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Official URL: https://www.medizininformatik-initiative.de/fhir/summary-reports/ImplementationGuide/summary-reports				Version: 0.1.0
Draft as of 2025-11-03				Computable Name: SummaryReports

FHIR Summary Reports

Overview

This Implementation Guide provides a FHIR-based framework for generating basic population-level demographic summary statistics from patient data. It focuses on age distribution analysis and gender stratification using Clinical Quality Language (CQL) and FHIR Measure resources.

Features

• Population Demographics: Calculate age distributions using 5-year age groups matching German census data structure
• Multiple Stratification Approaches: Support for both separate and composite demographic stratification
• CQL-Based Calculations: Age calculation logic
• Census Integration: Alignment with German Census 2011 and 2022 data structure for comparative analysis

FHIR Artifacts

Libraries

Resource	Purpose	Description
Stratifier Age Gender	CQL logic for demographic calculations	Contains age calculation functions, 5-year age grouping and gender stratification

Measures

Resource	Approach	Purpose
Age Gender CQL	Separate Stratifiers	Independent age and gender demographic analysis
Composite Gender Age CQL	Composite Stratifiers	Cross-tabulated age×gender combinations for detailed demographic intersections

Example Reports

German Census Data (2011 vs 2022)

Resource	Census Year	Format
Zensus 2011 Report	2011	Separate stratifiers
Zensus 2022 Report	2022	Separate stratifiers
Zensus 2011 Composite	2011	Composite format
Zensus 2022 Composite	2022	Composite format

Bundle Resources

Resource	Purpose
Measure Library Transaction Bundle	Complete artifact deployment bundle

Stratification Approaches

Separate Stratifiers

• Structure: Multiple independent stratifiers (age and gender analyzed separately)
• Use Case: Basic demographic summaries
• Output: Individual dimension counts (e.g., "6 males, 4 females" + "3 aged 40-44, 2 aged 45-49")

Composite Stratifiers

• Structure: Single stratifier with component dimensions
• Use Case: Detailed demographic intersection analysis
• Output: Cross-tabulated combinations (e.g., "2 males aged 40-44, 1 female aged 40-44")

Age Group Classifications

The implementation provides age classification scheme:

• 5-Year Groups: 0-4, 5-9, 10-14, …, 85-89, 90+ (matches German census structure)

Technical Implementation

CQL Functions

• AgeInYears, AgeInMonths, AgeInDays - Age calculations
• AgeFiveYearGroups - German census-aligned age grouping
• Gender - Gender stratification

FHIR Resource Types

• Measure: Population measure definitions with CQL expressions
• Library: CQL logic packaging for reuse across measures
• MeasureReport: Example outputs demonstrating expected results