Authors Statistics from 2 Countries
| Country | Count of Articles |
|---|---|
| Indonesia | 1 |
| United States | 1 |
Articles
Initial Purchase Decision: A Gender Differences Perspective
Abstract
This study aims to investigate gender-based differences in online purchasing decisions by analyzing how several key factors influence men and women. The research focused on students in Sikka Regency, who were chosen using a quota sampling technique that divided the participants into male and female groups to ensure balanced representation. To analyze the data, independent t-tests were applied to identify whether significant behavioral differences existed between the two genders, while multiple regression analysis was employed to measure the relative strength and impact of each influencing factor. The results reveal that the behavioral gap between men and women in terms of online shopping is relatively minor. However, the pattern of influential factors shows a consistent trend across both genders, where store ratings emerged as the most dominant determinant, followed by shopping through live streaming sessions and consumer reviews. Even though the sequence of influence remains the same, female respondents were found to be more actively involved in online shopping activities and demonstrated higher sensitivity to these influencing factors compared to their male counterparts.
Read full articleIntegrating Natural Language Processing and Machine Learning for Documentation-Driven Billing Anomaly Detection in Medicare and Medicaid: A Quantitative Framework for Long-Term Care Facilities
Abstract
Improper payments remain one of the most consequential operational and fiscal risks in U.S. public health insurance. For fiscal year 2025, the Centers for Medicare & Medicaid Services (CMS) reported improper payments of $28.83 billion in Medicare fee-for-service, $23.67 billion in Medicare Part C, $4.23 billion in Medicare Part D, $37.39 billion in Medicaid, and $1.37 billion in the Children’s Health Insurance Program. A large share of these losses is documentation-related rather than purely utilization-related, yet most operational detection systems still analyze structured claims without reading the clinical narratives that should substantiate those claims. This paper rebuilds the documentation-driven anomaly detection problem as a joint information problem: what was clinically documented, what was coded, and what was ultimately billed. Using current CMS, HHS, AHRQ, and OIG data, combined with the peer-reviewed literature on clinical natural language processing (NLP), automated coding, and healthcare fraud analytics, the paper develops a hybrid framework for long-term care (LTC) settings that links (1) NLP extraction of diagnoses, services, and severity indicators from unstructured notes, (2) machine-learning detection of anomalous claims behavior, and (3) a cross-referencing engine that scores documentation-billing consistency. Because linked, public LTC note-claim corpora are not available for true patient-level validation, the quantitative contribution of the paper is national and programmatic rather than encounter-level: it documents the financial exposure in nursing care, identifies documentation-dominant error structures, and models conservative savings scenarios from better documentation integrity and automated pre-bill review. The results show that directly quantified documentation-related exposure in FY2025 Medicare fee-for-service and Medicaid alone was approximately $47.65 billion. If integrated documentation-aware anomaly detection reduced that exposure by 10% to 30%, annual savings could plausibly range from $4.77 billion to $14.30 billion before considering secondary spillovers into Medicare Advantage, Part D, or downstream audit efficiencies. The article concludes that documentation-grounded anomaly detection is not merely a technical enhancement; it is a payment-integrity strategy with measurable fiscal and compliance relevance for LTC operators and public payers.
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