The MHMDS is a regular return of data generated by providers of adult secondary mental health services in England, in the course of delivering services to patients. From Q1 2011/12 onwards, the MHMDS also includes data from Independent Sector Organisations and is processed using the new system. Full details of the methods used in processing can be found in the MHMDS Version 4 User Guidance and Appendices (see related links). The MHMDS dataset is received by the HSCIC as record level anonymised data from patient administration systems, Care Programme Approach systems and Mental Health Act administration systems. Changes to this publication From April 2013 the submission of MHMDS data will be made every month, rather than every quarter, to support the implementation of Payment by Results for mental health. From April 2013 there are also NHS wide changes as a result of the Health and Social Care Act 2012. As a result, the frequency and content of this publication will be changing after the publication of Q4 2012/13 Final data. Further details will be provided in a Methodological Change Paper to be made available on this web site in early 2013.

Objective: Routine primary care data may be used for the derivation of clinical prediction rules and risk scores. We sought to measure the impact of a decision support system (DSS) on data completeness and freedom from bias.

Materials and Methods: We used the clinical documentation of 34 UK General Practitioners who took part in a previous study evaluating the DSS. They consulted with 12 standardized patients. In addition to suggesting diagnoses, the DSS facilitates data coding. We compared the documentation from consultations with the electronic health record (EHR) (baseline consultations) vs. consultations with the EHR-integrated DSS (supported consultations). We measured the proportion of EHR data items related to the physician’s final diagnosis. We expected that in baseline consultations, physicians would document only or predominantly observations related to their diagnosis, while in supported consultations, they would also document other observations as a result of exploring more diagnoses and/or ease of coding.

Results: Supported documentation contained significantly more codes (IRR=5.76 [4.31, 7.70] P<0.001) and less free text (IRR = 0.32 [0.27, 0.40] P<0.001) than baseline documentation. As expected, the proportion of diagnosis-related data was significantly lower (b=-0.08 [-0.11, -0.05] P<0.001) in the supported consultations, and this was the case for both codes and free text.

Conclusions: We provide evidence that data entry in the EHR is incomplete and reflects physicians’ cognitive biases. This has serious implications for epidemiological research that uses routine data. A DSS that facilitates and motivates data entry during the consultation can improve routine documentation.

Prior to downloading data, please download the README file. This dataset contains chemistry results from marine sediment sample collected from offshore (subtidal) and beaches (intertidal) from Puget Sound. It can be filtered by "Site Type". See the Sediment Monitoring Program Website for more information about the program and the Science Section Documents Database to see sampling and analysis plans and reports related to this dataset.

This table contains 96 series, with data for years 2001 - 2001 (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (Not all combinations are available): Geography (1 items: Canada ...) Health care setting most often contacted for routine or on-going care (6 items: Routine or on-going care; physician's office; Routine or on-going care; hospital; Routine or on-going care; community health centre; Routine or on-going care; walk-in clinic ...) Time of day, health care setting most often contacted for routine or on-going care (2 items: Routine or on-going care during regular hours; Routine or on-going care during evenings and weekends ...) Characteristics (8 items: Number of persons; Coefficient of variation; number of persons; Low 95% confidence interval; number of persons; High 95% confidence interval; number of persons ...).

Regression ranks among the most popular statistical analysis methods across many research areas, including psychology. Typically, regression coefficients are displayed in tables. While this mode of presentation is information-dense, extensive tables can be cumbersome to read and difficult to interpret. Here, we introduce three novel visualizations for reporting regression results. Our methods allow researchers to arrange large numbers of regression models in a single plot. Using regression results from real-world as well as simulated data, we demonstrate the transformations which are necessary to produce the required data structure and how to subsequently plot the results. The proposed methods provide visually appealing ways to report regression results efficiently and intuitively. Potential applications range from visual screening in the model selection stage to formal reporting in research papers. The procedure is fully reproducible using the provided code and can be executed via free-of-charge, open-source software routines in R.

The statistic shows the preferred number of steps in the skincare routine among skincare shoppers in Vietnam in 2016 and 2018, measured by the number of purchased skincare categories. In 2016, most of the skincare shoppers preferred a 1-step skincare routine. In 2018, the 1-step skincare routine lost appeal while a 3-step skincare rountine gained popularity, increasing from 15 to 20 percent.

In 2020, the Washington State Legislature enacted Engrossed Substitute Senate Bill (ESSB) 6404 (Chapter 316, Laws of 2020, codified at RCW 48.43.0161), which requires that health carriers with at least one percent of the market share in Washington State annually report certain aggregated and de-identified data related to prior authorization to the Office of the Insurance Commissioner (OIC). Prior authorization is a utilization review tool used by carriers to review the medical necessity of requested health care services for specific health plan enrollees. Carriers choose the services that are subject to prior authorization review. The reported data includes prior authorization information for the following categories of health services: • Inpatient medical/surgical • Outpatient medical/surgical • Inpatient mental health and substance use disorder • Outpatient mental health and substance use disorder • Diabetes supplies and equipment • Durable medical equipment The carriers must report the following information for the prior plan year (PY) for their individual and group health plans for each category of services: • The 10 codes with the highest number of prior authorization requests and the percent of approved requests. • The 10 codes with the highest percentage of approved prior authorization requests and the total number of requests. • The 10 codes with the highest percentage of prior authorization requests that were initially denied and then approved on appeal and the total number of such requests. Carriers also must include the average response time in hours for prior authorization requests and the number of requests for each covered service in the lists above for: • Expedited decisions. • Standard decisions. • Extenuating-circumstances decisions. Engrossed Second Substitute House Bill 1357 added additional prescription drug prior authorization reporting requirements for health carriers beginning in reporting year 2024. Carriers were provided the opportunity to submit voluntary prescription drug prior authorization data for the 2023 reporting period. Prescription drug reporting was required for the 2024 reporting period.

This table contains 176 series, with data for years 2001 - 2001 (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (Not all combinations are available): Geography (11 items: Canada; Prince Edward Island; Nova Scotia; Newfoundland and Labrador ...) Time of day, accessing routine or on-going care (2 items: Accessing routine or on-going care during regular office hours; Accessing routine or on-going care during evenings and weekends ...) Characteristics (8 items: Number of persons; Low 95% confidence interval; number of persons; Coefficient of variation; number of persons; High 95% confidence interval; number of persons ...).

This table contains 16 series, with data for years 2001 - 2001 (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (Not all combinations are available): Geography (1 items: Canada ...) Time of day, needed routine or on-going care (2 items: Needed routine or on-going care during regular hours; Needed routine or on-going care during evenings and weekends ...) Characteristics (8 items: Number of persons; High 95% confidence interval; number of persons; Coefficient of variation; number of persons; Low 95% confidence interval; number of persons ...).

The results of the survey show that in 2021, 63 percent of millennial consumers in the United States took their skin care routine seriously, a slightly higher number compared to the 57 percent of Generation Z participants who claimed the same thing.

As of 2023, about ** percent of surveyed employees from companies in the United States of America and United Kingdom claim to use artificial intelligence (AI) in the logic-based task of data analysis. Approximately ** percent claim to use it for routine administrative tasks. These numbers are forecasted to grow, as the share of employees that wish to use the technology for both tasks is much higher, lying around ** percent.

This dataset provides comprehensive statistics and insights on the impact of business planning, funding success rates, software market growth, exit strategies, and gender-based disparities in entrepreneurship. It explores how strategic business plans influence funding, growth, and operational success, supported by key metrics and global trends projected through 2032.

This document, Innovating the Data Ecosystem: An Update of The Federal Big Data Research and Development Strategic Plan, updates the 2016 Federal Big Data Research and Development Strategic Plan. This plan updates the vision and strategies on the research and development needs for big data laid out in the 2016 Strategic Plan through the six strategies areas (enhance the reusability and integrity of data; enable innovative, user-driven data science; develop and enhance the robustness of the federated ecosystem; prioritize privacy, ethics, and security; develop necessary expertise and diverse talent; and enhance U.S. leadership in the international context) to enhance data value and reusability and responsiveness to federal policies on data sharing and management.

The purpose of the California Wellness Plan (CWP) Data Reference Guide (Reference Guide) is to provide access to the lowest-level data for each CWP Objective; lowest-level data source, instructions to access data, and additional details are described. Some CWP Objectives do not have program leads, data sources, baselines, and/or targets, but are included because they were a result of CDPH program or partner input and were felt to be important to the reduction of chronic disease incidence, prevalence, and health disparities. Agencies, programs and/or partners identified with an objective may be either data stewards and/or engaged in activities to achieve the target, but may not have adequate resources for statewide activities. Developmental Objectives will be updated as information becomes available. Background: The California Wellness Plan, California's Chronic Disease Prevention and Health Promotion Plan was released February 2014 by the California Department of Public Health (CDPH). The overarching goal of CWP is Equity in Health and Wellbeing; additional CWP Goals include: 1) Healthy Communities, 2) Optimal Health Systems Linked with Community Prevention, 3) Accessible and Usable Health Information, and 4) Prevention Sustainability and Capacity. All CWP objectives fall under the framework of Let's Get Healthy California Task Force priorities. California Wellness Plan Green text in the “Objective” column indicates updates that were made to the California Wellness Plan objectives in 2016.

An in-depth dataset covering business plan statistics in 2025, including success rates, planning impact, and industry insights.

According to a survey conducted in August 2024 among U.S. parents of children aged from seven to 11 years old, some 68 of parents reported that their pre-teen/teenage children had a "skincare routine".

This statistic presents data on the type of subscription plan consumers have for music streaming services in the United States as of March 2018. During a survey, 71 percent of respondents stated that they had a personal subscription plan for music streaming services.

Workout Data

The dataset provided includes the logged data of my own strength workouts following the 5/3/1 BBB routine. While some insights were derived in an article I published recently, there is an opportunity for the community to benefit from the open sourcing of this data.

Most notably, I haven't found time to come up with a way of training and applying performance metrics against the data which I have labeled; and I'm hoping that the work I've spent to prepare a decent dataset can be picked up by someone looking to try out computer vision but on a dataset that has a clearer use case than some of the toy datasets that are currently open sourced.

The goal is to try to build an ML model that takes either phone images or scans of workout sheets, and automatically transfer them into the more structured Excel format for easier data gathering.

Content

There are 3 folders contained in the dataset, all files within the folder are datestamped by filename as DD-MM-YYYY: Excel Data This is considerable as the labeled data to a matching phone image or scanned image. There is an Excel file for each workout performed. Phone Images These are images of the filled out workout sheets as taken by my Android phone. More recently I have stopped taking phone images of my workout sheets, but about 85% of the Excel data has a matching phone image. While these images represent a harder challenge for computer vision, the ease of taking these images makes them much more practical as a future deployable mobile application. Scanned Images These are scans of the filled out workout sheets as scanned on my HP Deskjet printer. These scans are higher quality than the mobile images, however the lack of quick and easy access to scanners means that it is harder to gain a userbase as a potential future product.

A variety of summary statistics for the Rescue Plan program aggregated by Council Investment Priority Areas including: number of individual recipients, number and percentage of individual recipients identifying as BIPOC, direct financial assistance to individual recipients, direct financial assistance to individual recipients identifying as BIPOC, number of business partners, number and percentage of BIPOC-owned business partners, number of business recipients, number and percentage of BIPOC-owned business recipients, number of non-profit partners, percent of non-profit partner staff and boards identifying as BIPOC, number of non-profit recipients, percent of non-profit recipient staff and boards identifying as BIPOC.-- Additional Information: Category: ARPA Update Frequency: As Necessary-- Metadata Link: https://www.portlandmaps.com/metadata/index.cfm?&action=DisplayLayer&LayerID=60987