Health systems around the world are facing a host of challenges, including rising costs, clinical-workforce shortages, aging populations requiring more care (for example, to treat chronic conditions), and increasing competition from nontraditional players.1 At the same time, consumers are expecting new capabilities (such as digital scheduling and telemedicine) and better experiences from health systems across their end-to-end care journeys.2 In response, health systems are increasing their focus on digital and AI transformation to meet consumer demands, address workforce challenges, reduce costs, and enhance the overall quality of care.3 However, despite acknowledging the importance of these efforts to future sustainability, many health system executives say their organizations are still not investing enough.
AI, traditional machine learning, and deep learning are projected to result in net savings of $200 billion to $360 billion in healthcare spending.4 But are health systems investing to capture these opportunities? We recently surveyed 200 global health system executives about their digital investment priorities and progress.5 Seventy-five percent of respondents reported their organizations place a high priority on digital and analytics transformation but lack sufficient resources or planning in this area.
What health systems can do and how they can learn from other industries
The goal of digital and AI transformation is to fundamentally rewire how an organization operates, building capabilities to drive tangible business value (such as patient acquisition and experience, clinical outcomes, operational efficiency, and workforce experience and retention) through continuous innovation. Delivering digital value for health systems requires investment and new ways of working.
Building partnerships. Scale is crucial to value creation. But the definition of at-scale systems has changed in the past few years; today, it takes more than $13 billion to be a top 20 system by revenue, and many have reached their current position through inorganic growth.6 Partnerships (joint ventures and alliances) may offer a promising avenue to access new capabilities, increase speed to market, and achieve capital, scale, and operational efficiencies.7
Moving beyond off-the-shelf solutions. History shows that deploying technology—such as electronic health records (EHRs)—on top of broken processes and clinical workflows does not lead to value. Realizing value from healthcare technology will require a reimagination (and standardization) of clinical workflows and care models across organizations. For example, optimizing workflows to enable more appropriate delegation, with technical enablement, could yield a potential 15 to 30 percent net time savings over a 12-hour shift. This could help close the nursing workforce gap by up to 300,000 inpatient nurses.8
Using the cloud for modernization. Health systems are increasingly building cloud-based data environments with defined data products to increase data availability and quality. Health systems can also use cloud-hosted, end-user-focused platforms (such as patient or clinician apps) that integrate multiple other applications and experiences to simplify stakeholders’ interactions with the system.
Operating differently. Operating differently entails fundamental changes in structure (flatter, empowered, cross-functional teams), talent (new skill sets and fully dedicated teams), ways of working (outcome orientation, agile funding, and managing products, not projects), and technology (modular architecture, cloud-based data systems, and reduced reliance on the monolithic EHR). With these changes, some health systems have begun to see real value within six months. Building a digital culture helps the transformation succeed over time.9
Cautiously embracing gen AI. Gen AI has the potential to affect everything from continuity of care and clinical operations to contracting and corporate functions. Health system executives and patients have concerns about the risks of AI, particularly in relation to patient care and privacy. Managing these risks entails placing business-minded legal and risk-management teams alongside AI and data science teams.10 Organizations could also implement a well-informed risk-prioritization strategy.
Digital and AI investments provide health systems with opportunities to address the many challenges they face. Successful health systems will invest in areas with the greatest potential impact while removing barriers—for example, by upgrading legacy infrastructure. Health systems that make successful investments in digital and analytics capabilities could see substantial benefits and position themselves to benefit from the $200 billion to $360 billion opportunity.11
