Industry Voices—Hospitals Are Fueling AI Innovation, Should They Own A Piece Of It?

What does a “fair” partnership between hospitals and AI developers look like? As hospital workflows, clinician expertise, and rich clinical data increasingly fuel the performance and market value of vendors’ AI tools, providers face a critical question: how can they share in that upside while protecting patients, preserving trust, and avoiding new forms of liability? 

This piece examines emerging models for data sharing, governance and value capture, offering health systems practical frameworks to ensure they benefit from the innovation that their data helps create.

Hospitals embracing AI

Increasingly in the past few years, hospitals have been developing and licensing AI tools to reduce clinician burnout, improve clinical care and streamline operations. Developed tools range from those created entirely in-house, to those developed in partnership with both new and established AI companies. 

As healthcare costs increase, hospitals view the adoption of AI tools as a cost-saving measure. In particular, the adoption of AI tools by hospitals is projected to amount to a whopping $900 million in savings in hospital care costs by 2050. Given the cost savings and efficiency benefits, hospitals have increased investment and adoption of AI tools at a rapid pace in recent years. 

Existing data sharing models

Due to the large amounts of data hospitals utilize on a daily basis, they have engaged in large-scale data-sharing collaborations to ensure continuity of care. 

Health Information Exchanges (“HIEs”) allow the exchange of health care information across organizations within a region, community, or hospital system. HIEs have been developed by both private and public institutions to help facilitate rapid information exchange. 

Hospitals have also relied on the expertise of big tech companies to help facilitate their information exchange. In June 2025, CMS hosted an event with tech companies, including a major cloud services provider and a leading search and advertising platform, to begin laying the foundation for a digital health ecosystem to improve patient outcomes. 

In January of this year, a regional health system (Editor's note: this organization is a client of the authors and/or the authors' firm) migrated its entire technology infrastructure to the cloud, utilizing a major cloud computing platform. Additionally, hospitals have started to monetize de-identified data, as patient consent is not required for commercial use of properly de-identified data. Particularly, pharmaceutical companies have found de-identified data useful to determine how to target clinical trials and refine marketing strategies, producing a large and lucrative secondary market for hospitals to sell de-identified data.

Industry changes due to AI advancement and proliferation

There has been a recent shift in the hospital industry from episodic data transfer to continuous data systems utilizing AI to enable proactive patient care. This transition integrates continuous monitoring through items like wearables and smart beds to replace periodic checks by nurses, improve operational efficiency via command centers and facilitate seamless data sharing. The advantage of continuous monitoring is that it provides real-time vital measurements for patients, which reduces the risk of missing signs of deterioration compared to intermittent checkups of patients, all at a cost savings. 

Additionally, hospitals and healthcare providers have shifted their priorities from one-off technology innovation projects to embedded AI enterprise ecosystems. A large technology company (Editor's Note: this company is a client of the authors and/or the authors' firm) launched an agentic health assistant developed by its primary care subsidiary. The system connects with health information exchanges to provide personalized triage insights derived from a patient’s medical history. A large academic health system (Editor's Note: this company is a client of the authors and/or the authors' firm) expanded the deployment of an enterprise AI platform to more than 2,800 clinicians across 25 hospitals following user surveys showing a 61% reduction in cognitive load and a 77% increase in work satisfaction. 

Given the positive data of enterprise implementation of AI systems, hospitals across the nation are moving to enterprise AI implementation.

Legal and business considerations

As hospitals pursue partnerships with AI developers, several legal and business considerations warrant careful attention. 

When hospitals enter into joint ventures with for-profit AI companies, they must navigate complex tax issues to preserve their tax-exempt status, a challenge that, while not unique to AI, is heightened by the rapid pace of these collaborations, where tax considerations are often overlooked. Additionally, hospitals must remain vigilant about HIPPA compliance and the preservation of patient trust. Partnerships involving the sharing of clinical data with technology companies carry inherent reputational risk, as demonstrated by prior class action litigation alleging the improper disclosure of patient records to commercial partners. 

To mitigate these concerns, hospitals should ensure that any data used for commercial purposes is properly de-identified in accordance with HIPAA's Safe Harbor or Expert Determination methods. Parties to data sharing collaborations must clearly delineate data rights at the outset of any partnership, addressing whether the hospital, the AI developer or both will retain ownership of and access to the underlying data and any derivative datasets generated through the collaboration. Also, AI governance frameworks are essential to ensure that deployed models remain clinically valid, free from bias, and subject to ongoing human oversight. Finally, hospitals should prioritize risk mitigation through carefully negotiated contractual provisions, including comprehensive indemnification clauses, representations and warranties regarding data security, and clearly defined liability allocation to protect the institution in the event of model failure, data breach or regulatory enforcement action.

Approach AI with strategic rigor

As hospitals continue to invest in and adopt AI tools at a fast pace, the question is no longer whether these institutions will utilize AI tools, but how these tools will be utilized. Hospitals possess vast repositories of clinical data, deep clinical expertise and established patient relationships, which can be refined with AI technology. 

To avoid legal and regulatory exposure, health systems must approach AI collaborations with the same rigor they apply to other strategic transactions: clearly delineating data ownership rights, ensuring robust HIPAA compliance and patient trust, implementing comprehensive AI governance frameworks and negotiating contractual protections that appropriately allocate risk. By doing so, hospitals can secure both the clinical benefits and the cost savings that AI collaboration can produce.

Originally published by Fierce Healthcare, 3 April 2026.

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