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Health care’s quest for an enterprisewide AI strategy

As adoption of artificial intelligence increases, health care organization leaders should consider an enterprisewide AI strategy to transform care models

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 a trustworthy 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).

One of the health plan interviewees mentioned that they’re solidifying data governance through a central repository to have one version of truth. They’re also strengthening their data management practices and investing in what they call “explainable AI” to address unconscious bias. For example, instead of providing a social security number for member matching purposes, they’re now tokenizing or deidentifying the data they share with external entities. Another example is an academic medical center in the United States that’s tackling AI bias by continually evaluating the purpose of the data and reasons behind using AI over the course of its lifetime to account for and correct data biases.3

Deploying initiatives to tackle AI bias in a trustworthy way

Deloitte study shows that AI bias may be more prevalent within organizations than executives are aware. This bias can aid faulty decision-making, and damage consumer trust and stakeholder relationships. In health care, it can prove more costly as it can directly impact lives. For instance, Deloitte’s research on addressing bias shows how faulty algorithms are causing racial bias in the United States, and in effect reducing access and quality of care for the Black population.

The leaders we interviewed suggested that it’s important for health care organizations to not only understand how AI models predict outcomes across different groups, but also to hold themselves accountable for making sure the models are trained accurately and reflective of the populations they serve. The interviewees echoed the various business risks emanating from AI, including bias, security, privacy, and reliability issues—and confirmed that many health care organizations are now acknowledging these risks and working toward addressing them.

Trustworthy AI, a book by Beena Ammanath, executive director of the Deloitte AI Institute, highlights several characteristics of trustworthy AI: fair and impartial, robust and reliable, respectful of privacy, safe and secure, responsible and accountable, and transparent and explainable. These qualities can help organizations to safeguard ethics, build a trustworthy AI strategy, and accelerate the social and economic benefits required to maintain global competitiveness. When we asked our health care respondents about these dimensions in our survey, 78% were confident that their organizations respect data privacy, 75% have an accountability mechanism, 73% are safe from cyber risks, and 68% have transparent/explainable algorithms (figure 3).

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The barriers health care organizations face in achieving AI maturity

Making integrated data more accessible: Our interviewees mentioned that AI succeeds in its purpose when “there is a rich trove of data” and when “multiple datasets can talk to each other.” However, health care organizations often are challenged by siloed, unstructured, and at times, incomplete and inaccurate data. To add to this, patient-level data is sensitive and highly regulated, which makes it more difficult for AI applications to gain access to it. Lack of access to clean, integrated datasets hinders the ability to train high-performance AI models and deploy them at scale. According to one health plan interviewee, the organization’s leaders are focused on generating AI-powered insights to drive outcomes, improve quality, and reduce costs. To achieve this goal, a key is building an integrated health care dataset, which includes health plan data (e.g., enrollment and claims data), provider data (e.g., electronic medical records), and third-party data (e.g., health equity and ESG-related data).

To help ensure that AI algorithm models are robust and equitable, health care stakeholders recognize the importance of including data around drivers of health. For example, the Biden administration formed the Equitable Data Working Group to establish equitable data practices by generating disaggregated statistical estimates to depict experiences of historically underserved groups using survey data, increasing non-federal research and community access to disaggregated data, and conducting robust equity assessments of federal programs. Using equitable data in AI models can highlight opportunities to improve outcomes for underserved communities.4

Establishing an enterprisewide AI strategy: Our survey findings showed that just one in three executives strongly agree that their organization has an enterprisewide AI strategy, their leaders communicate the vision effectively, and AI differentiates them competitively (figure 4). Our interviewees discussed how, at times, AI loses out to other organizational priorities, and may not be featured in the enterprisewide strategy. As one of the interviewees pointed out, “there are a growing number of AI-powered point applications across the enterprise, but most are utility-based, and there isn’t a unified organizationwide strategy on AI.”

Keys to achieving AI maturity for health care organizations

Our interviewees and survey respondents highlighted three principles that health care leaders should embrace to achieve enterprisewide AI-led transformation:

1. Gaining senior leadership support: An executive champion is essential for crafting and translating this vision into achievable action steps and milestones, according to our interviewees. They discussed how the cultural characteristics set by executive leadership—namely the ability to make tough decisions, manage change, and partner with the workforce on key decisions—are as much as or more important to achieving AI maturity than the organization’s core technology capabilities. Health care organizations should build an AI culture with leaders driving support through communication and transparency at all organizational levels. While critical, gaining leadership support is just one of the factors that help ensure success. In fact, most survey respondents named leadership support, strong data, and technical capabilities the top three factors most critical to success of AI implementation.

2. Elevating talent beyond technical skills: The survey respondents believe that their organizations need a mix of technical (AI expertise, data science, project management) and professional (critical thinking, adaptability) skills to achieve broader success of AI initiatives. The survey results also showed that health care organizations are undertaking significant changes in workflows and workforce to scale AI initiatives and achieve greater maturity (figure 5). For instance, four in five respondents said that their organizations are changing how they create teams, roles, and workflows to take advantage of AI. The same number of respondents said that their organizations are investing in change management and AI readiness by creating new AI jobs and by training the workforce to better integrate AI into their workflows. One of our interviewees expressed the need for a training program or curriculum that, in a simplified way, teaches their typical health care staff just enough about AI, without overwhelming them with the science behind it. This can enable them to have a meaningful conversation about AI applications they use.

3. Pursuing the right ecosystem partnerships: To adopt an enterprisewide AI strategy, organizations should consider collaborating effectively and making strategic decisions on build versus buy. More than one-third of the survey respondents believe that cloud vendors, IT professionals, and consulting firms are the key ecosystem partners for their respective organization’s AI success. Interviewees mentioned that health systems are still at a nascent stage when it comes to developing the technical architecture needed to train, integrate, and validate AI models at scale. Health systems aren’t yet designed to deploy AI at an enterprisewide level. To address this issue, health care technology vendors offer product packages that help enable health systems to quickly train and validate models, which allows them to focus more resources on designing workflows and improving clinical outcomes. Platform-enabled ecosystems can bring together ecosystem participants on a digital network and help health care organizations improve and expand their services by providing opportunities to expand customer reach, access new capabilities, and increase revenue.

The path to an AI-fueled future for health care organizations

Comparing the survey findings shows that health care organizations have made progress on their AI initiatives since 2019. The pandemic, in part, has provided avenues for health care organizations to quickly turn AI pilots into full-scale implementation in many functions, according to our interviewees. Here’s how the path to an enterprisewide AI transformation moves through the health care value chain (figure 6):

  • Operations: AI-enabled digital authorization for fast and frictionless decision-making
  • Consumer: AI-enabled personalized services that help in omnichannel engagement for consumer needs
  • Clinical: AI-enabled care that focuses on prevention, monitoring, and delivery of care
  • Performance: Autonomous monitoring that helps in real-time anomaly and trend identification
  • Workforce: AI-enabled smart workforce management by optimizing resource and talent allocations

When health care organizations apply AI across the value chain, they can improve consumer health and well-being and support better outcomes while also boosting organizational efficiency and reducing costs.

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The Deloitte Center for Health Solutions


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    Rajiv Leventhal, “Healthcare leaders expect AI to unseat telehealth as top digital health investment,” Healthcare Innovation, May 7, 2021.


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    OECD, “OECD policy responses to coronavirus (COVID-19): Using artificial intelligence to help combat COVID-19,” accessed May 30, 2022; Vanita Ahuja and Lekshmi V. Nair, “Artificial intelligence and technology in COVID era:A narrative review,” Journal of Anaesthesiology Clinical Pharmacology 37, no. 1 (2021): pp. 28–34.


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    Seth Joseph, “Artificial intelligence myth vs reality: Where do healthcare experts think we stand?,” Forbes, September 30, 2021.


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    The White House, A vision for equitable data: Recommendations from the Equitable Data Working Group, accessed May 30, 2022.


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Project Team: Apoorva Singh contributed to the secondary research for this project. 

The authors would like to thank Jay Bhatt, Kate Fusillo Schmidt, Siri Anderson, Abha Kulkarni, Rebecca Knutsen, Hannah Bachman, Laura DeSimio, Zion Bereket, and the many others who contributed to the success of this project.

This study would not have been possible without our research participants who graciously agreed to participate in the interviews. They were generous with their time and insights.

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