Clinical Measures

Clinical research and quality improvement depend on standardized calculations: Body Mass Index for obesity screening, eGFR for kidney function assessment, HbA1c trends for diabetes management, CHADS₂-VASc scores for stroke risk stratification, etc. Yet many organizations lack a centralized, validated repository for these measures. Analysts reinvent calculations in SQL, Excel, and Python. Definitions drift between teams. Documentation disappears. Results become irreproducible.

The Clinical Measures module solves this by providing a single authoritative environment for managing clinically validated calculations across your organization. Define a measure once: with clinical documentation, evidence references, test cases, and SQL logic: then apply it consistently across cohorts, research studies, quality reporting, and patient-level analytics.

Clinical Measures Calculation

This video demonstrates the interface visually without audio narration.

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Why Clinical Measures Matter

Consider a simple scenario: You need to identify patients with poorly controlled diabetes (HbA1c >9%) for quality improvement outreach.

Without standardized measures:

• Data scientist writes SQL query defining "poorly controlled" as HbA1c >9%
• Quality team uses Excel formula with threshold of 8.5%
• Clinical team references different calculation in EHR reports
• Results conflict. Teams spend hours reconciling definitions. Patient outreach delayed.

With Clinical Measures module:

• Single "Diabetes Control Status" measure defined once with HbA1c >9% threshold
• Clinical documentation explains rationale (ADA guidelines)
• SQL logic validated with test cases
• All teams query the same calculation
• Results are consistent, reproducible, and auditable

This same principle extends to hundreds of clinical calculations: from simple vital sign conversions to complex risk scores incorporating dozens of variables.

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Module Overview

The Clinical Measures module consists of two integrated components working together:

Clinical Measures Library

Searchable catalog of all available measures with clinical documentation, formulas, and validation logic

Patient Metrics

Cohort-based interface for computing selected measures across patient populations and reviewing results

At-a-Glance Dashboard Metrics

Four summary indicators provide instant visibility into measure activity across your organization:

Total Measures

Complete inventory of defined clinical calculations available in the system

Active Measures

Measures currently enabled for computation and analysis

Failed Measures

Measures with validation or execution failures requiring attention

Outstanding Measures

Defined measures awaiting evaluation or execution

These metrics help administrators and analysts quickly assess measure readiness, identify configuration issues, and monitor pipeline health.

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Clinical Measures Library

The library presents all available measures in an intuitive card-based interface. Each card displays essential information at a glance:

Example measure card:

Body Mass Index (BMI)

• Category: Endocrinology, Nutrition
• Description: Weight-to-height ratio for assessing obesity risk
• Normal Range: 18.5 - 24.9 kg/m²
• Status: Active

Click card to view full details, documentation, and patient-level results

Search and Filter

Locate relevant measures efficiently using multiple strategies:

• Keyword search: Find measures by name, description, or clinical terms (e.g., "kidney function" returns eGFR, creatinine clearance, BUN/Cr ratio)
• Category filters: Browse by clinical domain (Cardiology, Pulmonology, Endocrinology, etc.)
• Sort options: Order by name, clinical area, creation date, or usage frequency

This makes it easy to discover measures during cohort design, model development, or clinical review workflows.

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Creating a New Clinical Measure

The measure creation workflow ensures every calculation is clinically validated, technically sound, and fully documented. Click New Clinical Measure to begin.

Step 1: Basic Information

Establish the measure's identity and scope:

Example:

• Measure Name: Body Mass Index (BMI)
• Data Type: Continuous numeric value
• Clinical Description: Calculates body mass index from weight and height measurements. Used to assess weight status and obesity-related health risks.

Step 2: Usage Documentation

Provide practical guidance for clinical and analytics teams:

When to Use:

• Screening for overweight and obesity
• Assessing cardiovascular disease risk factors
• Monitoring weight management programs
• Evaluating metabolic syndrome criteria

How to Use:

• Requires recent weight and height measurements from the same date
• Calculated automatically when both measurements are available
• Track changes over time to monitor weight trends

Why It Matters:

• Identifies patients at increased risk for obesity-related conditions
• Guides interventions for weight management and lifestyle modification
• Required for metabolic syndrome diagnosis and bariatric surgery evaluation
• Supports population health initiatives targeting obesity prevention

This documentation ensures everyone understands the measure's purpose and appropriate use.

Step 3: Ranges and Thresholds

Define interpretation boundaries and clinical decision points:

BMI Reference Ranges (Adults):

• Underweight: <18.5 kg/m²
• Normal weight: 18.5-24.9 kg/m²
• Overweight: 25.0-29.9 kg/m²
• Obesity (Class I): 30.0-34.9 kg/m²
• Obesity (Class II): 35.0-39.9 kg/m²
• Obesity (Class III): ≥40 kg/m²

Clinical Alerts:

• BMI <18.5: Flag for malnutrition screening and nutritional assessment
• BMI ≥30: Alert for obesity counseling and comorbidity screening
• BMI ≥40: Consider bariatric surgery evaluation

Step 4: Evidence and References

Link to authoritative sources supporting the measure:

Supporting Evidence:

• WHO Expert Consultation. Appropriate body-mass index for Asian populations. Lancet. 2004;363(9403):157-163
• CDC: About Adult BMI (2022)
• Jensen MD et al. 2013 AHA/ACC/TOS Guideline for the Management of Overweight and Obesity in Adults
• Institutional Protocol: Obesity Screening and Management, v2.1 (2024)

This establishes clinical validity and supports regulatory compliance.

Step 5: Technical Definition

Provide the precise implementation blueprint:

Formula:

BMI = weight (kg) / height (m)²

SQL Implementation:

WITH weights AS (
    SELECT
        m.person_id,
        m.measurement_date::date AS mdate,
        m.value_as_number AS weight_kg,
        m.unit_concept_id,
        ROW_NUMBER() OVER (
            PARTITION BY m.person_id, m.measurement_date::date
            ORDER BY COALESCE(m.measurement_datetime, m.measurement_date) DESC,
                     m.measurement_id DESC
        ) AS rn
    FROM tpj.measurement m
    WHERE m.measurement_concept_id = 3013762 -- Body weight
      AND m.value_as_number IS NOT NULL
      AND m.value_as_number > 0
),
heights AS (
    SELECT
        m.person_id,
        m.measurement_date::date AS mdate,
        m.value_as_number AS height_cm,
        m.unit_concept_id,
        ROW_NUMBER() OVER (
            PARTITION BY m.person_id, m.measurement_date::date
            ORDER BY COALESCE(m.measurement_datetime, m.measurement_date) DESC,
                     m.measurement_id DESC
        ) AS rn
    FROM tpj.measurement m
    WHERE m.measurement_concept_id = 3036277 -- Body height
      AND m.value_as_number IS NOT NULL
      AND m.value_as_number > 0
)
SELECT
    p.person_id,
    w.weight_kg,
    h.height_cm,
    h.height_cm / 100.0 AS height_m,
    ROUND(w.weight_kg / POWER((h.height_cm / 100.0), 2), 2) AS bmi,
    w.mdate AS measurement_date,
    wu.concept_name AS weight_unit,
    hu.concept_name AS height_unit
FROM tpj.person p
JOIN weights w ON p.person_id = w.person_id AND w.rn = 1
JOIN heights h ON p.person_id = h.person_id AND h.rn = 1 AND h.mdate = w.mdate
LEFT JOIN vocab.concept wu ON w.unit_concept_id = wu.concept_id
LEFT JOIN vocab.concept hu ON h.unit_concept_id = hu.concept_id
WHERE p.person_id = {person_id}
ORDER BY w.mdate DESC

Test Cases:

• Weight 70 kg, Height 175 cm → BMI = 22.9 kg/m² (Normal weight)
• Weight 90 kg, Height 165 cm → BMI = 33.1 kg/m² (Obesity Class I)

Input Validation:

• Weight must be >0 and <500 kg
• Height must be >0 and <300 cm
• Weight and height measurements must be from the same date

Once complete, click Create Calculation to save the measure to the library.

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Viewing Measure Details

Click any measure card to open a detailed modal showing comprehensive information and patient-level results.

Description Section

Extended clinical documentation including purpose, methodology, interpretation guidance, and usage notes.

Cohort Selector

Choose which patient population to evaluate:

• All Patients (default)
• Diabetes Cohort
• Obesity Management Program
• Heart Failure Registry
• (Any cohort defined in the system)

Patient Results Table

Dynamically computed results for the selected cohort:

Example: BMI calculation results

Patient ID	Name	Gender	Age	Evaluation Date	BMI (kg/m²)
P001234	Patient A	F	68	2024-12-15	33.1 (Obesity Class I)
P005678	Patient B	M	54	2024-12-14	22.9 (Normal)
P009012	Patient C	F	72	2024-12-13	36.8 (Obesity Class II)

If no qualifying data exists (e.g., no recent weight or height measurements), "No Data" placeholder is shown.

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Patient Metrics Interface

The Patient Metrics tab enables cohort-wide evaluation of multiple measures simultaneously. This supports quality reporting, research cohort characterization, and population health monitoring.

Workflow

1

Select Cohort

Choose patient population (e.g., "Type 2 Diabetes Patients")

↓

2

Select Metrics

Click "Select Metrics" and choose measures (e.g., HbA1c, BMI, Blood Pressure, LDL Cholesterol)

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3

Apply Filters (Optional)

Refine population by age, gender, or other demographics

↓

4

Review Results

View patient-level calculations in structured table, export for analysis

Results Table

The output displays all selected metrics across the cohort:

Example: Diabetes cohort with multiple measures

Patient ID	Age	Gender	HbA1c (%)	BMI (kg/m²)	SBP (mmHg)	LDL (mg/dL)
P001234	68	F	9.2	33.1	145	142
P005678	54	M	7.8	22.9	138	98
P009012	72	F	10.1	36.8	152	156

Each column represents a different clinical measure computed for the same evaluation date

Select Metrics Modal

Fine-grained control over which measures appear in the results table:

• Bulk select/deselect: Toggle all measures at once
• Search: Filter by measure name or clinical domain
• Preview: View name and short description before selecting
• Checkbox toggle: Include or exclude individual measures

Changes apply immediately to update the results table.

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Best Practices for Measure Design

To ensure quality, interpretability, and reuse across your organization:

Standardized Naming

Use consistent naming conventions across clinical domains. Include units where applicable (e.g., "BMI (kg/m²)" or "HbA1c (%)").

Validated Formulas

Define clinically validated formulas with test cases demonstrating expected outputs for known inputs.

Reference Ranges

Document normal ranges, abnormal thresholds, and units to support clinical interpretation.

Clinical Evidence

Link to peer-reviewed literature, clinical guidelines, or institutional protocols supporting the measure.

Input Validation

Implement technical guardrails preventing invalid inputs (e.g., negative weights, impossible vital signs).

Discoverability

Use category tags and detailed descriptions to improve measure discovery across teams.

Usage Documentation

Provide clear guidance for when, how, and why to use each measure in clinical and analytics workflows.

Version Control

Track changes to formulas and thresholds over time, maintaining reproducibility for historical analyses.

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Why This Matters

Without a centralized clinical measures repository:

• Calculation drift: Teams implement different versions of the same measure
• Lost documentation: Formula rationale disappears when analysts change roles
• Wasted effort: Each project reinvents calculations from scratch
• Irreproducible results: Research findings can't be validated or replicated

With the Clinical Measures module:

• Standardization: One authoritative definition used consistently across all applications
• Transparency: Complete documentation of formulas, evidence, and validation
• Efficiency: Define once, reuse everywhere: cohorts, registries, dashboards, research
• Quality: Peer-reviewed formulas with test cases and clinical validation
• Auditability: Full lineage from measure definition to patient-level calculation

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Strategic Value

The Clinical Measures module provides the computational foundation for analytics across the platform, enabling:

Quality Measurement

Track HEDIS, CMS, and institutional quality metrics with standardized, auditable calculations.

Research Cohorts

Define inclusion criteria using validated measures, ensuring reproducible cohort selection.

Risk Stratification

Apply consistent risk scores and clinical thresholds across patient populations.

Longitudinal Tracking

Monitor patient trajectories over time using standardized calculations at each timepoint.

Decision Support

Power clinical alerts, recommendations, and treatment pathways with real-time measure evaluation.

Regulatory Reporting

Generate compliant reports using measures with documented evidence and validation.

By maintaining a centralized, documented, and validated catalog of clinical measures, the platform ensures reproducible analytics, high-integrity analyses, and standardized logic across all secondary use applications.

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Summary

The Clinical Measures module transforms how healthcare organizations define and operationalize clinical calculations. Instead of scattered formulas across spreadsheets and scripts, you build a single authoritative library where every measure is:

• Clinically validated with evidence references and peer review
• Technically tested with validation cases and input guardrails
• Fully documented with usage guidance and interpretation notes
• Consistently applied across all cohorts, studies, and workflows
• Completely auditable with transparent logic and provenance

From population-level quality reporting to patient-level clinical monitoring, standardized measures power accurate, reproducible, and trustworthy analytics across your healthcare data ecosystem.