100+ datasets found
  1. C

    Patient Demographics

    • data.chhs.ca.gov
    • healthdata.gov
    • +2more
    csv, pdf, zip
    Updated Aug 29, 2024
    + more versions
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    Department of State Hospitals (2024). Patient Demographics [Dataset]. https://data.chhs.ca.gov/dataset/patient-demographics
    Explore at:
    pdf(104096), csv(208), csv(167), pdf(91406), csv(212), pdf(93731), csv(187), csv(1072), csv(194), csv(307), csv(182), pdf(107720), pdf(102502), pdf(104586), csv(553), pdf(86902), csv(862), zip, csv(1209), pdf(103183), csv(1784), csv(2016), csv(191), csv(1144), csv(896), pdf(106532), pdf(97992), csv(834), csv(176), csv(206)Available download formats
    Dataset updated
    Aug 29, 2024
    Dataset authored and provided by
    Department of State Hospitals
    Description

    Department of State Hospitals Patient Population Demographic (Fiscal Effective Dates: 2010-2020)

  2. f

    Patient demographic data (for n = 171 patients).

    • plos.figshare.com
    xls
    Updated May 31, 2023
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    Myres W. Tilghman; Susanne May; Josué Pérez-Santiago; Caroline C. Ignacio; Susan J. Little; Douglas D. Richman; Davey M. Smith (2023). Patient demographic data (for n = 171 patients). [Dataset]. http://doi.org/10.1371/journal.pone.0035401.t001
    Explore at:
    xlsAvailable download formats
    Dataset updated
    May 31, 2023
    Dataset provided by
    PLOS ONE
    Authors
    Myres W. Tilghman; Susanne May; Josué Pérez-Santiago; Caroline C. Ignacio; Susan J. Little; Douglas D. Richman; Davey M. Smith
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    MSM = men who have sex with men; IDU = injection drug users.§Age was determined at the time of acquisition of the first chronological sample collected from an individual patient that was included in the analysis.

  3. o

    Patient

    • ckm.openehr.org
    • arketyper.no
    txt
    Updated May 22, 2009
    + more versions
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    (2009). Patient [Dataset]. https://ckm.openehr.org/ckm/archetypes/1013.1.821
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    txtAvailable download formats
    Dataset updated
    May 22, 2009
    License

    Attribution-ShareAlike 3.0 (CC BY-SA 3.0)https://creativecommons.org/licenses/by-sa/3.0/
    License information was derived automatically

    Description

    Patient demographic data. Clinical Knowledge Manager (CKM)

  4. Age of health center patient vs. overall population in the U.S. in 2022

    • statista.com
    Updated Jun 26, 2024
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    Statista (2024). Age of health center patient vs. overall population in the U.S. in 2022 [Dataset]. https://www.statista.com/statistics/754579/patient-share-health-centers-in-us-by-age/
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    Dataset updated
    Jun 26, 2024
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    2022
    Area covered
    United States
    Description

    In 2022, children and teens are over-represented as health center patients compared to their proportion in the population. This statistic depicts the age distribution of health center patients compared to overall U.S. population as of 2022.

  5. f

    Observational Study Assessing Demographic, Economic and Clinical Factors...

    • plos.figshare.com
    docx
    Updated May 31, 2023
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    Georgios Hadjigeorgiou; Efthimios Dardiotis; Georgios Tsivgoulis; Triantafyllos Doskas; Damianos Petrou; Nikolaos Makris; Nikolaos Vlaikidis; Thomas Thomaidis; Athanasios Kyritsis; Nikolaos Fakas; Xoulietta Treska; Clementine Karageorgiou; Stefania Sotirli; Christos Giannoulis; Dimitra Papadimitriou; Ioannis Mylonas; Evaggelos Kouremenos; Georgios Vlachos; Dimitrios Georgiopoulos; Despoina Mademtzoglou; Michalis Vikelis; Elias Zintzaras (2023). Observational Study Assessing Demographic, Economic and Clinical Factors Associated with Access and Utilization of Health Care Services of Patients with Multiple Sclerosis under Treatment with Interferon Beta-1b (EXTAVIA) [Dataset]. http://doi.org/10.1371/journal.pone.0113933
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    docxAvailable download formats
    Dataset updated
    May 31, 2023
    Dataset provided by
    PLOS ONE
    Authors
    Georgios Hadjigeorgiou; Efthimios Dardiotis; Georgios Tsivgoulis; Triantafyllos Doskas; Damianos Petrou; Nikolaos Makris; Nikolaos Vlaikidis; Thomas Thomaidis; Athanasios Kyritsis; Nikolaos Fakas; Xoulietta Treska; Clementine Karageorgiou; Stefania Sotirli; Christos Giannoulis; Dimitra Papadimitriou; Ioannis Mylonas; Evaggelos Kouremenos; Georgios Vlachos; Dimitrios Georgiopoulos; Despoina Mademtzoglou; Michalis Vikelis; Elias Zintzaras
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Multiple sclerosis (MS) results in an extensive use of the health care system, even within the first years of diagnosis. The effectiveness and accessibility of the health care system may affect patients' quality of life. The aim of the present study was to evaluate the health care resource use of MS patients under interferon beta-1b (EXTAVIA) treatment in Greece, the demographic or clinical factors that may affect this use and also patient satisfaction with the health care system. Structured interviews were conducted for data collection. In total, 204 patients (74.02% females, mean age (SD) 43.58 (11.42) years) were enrolled in the study. Analysis of the reported data revealed that during the previous year patients made extensive use of health services in particular neurologists (71.08% visited neurologists in public hospitals, 66.67% in private offices and 48.53% in insurance institutes) and physiotherapists. However, the majority of the patients (52.45%) chose as their treating doctor private practice neurologists, which may reflect accessibility barriers or low quality health services in the public health system. Patients seemed to be generally satisfied with the received health care, support and information on MS (84.81% were satisfied from the information provided to them). Patients' health status (as denoted by disease duration, disability status and hospitalization needs) and insurance institute were found to influence their visits to neurologists. Good adherence (up to 70.1%) to the study medication was reported. Patients' feedback on currently provided health services could direct these services towards the patients' expectations.

  6. A

    ‘Patient Demographics’ analyzed by Analyst-2

    • analyst-2.ai
    Updated Jan 26, 2022
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    Analyst-2 (analyst-2.ai) / Inspirient GmbH (inspirient.com) (2022). ‘Patient Demographics’ analyzed by Analyst-2 [Dataset]. https://analyst-2.ai/analysis/data-gov-patient-demographics-70c1/latest
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    Dataset updated
    Jan 26, 2022
    Dataset authored and provided by
    Analyst-2 (analyst-2.ai) / Inspirient GmbH (inspirient.com)
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Analysis of ‘Patient Demographics’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/5c61d59f-d72b-4643-9445-0c6420fd4f2f on 26 January 2022.

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

    Department of State Hospitals Patient Population Demographic (Fiscal Effective Dates: 2010-2018)

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

  7. Patient demographics and clinical data.

    • plos.figshare.com
    xls
    Updated Jun 1, 2023
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    Edel Marie Quinn; Mark A. Corrigan; John O’Mullane; David Murphy; Elaine A. Lehane; Patricia Leahy-Warren; Alice Coffey; Patricia McCluskey; Henry Paul Redmond; Greg J. Fulton (2023). Patient demographics and clinical data. [Dataset]. http://doi.org/10.1371/journal.pone.0078786.t001
    Explore at:
    xlsAvailable download formats
    Dataset updated
    Jun 1, 2023
    Dataset provided by
    PLOShttp://plos.org/
    Authors
    Edel Marie Quinn; Mark A. Corrigan; John O’Mullane; David Murphy; Elaine A. Lehane; Patricia Leahy-Warren; Alice Coffey; Patricia McCluskey; Henry Paul Redmond; Greg J. Fulton
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    *Mean ankle brachial indices were all greater than 1 despite one patient having arterial disease; this was due to this same patient also having diabetes mellitus.

  8. d

    Dataplex: All CMS Data Feeds | Access 1519 Reports & 26B+ Rows of Data |...

    • datarade.ai
    .csv
    Updated Aug 14, 2024
    + more versions
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    Dataplex (2024). Dataplex: All CMS Data Feeds | Access 1519 Reports & 26B+ Rows of Data | Perfect for Historical Analysis & Easy Ingestion [Dataset]. https://datarade.ai/data-products/dataplex-all-cms-data-feeds-access-1519-reports-26b-row-dataplex
    Explore at:
    .csvAvailable download formats
    Dataset updated
    Aug 14, 2024
    Dataset authored and provided by
    Dataplex
    Area covered
    United States of America
    Description

    The All CMS Data Feeds dataset is an expansive resource offering access to 118 unique report feeds, providing in-depth insights into various aspects of the U.S. healthcare system. With over 25.8 billion rows of data meticulously collected since 2007, this dataset is invaluable for healthcare professionals, analysts, researchers, and businesses seeking to understand and analyze healthcare trends, performance metrics, and demographic shifts over time. The dataset is updated monthly, ensuring that users always have access to the most current and relevant data available.

    Dataset Overview:

    118 Report Feeds: - The dataset includes a wide array of report feeds, each providing unique insights into different dimensions of healthcare. These topics range from Medicare and Medicaid service metrics, patient demographics, provider information, financial data, and much more. The breadth of information ensures that users can find relevant data for nearly any healthcare-related analysis. - As CMS releases new report feeds, they are automatically added to this dataset, keeping it current and expanding its utility for users.

    25.8 Billion Rows of Data:

    • With over 25.8 billion rows of data, this dataset provides a comprehensive view of the U.S. healthcare system. This extensive volume of data allows for granular analysis, enabling users to uncover insights that might be missed in smaller datasets. The data is also meticulously cleaned and aligned, ensuring accuracy and ease of use.

    Historical Data Since 2007: - The dataset spans from 2007 to the present, offering a rich historical perspective that is essential for tracking long-term trends and changes in healthcare delivery, policy impacts, and patient outcomes. This historical data is particularly valuable for conducting longitudinal studies and evaluating the effects of various healthcare interventions over time.

    Monthly Updates:

    • To ensure that users have access to the most current information, the dataset is updated monthly. These updates include new reports as well as revisions to existing data, making the dataset a continuously evolving resource that stays relevant and accurate.

    Data Sourced from CMS:

    • The data in this dataset is sourced directly from the Centers for Medicare & Medicaid Services (CMS). After collection, the data is meticulously cleaned and its attributes are aligned, ensuring consistency, accuracy, and ease of use for any application. Furthermore, any new updates or releases from CMS are automatically integrated into the dataset, keeping it comprehensive and current.

    Use Cases:

    Market Analysis:

    • The dataset is ideal for market analysts who need to understand the dynamics of the healthcare industry. The extensive historical data allows for detailed segmentation and analysis, helping users identify trends, market shifts, and growth opportunities. The comprehensive nature of the data enables users to perform in-depth analyses of specific market segments, making it a valuable tool for strategic decision-making.

    Healthcare Research:

    • Researchers will find the All CMS Data Feeds dataset to be a robust foundation for academic and commercial research. The historical data, combined with the breadth of coverage across various healthcare metrics, supports rigorous, in-depth analysis. Researchers can explore the effects of healthcare policies, study patient outcomes, analyze provider performance, and more, all within a single, comprehensive dataset.

    Performance Tracking:

    • Healthcare providers and organizations can use the dataset to track performance metrics over time. By comparing data across different periods, organizations can identify areas for improvement, monitor the effectiveness of initiatives, and ensure compliance with regulatory standards. The dataset provides the detailed, reliable data needed to track and analyze key performance indicators.

    Compliance and Regulatory Reporting:

    • The dataset is also an essential tool for compliance officers and those involved in regulatory reporting. With detailed data on provider performance, patient outcomes, and healthcare utilization, the dataset helps organizations meet regulatory requirements, prepare for audits, and ensure adherence to best practices. The accuracy and comprehensiveness of the data make it a trusted resource for regulatory compliance.

    Data Quality and Reliability:

    The All CMS Data Feeds dataset is designed with a strong emphasis on data quality and reliability. Each row of data is meticulously cleaned and aligned, ensuring that it is both accurate and consistent. This attention to detail makes the dataset a trusted resource for high-stakes applications, where data quality is critical.

    Integration and Usability:

    Ease of Integration:

    • The dataset is provided in a CSV format, which is widely compatible with most data analysis tools and platforms. This ensures that users can easily integrate the data into their existing wo...
  9. Cystic Fibrosis Patient Demographics

    • dtechtive.com
    • find.data.gov.scot
    Updated Aug 11, 2023
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    CYSTIC FIBROSIS TRUST (2023). Cystic Fibrosis Patient Demographics [Dataset]. https://dtechtive.com/datasets/25766
    Explore at:
    Dataset updated
    Aug 11, 2023
    Dataset provided by
    Cystic Fibrosis Trust
    Area covered
    United Kingdom
    Description

    The UK Cystic Fibrosis Registry Demographic is made up of data items relating key demographic information about CF patients, relating to their diagnosis and genotype.

  10. Population Health Management Market Analysis, Size, and Forecast 2025-2029:...

    • technavio.com
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    Technavio, Population Health Management Market Analysis, Size, and Forecast 2025-2029: North America (US and Canada), Europe (France, Germany, Italy, UK), Asia (China, India, Japan, South Korea), and Rest of World (ROW) [Dataset]. https://www.technavio.com/report/population-health-management-market-industry-analysis
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    Dataset provided by
    TechNavio
    Authors
    Technavio
    Time period covered
    2021 - 2025
    Area covered
    Global, United States
    Description

    Snapshot img

    Population Health Management Market Size 2025-2029

    The population health management market size is forecast to increase by USD 19.40 billion at a CAGR of 10.7% between 2024 and 2029.

    The Population Health Management Market is experiencing significant growth, driven by the increasing adoption of healthcare IT solutions and the rising focus on personalized medicine. The implementation of electronic health records (EHRs) and other digital health technologies has enabled healthcare providers to collect and analyze large amounts of patient data, facilitating proactive care and population health management. Moreover, the trend towards personalized medicine, which aims to tailor healthcare treatments to individual patients based on their unique genetic makeup and health history, is further fueling the demand for PHM solutions. However, the high cost of installing and implementing these platforms poses a significant challenge for market growth.
    Despite this, the potential benefits of PHM, including improved patient outcomes, reduced healthcare costs, and enhanced population health, make it an attractive area for investment and innovation. Companies seeking to capitalize on these opportunities must navigate the challenges of data privacy and security, interoperability, and integration with existing healthcare systems. By addressing these challenges and focusing on delivering actionable insights from patient data, PHM solution providers can help healthcare organizations optimize their resources, improve patient care, and ultimately, improve population health.
    

    What will be the Size of the Population Health Management Market during the forecast period?

    Request Free Sample

    The market is experiencing significant growth, driven by the increasing focus on accountable care organizations (ACOs) and payer organizations to improve health outcomes and reduce costs. Healthcare professionals are leveraging big data, data analytics services, and clinical data integration to develop personalized care plans and implement intervention strategies for various populations. Telehealth services have become essential in population health management, enabling care coordination, health promotion, and health navigation for patients. Health equity is a critical factor in population health management, with a growing emphasis on addressing disparities and ensuring equal access to care.
    Data security and interoperability standards are essential in population health management, as healthcare providers exchange sensitive patient data for risk adjustment, care pathways, and quality reporting. Data mining and data visualization tools are used to identify health behavior changes and lifestyle modifications, leading to better health outcomes. Consumer health technology, such as patient engagement tools and wearable technology, are playing an increasingly important role in population health management. Health coaching and evidence-based medicine are intervention strategies used to prevent diseases and improve health outcomes. In summary, the market in the US is characterized by the adoption of precision medicine, health literacy, clinical guidelines, and personalized care plans.
    The market is driven by the need for care coordination, data analytics, and patient engagement to improve health outcomes and reduce costs. The use of data security, data mining, and interoperability standards ensures the effective exchange and utilization of health data.
    

    How is this Population Health Management Industry segmented?

    The population health management industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

    Component
    
      Software
      Services
    
    
    End-user
    
      Large enterprises
      SMEs
    
    
    Delivery Mode
    
      On-Premise
      Cloud-Based
      Web-Based
      On-Premise
      Cloud-Based
    
    
    End-Use
    
      Providers
      Payers
      Employer Groups
      Government Bodies
      Providers
      Payers
      Employer Groups
    
    
    Geography
    
      North America
    
        US
        Canada
    
    
      Europe
    
        France
        Germany
        Italy
        UK
    
    
      APAC
    
        China
        India
        Japan
        South Korea
    
    
      Rest of World
    

    By Component Insights

    The software segment is estimated to witness significant growth during the forecast period.

    The market's software segment is experiencing significant growth and innovation. Healthcare organizations are utilizing these solutions to effectively manage and enhance the health outcomes of diverse populations. The software component incorporates various tools that collect, analyze, and utilize health data for informed decision-making. Population health management platforms gather data from multiple sources, such as electronic health records, claims data, and patient-generated data. These platforms employ advanced analytics to generate valuable insi

  11. f

    Patient demographic data and CIS scores.

    • plos.figshare.com
    xls
    Updated May 30, 2023
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    Woon-Man Kung; Shuo-Tsung Chen; Chung-Hsiang Lin; Yu-Mei Lu; Tzu-Hsuan Chen; Muh-Shi Lin (2023). Patient demographic data and CIS scores. [Dataset]. http://doi.org/10.1371/journal.pone.0074267.t001
    Explore at:
    xlsAvailable download formats
    Dataset updated
    May 30, 2023
    Dataset provided by
    PLOS ONE
    Authors
    Woon-Man Kung; Shuo-Tsung Chen; Chung-Hsiang Lin; Yu-Mei Lu; Tzu-Hsuan Chen; Muh-Shi Lin
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    M, male; F, female; TBI, traumatic brain injury; EDH, epidural hematoma; SDH, subdural hematoma; ICH, intracerebral hematoma; BG, basal ganglion; F, frontal; T, temporal; P, parietal; DC, decompressive craniectomy; Uni+HR, unilateral craniectomy+removal of hematoma; Bil+HR, bilateral craniectomy+removal of hematoma; CIS, cranial index of symmetry; CAD, computer-assisted design.

  12. Hospice Utilization - Patient Demographics

    • data.chhs.ca.gov
    xlsx, zip
    Updated Jun 24, 2025
    + more versions
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    Department of Health Care Access and Information (2025). Hospice Utilization - Patient Demographics [Dataset]. https://data.chhs.ca.gov/dataset/hospice-utilization-patient-demographics
    Explore at:
    xlsx(37776), zip, xlsx(10024)Available download formats
    Dataset updated
    Jun 24, 2025
    Dataset authored and provided by
    Department of Health Care Access and Information
    Description

    The dataset contains counts of inpatient visits leading to a discharge to hospice care. Inpatient visits included in the counts consist of individuals aged 18 or over with a discharge disposition leading to home or facility hospice care. The total counts per each individual year can be viewed based on different patient characteristics, including patient age groups, individual counties of residence, primary payer type, diagnosis category, and patient sex/race/ethnicity. The disease categories include circulatory conditions, diabetes, malignant/benign neoplasms, malnutrition, neurodegenerative disease, renal failure or other kidney diagnoses, respiratory conditions and circulatory conditions. The categories represent common groupings of diagnoses seen in other studies related to hospice care and were created by grouping together relevant medical MSDRG codes in the HCAI inpatient data.

  13. f

    Patient demographics and clinical data.

    • figshare.com
    • plos.figshare.com
    xls
    Updated Jun 10, 2023
    + more versions
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    Philipp Bäumer; Markus Weiler; Maurice Ruetters; Frank Staub; Thomas Dombert; Sabine Heiland; Martin Bendszus; Mirko Pham (2023). Patient demographics and clinical data. [Dataset]. http://doi.org/10.1371/journal.pone.0049742.t001
    Explore at:
    xlsAvailable download formats
    Dataset updated
    Jun 10, 2023
    Dataset provided by
    PLOS ONE
    Authors
    Philipp Bäumer; Markus Weiler; Maurice Ruetters; Frank Staub; Thomas Dombert; Sabine Heiland; Martin Bendszus; Mirko Pham
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Patient demographics and clinical data.

  14. The GERAS Study - US

    • gaaindata.org
    Updated Feb 9, 2024
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    The Global Alzheimer's Association Interactive Network (2024). The GERAS Study - US [Dataset]. https://www.gaaindata.org/partner/GERAS-US
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    Dataset updated
    Feb 9, 2024
    Dataset provided by
    Alzheimer's Associationhttps://www.alz.org/
    Area covered
    Description

    The GERAS Study-US was a prospective, multicenter, observational study that aimed to assess societal costs and resource use associated with AD among patients and their primary caregivers across 76 sites in the United States. Data includes demographics/clinical characteristics; current medication; patient cognitive, functional, and behavioral assessments; patient and caregiver health-related quality of life; and patient and caregiver resource use. The data are available via the ADDI AD Workbench.

  15. Demographic data on patients and healthy controls.

    • plos.figshare.com
    xls
    Updated Jun 1, 2023
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    Martin Göttlich; Thomas F. Münte; Marcus Heldmann; Meike Kasten; Johann Hagenah; Ulrike M. Krämer (2023). Demographic data on patients and healthy controls. [Dataset]. http://doi.org/10.1371/journal.pone.0077336.t001
    Explore at:
    xlsAvailable download formats
    Dataset updated
    Jun 1, 2023
    Dataset provided by
    PLOShttp://plos.org/
    Authors
    Martin Göttlich; Thomas F. Münte; Marcus Heldmann; Meike Kasten; Johann Hagenah; Ulrike M. Krämer
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Notes: UPDRS = Unified Parkinson’s Disease Rating Scale.

  16. d

    Doorda UK Health Data | Demographic Patient Data: 20 Data Sources | Local...

    • datarade.ai
    .csv
    Updated Nov 6, 2024
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    Doorda (2024). Doorda UK Health Data | Demographic Patient Data: 20 Data Sources | Local Health Insights for 1.8M Postcodes [Dataset]. https://datarade.ai/data-products/doorda-uk-health-data-20-data-sources-business-intelligen-doorda
    Explore at:
    .csvAvailable download formats
    Dataset updated
    Nov 6, 2024
    Dataset authored and provided by
    Doorda
    Area covered
    United Kingdom
    Description

    Doorda's UK Health Data provides a comprehensive database covering 1.8M postcodes sourced from 20 data sources, offering unparalleled insights for local area health insights and analytics purposes.

    Volume and stats: - 1.8M Postcodes - UK Coverage - Age and Gender bands

    Our Health Data offers a multitude of use cases: - Market Analysis - Geodemographic Insights - Risk Management - Location Planning

    The key benefits of leveraging our Health Data include: - Data Accuracy - Informed Decision-Making - Competitive Advantage - Efficiency - Single Source

    Covering a wide range of industries and sectors, our data empowers organisations to make informed decisions, uncover market trends, and gain a competitive edge in the UK market.

  17. Patient Demographics - 4kwc-pn5b - Archive Repository

    • healthdata.gov
    application/rdfxml +5
    Updated Apr 8, 2025
    + more versions
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    (2025). Patient Demographics - 4kwc-pn5b - Archive Repository [Dataset]. https://healthdata.gov/dataset/Patient-Demographics-4kwc-pn5b-Archive-Repository/7nc9-sgid
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    application/rssxml, xml, tsv, json, application/rdfxml, csvAvailable download formats
    Dataset updated
    Apr 8, 2025
    Description

    This dataset tracks the updates made on the dataset "Patient Demographics" as a repository for previous versions of the data and metadata.

  18. Clinical and Demographic Patient Data.

    • plos.figshare.com
    xls
    Updated Jun 7, 2023
    + more versions
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    Haggai Sharon; Yotam Pasternak; Eti Ben Simon; Michal Gruberger; Nir Giladi; Ben Zion Krimchanski; David Hassin; Talma Hendler (2023). Clinical and Demographic Patient Data. [Dataset]. http://doi.org/10.1371/journal.pone.0074711.t001
    Explore at:
    xlsAvailable download formats
    Dataset updated
    Jun 7, 2023
    Dataset provided by
    PLOShttp://plos.org/
    Authors
    Haggai Sharon; Yotam Pasternak; Eti Ben Simon; Michal Gruberger; Nir Giladi; Ben Zion Krimchanski; David Hassin; Talma Hendler
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Clinical and Demographic Patient Data.

  19. i

    Patient Population by Provider Type - Dataset - The Indiana Data Hub

    • hub.mph.in.gov
    Updated Sep 14, 2017
    + more versions
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    (2017). Patient Population by Provider Type - Dataset - The Indiana Data Hub [Dataset]. https://hub.mph.in.gov/dataset/patient-population-by-provider-type
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    Dataset updated
    Sep 14, 2017
    Description

    This dataset is grouped by service provider type, and provides information about the number of recipients, number of claims, and dollar amount for given diagnosis claims. Restricted to claims with service date between 01/2012 to 12/2017. Restricted to claims with a primary diagnosis only. Restricted to top 100 most frequent diagnosis codes that are marked as primary diagnosis of a claim. Provider is the rendering provider in the claim. This data is for research purposes and is not intended to be used for reporting. Due to differences in geographic aggregation, time period considerations, and units of analysis, these numbers may differ from those reported by FSSA.

  20. d

    Patients Registered at a GP Practice

    • digital.nhs.uk
    Updated Feb 13, 2025
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    (2025). Patients Registered at a GP Practice [Dataset]. https://digital.nhs.uk/data-and-information/publications/statistical/patients-registered-at-a-gp-practice
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    Dataset updated
    Feb 13, 2025
    License

    https://digital.nhs.uk/about-nhs-digital/terms-and-conditionshttps://digital.nhs.uk/about-nhs-digital/terms-and-conditions

    Time period covered
    Feb 1, 2025
    Description

    Data for this publication are extracted each month as a snapshot in time from the Primary Care Registration database within the Personal Demographics Service (PDS) system. This release is an accurate snapshot as at 1 February 2025. GP Practice; Primary Care Network (PCN); Sub Integrated Care Board Locations (SICBL); Integrated Care Board (ICB) and NHS England Commissioning Region level data are released in single year of age (SYOA) and 5-year age bands, both of which finish at 95+, split by gender. In addition, organisational mapping data is available to derive PCN; SICBL; ICB and Commissioning Region associated with a GP practice and is updated each month to give relevant organisational mapping. Quarterly publications in January, April, July and October will include Lower Layer Super Output Area (LSOA) populations.

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Department of State Hospitals (2024). Patient Demographics [Dataset]. https://data.chhs.ca.gov/dataset/patient-demographics

Patient Demographics

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pdf(104096), csv(208), csv(167), pdf(91406), csv(212), pdf(93731), csv(187), csv(1072), csv(194), csv(307), csv(182), pdf(107720), pdf(102502), pdf(104586), csv(553), pdf(86902), csv(862), zip, csv(1209), pdf(103183), csv(1784), csv(2016), csv(191), csv(1144), csv(896), pdf(106532), pdf(97992), csv(834), csv(176), csv(206)Available download formats
Dataset updated
Aug 29, 2024
Dataset authored and provided by
Department of State Hospitals
Description

Department of State Hospitals Patient Population Demographic (Fiscal Effective Dates: 2010-2020)

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