De-identified data is outside the HIPAA Privacy Rule, which is why research teams want it. But the standard in 45 CFR 164.514 is stricter than removing names. This guide explains both methods, limited data sets and re-identification codes, and how to design study data so sharing is easier later. Not legal advice.
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| Keep as is | Generalise | Remove | |
|---|---|---|---|
| Participant name | |||
| Date of birth | |||
| Visit date | |||
| ZIP code (5 digit) | |||
| Systolic BP (mmHg) | |||
| Study subject code (random) |
The short answer
Context
The HIPAA Privacy Rule applies to covered entities (health plans, clearinghouses and most providers) and their business associates. A hospital that shares data with a sponsor, or a university that releases a dataset to another group, needs either a permitted basis under the rule, such as an individual authorisation or a waiver of authorisation by an IRB or privacy board, or the data must be de-identified. Once data is properly de-identified under 164.514, it is no longer protected health information, and the Privacy Rule no longer restricts its use. That is why de-identification is the cleanest route for secondary analyses, data sharing with collaborators and publication of datasets.
It is also easy to get wrong. People assume that replacing a name with a code is enough, or that pseudonymised data under the GDPR is equivalent to HIPAA de-identified data. It is not. Our HIPAA versus GDPR guide sets out why the two tests differ, and authorization versus informed consent explains the other basis for using identifiable data. A sponsor that is not a covered entity may not be bound by HIPAA directly, but the sites that supply the data usually are, and the data arrives with their constraints. Whether HIPAA applies to your organisation is a question for your privacy officer.
Method 1
Under 164.514(b)(2), the following identifiers of the individual or of relatives, employers or household members of the individual must be removed. The list below paraphrases the rule, so check the regulation text for the exact wording.
In plain terms, the categories are:
Removing the identifiers is not enough. The covered entity must also have no actual knowledge that the remaining information could be used, alone or in combination, to identify an individual. Free-text fields are where this fails most often: a note that says the patient is the mayor, or a rare diagnosis in a small community, can identify someone even with the 18 categories gone. Imaging with burned-in text and audio with spoken names are further examples, and the identifier list does not make every field safe by itself.
The date rule is the one that hurts research most. Study data is built on dates: visit dates, onset dates, event dates. Safe Harbor removes everything except the year, which breaks time-to-event analyses unless the analysis uses derived intervals, such as study day or days since randomisation, computed before the dates are removed. That shift has to be designed into the dataset, not discovered at release. Geography is the other pain point, and a pooled dataset with country-level location is usually fine while site-level small-area location may not be.
Method 2
Under 164.514(b)(1), data can be treated as de-identified if a person with appropriate knowledge of and experience with generally accepted statistical and scientific principles and methods for rendering information not individually identifiable applies those methods and determines that the risk is very small that the information could be used, alone or in combination with other reasonably available information, by an anticipated recipient to identify an individual. The expert must document the methods and the results of the analysis that justify the determination. The rule does not require a specific title, so a statistician is common but is not the only qualification the text allows.
In practice, Expert Determination suits cases where you need to keep dates, finer geography or rare clinical detail. The expert evaluates the data, the recipients, the release conditions and the other data they could combine it with, and may require transformations such as date shifting, generalisation or suppression, and conditions such as a data use agreement or no re-identification clause. The determination is specific to the dataset and the release: change the recipient or add a variable, and it may need to be revisited. HHS Office for Civil Rights has published guidance on both methods, and you should read the current version before you commission one.
Choosing
| Safe Harbor | Expert Determination | Limited data set | |
|---|---|---|---|
| Legal basis | 164.514(b)(2) | 164.514(b)(1) | 164.514(e) |
| Still PHI? | No, if conditions met | No, if determination holds | Yes: still PHI, used under a data use agreement |
| Dates and geography | Year only; no small geography | Can retain more, as the expert allows | May include dates, city, state and ZIP |
| Who decides | The covered entity applies the list | A qualified expert documents the finding | The covered entity and the recipient sign a data use agreement |
| Effort | Low to moderate; free-text review needed | Higher: external analysis and documentation | Moderate: removal of direct identifiers plus legal agreement |
| Good fit | Public datasets, simple tables, pooled outputs | Rich longitudinal data shared with named recipients | Research, public health or operations use with a known recipient |
A limited data set is not de-identified. Treat the data use agreement as a core control.
Decide in the free sandbox which fields are identifiers, which can be derived, and which roles will see them, before the first real participant is enrolled.
Codes
Research data is normally coded. HIPAA allows a covered entity to assign a code that lets it re-identify the data later, and the code does not count as an identifier if two conditions in 164.514(c) are met. The code must not be derived from or related to information about the individual, for example it must not be built from initials, birth date or a medical record number. The covered entity also must not use or disclose the code or the mechanism for re-identification for any other purpose, and must not disclose the mechanism. A random study subject number that sits in a separate key held by the site meets the idea well. A hash of the record number does not.
This is where system design helps. If the platform separates identity from clinical data, the sponsor team can work from coded records while only the site holds the key. Capture works this way: site coordinators see participant names while researchers see coded IDs, and access is limited by role and by row-level security at the database layer. See data pseudonymisation in EDC for how to apply it. Remember that a coded dataset where the key still exists at the site is still PHI in the hands of the site until it is properly de-identified, so the code is a design control, not a legal conclusion.
Workflow
Public release, named collaborator or regulator? The answer decides which method is realistic and whether a data use agreement is available.
List every variable, including free text, images, device IDs and derived fields. Mark each as identifier, quasi-identifier or clinical.
Derive study day, intervals and age bands from dates before removing them, and keep the derivation logic in the analysis plan.
Use Safe Harbor if the dataset survives the date and geography rules. Use Expert Determination if it does not, and commission it before the release date, not after.
Scan notes and file names, and review images for burned-in identifiers. Remove or redact what you cannot justify keeping.
Keep the method, the list of removed fields, the reviewer and the date with the release package, and repeat the check if the dataset or recipient changes.
In Capture
Capture does not de-identify data for you and does not decide what is identifiable: that is a judgement for your privacy officer or expert. What it does is help you design out identifiers. Collect only what the protocol needs, keep names with site coordinators, give researchers coded IDs, and use calculated fields such as age from date of birth for analysis. Exports can be filtered by date range and by site, come with an automatic data dictionary, and can be produced as CSV, Excel or SDTM datasets.
Identity (site coordinator only)
Participant name
Visible to site coordinators only
Hidden from researchersDate of birth
Analysis fields (researcher view)
Age at screening
AGESystolic blood pressure
SYSBPSafe Harbor under 45 CFR 164.514(b)(2), which removes 18 categories of identifiers and requires no actual knowledge that the rest could identify someone, and Expert Determination under 164.514(b)(1), where a qualified expert finds and documents that the risk of identification is very small.
Not automatically. A code is acceptable only if it is not derived from information about the individual and the key and mechanism are not disclosed. Other identifiers, such as dates and small geography, may still need to be removed or generalised.
Under Safe Harbor, only the year may stay. Use derived intervals such as study day, or use Expert Determination, or a limited data set under a data use agreement if the recipient is known.
A dataset with direct identifiers removed but which may keep elements such as dates, city, state and ZIP code. It is still PHI and may be used for research, public health or health care operations under a data use agreement with the recipient (164.514(e)).
The rule requires a person with appropriate knowledge of and experience with generally accepted statistical and scientific principles and methods for rendering information not individually identifiable. It does not name a specific profession.
The tests are different. Data that is de-identified under HIPAA may still be personal data under the GDPR if individuals can be identified by reasonable means, and pseudonymised data stays personal data. If you share across regions, assess both.
No. Capture separates identity from clinical data by role, gives researchers coded IDs and exports filtered, documented datasets, but the decision whether a dataset meets the HIPAA standard rests with your privacy officer or expert.
No. It is orientation for research, QA and privacy teams. Read 45 CFR 164.514 and the current HHS guidance, and take advice before you release data.
Keep exploring
HIPAA vs GDPR trial data
Two regimes compared.
Data pseudonymisation in EDC
Design the separation of identity.
HIPAA authorization vs informed consent
The other basis for identifiable data.
HIPAA-compliant clinical trial software
Software fit.
Data processing agreement for trials
The contract side under the GDPR.
Security
Hosting and access controls.
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