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This guidance is intended to assist covered entities to understand what is de-identification, the general process by which de-identified information is created, and the options available for performing de-identification. In developing this guidance, the Office for Civil Rights OCR solicited input from stakeholders with practical, technical and policy experience in de-identification.
OCR convened stakeholders at a workshop consisting of multiple panel sessions held March , , in Washington, DC.
The workshop was open to the public and each panel was followed by a question and answer period. Read the Full Guidance. Protected health information is information, including demographic information, which relates to:. By contrast, a health plan report that only noted the average age of health plan members was 45 years would not be PHI because that information, although developed by aggregating information from individual plan member records, does not identify any individual plan members and there is no reasonable basis to believe that it could be used to identify an individual.
The relationship with health information is fundamental. Identifying information alone, such as personal names, residential addresses, or phone numbers, would not necessarily be designated as PHI. For instance, if such information was reported as part of a publicly accessible data source, such as a phone book, then this information would not be PHI because it is not related to heath data see above.
If such information was listed with health condition, health care provision or payment data, such as an indication that the individual was treated at a certain clinic, then this information would be PHI. In general, the protections of the Privacy Rule apply to information held by covered entities and their business associates. HIPAA defines a covered entity as 1 a health care provider that conducts certain standard administrative and financial transactions in electronic form; 2 a health care clearinghouse; or 3 a health plan.
A covered entity may use a business associate to de-identify PHI on its behalf only to the extent such activity is authorized by their business associate agreement.
The increasing adoption of health information technologies in the United States accelerates their potential to facilitate beneficial studies that combine large, complex data sets from multiple sources. The process of de-identification, by which identifiers are removed from the health information, mitigates privacy risks to individuals and thereby supports the secondary use of data for comparative effectiveness studies, policy assessment, life sciences research, and other endeavors.
The Privacy Rule was designed to protect individually identifiable health information through permitting only certain uses and disclosures of PHI provided by the Rule, or as authorized by the individual subject of the information. These provisions allow the entity to use and disclose information that neither identifies nor provides a reasonable basis to identify an individual. Both methods, even when properly applied, yield de-identified data that retains some risk of identification.
Although the risk is very small, it is not zero, and there is a possibility that de-identified data could be linked back to the identity of the patient to which it corresponds. Regardless of the method by which de-identification is achieved, the Privacy Rule does not restrict the use or disclosure of de-identified health information, as it is no longer considered protected health information.
Section Under this standard, health information is not individually identifiable if it does not identify an individual and if the covered entity has no reasonable basis to believe it can be used to identify an individual. Health information that does not identify an individual and with respect to which there is no reasonable basis to believe that the information can be used to identify an individual is not individually identifiable health information. Sections As summarized in Figure 1, the Privacy Rule provides two methods by which health information can be designated as de-identified.
Figure 1. A covered entity may determine that health information is not individually identifiable health information only if: 1 A person with appropriate knowledge of and experience with generally accepted statistical and scientific principles and methods for rendering information not individually identifiable: i Applying such principles and methods, 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 who is a subject of the information; and ii Documents the methods and results of the analysis that justify such determination; or.
B All geographic subdivisions smaller than a state, including street address, city, county, precinct, ZIP code, and their equivalent geocodes, except for the initial three digits of the ZIP code if, according to the current publicly available data from the Bureau of the Census: 1 The geographic unit formed by combining all ZIP codes with the same three initial digits contains more than 20, people; and 2 The initial three digits of a ZIP code for all such geographic units containing 20, or fewer people is changed to C All elements of dates except year for dates that are directly related to an individual, including birth date, admission date, discharge date, death date, and all ages over 89 and all elements of dates including year indicative of such age, except that such ages and elements may be aggregated into a single category of age 90 or older.
De-identified health information created following these methods is no longer protected by the Privacy Rule because it does not fall within the definition of PHI. Of course, de-identification leads to information loss which may limit the usefulness of the resulting health information in certain circumstances. As described in the forthcoming sections, covered entities may wish to select de-identification strategies that minimize such loss.
The implementation specifications further provide direction with respect to re-identification , specifically the assignment of a unique code to the set of de-identified health information to permit re-identification by the covered entity. If a covered entity or business associate successfully undertook an effort to identify the subject of de-identified information it maintained, the health information now related to a specific individual would again be protected by the Privacy Rule, as it would meet the definition of PHI.
Disclosure of a code or other means of record identification designed to enable coded or otherwise de-identified information to be re-identified is also considered a disclosure of PHI.
A covered entity may assign a code or other means of record identification to allow information de-identified under this section to be re-identified by the covered entity, provided that: 1 Derivation. The code or other means of record identification is not derived from or related to information about the individual and is not otherwise capable of being translated so as to identify the individual; and 2 Security. The covered entity does not use or disclose the code or other means of record identification for any other purpose, and does not disclose the mechanism for re-identification.
The importance of documentation for which values in health data correspond to PHI, as well as the systems that manage PHI, for the de-identification process cannot be overstated. Esoteric notation, such as acronyms whose meaning are known to only a select few employees of a covered entity, and incomplete description may lead those overseeing a de-identification procedure to unnecessarily redact information or to fail to redact when necessary.
When sufficient documentation is provided, it is straightforward to redact the appropriate fields. See section 3. In the following two sections, we address questions regarding the Expert Determination method Section 2 and the Safe Harbor method Section 3.
The notion of expert certification is not unique to the health care field. Professional scientists and statisticians in various fields routinely determine and accordingly mitigate risk prior to sharing data.
The field of statistical disclosure limitation, for instance, has been developed within government statistical agencies, such as the Bureau of the Census, and applied to protect numerous types of data.
There is no specific professional degree or certification program for designating who is an expert at rendering health information de-identified. Relevant expertise may be gained through various routes of education and experience. Experts may be found in the statistical, mathematical, or other scientific domains. From an enforcement perspective, OCR would review the relevant professional experience and academic or other training of the expert used by the covered entity, as well as actual experience of the expert using health information de-identification methodologies.
The ability of a recipient of information to identify an individual i. This is because the risk of identification that has been determined for one particular data set in the context of a specific environment may not be appropriate for the same data set in a different environment or a different data set in the same environment. This issue is addressed in further depth in Section 2. The Privacy Rule does not explicitly require that an expiration date be attached to the determination that a data set, or the method that generated such a data set, is de-identified information.
However, experts have recognized that technology, social conditions, and the availability of information changes over time. Consequently, certain de-identification practitioners use the approach of time-limited certifications. In this sense, the expert will assess the expected change of computational capability, as well as access to various data sources, and then determine an appropriate timeframe within which the health information will be considered reasonably protected from identification of an individual.
Information that had previously been de-identified may still be adequately de-identified when the certification limit has been reached. When the certification timeframe reaches its conclusion, it does not imply that the data which has already been disseminated is no longer sufficiently protected in accordance with the de-identification standard.
Covered entities will need to have an expert examine whether future releases of the data to the same recipient e. In such cases, the expert must take care to ensure that the data sets cannot be combined to compromise the protections set in place through the mitigation strategy.
Of course, the expert must also reduce the risk that the data sets could be combined with prior versions of the de-identified dataset or with other publically available datasets to identify an individual. For instance, an expert may derive one data set that contains detailed geocodes and generalized aged values e.
The expert may certify a covered entity to share both data sets after determining that the two data sets could not be merged to individually identify a patient. This certification may be based on a technical proof regarding the inability to merge such data sets. Alternatively, the expert also could require additional safeguards through a data use agreement.
No single universal solution addresses all privacy and identifiability issues. Rather, a combination of technical and policy procedures are often applied to the de-identification task. OCR does not require a particular process for an expert to use to reach a determination that the risk of identification is very small.
However, the Rule does require that the methods and results of the analysis that justify the determination be documented and made available to OCR upon request. The following information is meant to provide covered entities with a general understanding of the de-identification process applied by an expert.
It does not provide sufficient detail in statistical or scientific methods to serve as a substitute for working with an expert in de-identification. A general workflow for expert determination is depicted in Figure 2. Stakeholder input suggests that the determination of identification risk can be a process that consists of a series of steps. First, the expert will evaluate the extent to which the health information can or cannot be identified by the anticipated recipients.
Second, the expert often will provide guidance to the covered entity or business associate on which statistical or scientific methods can be applied to the health information to mitigate the anticipated risk. The expert will then execute such methods as deemed acceptable by the covered entity or business associate data managers, i. Finally, the expert will evaluate the identifiability of the resulting health information to confirm that the risk is no more than very small when disclosed to the anticipated recipients.
Stakeholder input suggests that a process may require several iterations until the expert and data managers agree upon an acceptable solution. Regardless of the process or methods employed, the information must meet the very small risk specification requirement. Figure 2. Process for expert determination of de-Identification. Data managers and administrators working with an expert to consider the risk of identification of a particular set of health information can look to the principles summarized in Table 1 for assistance.
The principles should serve as a starting point for reasoning and are not meant to serve as a definitive list. In the process, experts are advised to consider how data sources that are available to a recipient of health information e. Linkage is a process that requires the satisfaction of certain conditions.
This is because of a second condition, which is the need for a naming data source, such as a publicly available voter registration database see Section 2. Without such a data source, there is no way to definitively link the de-identified health information to the corresponding patient. Finally, for the third condition, we need a mechanism to relate the de-identified and identified data sources.
The lack of a readily available naming data source does not imply that data are sufficiently protected from future identification, but it does indicate that it is harder to re-identify an individual, or group of individuals, given the data sources at hand.
Example Scenario Imagine that a covered entity is considering sharing the information in the table to the left in Figure 3. This table is devoid of explicit identifiers, such as personal names and Social Security Numbers. The information in this table is distinguishing, such that each row is unique on the combination of demographics i.
Beyond this data, there exists a voter registration data source, which contains personal names, as well as demographics i. Linkage between the records in the tables is possible through the demographics.
Figure 3. Linking two data sources to identity diagnoses. Thus, an important aspect of identification risk assessment is the route by which health information can be linked to naming sources or sensitive knowledge can be inferred.