Description: This session discussed three dramatic shifts in everyday economic relationships: 1 From matter to information, 2 From firms to individuals, and 3 From sellers to buyers. The instructor discussed application service providers and their impact on the economy and the Internet.

Description: CMS provides ACOs with monthly claims data for members, including their medical visits and ACO program eligibility. This data can be divided into Membership and Claims tables through the ETL process. However, when calculating PMPY at specific levels of detail using these tables, issues arise due to unrelated dimensions, leading to inaccurate and understated results. This article presents a solution to this problem by utilizing Tableau's data blending capabilities.

Description: Last year, the COVID pandemic significantly increased the pace of change in the life insurance industry, particularly in Accelerated Underwriting (AU). In the past, underwriting process changes occurred relatively infrequently and over a matter of years. Now, with new data sources and predictive models constantly evolving, the frequency of underwriting enhancements and decisioning changes, particularly in AU programs, is occurring every few months. With so much change occurring quickly, how can a company effectively monitor and understand the effects of these changes to make continuous adjustments with confidence and agility? Well-designed data visualizations allow a reviewer to navigate and comprehend detailed results quickly because our brains can process data in a picture format more than 15 times faster than text. Today’s advanced BI tools can make AU program monitoring more efficient and help companies adjust quickly to the results from program changes.

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.

Description: While it is now almost universally accepted that data can bring value to the insurance industry, there is little guidance on exactly how to make that value tangible. This session goes beyond advances in computing and storage technologies and is aimed directly at business leaders. This session will define what a data strategy is, discuss the main components, describe how to get started creating one, and will outline how to translate a strategy into execution.

Description: As the insurance industry continues to evolve due to a pandemic and rapidly changing economic and regulatory conditions, the role of data scientists and actuaries has become increasingly important. In an interview with the Atidot data science and actuarial team, we discuss what it is like to work at an InsurTech, how data scientists and actuaries can collaborate, and look at some concrete technical examples of where the innovation is having an impact. This article explores what collaboration can look like and how the role of data science and actuary may evolve in the InsurTech industry in the future.

Description: The barrier between humans and machines continues to blur, but curiously, this may have been prediction thousands of years ago in ancient myths. This article looks to how some of the early legends are becoming manifest in modern technology, particularly in the area of elder care. From automation, to humanoid assistants, to mind clones will the age of technology bring a new utopia?

Description: Natural Language Process (NLP) techniques can be used to find insights in large unstructured data sets and Word Embeddings are pervasive on a wide spectrum of NLP applications. These word representations improve downstream tasks such as Sentiment Analysis, Recommender System, and Topic Modeling. Learn how NLP transforms the Insurance Industry; and how word embeddings and pre-trained models improve the effectiveness and performance of NLP applications. NLP is increasing in importance for insurance companies. Additional application examples are available in session 36.