Category Archives: health communication

German publishers are pooling data to compete with Google and Facebook

German publishers have put aside traditional rivalry and gone all-in on a major data-pooling initiative.

Axel Springer, Gruner + Jahr, RTL owner Bertelsmann Group, and Der Speigel owner are among eight of the 10 biggest publishing groups in Germany to be pooling masses of reader data, from just under 1,000 websites including tabloid Bild, and other major titles.

The raw data goes into a single platform called Emetriq, a subsidiary owned by Deutsche Telekom, which sifts through and cleans it up, to create highly targeted, quality audience segments that publishers can use to boost their advertising packages.

Source: German publishers are pooling data to compete with Google and Facebook – Digiday

Oral Roberts University to Track Students’ Fitness | NBC Chicago

Oral Roberts University, a Christian university in Tulsa, announced earlier this month that all first-years must wear Fitbits — watches that track how much activity a person does. Their fitness data will be tracked by the school and will affect students’ grades.

Source: Oral Roberts University to Track Students’ Fitness | NBC Chicago

US insurers told use of external data in price opt. models will be subject to detailed regulatory scrutiny

US insurers told use of external data in price opt. models will be subject to detailed regulatory scrutiny p74

 

Potential Questions for Regulators to Ask Regarding the Use of Models in P&C Rate Filings
Insurers might use a model in the development of proposed rates and rating factors. The Task Force offers some potential questions a regulator could ask regarding the use of models in rate proposals.
Questions may include, but not be limited by, the following:
Model Description
1. Please provide a high-level description of the workings of the model that was used to select rates and rating factors that differ from the indicated.
2. What is the purpose of the model? What does the model seek to maximize or minimize (e.g. underwriting profit, retention, other) and explain.

Model Variables
3. How were the input variables for your model selected?
a. What is the support for the model variables, including the predictive values and error statistics for the model variables?
b. Are the parameters loss related, expense related, or related to the risk in some other way?

4. Which of the input variables are internal (customer-provided or deduced from customer-provided information) or external?
a. Identify whether each input variable is used in your rating plan.
b. For each external variable, please identify:
i. The owner or vendor of the data (e.g. Department of Motor Vehicles).
ii. Which variables are subject to the requirements of the federal Fair

Credit Reporting Act.
iii. How you ensure that the data are complete and accurate.
iv. The framework, if any, which provides consumers a means of correcting errors in the data pertaining to them.

Model Constraints & Output
5. What level of granularity is your model output (e.g. the class plan level, individual rating factors, or some other level such as household or demographic segment that is different than the rating plan)?
6. What are the limits (or constraints) for the selected rating plan factors, if any?
7. How do the modeled values compare to the company experience?

Note: Regulators should evaluate the particular filing and associated costs to insurers to determine the extent of questioning needed. Regulators should also consider the potential proprietary nature of modeling information and grant confidentiality as appropriate and if allowed under state law.

Privacy Implications of Health Information Seeking on the Web | March 2015 | Communications of the ACM

Privacy online is an increasingly popular field of study, yet it remains poorly defined. “Privacy” itself is a word that changes according to location, context, and culture. Additionally, the Web is a vast landscape of specialized sites and activities that may only apply to a minority of users—making defining widely shared privacy concerns difficult. Likewise, as technologies and services proliferate, the line between on- and offline is increasingly blurred. Researchers attempting to make sense of this rapidly changing environment are frequently stymied by such factors. Therefore, the ideal object of study is one that is inherently sensitive in nature, applies to the majority of users, and readily lends itself to analysis. The study of health privacy on the Web meets all of these criteria.

Source: Privacy Implications of Health Information Seeking on the Web | March 2015 | Communications of the ACM