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Issue Brief: Changing Financial Risks for Medigap Insurance, Part 1
considerations about how they are impacting Medigap. COVID-19’s Impacts The pandemic affected health insurers, payers ... their physicians and/or to require hospital stays (Table 1) in 2020 and 2021. However, two recent studies[1] ...- Authors: Kristi Bohn
- Date: Mar 2024
- Competency: External Forces & Industry Knowledge; Strategic Insight and Integration
- Publication Name: Health Watch
- Topics: Health & Disability; Health & Disability>Health insurance
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A Survey of the Fast-Evolving Landscape for Cell and Gene Therapy Coverage in the United States
availability of reasonable alternatives and (5) society’s prevailing opinion of what constitutes fair game as ... impending exponential increase in the number of U.S. Food and Drug Administration (FDA) approvals for ...- Authors: Ankit Nanda
- Date: May 2024
- Competency: External Forces & Industry Knowledge; Results-Oriented Solutions
- Publication Name: Health Watch
- Topics: Health & Disability; Health & Disability>Health insurance; Reinsurance>Health reinsurance
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Society of Actuaries Analyzes Pharmacy Ecosystem in New Study
drug costs. This report is a continuation in the SOA’s discussion on pharmacy financing and the role of actuaries ... 2016-2021, which includes about one-third of all U.S. commercial health insurance members. The hypothesis ...- Date: Mar 2024
- Competency: External Forces & Industry Knowledge; Results-Oriented Solutions
- Topics: Health & Disability; Health & Disability>Health insurance
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Issue Brief: Changing Financial Risks for Medigap Insurance, Part 2
50% in 2023.[1] This growth has cut into Medigap’s market share (though not overall enrollment counts) ... administrative and financial risk of enhancing benefits. MA’s broad annual flexibility to include dental, vision ...- Date: Mar 2024
- Competency: External Forces & Industry Knowledge; Strategic Insight and Integration
- Publication Name: Health Watch
- Topics: Health & Disability; Health & Disability>Health insurance; Public Policy; Public Policy
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Using Interpretable Machine Learning Methods: An Application to Health Insurance Fraud Detection
conroysimberg.com/blog/insurance-fraud-costs-the-u-s-308-billion- annually/#:~:text=The%20Coalition%2 ... between the fraudulent and non- fraudulent cases. Table 1 IMBALANCE IN DATASET Fraud Non-Fraud Number ...- Date: Jan 2024
- Topics: Health & Disability; Health & Disability>Health insurance; Technology & Applications; Technology & Applications>Artificial intelligence & machine learning
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A Patient Centric Approach to AI in Health Insurance
022-00594-w 2. Gier, Jaime, Missed appointments cost the U.S. healthcare system $150B each year (2017). https://www ... com/clinical-it/article/13008175/missed-appointments-cost- the-u s-healthcare-system-150b-each-year 3. Karen Van Nuys ...- Authors: Scott Damery
- Date: May 2024
- Competency: Results-Oriented Solutions
- Topics: Health & Disability; Health & Disability>Health insurance; Technology & Applications; Technology & Applications>Artificial intelligence & machine learning
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prod-design-customer-needs-risks
ReMark's Global Consumer Study delivers 10 key insights to empower customers. Insurers ... ReMark's Global Consumer Study delivers 10 key insights to empower customers. Insurers can design products ...- Date: Apr 2024
- Competency: External Forces & Industry Knowledge; Results-Oriented Solutions; Strategic Insight and Integration
- Topics: Health & Disability; Health & Disability>Health insurance; Life Insurance; Technology & Applications
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Using Interpretable Machine Learning Methods: An Application to Health Insurance Fraud Detection
Using Interpretable Machine Learning Methods: An Application to Health Insurance ... Postgraduate in Actuarial Data Science, SSSIHL Sai Siddhanth S, Postgraduate in Actuarial Data Science, SSSIHL Abhiishek ...- Date: Jan 2024
- Competency: External Forces & Industry Knowledge
- Topics: Health & Disability; Health & Disability>Health insurance; Technology & Applications; Technology & Applications>Artificial intelligence & machine learning