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<article class="article-card" data-search="Adjustments, Denials, and Reversals How to model and identify ADR behavior in claims data, with runnable SQL against Tuva input_layer.medical_claim for new IDs, same IDs, and hybrid patterns. Aaron Neiderhiser">
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<h3>Adjustments, Denials, and Reversals</h3>
<p class="card-author">Aaron Neiderhiser</p>
<p class="card-date">March 12, 2026</p>
<p class="card-copy">How to model and identify ADR behavior in claims data, with runnable SQL against Tuva input_layer.medical_claim for new IDs, same IDs, and hybrid patterns.</p>
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<article class="article-card" data-search="Measuring Same-Day Readmits How to measure same-day readmits for acute inpatient encounters in Tuva, including discharge-status patterns and same-primary-diagnosis checks. Aaron Neiderhiser Andreas Martinson">
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<h3>Measuring Same-Day Readmits</h3>
<p class="card-author">Aaron Neiderhiser &amp; Andreas Martinson</p>
<p class="card-date">March 4, 2026</p>
<p class="card-copy">How to measure same-day readmits for acute inpatient encounters in Tuva, including discharge-status patterns and same-primary-diagnosis checks.</p>
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<article class="article-card" data-search="Understanding Medicare ACO Assignment In Medicare ACOs member assignment is complex -- but it doesn't have to be. Mike Krahulec">
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<h3>Understanding Medicare ACO Assignment</h3>
<p class="card-author">Mike Krahulec</p>
<p class="card-date">February 19, 2026</p>
<p class="card-copy">In Medicare ACOs member assignment is complex -- but it doesn't have to be.</p>
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<article class="article-card" data-search="Customizing the Tuva Data Model How Tuva v0.17.0 preserves custom extension columns across the core data model with a configurable prefix-based pass-through pattern. Rabee Zyoud">
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<h3>Customizing the Tuva Data Model</h3>
<p class="card-author">Rabee Zyoud</p>
<p class="card-date">February 12, 2026</p>
<p class="card-copy">How Tuva v0.17.0 preserves custom extension columns across the core data model with a configurable prefix-based pass-through pattern.</p>
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<article class="article-card" data-search="Building a Claims Data Platform A practical architecture guide for building claims analytics platforms, covering ingestion, normalization, data quality, adjustment handling, and analytics-ready models. It outlines implementation tradeoffs and concrete design decisions for scaling payer and value-based care use cases. Aaron Neiderhiser">
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<h3>Building a Claims Data Platform</h3>
<p class="card-author">Aaron Neiderhiser</p>
<p class="card-date">April 17, 2024</p>
<p class="card-copy">A practical architecture guide for building claims analytics platforms, covering ingestion, normalization, data quality, adjustment handling, and analytics-ready models. It outlines implementation tradeoffs and concrete design decisions for scaling payer and value-based care use cases.</p>
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<article class="article-card" data-search="Intro to dbt for Healthcare An implementation-oriented introduction to dbt for healthcare data teams, focused on project structure, warehouse execution, Git workflows, and built-in testing/documentation. The post explains why these software engineering patterns materially improve analytics reliability and delivery speed. Aaron Neiderhiser">
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<h3>Intro to dbt for Healthcare</h3>
<p class="card-author">Aaron Neiderhiser</p>
<p class="card-date">January 31, 2024</p>
<p class="card-copy">An implementation-oriented introduction to dbt for healthcare data teams, focused on project structure, warehouse execution, Git workflows, and built-in testing/documentation. The post explains why these software engineering patterns materially improve analytics reliability and delivery speed.</p>
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<article class="article-card" data-search="The Problem with Healthcare Data A technical breakdown of the three core barriers to reliable healthcare analytics: normalization, data quality, and high-level concept generation. It frames why these issues persist across claims and clinical data and why they must be addressed in the data model itself. Aaron Neiderhiser">
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<h3>The Problem with Healthcare Data</h3>
<p class="card-author">Aaron Neiderhiser</p>
<p class="card-date">January 11, 2024</p>
<p class="card-copy">A technical breakdown of the three core barriers to reliable healthcare analytics: normalization, data quality, and high-level concept generation. It frames why these issues persist across claims and clinical data and why they must be addressed in the data model itself.</p>
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