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.

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.

Performance rates on frequently reported health care quality measures in the CMS Medicaid/CHIP Child and Adult Core Sets, for FFY 2022 reporting.

Source: Mathematica analysis of MACPro and Form CMS-416 reports for the FFY 2022 reporting cycle. Dataset revised September 2023. For more information, see the Children's Health Care Quality Measures and Adult Health Care Quality Measures webpages.

Data on long-form data quality indicators for 2021 Census commuting content, Canada, provinces and territories, census divisions and census subdivisions.

This dataset contains quality measures displayed on Nursing Home Compare, based on the resident assessments that make up the nursing home Minimum Data Set (MDS). Each row contains a specific measure for a nursing home and includes the four-quarter score average and scores for individual quarter.

Performance rates on frequently reported health care quality measures in the CMS Medicaid/CHIP Child and Adult Core Sets, for FFY 2019 reporting. Source: Mathematica analysis of MACPro and Form CMS-416 reports for the FFY 2019 reporting cycle. Dataset revised October 2020. For more information, see the Children's Health Care Quality Measures and Adult Health Care Quality Measures webpages.

This dataset includes information on quality control and data management of researchers and data curators from a social science organization. Four data curators and 24 researchers provided responses for the study. Data collection techniques, data processing strategies, data storage and preservation, metadata standards, data sharing procedures, and the perceived significance of quality control and data quality assurance are the main areas of focus. The dataset attempts to provide insight on the RDM procedures that are being used by a social science organization as well as the difficulties that researchers and data curators encounter in upholding high standards of data quality. The goal of the study is to encourage more investigations aimed at enhancing scientific community data management practices and guidelines.

Performance rates on frequently reported health care quality measures in the CMS Medicaid/CHIP Child and Adult Core Sets, for FFY 2021 reporting. Source: Mathematica analysis of MACPro and Form CMS-416 reports for the FFY 2021 reporting cycle. Dataset revised September 2023. For more information, see the Children's Health Care Quality Measures and Adult Health Care Quality Measures webpages.

This dataset includes 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. It contains state-wise data for the hospitals in United States for several inpatient psychiatric facility quality measures.

Performance rates on frequently reported health care quality measures in the CMS Medicaid/CHIP Child and Adult Core Sets, for FFY 2018 reporting.

Source: Mathematica analysis of MACPro and Form CMS-416 reports for the FFY 2018 reporting cycle. For more information, see the Children's Health Care Quality Measures and Adult Health Care Quality Measures webpages.

Data Quality Management Software Market Outlook

The global data quality management software market size was valued at approximately USD 1.5 billion in 2023 and is anticipated to reach around USD 3.8 billion by 2032, growing at a compound annual growth rate (CAGR) of 10.8% during the forecast period. This growth is largely driven by the increasing complexity and exponential growth of data generated across various industries, necessitating robust data management solutions to ensure the accuracy, consistency, and reliability of data. As organizations strive to leverage data-driven decision-making and optimize their operations, the demand for efficient data quality management software solutions continues to rise, underscoring their significance in the current digital landscape.

One of the primary growth factors for the data quality management software market is the rapid digital transformation across industries. With businesses increasingly relying on digital tools and platforms, the volume of data generated and collected has surged exponentially. This data, if managed effectively, can unlock valuable insights and drive strategic business decisions. However, poor data quality can lead to erroneous conclusions and suboptimal performance. As a result, enterprises are investing heavily in data quality management solutions to ensure data integrity and enhance decision-making processes. The integration of advanced technologies such as artificial intelligence (AI) and machine learning (ML) in data quality management software is further propelling the market, offering automated data cleansing, enrichment, and validation capabilities that significantly improve data accuracy and utility.

Another significant driver of market growth is the increasing regulatory requirements surrounding data governance and compliance. As data privacy laws become more stringent worldwide, organizations are compelled to adopt comprehensive data quality management practices to ensure adherence to these regulations. The implementation of data protection acts such as GDPR in Europe has heightened the need for data quality management solutions to ensure data accuracy and privacy. Organizations are thus keen to integrate robust data quality measures to safeguard their data assets, maintain customer trust, and avoid hefty regulatory fines. This regulatory-driven push has resulted in heightened awareness and adoption of data quality management solutions across various industry verticals, further contributing to market growth.

The growing emphasis on customer experience and personalization is also fueling the demand for data quality management software. As enterprises strive to deliver personalized and seamless customer experiences, the accuracy and reliability of customer data become paramount. High-quality data enables organizations to gain a 360-degree view of their customers, tailor their offerings, and engage customers more effectively. Companies in sectors such as retail, BFSI, and healthcare are prioritizing data quality initiatives to enhance customer satisfaction, retention, and loyalty. This consumer-centric approach is prompting organizations to invest in data quality management solutions that facilitate comprehensive and accurate customer insights, thereby driving the market's growth trajectory.

Regionally, North America is expected to dominate the data quality management software market, driven by the region's technological advancements and high adoption rate of data management solutions. The presence of leading market players and the increasing demand for data-driven insights to enhance business operations further bolster market growth in this region. Meanwhile, the Asia Pacific region is witnessing substantial growth opportunities, attributed to the rapid digitalization across emerging economies and the growing awareness of data quality's role in business success. The rising adoption of cloud-based solutions and the expanding IT sector are also contributing to the market's regional expansion, with a projected CAGR that surpasses other regions during the forecast period.

Component Analysis

The data quality management software market is segmented by component into software and services, each playing a pivotal role in delivering comprehensive data quality solutions to enterprises. The software component, constituting the core of data quality management, encompasses a wide array of tools designed to facilitate data cleansing, validation, enrichment, and integration. These software solutions are increasingly equipped with advanced features such as AI and ML algorithms, enabling automated data quality processes that si

Performance rates on frequently reported health care quality measures in the CMS Medicaid/CHIP Child and Adult Core Sets, for FFY 2017 reporting. Source: Mathematica analysis of MACPro and Form CMS-416 reports for the FFY 2017 reporting cycle. For more information, see the Children's Health Care Quality Measures and Adult Health Care Quality Measures webpages.

This data package contains quality measures such as Air Quality, Austin Airport, LBB Performance Report, School Survey, Child Poverty, System International Units, Weight Measures, etc.

Data on short-form data quality indicators for 2021 Census, Canada, provinces and territories, census metropolitan areas, census agglomerations and census subdivisions.

This dataset contains measures that evaluate the quality of care delivered by Health Homes for the Centers for Medicare & Medicaid Services (CMS) Core Set and Health Home State Plan Amendment (SPA). To support ongoing assessment of the effectiveness of the Health Home model, the CMS has established a recommended Core Set of health care quality measures that it intends to promulgate in the rulemaking process. The data used in the Health Home Quality Measures are taken from the following sources: • Medicaid Data Mart: Claims and encounters data generated from the Medicaid Data Warehouse (MDW). • QARR Member Level Files: Sample of the health plan eligible member’s quality. • New York State Delivery System Inform Incentive Program (DSRIP) Data Warehouse: Claims and encounters data generated from the Medicaid Data Warehouse (MDW).

Please refer to the Overview document for additional information.

Performance rates on frequently reported health care quality measures in the CMS Medicaid/CHIP Child and Adult Core Sets, for FFY 2015 reporting. Source: Mathematica analysis of MACPro and Form CMS-416 reports for the FFY 2015 reporting cycle. For more information, see the Children's Health Care Quality Measures and Adult Health Care Quality Measures webpages.

Evaluating Health Home Care Quality

CMS Core Set and Health Home SPA Measures

By Health Data New York [source]

About this dataset

This dataset provides comprehensive measures to evaluate the quality of medical services provided to Medicaid beneficiaries by Health Homes, including the Centers for Medicare & Medicaid Services (CMS) Core Set and Health Home State Plan Amendment (SPA). This allows us to gain insight into how well these health homes are performing in terms of delivering high-quality care. Our data sources include the Medicaid Data Mart, QARR Member Level Files, and New York State Delivery System Inform Incentive Program (DSRIP) Data Warehouse. With this data set you can explore essential indicators such as rates for indicators within scope of Core Set Measures, sub domains, domains and measure descriptions; age categories used; denominators of each measure; level of significance for each indicator; and more! By understanding more about Health Home Quality Measures from this resource you can help make informed decisions about evidence based health practices while also promoting better patient outcomes

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How to use the dataset

This dataset contains measures that evaluate the quality of care delivered by Health Homes for the Centers for Medicare & Medicaid Services (CMS). With this dataset, you can get an overview of how a health home is performing in terms of quality. You can use this data to compare different health homes and their respective service offerings.

The data used to create this dataset was collected from Medicaid Data Mart, QARR Member Level Files, and New York State Delivery System Incentive Program (DSRIP) Data Warehouse sources.

In order to use this dataset effectively, you should start by looking at the columns provided. These include: Measurement Year; Health Home Name; Domain; Sub Domain; Measure Description; Age Category; Denominator; Rate; Level of Significance; Indicator. Each column provides valuable insight into how a particular health home is performing in various measurements of healthcare quality.

When examining this data, it is important to remember that many variables are included in any given measure and that changes may have occurred over time due to varying factors such as population or financial resources available for healthcare delivery. Furthermore, changes in policy may also affect performance over time so it is important to take these things into account when evaluating the performance of any given health home from one year to the next or when comparing different health homes on a specific measure or set of indicators over time

Research Ideas

• Using this dataset, state governments can evaluate the effectiveness of their health home programs by comparing the performance across different domains and subdomains.
• Healthcare providers and organizations can use this data to identify areas for improvement in quality of care provided by health homes and strategies to reduce disparities between individuals receiving care from health homes.
• Researchers can use this dataset to analyze how variations in cultural context, geography, demographics or other factors impact delivery of quality health home services across different locations

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

See the dataset description for more information.

Columns

File: health-home-quality-measures-beginning-2013-1.csv | Column name | Description | |:--------------------------|:----------------------------------------------------| | Measurement Year | The year in which the data was collected. (Integer) | | Health Home Name | The name of the health home. (String) | | Domain | The domain of the measure. (String) | | Sub Domain | The sub domain of the measure. (String) | | Measure Description | A description of the measure. (String) | | Age Category | The age category of the patient. (String) | | Denominator | The denominator of the measure. (Integer) | | Rate | The rate of the measure. (Float) | | Level of Significance | The level of significance of the measure. (String) | | Indicator | The indicator of the measure. (String) |

Acknowledgements

...

Analysis of ‘2015 Child and Adult Health Care Quality Measures’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/58240558-6254-41f6-aeb2-38671a534ea9 on 26 January 2022.

--- Dataset description provided by original source is as follows ---

Performance rates on frequently reported health care quality measures in the CMS Medicaid/CHIP Child and Adult Core Sets, for FFY 2015 reporting.

Source: Mathematica analysis of MACPro and Form CMS-416 reports for the FFY 2015 reporting cycle. For more information, see the Children's Health Care Quality Measures and Adult Health Care Quality Measures webpages.

--- Original source retains full ownership of the source dataset ---

Performance rates on frequently reported health care quality measures in the CMS Medicaid/CHIP Child and Adult Core Sets, for FFY 2016 reporting. Source: Mathematica analysis of MACPro and Form CMS-416 reports for the FFY 2016 reporting cycle. For more information, see the Children's Health Care Quality Measures and Adult Health Care Quality Measures webpages.

Performance rates on frequently reported health care quality measures in the CMS Medicaid/CHIP Child and Adult Core Sets, for FFY 2014 reporting. Dataset contains both child and adult measures. Source: Mathematica analysis of FFY 2014 Child and Adult CARTS reports as of May 8, 2015, as published in the 2015 Secretary's Reports on the Quality of Care in Medicaid/CHIP.