HL7 Informative Document: Patient Information Quality Improvement (PIQI) Framework, Edition 1
1.0.0 - Informative 1
HL7 Informative Document: Patient Information Quality Improvement (PIQI) Framework, Edition 1
1.0.0 - Informative 1
HL7 Informative Document: Patient Information Quality Improvement (PIQI) Framework, Edition 1, published by HL7 Cross-Group Projects Work Group. This guide is not an authorized publication; it is the continuous build for version 1.0.0 built by the FHIR (HL7® FHIR® Standard) CI Build. This version is based on the current content of https://github.com/HL7/piqi/ and changes regularly. See the Directory of published versions
| Official URL: http://hl7.org/xprod/ig/uv/piqi/ImplementationGuide/hl7.xprod.uv.piqi | Version: 1.0.0 | ||||
| Draft as of 2026-07-28 | Computable Name: CrossParadigmPIQI | ||||
The ongoing digitization of healthcare has resulted in significant growth in the volume and diversity of patient-centric data, including electronic health records (EHRs), clinical registries, administrative claims, and digital health applications. While these data are foundational to interoperable exchange and secondary use, their utility is frequently constrained by variation in data representation, conformance to standards, and governance across systems.
The Patient Information Quality Improvement (PIQI) Framework (pronounced “picky”) defines a standardized, transportable, and reusable approach for assessing the quality of patient-centric health data. The framework is designed to operate as an ingestion, or “in-line,” data quality assessment capability, in which data quality evaluation is performed during data acquisition, transformation, or exchange. This approach supports near real-time assessment at points of data movement, enabling early detection and remediation of data quality issues and improving downstream reliability and reuse. Data usability is fundamentally subjective and specific to a use case. In the PIQI framework an Evaluation Rubric uses evaluation criteria to assess usability of the data for the Evaluation Rubric's use case.
The PIQI Framework defines a structured taxonomy of data quality dimensions that integrates both structural and contextual perspectives. Conventional data quality frameworks within healthcare interoperability primarily emphasize structural assessment, including validation of data element conformance to defined formats, value sets, and profiles. Common structural dimensions include completeness, conformance, consistency, and syntactic validity. These checks are necessary to ensure alignment with implementation guides and specifications; however, they do not determine whether data are suitable for a specific use case.
In addition to comprehensive structural assessment, PIQI enables contextual data quality assessment aligned to intended use. Data fitness is one component of data quality where determination of data’s appropriateness for use in a specific context is ultimately declared by the person (data consumer) responsible for evaluating whether a dataset is adequate for its intended purpose. Fitness for use assessment evaluates characteristics such as relevance, timeliness, and interpretability within a defined clinical, operational, or analytical context. These dimensions are dependent on the data consumer’s role and use case and are not fully captured by structural validation alone. As such, data that conform to a standard (e.g., Fast Healthcare Interoperability Resources (FHIR) resource profiles) may still be insufficient for decision-making if they lack required context or fitness for use
This approach supports a more comprehensive evaluation of data quality and directly addresses a core requirement for interoperable exchange: confidence in the reliability and trustworthiness of exchanged data. By presenting quality assessment outputs in a manner aligned to user expectations and use-case requirements, PIQI improves the interpretability and actionable value of data quality information.
The PIQI Framework is aligned with Health Level Seven (HL7) interoperability standards, including FHIR, and supports data usability assessment for use cases developed within a standard scoping framework, such as United States Core Data for Interoperability (USCDI) for receiving clinical data, or Common Payer Consumer Data Set (CPCDS) for receiving adjudicated claims. This alignment enables consistent application across systems participating in standards-based exchange.
By embedding data quality assessment within ingestion and exchange workflows, PIQI supports scalable implementation across heterogeneous environments without dependency on specific infrastructure. This enables implementers to incorporate data quality evaluation as a core component of interoperability workflows. Through its emphasis on standards alignment, portability, and use-case-driven assessment, PIQI provides a foundation for improving trust in exchanged patient data and supporting high-quality, data-driven healthcare delivery.
The HL7 Cross-Group Project (CGP) sponsored PIQI project is two-phased. This Informative Document is the first phase, in which the PIQI Framework, its components and approach to data quality evaluation are described. As an Informative Document, no HL7 product specific resources are defined herein. The second phase will produce a Cross-Paradigm implementation guide (CP IG) that defines how the PIQI Framework and approach can be utilized across formats, namely HL7 V2, Consolidated Clinical Document Architecture (C-CDA) and FHIR. Therefore, specifics about how the PIQI Framework relates to each HL7 product family is out of scope for the Informative Document and will be addressed in the PIQI CP IG.
The main sections of this Informative Document include:
Achieving higher data quality involves careful review and action throughout different stages of the health data lifecycle. However, the PIQI framework is primarily designed to objectively assess data quality at the point of data exchange—which is when data receivers first encounter the data. To best assess data quality, it's important to evaluate that data in as close to the format in which it was received as possible. When PIQI-based assessments uncover data quality issues, organizations should work to address these problems by consulting comprehensive, end-to-end standards for health record data quality, such as the following:
It's important to note that PIQI is not intended to assess clinical judgement or accuracy of the processes of the data source. Rather, PIQI can ensure that the data being utilized in decision support has been evaluated based on metrics important for the use cases.
This Informative Document was made possible by the thoughtful contributions of the following people and organizations:
Primary Authors:
Contributing Authors:
| IG | Package | FHIR | Comment |
|---|---|---|---|
  HL7 Informative Document: Patient Information Quality Improvement (PIQI) Framework, Edition 1 | hl7.xprod.uv.piqi#1.0.0 | R5 | |
 HL7 Terminology (THO) | hl7.terminology.r5#7.3.0 | R5 | Automatically added as a dependency - all IGs depend on HL7 Terminology |
  FHIR Extensions Pack | hl7.fhir.uv.extensions.r5#5.3.0 | R5 | |
 FHIR Tooling Extensions IG | hl7.fhir.uv.tools.r5#1.1.2 | R5 | for example references |
Package hl7.fhir.uv.extensions.r5#5.3.0 This IG defines the global extensions - the ones defined for everyone. These extensions are always in scope wherever FHIR is being used (built Sat, May 16, 2026 18:32+1000+10:00) |
Package hl7.fhir.uv.tools.r5#1.1.2 This IG defines the extensions that the tools use internally. Some of these extensions are content that are being evaluated for elevation into the main spec, and others are tooling concerns (built Tue, Mar 24, 2026 11:13+1100+11:00) |
There are no Global profiles defined
No use of external IP
IG © 2025+ HL7 Cross-Group Projects Work Group.
Package hl7.xprod.uv.piqi#1.0.0 based on FHIR 5.0.0.
Generated
2026-07-28
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