HL7 Informative Document: Patient Information Quality Improvement (PIQI) Framework, Edition 1
1.0.0-ballot - Informative 1 - Ballot
HL7 Informative Document: Patient Information Quality Improvement (PIQI) Framework, Edition 1
1.0.0-ballot - Informative 1 - Ballot
This page is part of the HL7 Informative Document: Patient Information Quality Improvement (PIQI) Framework, Edition 1 (v1.0.0-ballot: Informative 1 Ballot 1) based on FHIR (HL7® FHIR® Standard) v5.0.0. No current official version has been published yet. For a full list of available versions, see the Directory of published versions
In healthcare, both human providers and the software systems that support them critically depend on the availability of complete, correct, and current information. To provide this information, our industry has spent decades building the foundational infrastructure for interoperability between healthcare applications. Messaging standards, standard terminologies, and implementation guides all form part of this foundation. However, the last barrier to true interoperability is trust—specifically, trust that the data we receive meets the quality standards necessary to support our use cases rather than undermine them.
The PIQI Framework is designed to provide a standardized, community-based approach to evaluating data usability, with the goal of transcending siloed, subjective approaches to data quality assessment. The framework's flexible methodology enables quality measurement across different contexts while providing qualitative guidance tailored to various implementations of interoperable data exchange. By working across the family of HL7 products, the PIQI Framework supports not only the current state of the industry but also its evolution over time.
The following are examples of national or international specifications that outline technical and/or policy requirements via "core data" requirements to support interoperability through defined classes and elements within the classes, often bound to specific terminology assets. PIQI components can be assembled to evaluate data quality for any of these specifications, regardless of varying data formats, defined classes, elements or terminology bindings required by the applicable jurisdiction.
The United States Core Data for Interoperability (USCDI) establishes the technical and policy foundation for nationwide interoperable health information exchange. It defines a baseline set of data elements for exchange across care settings and use cases. USCDI is stewarded and adopted by the Assistant Secretary for Technology Policy/Office of the National Coordinator for Health Information Technology (ASTP/ONC) on behalf of the U.S. Department of Health and Human Services.
Health IT developers participating in the voluntary ASTP/ONC Certification Program must support the exchange of USCDI data elements as specified in the USCDI itself or in associated interoperability specifications. ASTP regulations—such as the Health Data, Technology, and Interoperability: Certification Program Updates, Algorithm Transparency, and Information Sharing Final Rule (HTI-1)—and the voluntary Standards Version Advancement Process (SVAP) identify which USCDI versions are required or optional as of their effective dates. Annual updates to HL7 FHIR US Core and C-CDA specifications align these standards with the latest USCDI versions.
Certification Program testing tools verify a health IT developer’s ability to exchange USCDI data using appropriate HL7 specifications and terminology. Real-world testing plans and reports evaluate whether these capabilities are correctly implemented using customer data. However, neither the test tools nor the real-world testing process assesses data quality or operate on real-time, in-transit data generated by health systems.
To address data quality, the HL7 C-CDA Rubric Criteria Standard was developed to evaluate structured data within C-CDA documents. The PIQI Framework expands on this approach, enabling health systems and organizations (e.g., Qualified Health Information Networks) to assess the quality of exchanged data—regardless of format. A USCDI-specific model within the PIQI Framework can identify and remediate data quality issues during exchange, improving patient care, reporting, clinical decision support, AI applications, and claims processing.
Implementing the framework supports a continuous data quality lifecycle of assessment, remediation, monitoring, and improvement. The PIQI Framework also serves as the data quality component within the Patient Demographic Data Quality Framework.
The Australian Core Data for Interoperability (AUCDI) is changing the approach to health data and is set to become a national asset focused on establishing an independent base of reusable, standardized information models and related artefacts. As clinical systems converge their internal data structures towards AUCDI, this common, consensus-based data foundation will reduce the need for data transformations and mappings, supporting safer and simpler interoperability.
The AUCDI is intentionally agnostic of:
- Any single clinical use case while being constructed as a foundation for many clinical use cases,
- Any single clinical system vendor while being strongly informed by functionality and data available in current clinical systems, and
- Any single technical implementation or exchange approach while providing the clinical data requirements for developing the FHIR AU Core specifications and subsequent Implementation on Guides (IG).
[Australian CDI]1
The Canadian Core Data for Interoperability (CACDI) defines a standardized set of essential health data elements and value sets in the context of a common data architecture to support interoperability and data exchange across the Canadian health care ecosystem. The CACDI represents the minimum data required to support standardized information capture and enable meaningful exchange of health information. It aims to standardize the capture, structure and exchange of health data across the health continuum by providing a foundation of standardized data elements applicable to multiple health care settings.
The CACDI is a subset of the Pan-Canadian Health Data Content Framework and works in tandem with CA Core+, the Fast Healthcare Interoperability Resources (FHIR) profiles created by Canada Health Infoway, to facilitate the meaningful exchange of health care information. Together, the Canadian Institute for Health Information’s and Canada Health Infoway’s efforts, including the development of national health data content and data exchange standards, will support the uninterrupted and accurate exchange of health information across Canada, aligning with Health Canada’s vision for a modern, integrated health care system" [Canadian CDI]2
Similar to it's applicability to the USCDI, the PIQI Framework can expand on the approach taken by the CACDI by enabling health systems and organizations to assess the quality of exchanged data—regardless of format. The PIQI Framework components are extensible by design, and can be adapted to address CACDI-specific elements and SAMs to define a CACDI evaluation rubric using applicable CACDI required terminology components.
The International Patient Summary (IPS) is a minimal and non-exhaustive set of basic clinical data of a patient, specialty-agnostic, condition-independent, but readily usable by all clinicians for cross-border patient care. This summarized version of the patient’s clinical data gives health professionals the essential information such as allergies, medications and problems needed to provide care in the case of an unexpected or unscheduled medical situation (e.g. emergency or accident). While this data is mainly intended to aid health professionals in providing unscheduled care, it can also be used to provide planned medical care (e.g. in the case of citizen movements or cross-organizational care paths).
The IPS provides guidance that seeks to align data expectations in the population of data sections and elements in the context of a patient summary. In addition, to be universally exchangeable and understood, a patient summary must rely as much as possible on structured data and multilingual international reference terminologies that are licensed at no cost for global use. In the case of SNOMED CT, SNOMED International has created the IPS Terminology, which is an open and free sub-ontology of SNOMED CT that references a core set of clinical concepts licensed at no-cost with the aim to serve the public good. The population of elements and matching of terminologies remain important elements of data quality in the context of IPS to inform downstream care.
Methods for assessing healthcare quality are rapidly evolving to become more patient-focused and more meaningful in supporting quality improvement. Recent advancements in digital data and measurement standards have made this evolution possible, but the accelerated deployment of digital quality measures (dQMs) presents several challenges despite their many benefits. Electronic clinical datasets offer many opportunities for assessing a variety of patient outcomes, however measuring quality of care is particularly reliant on the transformation of the data to a standardized format useable by a dQM. dQMs require standard data stored using common data interoperability definitions and preferred terminologies (codes) that represent the warehoused knowledge in a manner that can be directly queried by the dQM for clinical performance assessment. The need for standardized data to assess healthcare quality has resulted in formation of central data repositories (CDRs) for the explicit purpose of aggregating, deduplicating and normalizing patient data from multiple systems to support both research and digital quality measurement. Extracting, transforming, and loading (ETL) patient data from multiple systems into a dQM-compatible CDR is subject to rigorous evaluation to ensure that, as the information is being transformed, the integrity of the original data is preserved. The criteria for evaluation of these data in CDRs lacks an authoritative source, or gold standard reference, that explicitly defines those fitness expectations critical to assert that the data is adequate for use by quality measurement programs. The PIQI Framework enables highly reliable data quality assessments that can be applied to data used by digital quality measures universally, irrespective of the data source.
The Joint Commission stewards a variety of structure, process and outcome quality measures used across their accreditation and certification programs. Having accurate and reliable data ensures that healthcare organizations are evaluated fairly and consistently, leading to improved patient outcomes and enhanced operational efficiency. From The Joint Commission's perspective, high-quality data is essential for tying clinical care to quality improvement initiatives. This connection helps identify areas needing improvement, track progress over time, and implement evidence-based practices to enhance patient care. However, several prevalent pain points arise from poor data quality. These include data fragmentation, where information is scattered across multiple systems, making it difficult to obtain a comprehensive view of patient care. Inconsistent data entry practices lead to errors and discrepancies, undermining the reliability of the data. Outdated systems hinder seamless data integration, causing delays and inefficiencies. Additionally, the lack of standardized data formats and the challenge of maintaining data integrity across various platforms pose significant obstacles. Ensuring accurate, consistent, and complete data is used for quality measurement, ensuring healthcare organizations accredited by the Joint Commission can support better care for individuals, improved health for populations, and lower healthcare costs.
Providing real-time decision support requires data be complete and accessible to Clinical Decision Support (CDS) applications. The ability to provide accurate guidance and/or warnings is wholly dictated by the completeness, accessibility, context specificity, accuracy, and reliability of the data mapped to a CDS application. CDS has a very high bar for trustworthiness, as decisions made from information produced by CDS have a substantial impact on both patient safety and patient outcomes. Maturation of interoperability standards such as FHIR and data models such as USCDI have greatly increased the amount of data that is accessible by CDS applications, however a lack of standard data quality assessment frameworks has prevented a substantial amount of useable data from being leveraged for clinical decision-making supported by CDS applications.
CDS developed, deployed, and utilized at the point of care are based on very specific criteria and supporting documentation. Clinical end users often have strong opinions about the quality of electronic patient data used to inform care and missing or incomplete patient information impacts their ability to trust the information generated by a CDS application. This missing information is not limited to discreet clinical variables and often includes contextual parameters which are essential for the data to be interpreted correctly by the CDS application. The relative context within which CDS is providing alerts or reminders requires accurate interpretation of ALL relevant variables (both clinical and contextual) to ensure that patients are receiving the highest quality of care and are not harmed by misinformation.
Data derived from external sources is much less likely to be included in CDS criteria due to its unmeasured quality. Terminology mapping can be used to offset semantic differences but is not a proxy for effective data quality assessment. CDS tools are scraping or “hooking” data in real time from data directly entered into the medical record. While structured data is often preferred for clinical documentation due to its potential reusability, limitations at the point of data entry result in the loss of rich contextual information and increased frustration by providers when fields do not align with the patient context. Poor data quality can result in a compounding proliferation of copy-paste errors which directly affect the ability of CDS to provide rich, context-specific information to the care team. Increasingly, AI is being deployed to “correct” and standardize notes as they are entered into the medical record by a clinician or patient (e.g., dictation), however scalability of these applications is limited as they are rarely developed using standard reference models for data fitness.
As AI is increasingly leveraged for CDS use cases, it is important, if not crucial, that we endorse data quality measurement standards to improve health outcomes at scale. The situational nature of CDS requirements and the presentation of information to the end user complicate the broad adoption of CDS, essentially because the rich context contained within unstructured data are not leveraged, preventing the reuse of valuable data. The absence of a standard data quality framework such as PIQI guiding the development and deployment of CDS prevents the reuse of valuable data. The efficacy of clinical AI resources is directly related to the quality of the data upon which it is developed and broad deployment of the PIQI framework will have a substantial positive impact on patient outcomes.
Public health programs are responsible for the health and safety of both large populations within their jurisdiction as well as individuals. Having access to timely, complete, and high-quality data is key to driving public health decisions and the effective and efficient application of public resources. Public health uses individual patient data for outbreak management, contact tracing, test positivity calculations, case follow up prioritization, immunization recommendations and exposure prevention. A core feature of most public health programs is to leverage mandated and optional reporting requirements to collate and curate data received from a wide variety of data sources often via thousands or tens of thousands individual connections. Applying common data quality assessments to information submitted from myriad sources is key to ensuring that data is reliable and comparable regardless of the origin of the data so that public health programs can optimize decision making and resource allocation. Data quality is also important when public health shares data back with healthcare providers and other authorized users where it can be leveraged to provide better care for individuals and populations. Ensuring that data being shared by public health is timely, of good quality and is reliable builds trust with trading partners and enhances the flow of information in both directions. Finally, public health is heavily dependent on sharing data between programs within the same jurisdiction and between jurisdictions. The ability to share reliable data within public health reduces burden not only on the programs themselves but also on healthcare providers and other data submitters by minimizing duplicate data submission. For these reasons, public health has a strong incentive to apply the standardized, modular, and reusable PIQI Framework for data quality analysis and improvement.
Regulatory bodies depend on high-quality, high-integrity real-world data to make informed decisions that significantly impact public health and safety. The integrity and quality of these data are crucial, as they ensure that the information used is accurate, reliable, and trustworthy.
High-quality data allows regulatory bodies to effectively reuse information across various scenarios, enhancing their decision-making processes. This foundation is essential for making sound decisions regarding the approval, recall, and monitoring of clinical devices and products, ultimately safeguarding patients from potential harm.
In value-based care, accurate data is vital for evaluating patient outcomes and controlling costs. Reliable data is also critical in product approvals and recalls, ensuring that only safe and effective products reach the market. Post-market surveillance depends on high-quality data to continuously monitor the safety and efficacy of products. Additionally, population health reporting and analytics require precise data to identify trends and inform public health strategies. By maintaining the highest data quality standards, regulatory bodies can better protect and promote public health across these diverse areas.
Data integration and interoperability are top priorities for healthcare payers, but integrating data from diverse sources remains a complex challenge. Health insurance payers face costly data quality issues that impact member management, claims processing, regulatory compliance, and operational efficiency.
Payers rely upon data obtained from diverse sources, each often acquired in unique formats (e.g., flat files, CSV, non-standard XML, JSON, EDI standards) using a variety of vocabularies (proprietary and terminology standards) which require significant transformation to aggregate, analyze and exchange these data.
Payers receive data in a variety of healthcare exchange and terminology standards formats including HL7 FHIR, C-CDA’s and Version 2, EDI X12N, ICD, CPT and HCPCS, SNOMED CT, and LOINC. Payers use healthcare data submitted by providers to evaluate requests for prior authorization and to accurately adjudicate healthcare claims. In addition, these data are used to show that quality measures are being addressed as well as to ensure that appropriate Risk Adjustments are applied to their members.
The availability of consistent, accurate, and plausible data are paramount to correct outcomes for all of these use cases. The primary data quality issues that confront healthcare payers include:
The PIQI Framework can significantly address the critical need for consistent, high-quality, and interoperable patient information that healthcare care payers obtain from diverse data sources using PIQI standardized data assessments to identify specific issues affecting data integrity, accuracy, conformity, and availability. The PIQI can provide detailed insight into the root causes of specific data quality issues and provides a feedback loop to help data sources adjust their processes to meet quality requirements and improve overall data quality. These improvements are useful to payers, healthcare providers, and most importantly to the people they serve.
Health Information Exchanges (HIEs) serve as essential intermediaries within the healthcare ecosystem, enabling the flow of information among providers, payers, public health agencies, and other stakeholders. Because of this role, HIEs are uniquely positioned to apply the Patient Information Quality Improvement (PIQI) Framework, which focuses on evaluating and improving the quality of data as it moves between systems. Unlike traditional data quality approaches that focus on information stored within a single organization or system, the PIQI Framework is centered on data in motion. This aligns closely with the operational function of an HIE, which facilitates real-time exchange of diverse types of health-related data across a variety of technical standards and organizational boundaries. Applying the PIQI Framework at this layer of data exchange offers an opportunity to assess the quality and usability of information at the moment it is shared, helping to identify and address issues before they propagate across systems. This creates potential value for all participants connected to the HIE, offering increased transparency into the reliability and completeness of the data they receive. The ability to observe patterns across multiple sources may also support identification of common data quality challenges and inform collaborative improvement efforts. Because this work occurs at a shared intersection point, the resulting benefits can extend across the broader network of stakeholders. As healthcare increasingly relies on high-quality information to support initiatives such as quality measurement, public health reporting, care coordination, prior authorization, and population health management, the ability to evaluate and improve data during transmission becomes more important. HIEs can help ensure that the data flowing through their networks is not only standardized but also fit for its intended use. Applying the PIQI Framework within the HIE context offers a scalable and collaborative approach to improving data quality, building trust, supporting compliance with emerging regulations, and enabling a wide range of downstream use cases. In this way, HIEs play a valuable role in advancing data quality as a shared priority across the healthcare ecosystem.
Laboratory results make up the largest percentage of data that drive clinical decisions, as well as follow up for public health, regulatory or research purposes. Often these results are produced at outside organizations, so being able to trust that the data are accurately reflecting the patient’s condition and are received in a timely manner is critical for all these use cases. For some lab tests the order must contain accurate clinical information (for example the overall volume of urine collected as input for a calculated result, or the patient’s sex and/or age to determine the correct reference range) for the laboratory to be able to perform testing. Missing or inaccurate data will require human intervention, delaying the testing and resulting process. Increasing the quality of laboratory data in electronic health care systems will serve many of the use cases listed on this page.
Measuring and understanding objective data quality is relevant for Electronic Health Records (EHRs) when they exchanging or receiving patient information. When an EHR receives patient data from another healthcare system during hospital transfers, specialist referrals, or lab result transmissions, PIQI could evaluate the incoming data quality in real-time before incorporation into the patient record, preventing poor-quality data from contaminating the EHR and alerting clinicians to potential reliability issues. Additionally, healthcare organizations could use PIQI to assess their interoperability readiness before participating in health information exchanges or implementing new connections, ensuring their EHR's outgoing data meets quality standards expected by receiving systems, which is particularly relevant for USCDI compliance and information blocking regulations. The framework also enables vendor performance monitoring, allowing organizations to objectively measure the quality of data coming from different EHR modules, third-party applications, or data sources, providing metrics for vendor accountability while identifying which systems consistently provide high-quality versus problematic data. Finally, as data quality becomes increasingly important for regulatory programs like Medicare reporting, public health surveillance, and quality measures, EHRs could use PIQI to ensure outgoing data meets regulatory quality standards before submission. The key advantage of PIQI in these EHR scenarios is that it evaluates data quality "in flight" without storing PHI, making it suitable for real-time assessment during the normal flow of clinical operations rather than requiring separate data quality analysis processes.
Healthcare providers encounter significant hurdles in generating high-quality, interoperable clinical data across various electronic health record (EHR) systems. These challenges include dealing with incomplete, outdated, or duplicate problem lists; deciphering ambiguous or inconsistently applied terminologies; and managing the increasing demand for structured documentation that may disrupt clinical workflows.
A delicate balance exists between the need for thorough clinical documentation and the requirements for structured data capture. For instance, vital clinical information often resides in free-text notes, yet many systems and quality initiatives rely exclusively on structured fields for reporting, decision support, and risk adjustment. This disconnect can lead to critical data being underrepresented in secondary-use datasets.
Providers also face limited feedback mechanisms to assess whether their data entries satisfy regulatory, operational, or quality standards. This lack of transparency can impede efforts to improve documentation practices and diminish trust in the EHR as a reliable source of information.
The PIQI Framework serves as a powerful tool to evaluate and enhance data quality from the provider's perspective. By utilizing modular, reusable assessments (SAMs) and structured evaluation profiles, PIQI helps pinpoint actionable gaps—such as missing coded entries, improper use of value sets, or misalignment with USCDI and US Core standards. Integrating provider-centric rubrics bridges the gap between real-time documentation and downstream data applications.
Ultimately, aligning the PIQI Framework with clinical workflows and provider constraints fosters greater engagement, reduces documentation burdens, and elevates the quality and utility of clinical data across the healthcare landscape.
IG © 2025+ HL7 Cross-Group Projects Work Group. Package hl7.xprod.uv.piqi#1.0.0-ballot based on FHIR 5.0.0. Generated 2025-08-11
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