Should patients—or any of 6 other stakeholder groups—get paid for AI in healthcare?
The commoditization of health data raises questions about who is owed what, and in what proportion, when artificial intelligence renders the data clinically useful and thereby financially profitable.
An obvious avenue of inquiry focuses on legal ramifications. However, preceding these are fundamental moral and ethical considerations. Two radiology researchers at Mayo Clinic Florida flesh out the basics in an opinion piece published online Aug. 3 in Radiology: Artificial Intelligence [1].
“There is no consensus on who owns medical data, or for how long,” write Colin Rowell, MD, and Ronnie Sebro, MD, PhD. “There are multiple stakeholders and multiple individuals who are essential when creating an AI system.”
After tracing the typical path of a commercialized AI system from the conception of an idea through the deployment and management of a model, the authors offer numerous points to ponder. Among the most compelling are thought exercises involving potentially legitimate claims to compensation among seven groups:
1. Patients, it could be argued, are the rightful owners of any data created from their bodies, Rowell and Sebro suggest. Cells, images, demographic data, patient outcomes—“if that data is used to create a lucrative AI system, then it can be argued that this data has value.” More:
It can be argued further that the patients to whom this data belongs should receive some compensation for use of their data in the development of these lucrative AI systems.”
Extending the AI patient puzzler to the viewpoint of family members, the authors present the case of Henrietta Lacks. The HeLa cell line derived from Henrietta’s tumor “has proven instrumental in the field of cancer research,” they note, adding that the line was created using her cervical cancer cells “without her permission or even knowledge. Neither she nor her family benefited from the pioneering of this first, immortalized cell line.”
Will the same be said of those patients whose data serve to train AI systems?”
2. Healthcare professionals must attend to a patient, diagnose a disease or condition, request tests or imaging, interpret those tests, communicate findings to the patient and enter data into a system where it can be later accessed for creating AI systems, the authors point out. “Therefore, during regular clinical care, healthcare professionals create an asset (data) that has value.” More:
It takes healthcare professionals with years of training, knowledge, and expertise to help train healthcare AI systems by annotating which patient data correlate to which disease, pathology, or outcome of interest. Furthermore, healthcare professionals also actively create annotations or diagnoses to be used to train and validate AI systems. This process can be quite time-consuming for healthcare professional experts in their respective fields.”
3. Healthcare systems invest in infrastructure used to build AI systems. This includes labs, data storage facilities, and EHR hardware and software. Healthcare systems “also must bear other costs such as penalties associated with any data breach.”
Because healthcare systems house and curate the data for AI systems, they also have a claim to data ownership. Healthcare systems also may develop AI systems and decision support systems in-house, and therefore may have financial claims to these systems.”
4. Health insurance companies store patient data and healthcare professional data to meet their business needs. These companies have ownership claims to the data “because they invest indirectly in the infrastructure needed to create data used by healthcare AI systems.
Private health insurers also may create, develop, and maintain AI systems, and therefore may have financial claims to these systems. As data has become a new commodity, the health insurance company’s claim to patients’ private health information may prove to be lucrative.”
5. U.S. taxpayers contribute to the budget of the Centers for Medicare and Medicaid Services, which sends patient data into large databases used for AI research. These large databases “are the result of millions of dollars of payments by US taxpayers,” Rowell and Sebro note.
The argument can be made that data and products derived from data arising from these databases should be made available to the US public for free, as they were financed by US taxpayers.”
6. AI companies create healthcare AI systems, expending capital to obtain data, pay developers, conduct marketing and maintain their software and hardware, the authors state.
For these reasons, AI companies also have ownership claims on AI systems. AI companies usually have software license agreements that state that the licensor (AI company) owns the AI system.”
7. AI developers “contribute heavily to developing an algorithm that, in many cases, can run for long periods of time to generate income for the software company through sales to healthcare systems.”
It seems the software companies stand to financially benefit most from the revolution that AI promises to bring. How this potential revenue would be distributed throughout these software companies to software developers and shareholders is a separate question entirely, though one imagines the shareholders of these companies would certainly stand to gain.”
Under the healthcare systems section, Rowell and Sebro describe a scenario in which an institution’s oncologists share patient data with a consulting AI company that’s working on software aimed at helping the institution’s cancer patients.
Is that store of presumably anonymized data “owned by the oncologists, the healthcare system or the radiology department that stores the imaging data?” they ask. “Is the data also co-owned by others such as the patient whose data are being shared?”
What about the health system itself, the oncology department at large, the treating physician and/or the chief medical informatics officer?
“Further work is required to understand the intramural ownership and extramural sharing of data within healthcare systems,” the authors assert, adding that more research also is needed to identify good practices around data sharing for AI development.
Rowell and Sebro conclude:
Multiple stakeholders and multiple individuals are essential when creating an AI system. Dissecting the individual contribution of each stakeholder and each individual to the development of an AI system is difficult and, in some cases, intractable. An urgent discussion is required in the scientific community to really understand data ownership as it pertains to medical AI and how its use will be reimbursed.”
Dave P. has worked in journalism, marketing and public relations for more than 30 years, frequently concentrating on hospitals, healthcare technology and Catholic communications. He has also specialized in fundraising communications, ghostwriting for CEOs of local, national and global charities, nonprofits and foundations.