Electronic Health Record Time Allocation Among Primary Care Clinicians at the Veterans Health Administration Using Virtual Observations.

Background: Prior research has shown that primary care clinicians (PCPs) spend a large portion of clinic visits on tasks within the electronic health record (EHR). However, no time allocation studies have been done in the Veterans Health Administration (VHA) and little is known about EHR time spent during virtual visits.

Objective: To estimate the proportion of clinician time spent working within the EHR during primary care visits at VHA clinics.

Lipoprotein(a) Associated Molecules are Prominent Components in Plasma and Valve Leaflets in Calcific Aortic Valve Stenosis.

The gene is the only monogenetic risk factor for calcific aortic valve stenosis (CAVS). Oxidized phospholipids (OxPL) and lysophosphatidic acid generated by autotaxin (ATX) from OxPL are pro-inflammatory. Aortic valve leaflets were categorized pathologically from Both ATX-apoB and ATX-apo(a) were measureable in plasma.

National substance use patterns on Twitter.

Purpose: We examined openly shared substance-related tweets to estimate prevalent sentiment around substance use and identify popular substance use activities. Additionally, we investigated associations between substance-related tweets and business characteristics and demographics at the zip code level.

Methods: A total of 79,848,992 tweets were collected from 48 states in the continental United States from April 2015-March 2016 through the Twitter API, of which 688,757 were identified as being related to substance use.

Geotagged US Tweets as Predictors of County-Level Health Outcomes, 2015-2016.

Objectives: To leverage geotagged Twitter data to create national indicators of the social environment, with small-area indicators of prevalent sentiment and social modeling of health behaviors, and to test associations with county-level health outcomes, while controlling for demographic characteristics.

Methods: We used Twitter's streaming application programming interface to continuously collect a random 1% subset of publicly available geo-located tweets in the contiguous United States. We collected approximately 80 million geotagged tweets from 603 363 unique Twitter users in a 12-month period (April 2015-March 2016).

Leveraging geotagged Twitter data to examine neighborhood happiness, diet, and physical activity.

Objectives: Using publicly available, geotagged Twitter data, we created neighborhood indicators for happiness, food and physical activity for three large counties: Salt Lake, San Francisco and New York.

Methods: We utilize 2.8 million tweets collected between February-August 2015 in our analysis.