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.

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

BackgroundRoutine Data Quality Assessments (RDQAs) were developed to measure and improve facility-level electronic medical record (EMR) data quality. We assessed if RDQAs were associated with improvements in data quality in KenyaEMR, an HIV care and treatment EMR used at 341 facilities in Kenya.MethodsRDQAs assess data quality by comparing information recorded in paper records to KenyaEMR. RDQAs are conducted during a one-day site visit, where approximately 100 records are randomly selected and 24 data elements are reviewed to assess data completeness and concordance. Results are immediately provided to facility staff and action plans are developed for data quality improvement. For facilities that had received more than one RDQA (baseline and follow-up), we used generalized estimating equation models to determine if data completeness or concordance improved from the baseline to the follow-up RDQAs.Results27 facilities received two RDQAs and were included in the analysis, with 2369 and 2355 records reviewed from baseline and follow-up RDQAs, respectively. The frequency of missing data in KenyaEMR declined from the baseline (31% missing) to the follow-up (13% missing) RDQAs. After adjusting for facility characteristics, records from follow-up RDQAs had 0.43-times the risk (95% CI: 0.32–0.58) of having at least one missing value among nine required data elements compared to records from baseline RDQAs. Using a scale with one point awarded for each of 20 data elements with concordant values in paper records and KenyaEMR, we found that data concordance improved from baseline (11.9/20) to follow-up (13.6/20) RDQAs, with the mean concordance score increasing by 1.79 (95% CI: 0.25–3.33).ConclusionsThis manuscript demonstrates that RDQAs can be implemented on a large scale and used to identify EMR data quality problems. RDQAs were associated with meaningful improvements in data quality and could be adapted for implementation in other settings.

Data Quality Scorecards Market Outlook

According to our latest research, the global Data Quality Scorecards market size in 2024 stands at USD 1.42 billion, reflecting robust demand across diverse sectors. The market is projected to expand at a CAGR of 14.8% from 2025 to 2033, reaching an estimated USD 4.45 billion by the end of the forecast period. Key growth drivers include the escalating need for reliable data-driven decision-making, stringent regulatory compliance requirements, and the proliferation of digital transformation initiatives across enterprises of all sizes. As per our latest research, organizations are increasingly recognizing the significance of maintaining high data quality standards to fuel analytics, artificial intelligence, and business intelligence capabilities.

One of the primary growth factors for the Data Quality Scorecards market is the exponential rise in data volumes generated by organizations worldwide. The digital economy has led to a surge in data collection from various sources, including customer interactions, IoT devices, and transactional systems. This data explosion has heightened the complexity of managing and ensuring data accuracy, completeness, and consistency. As a result, businesses are investing in comprehensive data quality management solutions, such as scorecards, to monitor, measure, and improve the quality of their data assets. These tools provide actionable insights, enabling organizations to proactively address data quality issues and maintain data integrity across their operations. The growing reliance on advanced analytics and artificial intelligence further amplifies the demand for high-quality data, making data quality scorecards an indispensable component of modern data management strategies.

Another significant growth driver is the increasing regulatory scrutiny and compliance requirements imposed on organizations, particularly in industries such as BFSI, healthcare, and government. Regulatory frameworks such as GDPR, HIPAA, and CCPA mandate stringent controls over data accuracy, privacy, and security. Non-compliance can result in severe financial penalties and reputational damage, compelling organizations to adopt robust data quality management practices. Data quality scorecards help organizations monitor compliance by providing real-time visibility into data quality metrics and highlighting areas that require remediation. This proactive approach to compliance not only mitigates regulatory risks but also enhances stakeholder trust and confidence in organizational data assets. The integration of data quality scorecards into enterprise data governance frameworks is becoming a best practice for organizations aiming to achieve continuous compliance and data excellence.

The rapid adoption of cloud computing and digital transformation initiatives across industries is also fueling the growth of the Data Quality Scorecards market. As organizations migrate their data infrastructure to the cloud and embrace hybrid IT environments, the complexity of managing data quality across disparate systems increases. Cloud-based data quality scorecards offer scalability, flexibility, and ease of deployment, making them an attractive option for organizations seeking to modernize their data management practices. Moreover, the proliferation of self-service analytics and business intelligence tools has democratized data access, necessitating robust data quality monitoring to ensure that decision-makers are working with accurate and reliable information. The convergence of cloud, AI, and data quality management is expected to create new opportunities for innovation and value creation in the market.

From a regional perspective, North America continues to dominate the Data Quality Scorecards market, driven by the presence of leading technology vendors, high adoption rates of advanced analytics, and stringent regulatory frameworks. However, the Asia Pacific region is expected to witness the fastest growth during the forecast period, fueled by rapid digitalization, increasing investments in IT infrastructure, and growing awareness of data quality management among enterprises. Europe also represents a significant market, characterized by strong regulatory compliance requirements and a mature data management ecosystem. Latin America and the Middle East & Africa are emerging markets, with increasing adoption of data quality solutions in sectors such as BFSI, healthcare, and government. The global market landscape is evolving rapidly, with regional

Biennial Business Survey data summary for Quality of Business Services survey results. The Business Survey question that relates to this dataset is: “Quality of services provided by City of Tempe.” Respondents are asked to rate their satisfaction level using a scale of 1 to 5, where 1 means "Very Dissatisfied" and 5 means "Very Satisfied".This page provides data for the Quality of Business Services performance measure. The performance measure dashboard is available at 5.01 Quality of Business Services.Additional InformationSource: Business Survey (Vendor: ETC Institute) Contact: Wydale HolmesContact E-Mail: wydale_holmes@tempe.govData Source Type: .pdf, ExcelPreparation Method: The City contracts with a vendor to conduct the survey, analyze the data, and prepare for publication.Publish Frequency: Every other yearPublish Method: Manual, .pdfData Dictionary

This operations dashboard shows historic and current data related to this performance measure. The performance measure dashboard is available at 5.01 Quality of Business Services. Data Dictionary

No description provided

Load and view a real-world dataset in RStudio

• Calculate “Measure of Frequency” metrics

• Calculate “Measure of Central Tendency” metrics

• Calculate “Measure of Dispersion” metrics

• Use R’s in-built functions for additional data quality metrics

• Create a custom R function to calculate descriptive statistics on any given dataset

Healthcare Data Quality Tools Market Outlook

According to our latest research, the global healthcare data quality tools market size reached USD 1.82 billion in 2024. The market is expected to exhibit a strong compound annual growth rate (CAGR) of 16.9% from 2025 to 2033, driven by the increasing digitization of healthcare systems, regulatory mandates, and the rising emphasis on data-driven decision-making in healthcare. By 2033, the market is forecasted to achieve a value of USD 7.13 billion. This robust expansion is primarily fueled by the growing need for accurate, complete, and reliable health data to improve patient outcomes, streamline operations, and ensure compliance with evolving healthcare regulations.

The healthcare data quality tools market is experiencing significant growth due to the surging adoption of electronic health records (EHRs) and the rapid digital transformation within the healthcare sector. As healthcare organizations increasingly transition from paper-based systems to digital platforms, the volume and complexity of healthcare data have grown exponentially. This shift has amplified the need for data quality tools that can cleanse, standardize, and validate large datasets, ensuring that critical clinical and administrative decisions are based on accurate and consistent information. The integration of advanced analytics and artificial intelligence (AI) in healthcare data management further accelerates the demand for robust data quality solutions, enabling organizations to unlock actionable insights from their data assets.

Another key growth factor for the healthcare data quality tools market is the stringent regulatory environment governing healthcare data management. Regulatory bodies such as HIPAA in the United States and GDPR in Europe have established strict guidelines for data privacy, security, and accuracy, compelling healthcare organizations to invest in tools that ensure compliance. Non-compliance can result in severe penalties and reputational damage, making data quality management a top priority. Additionally, the increasing adoption of value-based care models and the emphasis on population health management require high-quality data to track patient outcomes, measure performance, and optimize resource allocation. This regulatory and operational landscape is driving sustained investments in healthcare data quality tools globally.

The proliferation of connected medical devices, telemedicine platforms, and health information exchanges has further contributed to the complexity of healthcare data ecosystems. These advancements generate vast amounts of structured and unstructured data from diverse sources, including patient records, imaging systems, wearable devices, and administrative databases. Ensuring the interoperability and consistency of such heterogeneous data is a significant challenge, necessitating advanced data quality tools that can handle multiple data types and formats. As healthcare organizations strive to harness the full potential of big data and predictive analytics, the importance of data quality tools in enabling reliable and actionable insights cannot be overstated.

From a regional perspective, North America currently dominates the healthcare data quality tools market, accounting for the largest revenue share in 2024. The region’s leadership is attributed to its advanced healthcare IT infrastructure, high adoption of EHRs, and strong regulatory frameworks. However, Asia Pacific is expected to register the fastest growth during the forecast period, supported by increasing healthcare digitization, government initiatives to modernize healthcare systems, and rising investments in health IT. Europe also remains a significant market, driven by stringent data protection regulations and the widespread implementation of digital health initiatives across the region.

Component Analysis

The healthcare data quality tools market by component is broadly segmented into software and services. The software segment comprises standalone and integrated solutions designed to automate data cleansing, profiling, integration, enrichment, and monitoring processes within healthcare organizations. These solutions are increasingly incorporating advanced technologies such as artificial intelligence, machine learning, and natural language processing to enhance data accuracy and streamline workflows. The growing need to manage large volumes of healthcare data efficiently and the rising

Population-based Cancer Registries (PBCRs) are tasked with collecting high-quality data, important for monitoring cancer burden and its trends, planning and evaluating cancer control activities, clinical and epidemiological research and development of health policies. The main indicators to measure data quality are validity, completeness, comparability and timeliness. The aim of this article is to evaluate the quality of PBCRs data collected in the first ENCR-JRC data call, dated 2015.MethodsAll malignant tumours, except skin non-melanoma, and in situ and uncertain behaviour of bladder were obtained from 130 European general PBCRs for patients older than 19 years. Proportion of cases with death certificate only (DCO%), proportion of cases with unknown primary site (PSU%), proportion of microscopically verified cases (MV%), mortality to incidence (M:I) ratio, proportion of cases with unspecified morphology (UM%) and the median of the difference between the registration date and the incidence date were computed by sex, age group, cancer site, period and PBCR.ResultsA total of 28,776,562 cases from 130 PBCRs, operating in 30 European countries were included in the analysis. The quality of incidence data reported by PBCRs has been improving across the study period. Data quality is worse for the oldest age groups and for cancer sites with poor survival. No differences were found between males and females. High variability in data quality was detected across European PBCRs.Conclusionthe results reported in this paper are to be interpreted as the baseline for monitoring PBCRs data quality indicators in Europe along time.

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

Ever wondered how bad the air really is? Wonder no more! We've got 14 years of hourly data from 553 stations across India proving that yes, it's probably worse than you thought.

Perfect for data scientists who want to predict the unpredictable and researchers who enjoy charts that trend upward in all the wrong ways.

🏆 The Masterminds Behind This Chaos

Curated by: - @omsandeeppatil - Guy who decided counting particles in air was fun - @durvadongre - Partner in crime

Brought to you by: Project Parisar - Because someone has to keep track of this mess

🎯 What Can You Do With This?

• Predict the future: Will tomorrow's air be soup or just thick fog?
• Machine Learning: Teach computers to be as pessimistic about air quality as we are
• Health Research: Correlate coughing patterns with PM2.5 spikes
• Climate Studies: Document the slow-motion environmental apocalypse
• Urban Planning: Help cities figure out where NOT to put playgrounds

📁 How We Organized This Disaster

├── stations.csv         # All 553 ways we measure disappointment
└── data/
  ├── Andhra-Pradesh/
  │  ├── AP01.csv      # Local air quality: "Meh"
  └── [More States of Despair]/

🗂️ What's Inside

stations.csv - Station Hall of Fame

Your guide to 553 locations where we scientifically measure "yikes":

Individual Station Files - The Daily Grind

🌫️ The Main Villains: - pm2.5 - Tiny particles of regret (μg/m³) - pm10 - Bigger particles of regret (μg/m³) - no2 - Nitrogen's angry cousin (μg/m³) - so2 - Sulfur's contribution to chaos (μg/m³) - co - The silent but deadly friend (mg/m³) - ozone - Good upstairs, bad downstairs (μg/m³)

🧪 The Chemical Ensemble Cast: - benzene, toluene, xylene - The aromatic troublemakers (μg/m³) - nh3 - Ammonia, because why not? (μg/m³)

🌡️ Weather Accomplices: - rh - Humidity (makes everything stickier) (%) - ws - Wind speed (how fast help is blowing away) (m/s) - wd - Wind direction (where the blame is coming from) (°) - bp - Barometric pressure (atmospheric mood swings) (hPa)

📅 Time & Place Stamps: - timestamp - When exactly everything went wrong - station_id - Which station witnessed this particular tragedy

🌍 Geographic Coverage

From the bustling metros to sleepy hill stations, we've got disappointing air quality data everywhere! Mumbai's industrial charm, Delhi's winter wonderland of smog, and even those "pristine" hill stations that aren't so pristine anymore.

📈 Data Quality

• 85% complete (The other 15% probably gave up measuring)
• CPCB validated (Officially certified disappointment)
• Missing values clearly marked (Honesty in despair)

🛠️ Tech Specs

Format: CSV (Because even environmental disasters need spreadsheets)
Encoding: UTF-8 (International standard for documenting problems)
Missing Values: When even the sensors couldn't handle it

🔍 Perfect For

• PhD students who hate themselves
• Data scientists with a dark sense of humor
• Anyone building the next "AirpocalypseNow" app
• Researchers documenting the end times

📚 How to Cite This Masterpiece

Patil, O.S., Dongre, D. (2024). "India Air Quality Dataset: 
14 Years of Scientifically Measuring How Screwed We Are." 
Project Parisar. Available at: [kaggle-url]

🤝 Want to Help?

Project Parisar welcomes contributions! Because misery loves company, and data cleaning is a team sport.

📞 Questions?

Hit up @omsandeeppatil or @durvadongre - they're the brave souls who actually organized this chaos.

🏷️ Tags

environmental-disaster data-science time-series india air-pollution machine-learning public-health why-we-cant-have-nice-things

Disclaimer: No air particles were harmed in the making of this dataset. They're doing just fine, unfortunately.

Data Quality Scorecards Market Outlook

According to our latest research, the Global Data Quality Scorecards Market size was valued at $1.4 billion in 2024 and is projected to reach $4.2 billion by 2033, expanding at a robust CAGR of 13.2% during the forecast period of 2025–2033. The primary growth driver for this market is the increasing reliance on data-driven decision-making across enterprises, which necessitates stringent data quality management to ensure accuracy, compliance, and business agility. As organizations globally accelerate digital transformation initiatives, the demand for comprehensive data quality scorecard solutions is surging, enabling businesses to monitor, measure, and improve data integrity and reliability across diverse operational environments.

Regional Outlook

North America currently dominates the Data Quality Scorecards Market, accounting for the largest market share in 2024. The region’s leadership stems from the early adoption of advanced data management technologies, a mature IT infrastructure, and stringent regulatory requirements, particularly in sectors such as BFSI, healthcare, and government. Organizations in the United States and Canada are investing heavily in robust data governance frameworks, which in turn drives the adoption of data quality scorecards. Major technology players headquartered in this region also contribute to rapid product innovation and ecosystem development. As a result, North America is expected to maintain its market leadership, with a projected market value exceeding $1.5 billion by 2033.

The Asia Pacific region is anticipated to register the fastest growth in the Data Quality Scorecards Market, with a projected CAGR surpassing 15% during the forecast period. This growth is primarily fueled by rapid digitalization, expanding IT and telecommunications sectors, and increasing regulatory focus on data privacy and quality in countries such as China, India, Japan, and South Korea. Enterprises in this region are increasingly adopting cloud-based data quality solutions to support large-scale data integration and analytics projects. Furthermore, government-led digital transformation initiatives and significant investments in smart city projects are propelling the demand for efficient data quality management tools. The region’s burgeoning e-commerce and financial services industries are also key contributors to this robust growth trajectory.

Emerging economies in Latin America, the Middle East, and Africa are gradually embracing data quality scorecards, although adoption remains at a nascent stage compared to developed markets. Challenges such as limited IT infrastructure, budget constraints, and a shortage of skilled data professionals hinder market penetration. However, the growing awareness of the importance of data quality for regulatory compliance and operational efficiency is driving gradual uptake. Localized demand is further influenced by sector-specific needs in banking, government, and retail, where accurate data is crucial for risk management and customer engagement. Policy reforms aimed at enhancing data security and digital transformation are expected to create new opportunities for market players in these regions over the coming years.

Report Scope

This operations dashboard shows historic and current data related to this performance measure.The performance measure dashboard is available at 3.36 Quality of City Services. Data Dictionary

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.

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.

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.

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.

Quality of life is a measure of comfort, health, and happiness by a person or a group of people. Quality of life is determined by both material factors, such as income and housing, and broader considerations like health, education, and freedom. Each year, US & World News releases its “Best States to Live in” report, which ranks states on the quality of life each state provides its residents. In order to determine rankings, U.S. News & World Report considers a wide range of factors, including healthcare, education, economy, infrastructure, opportunity, fiscal stability, crime and corrections, and the natural environment. More information on these categories and what is measured in each can be found below:

Healthcare includes access, quality, and affordability of healthcare, as well as health measurements, such as obesity rates and rates of smoking. Education measures how well public schools perform in terms of testing and graduation rates, as well as tuition costs associated with higher education and college debt load. Economy looks at GDP growth, migration to the state, and new business. Infrastructure includes transportation availability, road quality, communications, and internet access. Opportunity includes poverty rates, cost of living, housing costs and gender and racial equality. Fiscal Stability considers the health of the government's finances, including how well the state balances its budget. Crime and Corrections ranks a state’s public safety and measures prison systems and their populations. Natural Environment looks at the quality of air and water and exposure to pollution.

This dataset contains information on Programa Nacional Cuna Más (Cuna Mas, hereinafter), Peru’s largest early childhood development program established in 2012. It focuses on one of the two services provided by Cuna Mas known as Servicio de Acompanamiento a Familias (SAF), a home visiting program that operates in rural areas and provides one-hour weekly home visits to children aged 0-36 months and their caregiver. The objective of the study was to compare different instruments to measure the quality of home visiting programs. Between August and October 2015, three instruments were administered to a sample of 554 children enrolled in Cuna Mas and receiving home visits at the time of data collection, and on their 176 home visitors who regularly work with 80 supervisors.