Publications by authors named "Michael Suesserman"
Procedure code overutilization detection from healthcare claims using unsupervised deep learning methods.
BMC Med Inform Decis Mak
September 2023
Background: Fraud, Waste, and Abuse (FWA) in medical claims have a negative impact on the quality and cost of healthcare. A major component of FWA in claims is procedure code overutilization, where one or more prescribed procedures may not be relevant to a given diagnosis and patient profile, resulting in unnecessary and unwarranted treatments and medical payments. This study aims to identify such unwarranted procedures from millions of healthcare claims.
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- Hospital readmission rates are high and pose financial challenges for healthcare systems, making them a key measure of care quality.
- This study uses machine learning and survival analysis to predict hospital readmission risks by analyzing patient demographics and discharge data.
- The findings reveal that the Weibull distribution model performs best, while embeddings of diagnosis codes do not enhance model effectiveness, and that model performance varies over time, suggesting the need for different models for assessing quality of care at various points.