Sector News

Health care’s quest for an enterprisewide AI strategy

July 10, 2022
Life sciences

The health care industry’s use of artificial intelligence (AI) traditionally has lagged other industries. However, the COVID-19 pandemic created the right set of internal and external conditions for digital transformation across the health care industry—and AI is one of the biggest areas of investments.1 AI is showing signs of maturity in the health care space: Organizations are using AI to improve the efficiency of internal processes and they’re better prepared to manage AI’s potential risks.

Now that investments in AI tools and capabilities are increasing, health care leaders are tasked with establishing the right enterprisewide AI strategy for their organizations. As we concluded in “Smart use of artificial intelligence in health care,” AI-enabled solutions can provide many benefits for organizations, such as immediate returns through cost reduction and better consumer engagement, but there’s still a lot of work to be done. That means putting strategies into action on a functional level by communicating a clear AI vision, helping the workforce operationalize AI, and finding the right ecosystem partners to supplement technical needs.

Deloitte’s most recent State of AI in the Enterprise survey, conducted with 2,875 global technology executives across all industries, found that while AI is rapidly changing, it’s not fully evolved. To understand where hospitals, health systems, and health plans stand on the adoption and maturity of AI, and what levers leaders can take to improve clinical decision-making, make processes more efficient, and lower costs, the Deloitte Center for Health Solutions analyzed a subset of the survey, specifically the responses of 220 global health care executives. To supplement the survey responses, we conducted interviews with four health system and health plan technology leaders.

Health care organizations are increasingly adopting AI and preparing for its risks
The COVID-19 pandemic has highlighted the strategic importance of AI in health care. In fact, it has served as a catalyst for health care organizations to begin to adopt AI enterprisewide rather than rolling out fragmented, single-solution initiatives. Health care organizations began using AI to battle the pandemic in many facets of care delivery from assisting with patient screenings, monitoring COVID-19 symptoms, and diagnosing and triaging patients, to developing treatments, automating hospital operational functions, and promoting public health.2

As applications and uses of AI in care delivery become more common, health care organizations are beginning to recognize more opportunities to use AI and are increasing their investments. In fact, 85% of survey respondents said they expect their AI investments to increase in the next fiscal year (2022–23) compared to 73% of respondents in our previous study (figure 1).

The increase in investments isn’t surprising, as 90% of the health care leaders surveyed believe that AI initiatives are important for their organizations to remain competitive in the market. When asked about their organization’s approach to technology innovation, 80% self-reported that they are either edge experimenters (organizations that tend to be first adopters of new technology or first to try new approaches and test unknown use cases) or fast followers (organizations that typically are next in line to adopt after some experimentation).

As adoption of AI increases, so can the risks. AI’s potential risks make it even more important for health care organizations to establish appropriate governance and oversight of algorithms and data (see sidebar, “Deploying initiatives to tackle AI bias in atrustworthy way,” for more information).

Our survey shows that health care organizations are better prepared to manage AI’s potential risks compared to 2019. Survey respondents reported doing a better job compared to 2019 on risks arising from cybersecurity vulnerabilities, unethical AI systems, and perceived job losses resulting from AI automation (figure 2). READ MORE

by Kumar Chebrolu, Maulesh Shukla, Hemnabh Varia, Kylie Cherco


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