Description: En el seminario hablaremos sobre los nuevos flujos de tarificación, y nos enfocaremos en la inclusión del análisis geográfico en el proceso de modelado para optimizar la extracción del riesgo en modelos de Machine Learning.

Description: Level funded products often target the small group market (2-50) and can result in many manual and tedious processes for underwriting to perform risk assessment. Learn how a carrier used technology to help automate risk assessment without the need for paper medical questionnaires, and exposed a risk engine for broker quoting to enable near real-time of pricing for risk-based products. The approach reduced labor and allowed underwriting to focus solely on fallout cases while also improving pricing consistency. Discuss broader trends in the health market for risk assessment, including new machine learning cost predictions that can be used for automation and better predictions of future costs. Discussion topics will include underwriting guidelines, business rules, risk scores, and machine learning. By attending this session, you will have a better idea of new trends in risk assessment for small groups where no claims experience is available. And, better understand business rules that are common in underwriting for flags for review as well as how risk scores and machine learning can be used during the underwriting process.

Description: CMS has required hospitals to publish their chargemaster rates, cash discounts and negotiated rates as of January 1, 2021. Our panel is comprised of a data strategist, an executive from a large hospital system and an actuary.

Description: Discuss various analytics and use cases using payer published reimbursement contract data under federal mandate of Transparency in Coverage. These contract rates are publicly available in machine readable format (MRF) starting July 1, 2022, and refreshed monthly. By attending the session, you will have a better understanding of the state-of-art development and analytics using MRF data, including the technologies used to extract and analyze huge MRF files, validation and limitations of published rates, and their use cases.

Description: This session will examine bias from a social science perspective. It will discuss the three types of systemic biases using the National Institute of Standards and Technology (NIST) model. The NIST model details the three types as historical bias, societal bias, and institutionalbias. The session will also provide several examples of each type to provide a framework for identifying the social structure of bias.