By MICHAEL MILLENSON

The latest draft government strategic plan for health information technology pledges to support health information sharing among individuals, health care providers and others “so that they can make informed decisions and create better health outcomes.”

Those good intentions notwithstanding, the current health data landscape is dramatically different from when the organizational author of the plan, the Office of the National Coordinator for Health IT, formed two decades ago. As Price and Cohen have pointed out, entities subject to federal Health Insurance Portability and Accountability Act (HIPAA) requirements represent just the tip of the informational iceberg. Looming larger are health information generated by non-HIPAA-covered entities, user-generated health information, and non-health information being used to generate inferences about treatment and health improvement.

Meanwhile, the content of health information, its capabilities, and, crucially, the loci of control are all undergoing radical shifts due to the combined effects of data democratization and artificial intelligence. The increasing sophistication of consumer-facing AI tools such as biometric monitoring and web-based analytics are being seen as a harbinger of “fundamental changes” in interactions between health care professionals and patients.

In that context, a framework of information sharing I’ve called “collaborative health” could help proactively create a therapeutic alliance designed to respond to the emerging new realities of the AI age.

The term (not be confused with the interprofessional coordination known as “collaborative care”) describes a shifting constellation of relationships for health maintenance and sickness care shaped by individuals based on their life circumstances. At a time when people can increasingly find, create, control, and act upon an unprecedented breadth and depth of personalized information, the traditional care system will often remain a part of these relationships, but not always. For example, a review of breast cancer apps found that about one-third now use individualized, patient-reported health data obtained outside traditional care settings.

Collaborative health has three core principles: shared information, shared engagement, and shared accountability. They are meant to enable a framework of mutual trust and obligation with which to address the clinical, ethical, and legal issues AI and data democratization are bringing to the fore. As the white paper AI Rights for Patients noted, digital technologies can be vital tools, but they can also expose patients to privacy breaches, illegal data sharing and other “cyber harms.” Involving patients “is not just a moral imperative; it is foundational to the responsible and effective deployment of AI in health and in care.” (While “responsible” is not defined, one plausible definition might be “defensible to a jury.”)

Below is a brief description of how collaborative health principles might apply in practice.

Shared information

While the OurNotes initiative represents a model for co-creation of information with clinicians, important non-traditional inputs that should be shared are still generally absent from the record. These might include not just patient-provided data from vetted wearables and sensors, but also information from important non-traditional providers, such as the online fertility companies often accessed through an employee benefit. Whatever is in the record, the 21^st Century Cures Act and subsequent regulations addressing interoperability through mechanisms such as Fast Healthcare Interoperability Resources more commonly known as FHIR have made much of that information available for patients to access and share electronically with whomever they choose.

Provider sharing of non-traditional information that comes from outside the EHR could be more problematic. So-called “commercially available information,” not protected by HIPAA, is being used to generate inferences about health improvement interventions. Individually identified data can include shopping habits, online searches, living arrangements and many other variables analyzed by proprietary AI algorithms that have undergone no public scrutiny for accuracy or bias. Since use by providers is often motivated by value-based payment incentives, voluntary disclosure will distance clinicians from a questionable form of surveillance capitalism.