Information that reveals or relates to a consumer’s health has become one of the most sensitive and closely scrutinized categories of PI in the modern privacy landscape. Once regulated primarily within traditional healthcare and insurance settings under HIPAA, the concept of “health data” has expanded, and now encompasses a wide range of information from consumer‑facing products and services, mobile applications, wearable devices, digital advertising, data analytics and digital content platforms. To date, dozens of laws have been passed across the United States seeking to regulate the collection and use of this broad category of information. However, fragmented approaches to defining and protecting it have left many companies confused, frustrated and potentially vulnerable to enforcement risks.
It is against this backdrop that the Network Advertising Initiative (NAI) released in early 2026 its Factor Analysis for Health‑Related Sensitive Personal Information (Factor Analysis) – providing a structured, contextual framework for classifying non-HIPAA health data based on five factors, and offering organizations with a disciplined guidepost to help reason through difficult classification decisions in an increasingly risky environment.
This article explores the regulatory evolution that has made health data compliance more challenging, explains why context‑driven frameworks like the NAI’s Factor Analysis are increasingly valuable and highlights additional best practices for making navigating the health data landscape manageable.
See “Addressing the Operational Complexities of Complying With the Washington My Health My Data Act” (Apr. 3, 2024).
The U.S. Health Data Framework
Over the last 30 years, the regulation of health data in the United States has undergone dramatic changes.
Evolution of Data in the HIPAA Context
In the late 1990s, HIPAA established protections for a narrow set of “protected health information” (PHI) created, collected, used or disclosed in the course of healthcare treatment or payment. Notably, HIPAA’s obligations only extend to “covered entities” such as hospitals, healthcare providers, counselors, pharmacies and health plans, and “business associates” that process PHI on behalf of covered entities. HIPAA limits the circumstances under which PHI can be disclosed to other types of entities, but once that occurs, HIPAA does not apply.
When consumer use of the internet and connected devices took off in the early and mid‑2000s, so too did the amount of data collected about consumers. This included information related to a consumer’s health, such as browsing history about a specific disease or diagnosis, data collected from wearable health trackers and purchase data relating to certain medications. This data generally falls outside of HIPAA and had received no specific regulatory protection – until, that is, state legislatures began passing their own comprehensive privacy laws, starting with the CCPA in 2018.
See “Lessons From the Continued Uptick in HIPAA Enforcements” (Feb. 8, 2017).
Heightened Protections Under U.S. State Consumer Privacy Laws
As of April 2026, more than 20 U.S. states have passed comprehensive privacy laws addressing the way consumer PI, including online data, is collected and used. These laws generally impose heightened protections on “sensitive” categories of personal data, including information and “inferences” made about a consumer’s health. A smaller number of states have passed health-specific privacy laws that impose even stronger restrictions on a broader set of data and prohibit the use of such data for advertising outright.
U.S. state privacy laws do not adopt a uniform definition of what constitutes health data. For example, Washington State’s My Health My Data Act defines “consumer health data” as any PI that identifies the consumer’s past, present, or future physical or mental health status, including data that identifies a consumer seeking any service to assess, measure, improve, or learn about their mental or physical health. Other states’ comprehensive laws focus on data that “reveals” a mental or physical health condition. The CCPA definition of health data includes any PI “collected and analyzed concerning a consumer’s health.”
These fragmented definitions have left companies that deal in consumer data paralyzed. Does browsing history related to aspirin reveal a consumer’s health condition? Does the fact that a consumer read an article about breast cancer indicate a medical diagnosis? Does purchase data for running shoes identify an individual seeking to improve their health? Many of the most challenging compliance questions arise at the margins, where certain data points may not obviously be health-related, but could be combined, analyzed or used in a way that triggers heightened legal scrutiny.
A well-known example is the Target pregnancy score case, where an investigative journalist revealed the retail giant assigned pregnancy prediction scores to consumers based on their otherwise non-sensitive purchase history and demographic data. With very little regulatory guidance to rely on, and facing significant financial, organizational and reputational risks, many companies are creating internal processes to classify data elements, with little ability to benchmark their efforts against their peers.
See “Examining the Washington Attorney General’s FAQs on the My Health My Data Act” (Sep. 13, 2023).
Market Impacts
The growing complexity of health data privacy regulations has practical consequences for organizations attempting to operate responsibly and compliantly.
Faced with uncertainty, some organizations take a cautious approach by treating broad categories of health‑adjacent data as sensitive. For example, some companies choose to remove third-party data collection technologies from any web pages that contain health and wellness products, like supplements or skin creams, to avoid any implication that it collects or sells PI “relating” to a consumer’s health. While this approach may reduce regulatory exposure, it can also create operational burdens, disproportionately limit data use and reduce the availability of information that consumers find valuable. Other organizations, however, underestimate the risk and classify data too narrowly, exposing themselves to enforcement actions, litigation and reputational harm.
The absence of clear guidelines to define the scope of non-HIPAA health data makes it increasingly difficult for companies to calibrate their risks. Various entities could reasonably reach different conclusions about the same data depending on the nuances of a specific analysis, adding more complexity to an already fraught landscape and leaving many companies unsure as to how to move forward.
See our two-part series on Washington’s aggressive health privacy law: “Right to Sue and Onerous Consent Obligations” (May 3, 2023), and “Ten Compliance Priorities” (May 10, 2023).
The NAI’s Response to Regulatory Uncertainty: A Compass, Not a Road Map
The NAI has long served as a self‑regulatory organization for the digital advertising industry, translating evolving legal requirements into practical standards for responsible data use. In continuing this work, the NAI’s Factor Analysis serves as a framework to guide determinations about when data is likely to be considered “health data” and when it is not.
Importantly, given the breadth and variation among statutory health data definitions and the diversity of data use cases, there is no single solution to defining health data. The NAI’s Factor Analysis does not provide binary answers. Instead, it provides a helpful framework for pointing companies in the correct direction rather than a prescriptive solution for coming to a definitive answer on data classification.
The NAI’s Five Factors
The NAI establishes five factors to assist businesses in evaluating whether a specific piece of information may be considered “health-related sensitive personal information,” taking into account the varying definitions across U.S. jurisdictions. These factors are described below.
Factor 1: Source
The first factor examines where the data originates. Source matters because it shapes expectations and risk. Data that originates in a context explicitly tied to healthcare or medical decision‑making may carry additional risk and sensitivity compared to data collected in a broader consumer context, particularly where attenuated from medical services or diagnoses. However, source alone is insufficient to identify health data. Consumer‑generated data, browsing data or purchase data could still be considered health data depending on other factors, including how it is combined or used. The source inquiry therefore serves as a starting point, not a safe harbor.
Factor 2: Content
The second factor focuses on what the data actually reveals, either explicitly or implicitly. Certain information such as diagnoses, treatment details, prescription data or identifiable medical conditions may clearly point toward health status. Other information may only become health‑related through additional inference.
This factor acknowledges that sensitivity falls along a spectrum. Data that directly identifies a specific health condition is generally more sensitive than data that merely correlates with health outcomes or interests. For example, the difference between buying low-sugar foods and buying diabetes medication is significant. Importantly, the Factor Analysis acknowledges that inferred health information can be just as sensitive as observed data, particularly where inferences are individualized, accurate and precise. However, by emphasizing content rather than labels, the analysis under Factor 2 encourages organizations to look beyond how data is categorized internally and instead evaluate what it would reasonably communicate about an individual if used or disclosed.
Factor 3: Use
The third factor acknowledges that even relatively benign data can take on heightened sensitivity if it is used for health‑related targeting, personalization or decision‑making. In the Target pregnancy score case for example, common purchase trends, when combined and analyzed, were used to identify and direct ads to apparently pregnant shoppers. However, taking the individual data points alone, the outcome is less clear. This use‑based perspective reflects how regulators have increasingly framed their analyses. Data that might otherwise appear innocuous can raise concerns when deployed in ways that meaningfully affect individuals’ access to health information, products or services. Conversely, certain uses may reduce sensitivity, such as when individually sensitive data is aggregated in applications that do not meaningfully impact a specific person.
See “Takeaways From FTC’s Orders Targeting Digital Health Companies” (May 8, 2024).
Factor 4: Consumer Expectations
The fourth factor centers on the consumer perspective, asking what a reasonable consumer would understand about the collection and use of the data based on the environment in which the data was collected, disclosures made to the consumer and certain norms associated with the product or service. In instances where a consumer would reasonably expect a heightened level of privacy, such as in relation to certain vulnerable classifications like reproductive health or pregnancy, data is more likely to be viewed as sensitive. However, it is important to note that consumer expectations are dynamic and highly fact-dependent, meaning this factor could weigh in favor of data being considered health-related in some circumstances but not in others.
Factor 5: Harm
The final factor examines the potential for harm that could result from the collection, use or disclosure of the information. Harm may be tangible or intangible and can include financial loss, discrimination, stigma or emotional distress. Even in instances where the risk under the previous four factors is low, the fifth factor is a reminder that sensitivity is ultimately about impact and optics.
None of the NAI’s factors is dispositive alone. Rather, organizations should evaluate how each of the factors interact when applied to their specific factual instance, considering their own risk tolerance.
Conducting an analysis under the NAI Factor Analysis may not provide black-and-white answers in many cases. Instead, organizations might find it more useful to classify low, medium and high risks to guide business decisions and risk mitigation strategies. This holistic analysis fits with how many organizations manage real‑world compliance risks and helps them avoid being underinclusive or overinclusive in their data classification strategy.
Practical Steps for Mitigating Health Data Risks
Just as no singular NAI factor stands alone, a factors-based analysis is not, on its own, a health data privacy program. Integrating the NAI Factor Analysis into existing privacy programs and processes will help make the framework more useful to business clients and provide evidence to demonstrate compliance with health data privacy regulations. To this end, below are some suggestions for incorporating the NAI Factor Analysis into a broader privacy program.
Update Data Review Policies
Organizations that decide to utilize the NAI Factor Analysis will also need to decide who will be responsible for applying it, which data will be analyzed and when. These details will look much different for a publisher that is managing the placement of advertising pixels on its website than they will for a data vendor that is classifying thousands of variables. Updating relevant policies will help clarify roles and responsibilities and demonstrate a systematic approach toward determining what is and is not health data.
Conduct Training
Applying the NAI Factor Analysis in practice involves a significant amount of judgment and discretion. If multiple employees are responsible for analyzing an organization’s data, it is possible that they will reach divergent results on similar data. Providing training can help establish consistency from the outset.
Repeat Reviews
Sensitive data reviews – whether or not they use the NAI Factor Analysis – are not one-time efforts. Statutory definitions of health data, industry practices and consumer expectation will continue to change. These are all ingredients in the NAI Factor Analysis, and the outcomes of such an analysis will likely change over time. Setting a cadence for review will depend on several considerations, including how much data is at issue (e.g., a simple mobile app versus a dataset containing thousands of variables), how rapidly an organization’s data changes, and what proportion of the organization’s data sits in a middle category of risk and is most susceptible to changes in industry and consumer norms.
See “Healthline’s Record-Setting CCPA Settlement Offers Lessons on Transparency and Opt-Outs” (Aug. 6, 2025).
Aaron Burstein is a partner at Kelley Drye. He advises clients on complex privacy, data security and consumer protection issues, drawing on deep legal knowledge and extensive government experience. He provides practical guidance on compliance with federal and state privacy, information security and marketing laws, helping companies adapt business practices to manage risk amid evolving regulatory requirements, including under the CCPA. He also counsels on privacy and security issues arising in transactions and emerging technologies such as connected vehicles and drones. Before entering private practice, Burstein served in the FTC’s Division of Privacy and Identity Protection and as senior legal advisor to Commissioner Julie Brill, where he helped shape U.S. and international privacy policy, rulemaking and enforcement.
Meaghan Donahue is an associate at Kelley Drye. She advises clients on privacy law with a particular focus on adtech, marketing practices, and compliance with state privacy laws and federal regulatory frameworks. She assists with privacy policies, contracts and disclosures, and has developed practical resources and guidance addressing sensitive and health data issues. Before joining Kelley Drye, Donahue worked at the NAI, where she developed self‑regulatory standards for online advertising, focusing on compliance and policy initiatives.
