How Disease Burden Influences Medication Patterns for Medicare Beneficiaries: Implications for Policy

Provides benchmarks for assessing the quality of pharmaceutical care under the Medicare Part D prescription drug benefit. Examines how the beneficiaries? medication regimens evolve in the context of multiple chronic conditions and accumulating morbidity

Multimodal Machine Learning for Automated ICD Coding

This study presents a multimodal machine learning model to predict ICD-10 diagnostic codes. We developed separate machine learning models that can handle data from different modalities, including unstructured text, semi-structured text and structured tabular data. We further employed an ensemble method to integrate all modality-specific models to generate ICD-10 codes. Key evidence was also extracted to make our prediction more convincing and explainable. We used the Medical Information Mart for Intensive Care III (MIMIC -III) dataset to validate our approach. For ICD code prediction, our best-performing model (micro-F1 = 0.7633, micro-AUC = 0.9541) significantly outperforms other baseline models including TF-IDF (micro-F1 = 0.6721, micro-AUC = 0.7879) and Text-CNN model (micro-F1 = 0.6569, micro-AUC = 0.9235). For interpretability, our approach achieves a Jaccard Similarity Coefficient (JSC) of 0.1806 on text data and 0.3105 on tabular data, where well-trained physicians achieve 0.2780 and 0.5002 respectively.Comment: Machine Learning for Healthcare 201

The Medicare Part D Coverage Gap: Costs and Consequences in 2007

Analyzes data on Medicare Part D enrollees who reached the coverage gap and had to pay the full cost until they qualified for catastrophic coverage, who then stopped taking their medications or bought cheaper ones, and who received catastrophic coverage