Harnessing the cloud could power AI ambitions in health care

By Matt Crowson, M.D., specialist leader, and Sahithi Bayana, managing director, Deloitte Consulting LLP

Artificial intelligence—generative AI in particular—is a central theme at this year’s HIMSS conference in Orlando (March 11-15).¹ Dozens of sessions taking place this week will cover everything from the use of AI to accelerate health innovations to cybersecurity to ethical considerations.

The hype surrounding AI has become almost impossible to avoid. Many of our health care and life sciences clients feel pressure to develop use-cases and to incorporate the new technologies into their business practices. But AI and large language models (LLMs) have sophisticated computational requirements that legacy IT systems may not be equipped to handle. Organizations that have not yet transitioned their operations to the cloud likely aren’t quite ready to adopt AI (see From fax machines to GenAI, are health systems ready?).

In some cases, a fear of missing out appears to be driving the push toward AI. Some company leaders are regularly hearing about the benefits of Generative AI and they don’t want to miss the boat or lose their competitive edge. There is also a recognition that there are clear benefits to be gained by incorporating AI into processes. But without a cloud-based platform in place, AI will likely not be able to meet its full potential. Cloud technology, which offers substantial computing power, data storage, and security, is the engine that allows AI and Generative AI to succeed. Along with having a cloud foundation, organizations also should have the necessary skills and processes in place.

A modern cloud-based data architecture will likely be necessary to build and maintain Generative AI at scale. We have identified the following four key steps that organizations should consider as they try to determine the best use-cases for AI.

1. Define business objectives: Executives should try to gain a comprehensive understanding of the organization's strategic goals, operational challenges, and key performance indicators (KPIs). They should also identify areas where AI could add value, streamline processes, or drive innovation in alignment with those objectives.
2. Evaluate technological readiness: IT leaders should assess their organization's technological infrastructure, capabilities, and constraints to determine its readiness for implementing each AI use case. They should consider factors such as computing resources, software platforms, integration requirements, cybersecurity, and scalability. They should also try to identify any gaps or challenges that may hinder the successful adoption and deployment of AI initiatives and develop strategies to address them proactively.
3. Determine a financial business case: A business case is crucial for a Generative AI. A minimum viable product (MVP) should outline the initiative's purpose, potential benefits, and feasibility. An MVP can help move the needle by securing stakeholder buy-in, allocating resources effectively, and evaluating the anticipated return on investment. Additionally, a financial business case can offer a roadmap for implementation and help ensure alignment with strategic objectives and organizational priorities.
4. Assess data availability and quality: The availability, accessibility, and quality of data within the organization should be evaluated. Determine whether existing data assets are sufficient for AI initiatives or if additional data collection or enhancement efforts might be required. Assess data governance practices and ensure compliance with relevant regulations to help maintain data integrity and privacy.

By completing these steps, organizations can establish a solid foundation for identifying and prioritizing relevant use-cases for AI that align with their strategic objectives and operational realities.

AI has transformative potential

Building on the steps outlined above, integrating AI into organizational ambitions offers transformative potential. One of the advantages is the enhancement of efficiency and productivity across various operations. AI algorithms are adept at automating routine tasks, increasing operational throughput, and allowing humans to dedicate more time to strategic and creative endeavors. This shift can help optimize productivity and elevate employee satisfaction by reducing the burden of monotonous work.

Another potential benefit lies in the realm of data-driven decision-making. The ability to swiftly process and analyze vast datasets to extract actionable insights can be invaluable. AI excels in this domain, helping to equip organizations with the tools to make informed decisions that are aligned with their strategic objectives. Such precision in decision-making can be a cornerstone of successful business strategies, guiding organizations through the complexities of modern markets with agility and foresight.

Generative AI is the latest step along the journey toward the Future of Health that Deloitte first outlined in 2019. About 75% of health care organizations are experimenting with, or plan to scale, Generative AI across the enterprise, according to a survey conducted by the Deloitte Center for Health Solutions (see Generative AI to Reshape the Future of Health Care | Deloitte US). Here are several examples of where AI could be used to boost efficiencies and reduce costs:

• Administrative burdens: Manual processing is an unavoidable component of modern Western medicine. It has been integrated into care delivery and can pull clinicians away from patients. It can also contribute to administrative bloat. The United States spends more than $900 per person on administrative costs. AI could help reduce that spending by automating repetitive tasks (e.g., claims submission, coding, billing, collections), freeing up businesses office staff to focus on more complex work.
  
• Regulatory compliance: AI can be used to help ensure compliance with regulations or Joint Commission standards, for example. AI-enabled predictions could also make compliance-related decisions easier for physicians.
  
• Disease detection: AI could help detect disease in the earliest stages, sometimes years before a clinician could spot it. It might also help predict disease. Researchers are already experimenting with AI to detect pancreatic cancer and lung cancer in the earliest stages.³
  
• Patient outcomes: AI could scour electronic health records and develop predictive interventions that could lead to shorter hospital stays and improved patient outcomes. (see Can health IT teams find a silver lining in the Cloud?). 
  

CONCLUSION

Health care leaders should try to balance the AI hype with tangible business value, and make sure they are exploring and developing this emerging technology to help ensure that it is implemented safely, efficiently, and appropriately. Beginning with the fundamentals can help with the foundation. By modernizing through cloud technologies, organizations can be better positioned to implement and efficiently drive AI and Generative AI strategies. Innovative prospects exist, but without a proper foundation, new technologies will likely not be able to reach their full potential.

Acknowledgements: Bill Fera, M.D., Michael T. Black, Marlin Metzger, Akash Tayal, Randy Bush, Amit Chaudhary, Emily Schulte, Tejas Desai, Amod Bavare

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Endnotes:

¹HIMSS Global Health Conference & Exhibition

²How does the U.S. healthcare system compare to other countries?, Peter G. Peterson Foundation, July 12, 2023

³Promising new AI can detect early signs of lung cancer that doctors can't see, NBC News, April 11, 2023

This publication contains general information only and Deloitte is not, by means of this publication, rendering accounting, business, financial, investment, legal, tax, or other professional advice or services. This publication is not a substitute for such professional advice or services, nor should it be used as a basis for any decision or action that may affect your business. Before making any decision or taking any action that may affect your business, you should consult a qualified professional advisor.

Deloitte shall not be responsible for any loss sustained by any person who relies on this publication.

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