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.

Metrics used to give an indication of data quality between our test’s groups. This includes whether documentation was used and what proportion of respondents rounded their answers. Unit and item non-response are also reported.

The GOES-R Advanced Baseline Imager (ABI) Rainfall Rate Quantitative Precipitation Estimate (RRQPE) product contains an image with pixel values identifying the rainfall rate. The product includes data quality information that provides an assessment of the rainfall rate data values for on-earth pixels. The units of measure for the rainfall rate value is millimeters per hour. The product image is produced on the ABI fixed grid at 2 km resolution for the Full Disk coverage region. Product data is produced for geolocated source data to local zenith angles of 90 degrees for both daytime and nighttime conditions.

Data quality scale applied to the assessment of each measure in the FGT.

No description provided

Data from a weather station (Vilanova i la Geltrú) deployed at the Catalan coast from 2013 to 2014. The station at Vilanova i la Geltrú provides air temperature, wind speed and wind direction. Data from the Vilanova i la Geltrú weather station has been acquired every minute, then a quality control procedure has been applied following QARTOD guidelines. Afterwards the quality controlled data has been averaged in periods of 30 minutes (discarding data flagged as bad data). Every data point has an associated a quality control flag and standard deviation value. The quality control flag values are 1: good data, 2: not applied, 3: suspicious data, 4: bad data, 9: missing data. The standard deviation provides a measure of the variability of the data within the 30min time window used in the average.

This dataset presents the impact indicators of the data.gouv.fr platform. The mission of data.gouv.fr is to ensure the provision of quality open data to promote transparency and efficiency of public action while facilitating the creation of new services. These indicators aim to monitor the extent to which data.gouv.fr meets its objectives. Objective 1: data.gouv.fr promotes the discoverability of open data The aim here is to measure the extent to which users find the data they need. Indicator: percentage of users who answered positively to the question "Did you find what you were looking for?" Objective 2: data.gouv.fr promotes open data quality This is to measure whether data.gouv.fr makes it easy to publish and reference quality data. Indicator: Average quality score of the 1000 most viewed datasets on the platform. Objective 3: data.gouv.fr promotes the reuse of open data The aim here is to measure the extent to which data.gouv.fr facilitates interactions between data producers and re-users. Indicator: average time for a "legitimate" response to discussions on datasets (legitimate: reply by a member of the organisation publishing the dataset or by a member of the data.gouv.fr team.) Objective 4: data.gouv.fr facilitates access to information of the most important datasets This is to measure the extent to which data.gouv.fr participates in access to information. Indicator: number of datasets in the top 100 associated with "quality" reuse (quality reuse is an editorial choice of the data.gouv.fr. team) ## Data format This dataset shall comply with the "impact of a digital public service" data scheme aimed at ensuring a smooth publication of the impact statistics of digital public services. Its use makes it possible to compare and centralize data from different products, in order to facilitate their understanding and reuse. Read more ### Description of columns - "administration_rattachement" : aadministration to which the digital public service is attached. - public_numeric_service_name: name of the digital public service - "indicator": Name of indicator. - "value" : vvalue of the indicator, as determined on the date indicated in the field 'date'. - "unite_measure" : unity of the indicator - “is_target” : Indicates whether the value is a target value (projected to a future date) or whether it is an actual value (measured to a past date). - frequency_monitoring: frequency with which the indicator is consulted and used by the service. - "date": date when the indicator was measured, or when the target value is desired if it is a target. - est_periode: Boolean indicating whether the measurement is made over a period (true) or whether it is a stock (false). - date_start: date of the start of the measurement period, if the indicator covers a period of time. - “is_automated“: specifies whether data collection is automated (true) or manual (false). - "source_collection": specify how the collection is carried out: script, survey, manual collection... - insee_code : if the indicator is calculated at a certain geographical scale, specify the INSEE code of that scale. - dataviz_wish: indication for visualization producers of the appropriate type of dataviz for this indicator. - "comments": specify the known limitations and biases and justify the choice of the indicator despite its limitations.

The water quality index provides a single number (like a grade) that expresses overall water quality.

Each month Water Quality Specialists measure 8 water quality parameters at 53 streams. The parameters are temperature, dissolved oxygen, bacteria (fecal coliform), total nitrogen, total phosphorus, pH, total suspended sediment, and turbidity.

This dataset provides the expected and determined concentrations of selected inorganic and organic analytes for spiked reagent-water samples (calibration standards and limit of quantitation standards) that were used to calculate detection limits by using the United States Environmental Protection Agency’s (USEPA) Method Detection Limit (MDL) version 1.11 or 2.0 procedures, ASTM International’s Within-Laboratory Critical Level standard procedure D7783-13, and, for five pharmaceutical compounds, by USEPA’s Lowest Concentration Minimum Reporting Level procedure. Also provided are determined concentration data for reagent-water laboratory blank samples, classified as either instrument blank or set blank samples, and reagent-water blind-blank samples submitted by the USGS Quality System Branch, that were used to calculate blank-based detection limits by using the USEPA MDL version 2.0 procedure or procedures described in National Water Quality Laboratory Technical Memorandum 2016.02, http://wwwnwql.cr.usgs.gov/tech_memos/nwql.2016-02.pdf. The determined detection limits are provided and compared in the related external publication at https://doi.org/10.1016/j.talanta.2021.122139.

This dataset comes from the Annual Community Survey question related to satisfaction with the quality of the city website. Respondents are asked to provide their level of satisfaction related to the “Usefulness of the City's website” on a scale of 5 to 1, where 5 means "Very Satisfied" and 1 means "Very Dissatisfied" (without "don't know" as an option).

The survey is mailed to a random sample of households in the City of Tempe and has a 95% confidence level.

This page provides data for the City Website Quality Satisfaction performance measure. Click on the Showcases tab for any available stories or dashboards related to this data.

The performance measure dashboard is available at PMD 2.04 City Website Satisfaction (Coming Soon)

PMID: 2211

The Agency for Healthcare Research and Quality (AHRQ) Patient Safety Indicator 11 (PSI-11) Measure Rates dataset provides information on provider-level measure rates regarding one preventable complication (postoperative respiratory failure) for Medicare fee-for-service discharges. The PSI-11 measure data is solely reported for providers’ information and quality improvement purposes and are not a part of the Deficit Reduction Act (DRA) Hospital-Acquired Condition (HAC) Payment Provision or HAC Reduction Program.

The Open Data Maturity (ODM) assessment is carried out yearly and provides a benchmark of European countries development in the field of open data. It is based on the following dimensions:

• Policy: focusing on countries’ open data policies and strategies;
• Impact: looking into the activities to monitor and measure open data reuse and its impact;
• Portal: assessing portal functions and features that enable users to access open data via the national portal and support interaction within the open data community;
• Quality: focusing on mechanisms that ensure the quality of the (meta)data..

This assessment helps the countries to better understand their level of maturity, to capture their progress over time and to find areas for improvement. Additionally, the study provides an overview of best practices implemented across Europe that could be transferred to other national and local contexts.

The 35 participant countries in the 2022 edition are the 27 EU Member States, 3 European Trade Association (EFTA) countries (Norway, Switzerland, Iceland), 4 candidate countries (Albania, Montenegro, Serbia, Ukraine) and Bosnia and Herzegovina.

The scores of the ODM assessment for each participating country and the questionnaire used in the survey are provided as a re-usable dataset. The complete report and the methodology can be found under documentation.

This dataset contains data of the contaminants measured in the stations of the city of Barcelona. The update is carried out in intervals of one hour indicating whether the value is validated or not. The data of three days prior to the current one is also displayed.

A list of all Home Health Agencies that have been registered with Medicare. The list includes addresses, phone numbers, and quality measure ratings for each agency.

Performance rates on frequently reported health care quality measures in the CMS Medicaid/CHIP Child and Adult Core Sets, for FFY 2020 reporting.

Source: Mathematica analysis of MACPro and Form CMS-416 reports for the FFY 2020 reporting cycle. Dataset revised September 2021. For more information, see the Children's Health Care Quality Measures and Adult Health Care Quality Measures webpages.

This publication provides the most timely statistics available relating to NHS funded secondary mental health, learning disabilities and autism services in England. This information will be of use to people needing access to information quickly for operational decision making and other purposes. These statistics are derived from submissions made using version 3.0 of the Mental Health Services Dataset (MHSDS). NHS Digital review the quality and completeness of the submissions used to create these statistics on an ongoing basis. More information about this work can be found in the Accuracy and reliability section of this report. Fully detailed information on the quality and completeness of particular statistics in this release is not available due to the timescales involved in reviewing submissions and engaging with data providers. The information that has been obtained at the time of publication is made available in the Provider Feedback sections of the Data Quality Reports which accompany this release. Information gathered after publication is released in future editions of this publication series. More detailed information on the quality and completeness of these statistics and a summary of how these statistics may be interpreted is made available later in our Mental Health Bulletin: Annual Report publication series. All elements of this publication, other editions of this publication series, and related annual publication series' can be found in the Related Links below. Please note, the Quarter 4 Children and Young People Receiving Second Contact With Services measure was not included in the June 2018 publication. A validation of this data was undertaken and statistics for the full 2017-18 financial year are published here. This file can be found below as the "Number of children and young people accessing NHS funded community mental health services in England - April 2017 to March 2018". A correction was made to this publication at 10:00am on 12 July 2018. This correction replaced an older version of the Excel data file Number of children and young people accessing NHS funded community mental health services in England - April 2017 to March 2018 that was displayed in error. The update specifically relates to statistics for the England total, Frimley Health STP, NHS Slough CCG, NHS Windsor, Ascot and Maidenhead CCG figure reported in Table 1 of this file. A correction has been made to this publication on 10 September 2018. This amendment relates to statistics in the monthly CSV data file; the specific measures effected are listed in the “Corrected Measures” CSV. All listed measures have now been corrected. On 11 December 2018 the data quality reports were made available; however the accompanying CSV for the provisional report is not currently available; if you require this file please contact us at mh.analysis@nhs.net. NHS Digital apologises for any inconvenience caused.

This project provides a national unified database of residential building retrofit measures and associated retail prices and end-user might experience. These data are accessible to software programs that evaluate most cost-effective retrofit measures to improve the energy efficiency of residential buildings and are used in the consumer-facing website https://remdb.nrel.gov/ This publicly accessible, centralized database of retrofit measures offers the following benefits: Provides information in a standardized format Improves the technical consistency and accuracy of the results of software programs Enables experts and stakeholders to view the retrofit information and provide comments to improve data quality Supports building science R&D Enhances transparency This database provides full price estimates for many different retrofit measures. For each measure, the database provides a range of prices, as the data for a measure can vary widely across regions, houses, and contractors. Climate, construction, home features, local economy, maturity of a market, and geographic location are some of the factors that may affect the actual price of these measures. This database is not intended to provide specific cost estimates for a specific project. The cost estimates do not include any rebates or tax incentives that may be available for the measures. Rather, it is meant to help determine which measures may be more cost-effective. The National Renewable Energy Laboratory (NREL) makes every effort to ensure accuracy of the data; however, NREL does not assume any legal liability or responsibility for the accuracy or completeness of the information.

Psychiatric facilities that are eligible for the Inpatient Psychiatric Facility Quality Reporting (IPFQR) program are required to meet all program requirements, otherwise their Medicare payments may be reduced. Follow-Up After Hospitalization for Mental Illness (FUH) measure data on this table are marked as not available. Results for this measure are provided on a separate table.

This dataset contains a list of the quality measures displayed on Nursing Home Compare, that are based on Medicare claims data. Each row contains a specific quality measure for a specific nursing home and includes the risk-adjusted score.

Satisfaction, loyalty, and likelihood of referral are regarded by marketers and the Big Three diagnostics leading to retail profitability. However, as yet no-one has developed a model to capture all three of these constructs in the context of the internet. Moreover, although several attempts have been made to develop models to measure quality of website experience, no-one has sought to develop an instrument short enough to be of practical use as a quick customer satisfaction feedback form. In this research we sought to fill this void by developing and psychometrically testing a parsimonious model to capture the Big Three diagnostics, brief enough to be used in a commercial environment as a modal popup feedback form.