Healthcare leaders are optimistic about AI’s transformative potential but lack strategic plans, with just 6% having an approach, reports Bain & Company. Despite industry challenges like rising costs, COVID-19 impacts, and staff shortages, executives foresee AI streamlining processes, reducing inefficiencies, and aiding clinical decisions. However, hurdles like cost, expertise, and regulation hinder progress. Bain suggests focusing on low-risk AI pilot projects, evaluating build vs. buy options, and aligning AI strategies with overarching goals for success in the evolving healthcare landscape.
Amidst challenges arising from mounting costs, a recent report from Bain & Company reveals that three out of four C-suite executives believe that recent advancements in artificial intelligence (AI) will significantly reshape the healthcare sector. However, only a mere 6% of these leaders have established concrete plans to leverage this transformative technology.
The healthcare landscape has faced considerable strain in recent years due to the COVID-19 pandemic, critical staffing shortages, inflation, and other financial pressures. A Bain & Company report highlights that over 50% of U.S. hospitals concluded 2022 with negative financial margins, further emphasizing the industry’s difficulties.
Despite these obstacles, top executives remain hopeful about the future, especially regarding the potential cost savings that rapidly evolving generative AI technologies can offer.
Significant enthusiasm exists among health system leaders for AI and automation’s potential to streamline financial and operational processes, address administrative inefficiencies, and alleviate clinician burnout. They foresee substantial opportunities to enhance workflows, manage clinical documentation, analyze data, and more in the upcoming year.
Over the next two to five years, these leaders plan to implement AI-powered initiatives focused on predictive analytics, decision support, guided treatment insights, and similar areas, according to the report.
The feasibility of these endeavors has been boosted by the exponentially decreased cost of training AI and machine learning models since 2017, as noted by Bain. This reduction in costs opens the door to an array of new productivity-enhancing tools at a relatively low investment.
Although the report indicates that 75% of C-suite leaders are enthusiastic about generative AI’s potential to reshape the industry, a mere 6% of surveyed health systems have formulated a concrete strategy to harness this potential.
Nonetheless, this percentage is expected to rise as more provider organizations recognize the value of adopting generative AI and automation to address long-standing clinical, financial, and operational challenges.
Top priorities for applying generative AI within health systems, as outlined by the report, include optimizing charge capture and reconciliation, analyzing patient data, automating workflows, providing clinical decision support, implementing predictive analytics, enhancing telehealth and remote patient monitoring, managing call centers for administrative purposes, offering diagnostics and treatment recommendations, streamlining provider and patient workflows, and facilitating care coordination and health system navigation.
However, while optimism abounds, Bain & Company researchers caution that significant hurdles persist as health systems race to integrate these rapidly advancing technologies. Challenges include resource and cost constraints, a shortage of expertise, and regulatory and legal considerations.
The researchers also emphasize that focus and prioritization remain challenges, as executives grapple with extensive lists of potential generative AI investments. These debates often prove to be inefficient and time-consuming due to the fast pace of innovation.
In light of these developments, Bain proposes four guiding principles for healthcare executives to navigate generative AI adoption:
1. Begin with low-risk applications that have a narrow focus to gain experience and establish minimum viable products in repeatable use cases.
2. Choose between buying, partnering, or building AI solutions based on the availability of technology and the initiative’s importance.
3. Reinvest cost savings and experience gained from early AI initiatives into larger transformative projects as the technology matures.
4. Recognize that AI is not a strategy by itself; rather, it should align with overarching goals to build a true competitive advantage.
Eric Berger, a partner in Bain’s Healthcare & Life Sciences practice, underscores the industry’s dual perspective on AI, stating that while it presents both hype and opportunity, leading companies are taking this technology shift seriously. They are embarking on focused, low-stakes AI projects that offer short-term returns on investment while building the experience and confidence needed for more transformative initiatives.
