutions often entail changes to workflows and processes. But the COVID pandemic showed that rapid change is possible, with health care organizations adopting technology to support the shift to remote work and telehealth, for example.
Implementing GenAI follows the 10-20-70 formula: algorithms require about 10% of the effort, data and technology another 20%, and the remaining 70% goes to change management such as transforming operational processes and clinical workflows.
Our experience suggests that physicians and nurses are reluctant to adopt new technologies until they pass a high threshold of trust and reliability. When implementing GenAI, organizations can support staff by articulating a compelling vision, generating early proof points in small pilots, and including clinicians and staff throughout the process. With the right approach, technology can help an organization streamline a complex web of workflows. However, leaders need to support the effort with strong change management to ensure that staff feels heard and supported throughout the transition.
In caring for patients, providers are understandably concerned about risk. Organizations need to understand the risks that GenAI might entail—as well as the risks it can reduce—and build guardrails into the design of the new solutions.
Some health systems may not have the institutional expertise to understand how GenAI fits with their existing policy frameworks. The goal is not to formalize all policy implications in advance. Rather, organizations can start with a clear understanding of their current policy principles and then adapt them over time to incorporate GenAI across five broad areas:
Providers will need to commit to ongoing review processes as new GenAI applications and solutions emerge. For example, they can expect to reassess their policy and risk mitigation practices every 6 to 12 months.
In prioritizing use cases, it’s helpful to segment across two dimensions: potential value and ease of execution. (See Exhibit 2.) Often it’s not possible to quantify the full value of an initiative, including new revenue, cost management, and patient value. But organizations can simplify the process and make various options broadly comparable by using a basic, five-point scoring system, with five indicating the greatest potential value and one reflecting the least.
Regarding ease of execution, it’s helpful to understand the organization’s ability to implement a GenAI application and sustain it over time; that means determining whether the necessary people, platform, and partnerships are in place. Key questions relating to each of these include:
After weighing these considerations, providers can assign basic clothing sizes—S, M, L, or XL—to the ease of execution of each use case to determine the overall scope of the project. They can then select a portfolio of projects, including some quick wins that are relatively simple to implement and can generate value in a year or less, along with more ambitious and complex efforts that have greater potential impact over the long term.
Our experience shows that quick wins often focus on administrative support functions—areas characterized by high-volume, repetitive processes such as payments or appointment confirmations. In contrast, broad clinical decision support applications have greater potential value but are generally more complex. As with other digital solutions in health care, initial clinical GenAI offerings typically focus on narrow, disease-specific use cases, such as monitoring type 1 diabetes. Over time, the number of applications will grow to support a wider range of clinical decisions.
The forgotten step in many health care transformations is tracking progress and improving over time. The beauty of digitizing processes is that it simplifies the process of tracking and reporting key metrics. Optimally, each problem that the organization identifies—and the corresponding solutions—are linked to a a quantifiable goal, such as reducing costs in a specific process or function by 20% or increasing patient volume by 5% to 10%.
Generative AI has immense potential to transform hospitals and health systems. By applying the five-step process outlined here, providers can reduce costs, improve the patient and provider experience, and generate better clinical outcomes. GenAI will not replace the role of clinicians, but organizations that fail to leverage the technology will be overtaken by those that do. Do you have a portfolio of problems across your organization? As a leader, do you feel ready to leverage GenAI? Is your organization ready?
The authors thank Kazim Zaidi, Stephen Filler, Keith Fairbank, and Aryana Jacobs for their contributions to this publication.
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