Quantifying Diagnostic Signal Decay in Dementia: A National Study of Medicare Hospitalization Data

TL;DR

This study analyzes Medicare hospitalization data to quantify how diagnostic signals for dementia degrade over time and vary geographically, revealing patterns that impact AI healthcare models and research accuracy.

Contribution

It introduces a novel temporal sequence analysis of diagnostic code usage, highlighting systematic diagnostic signal decay and demographic disparities across U.S. counties.

Findings

Significant geographic and demographic variation in dementia coding.

Consistent transition from specific to non-specific codes over time.

Model explains 38% of variation in diagnostic pattern divergence.

Abstract

Background: Artificial intelligence (AI) models in healthcare depend on the fidelity of diagnostic data, yet the quality of such data is often compromised by variability in clinical documentation practices. In dementia, a condition already prone to diagnostic ambiguity, this variability may introduce systematic distortion into claims-based research and AI model development. Methods: We analyzed Medicare Part A hospitalization data from 2016-2018 to examine patterns of dementia-related ICD-10 code utilization across more than 3,000 U.S. counties. Using a clinically informed classification of 17 ICD-10 codes grouped into five diagnostic categories, we applied the transitive Sequential Pattern Mining (tSPM+) algorithm to model temporal usage structures. We then used matrix similarity methods to compare local diagnostic patterns to national norms and fit multivariable linear regressions to identify county-level demographic and structural correlates of divergence. Findings: We found substantial geographic and demographic variation in dementia-related diagnostic code usage. Non-specific codes were dominant nationwide, while Alzheimer's disease and vascular dementia codes showed pronounced variability. Temporal sequence analysis revealed consistent transitions from specific to non-specific codes, which suggest degradation of diagnostic specificity over time. Counties with higher proportions of rural residents, Medicaid-eligible patients, and Black or Hispanic dementia patients demonstrated significantly lower similarity to national usage patterns. Our model explained 38% of the variation in local-to-national diagnostic alignment.