Tag: Healthcare
All the articles with the tag "Healthcare".
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CCLF is not eligibility: where dual status gets lost in Medicare ACO data
How a coalesce and a latest-month rule in Tuva's CCLF + ALR connectors can turn full-dual Medicare beneficiaries into non-duals in CMS-HCC.
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Tuva's encounter grouper only groups claims. Here's what happens to your ADT encounters
Tuva's encounter grouper only runs on claims. Clinical ADT/EHR encounters are standardized and stacked in core.encounter, never matched or deduped.
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Full risk vs non-risk contracts: not the same axis as risk adjustment
Full risk and non-risk describe who bears financial risk in a healthcare contract. Risk adjustment describes how much gets paid. They share a word, not a mechanism.
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What's new in cms_hcc v1.0.0: the payer grain fix nobody's docs warned you about
cms_hcc v1.0.0 fixes a silent grain mismatch between int_members and int_disease_factors that dropped every disease factor for members who switched payers mid-year.
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Building a data quality dashboard on top of Tuva's DQI mart
Tuva's DQI mart produces fill rates and anomaly flags for every input field in a claims dataset. Here's how to surface that in a Streamlit dashboard with meaningful visualizations.
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Feature engineering from claims data for a Random Forest classifier
Healthcare claims have dozens of potential features for patient risk models. Here's how to select and validate features from a Tuva Project dataset for a Random Forest classifier predicting HCC gaps.
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From raw claims to RAF: what the data pipeline actually looks like
The path from a raw Medicare claim file to a patient's Risk Adjustment Factor score involves five distinct transformation layers. This is what each layer does and where the dbt models fit in.
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HCC suspecting explained from a data engineering perspective
HCC suspecting is about identifying conditions documented in prior years that haven't appeared in claims yet this year. This is what the data pipeline looks like and what the Tuva mart actually produces.