Description: This session explores the far-reaching changes to how clinical data will be sourced, processed and consumed in many payer use cases including risk adjustment, quality measurement, care management, claims adjudication, prior authorizations, and other payer functions. Robust, interoperable data exchange is a prerequisite for value-based care. Clinical data will be increasingly accessible to augment administrative (claims) data via standardized Fast Health Interoperability Resources (FHIR, pronounced 'fire'), at a fraction of the cost of a accessing a traditional medical record and in real-time. On May 1, 2020, HHS issued a final rule implementing interoperability and the patient access provisions of the 21st Century Cures Act of 2016, although enforcement has been delayed into 2021 because of the COVID-19 pandemic. The new regulations promote, “HHS’s vision and strategy for achieving a health information ecosystem within which all individuals, their personal representatives, their health care providers, and their payers are able to send, receive, find, and use electronic health information in a manner that is appropriate, secure, timely, and reliable to support the health and wellness of individuals through informed, shared decision-making, as well as to support consumer choice of payers and providers.”Once enforcement begins, CMS and the Office of the National Coordinator for Health Information Technology (ONC), have the authority to mandate adoption of interoperability requirements, and also penalize payers and providers for non-compliance or the delay and withholding of data.This ‘digital transformation’ of the clinical data ecosystem has far-reaching implications for enabling technologies including Natural Language Processing (NLP) and entire segments of services and vendors. It will eliminate many departmental barriers, duplication and error sources for health plans and impact every department and the underlying economics of delivering and managing care.

Description: The famous 80/20 rule in data science states that only 20% of your time working with data is spent driving insightful business analytics. The rest of your time is spent trying to query, clean and organize your data. Even with this knowledge, the majority of actuarial training/focus is on the 20 percent. This session takes a different approach, focusing solely on speeding up your work on the other 80 percent. Practical examples will be part of a code walkthrough in R.

Description: Actuarial Process Optimization (APO) is a technology-focused methodology for bringing together actuarial and technology teams to critically examine and optimize actuarial processes, specifically targeting processes that are most time consuming and have the highest risk of failure. From claims analytics to valuation and experience analysis, modern technology can help actuaries optimize their time and focus their attention on higher value tasks. In this session, we will explore opportunities for APO in the Health insurance industry and demonstrate how technology can be used to improve the actuarial process using an agile and targeted approach.

Description: This article discusses the publication of the “Encyclopedia of Quantitative Risk Assessment”

Description: 2021 ImpACT Conference - Session 10E: Azure Synapse for Actuaries | Solvency II Use Case 10.28.21 Recording

Description: Sponsored by the Society of Actuaries Research Expanding Boundaries Pool, this report examines an encompassing method to allocating economic capital in insurance conglomerates. Authored by Edward Furman, Yisub Kye, and Jianxi Su, the plan put forth unifies the centralized top-down and the detail-oriented bottom-up approaches

Description: The COVID-19 pandemic has disrupted the global economy at a level never imagined. The economic fallout has been catastrophic. Record high unemployment, staggering revenue loss, bankruptcy and an uncertain future have businesses—and consumers—searching for a beacon of hope. In the quest for a lifeline, actuarial teams are quickly being thrust into the spotlight

Description: The predictive analytics application growth in the Asia-Pacific region has been gaining momentum.

Description: The predictive analytics application growth in the Asia-Pacific region has been gaining momentum.

Description: Risk adjustment in government regulated health plans has been around for over two decades. Yet the strategies for promoting risk score accuracy haven't varied much during that time. Most health plans, physician groups, and vendors promote chart reviews and/or in-home assessments as the primary vehicle for collecting data not perceived to be present in the routine claims submission and adjudication stream. We know that most organizations trying to coalesce encounter data from multiple sources often fail to ensure that all relevant data is ultimately submitted to CMS or a state Medicaid agency. Crucial data, often with significant financial value, frequently gets lost in the data pipeline, somewhere between the provider's billing system and the health plan and/or its vendors. Because 837-formatted data is difficult to read, it complicates payers and vendor’s abilities to track encounter data so that every service delivered to patients is credited to the applicable risk adjustment formula. It is incumbent upon successful risk-adjusted entities to be able to track encounter data, identify processes and/or vendors which may be mangling or losing data, in order to ensure that risk scores are sufficiently accurate to be compliant with plan sponsor requirements and to ensure appropriate revenue to the health plan or provider group.