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

Evaluation

PIQI Evaluation Rubric

The Patient Information Quality Improvement (PIQI) Framework assesses patient data in the PIQI Data Model using an Evaluation Rubric.

The Evaluation Rubric is a sequenced collection of Evaluation Criteria.

Each Evaluation Criterion is comprised of a specific PIQI Model Entity an assigned Evaluation an optional Condition and the Scoring Effect.

The PIQI Model Entity can be an attribute, element, data class or the entire patient.

The Evaluation and Condition are both configured Simple Assessment Modules (SAMs).

If the Condition is configured, the Evaluation is only processed if the conditional SAM passes.

Each Evaluation that is processed triggers the Scoring Effect based upon the pass or fail result of the underlying SAM. If the SAM returns an indeterminant result (skip) the Evaluation is skipped. This implies a condition within the implementation of the SAM itself was not met.

Simple Assessment Modules

A SAM is a logical test that measures a specific data quality dimension for a model element or characteristic, following a defined pattern. The inputs depend on the pattern, but the output is always a simple result: pass, fail, or skip. SAMs can be grouped into hierarchical collections, allowing multiple checks to be combined in order to provide a broader picture of data quality for a given use case. Given that interaction with a PIQI evaluation endpoint is at the Rubric level, and not at the SAM level, the actual format for conveying these responses (e.g., "pass/fail" vs. "true/false" vs. "1/0") is an implementation decision that is outside the scope of this standard. For detailed information see SAMS

Evaluation Rubrics

Evaluation Rubrics represent a collection of sequenced SAM evaluations of specific entities in the PIQI Model along with the desired scoring effect.

Anatomy of an Evaluation Rubric

An Evaluation Rubric is comprised of the following components:

Evaluation Rubric Criteria

Each Evaluation Rubric contains a collection of criteria used for assessing and scoring patient messages. Each criterion has the following fields:

Example of Evaluation Rubric Definition JSON

{
    "evaluationRubric": [
        {
            "mnemonic": "Rubric_001",
            "name": "Sample Evaluation Rubric",
            "description": "This evaluation rubric assesses patient data quality based on specific criteria.",
            "version": "1.0",
            "source": "PIQI Alliance",
            "model": "PIQI_DataModel_ExtensionX",
            "modelVersion": "2.5",
            "criteria": [
                {
                    "sequence": 1,
                    "description": "Check if patient birth date is a valid date.",
                    "dataClass": "Patient",
                    "entity": "birthDate",
                    "SAMMnemonic": "Attr_IsValidDate",
                    "SAMShortName": "ValidDate",
                    "successNameOverride": "Valid Birth Date",
                    "failureNameOverride": "Invalid Birth Date",
                    "SAMParameters": [],
                    "conditionalSAM": "Attr_IsPopulated",
                    "conditionalSAMParameters": [
                        {
                            "parameterName": "FieldRequired",
                            "parameterValue": "true"
                        }
                    ],
                    "scoringEffect": "scoring",
                    "scoringWeight": 1,
                    "criticalityIndicator": false
                },
                {
                    "sequence": 2,
                    "description": "Verify if patient gender is populated.",
                    "dataClass": "Patient",
                    "entity": "gender",
                    "SAMMnemonic": "Attr_IsPopulated",
                    "SAMShortName": "GenderPopulated",
                    "successNameOverride": "Gender Present",
                    "failureNameOverride": "Gender Missing",
                    "SAMParameters": [],
                    "conditionalSAM": "",
                    "conditionalSAMParameters": [],
                    "scoringEffect": "informational",
                    "scoringWeight": 0,
                    "criticalityIndicator": false
                }
            ]
        }
    ]
}