Franklingregersen3861

Digitization of health records has allowed the health care domain to adopt data-driven algorithms for decision support. There are multiple people involved in this process a data engineer who processes and restructures the data, a data scientist who develops statistical models, and a domain expert who informs the design of the data pipeline and consumes its results for decision support. Although there are multiple data interaction tools for data scientists, few exist to allow domain experts to interact with data meaningfully. Designing systems for domain experts requires careful thought because they have different needs and characteristics from other end users. There should be an increased emphasis on the system to optimize the experts' interaction by directing them to high-impact data tasks and reducing the total task completion time. We refer to this optimization as amplifying domain expertise. Although there is active research in making machine learning models more explainable and usable, it focuses on the final outputs of the model. However, in the clinical domain, expert involvement is needed at every pipeline step curation, cleaning, and analysis. To this end, we review literature from the database, human-computer information, and visualization communities to demonstrate the challenges and solutions at each of the data pipeline stages. Next, we present a taxonomy of expertise amplification, which can be applied when building systems for domain experts. This includes summarization, guidance, interaction, and acceleration. Finally, we demonstrate the use of our taxonomy with a case study.

Although researchers are giving increased attention to blockchain-based personal health records (PHRs) and data sharing, the majority of research focuses on technical design. Very little is known about health care consumers' intentions to adopt the applications.

This study aims to explore the intentions and concerns of health care consumers regarding the adoption of blockchain-based personal health records and data sharing.

Three focus groups were conducted, in which 26 participants were shown a prototype of a user interface for a self-sovereign blockchain-based PHR system (ie, a system in which the individual owns, has custody of, and controls access to their personal health information) to be used for privacy and secure health data sharing. A microinterlocutor analysis of focus group transcriptions was performed to show a descriptive overview of participant responses. selleckchem NVivo 12.0 was used to code the categories of the responses.

Participants did not exhibit a substantial increase in their willingnessd health data sharing. However, their intentions may increase when the concerns and recommendations demonstrated in this study are considered in application design.Nearly all women use contraception in their lifetimes (1), although at any given time, they may not be using contraception for reasons such as seeking pregnancy, being pregnant or postpartum, or not being sexually active. Using data from the 2017-2019 National Survey of Family Growth (NSFG), this report provides a snapshot of current contraceptive status, in the month of interview, among women aged 15-49 in the United States. In addition to describing use of any method by age, Hispanic origin and race, and education, patterns of use are described for the four most commonly used contraceptive methods female sterilization; oral contraceptive pills; long-acting reversible contraceptives (LARCs), which include contraceptive implants and intrauterine devices; and the male condom.Chronic pain (1) and chronic pain that frequently limits life or work activities, referred to in this report as high-impact chronic pain (2), are among the most common reasons adults seek medical care (3) and are associated with decreased quality of life, opioid dependence, and poor mental health (1,4,5). This report examines chronic pain and high-impact chronic pain in the past 3 months among U.S. adults aged 18 and over by selected demographic characteristics and urbanization level.To help achieve and maintain a healthy body weight, support nutrient adequacy, and reduce the risk of chronic disease, the 2015-2020 Dietary Guidelines for Americans recommend following a healthy eating pattern across the lifespan (1). Some people adhere to specific eating patterns, otherwise known as special diets, for the purposes of weight loss or other health-related reasons. This report shows the percentage of U.S. adults who, on a given day, were on any special diet and specific types of special diets in 2015-2018 and trends from 2007-2008 through 2017-2018.Objective This report provides a general description of the background and operation of the first two rounds of the Research and Development Survey (RANDS), a series of cross-sectional surveys from probability-sampled commercial survey panels. The Division of Research and Methodology of the National Center for Health Statistics (NCHS) conducted the first two rounds of RANDS in 2015 and 2016. RANDS 1 and 2 are being used primarily for question design evaluation and for investigating statistical methodologies for estimation. Methods NCHS contracted with Gallup, Inc. to conduct RANDS 1 in Fall 2015 and RANDS 2 in Spring 2016. RANDS 1 and 2 were conducted using a web survey mode and included survey questions from the National Health Interview Survey (NHIS) that were specifically chosen to provide comparison and evaluation of the survey methodology properties of web surveys and traditional household surveys. In this report, some demographic and health estimates are provided from both sources to describe the RANDS data. Results In RANDS 1, 2,304 out of the original 9,809 invited panel members completed the survey, for a completion rate of 23.5%. In RANDS 2, 2,480 of the initial 8,231 invited respondents completed the survey, for a completion rate of 30.1%. RANDS 1 and 2 participants were similar to the quarterly NHIS participants with respect to sex, census region, and whether they had worked for pay in the previous week. Other characteristics varied, including age, race and ethnicity, and income. Most health estimates differed between RANDS and NHIS. Public-use versions of the RANDS data can be found at https//www.cdc.gov/nchs/rands. Conclusion RANDS is an ongoing platform for research to understand the properties of probability-sampled recruited panels of primarily web users, investigating and developing statistical methods for using such data in conjunction with large nationally representative health surveys, and for extending question-design evaluations.