9 datasets found
  1. NPPES Plan and Provider Enumeration System

    • kaggle.com
    zip
    Updated Mar 20, 2019
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    Centers for Medicare & Medicaid Services (2019). NPPES Plan and Provider Enumeration System [Dataset]. https://www.kaggle.com/cms/nppes
    Explore at:
    zip(0 bytes)Available download formats
    Dataset updated
    Mar 20, 2019
    Dataset authored and provided by
    Centers for Medicare & Medicaid Services
    License

    https://creativecommons.org/publicdomain/zero/1.0/https://creativecommons.org/publicdomain/zero/1.0/

    Description

    Context

    The CMS National Plan and Provider Enumeration System (NPPES) was developed as part of the Administrative Simplification provisions in the original HIPAA act. The primary purpose of NPPES was to develop a unique identifier for each physician that billed medicare and medicaid. This identifier is now known as the National Provider Identifier Standard (NPI) which is a required 10 digit number that is unique to an individual provider at the national level.

    Once an NPI record is assigned to a healthcare provider, parts of the NPI record that have public relevance, including the provider’s name, speciality, and practice address are published in a searchable website as well as downloadable file of zipped data containing all of the FOIA disclosable health care provider data in NPPES and a separate PDF file of code values which documents and lists the descriptions for all of the codes found in the data file.

    Content

    The dataset contains the latest NPI downloadable file in an easy to query BigQuery table, npi_raw. In addition, there is a second table, npi_optimized which harnesses the power of Big Query’s next-generation columnar storage format to provide an analytical view of the NPI data containing description fields for the codes based on the mappings in Data Dissemination Public File - Code Values documentation as well as external lookups to the healthcare provider taxonomy codes . While this generates hundreds of columns, BigQuery makes it possible to process all this data effectively and have a convenient single lookup table for all provider information.

    Fork this kernel to get started.

    Acknowledgements

    https://bigquery.cloud.google.com/dataset/bigquery-public-data:nppes?_ga=2.117120578.-577194880.1523455401

    https://console.cloud.google.com/marketplace/details/hhs/nppes?filter=category:science-research

    Dataset Source: Center for Medicare and Medicaid Services. This dataset is publicly available for anyone to use under the following terms provided by the Dataset Source - http://www.data.gov/privacy-policy#data_policy — and is provided "AS IS" without any warranty, express or implied, from Google. Google disclaims all liability for any damages, direct or indirect, resulting from the use of the dataset.

    Banner Photo by @rawpixel from Unplash.

    Inspiration

    What are the top ten most common types of physicians in Mountain View?

    What are the names and phone numbers of dentists in California who studied public health?

  2. Data from: Medicare Data

    • kaggle.com
    zip
    Updated Feb 12, 2019
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    Centers for Medicare & Medicaid Services (2019). Medicare Data [Dataset]. https://www.kaggle.com/cms/cms-medicare
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    zip(0 bytes)Available download formats
    Dataset updated
    Feb 12, 2019
    Dataset authored and provided by
    Centers for Medicare & Medicaid Services
    License

    https://creativecommons.org/publicdomain/zero/1.0/https://creativecommons.org/publicdomain/zero/1.0/

    Description

    Context

    In the United States, Medicare is a single-payer, national social insurance program administered by the U.S. federal government since 1966. It provides health insurance for Americans aged 65 and older who have worked and paid into the system through the payroll tax. Source: https://en.wikipedia.org/wiki/Medicare_(United_States)

    Content

    This public dataset was created by the Centers for Medicare & Medicaid Services. The data summarizes the utilization and payments for procedures, services, and prescription drugs provided to Medicare beneficiaries by specific inpatient and outpatient hospitals, physicians, and other suppliers. The dataset includes the following data.

    Common inpatient and outpatient services All physician and other supplier procedures and services All Part D prescriptions. Providers determine what they will charge for items, services, and procedures provided to patients and these charges are the amount that providers bill for an item, service, or procedure.

    Fork this kernel to get started.

    Acknowledgements

    https://bigquery.cloud.google.com/dataset/bigquery-public-data:medicare

    https://cloud.google.com/bigquery/public-data/medicare

    Dataset Source: Center for Medicare and Medicaid Services. This dataset is publicly available for anyone to use under the following terms provided by the Dataset Source - http://www.data.gov/privacy-policy#data_policy — and is provided "AS IS" without any warranty, express or implied, from Google. Google disclaims all liability for any damages, direct or indirect, resulting from the use of the dataset.

    Banner Photo by @rawpixel from Unplash.

    Inspiration

    What is the total number of medications prescribed in each state?

    What is the most prescribed medication in each state?

    What is the average cost for inpatient and outpatient treatment in each city and state?

    Which are the most common inpatient diagnostic conditions in the United States?

    Which cities have the most number of cases for each diagnostic condition?

    What are the average payments for these conditions in these cities and how do they compare to the national average?

  3. d

    Geocoded Medicaid office locations in the United States

    • search.dataone.org
    • dataverse.harvard.edu
    Updated Mar 6, 2024
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    Shafer, Paul; Palmer, Maxwell; Cho, Ahyoung; Lynch, Mara; Louis, Pierce; Skinner, Alexandra (2024). Geocoded Medicaid office locations in the United States [Dataset]. http://doi.org/10.7910/DVN/AVRHMI
    Explore at:
    Dataset updated
    Mar 6, 2024
    Dataset provided by
    Harvard Dataverse
    Authors
    Shafer, Paul; Palmer, Maxwell; Cho, Ahyoung; Lynch, Mara; Louis, Pierce; Skinner, Alexandra
    Time period covered
    Aug 1, 2023 - Dec 19, 2023
    Area covered
    United States
    Description

    Big “p” policy changes at the state and federal level are certainly important to health equity, such as eligibility for and generosity of Medicaid benefits. Medicaid expansion has significantly expanded the number of people who are eligible for Medicaid and the creation of the health insurance exchanges (Marketplace) under the Affordable Care Act created a very visible avenue through which people can learn that they are eligible. Although many applications are now submitted online, physical access to state, county, and tribal government Medicaid offices still plays a critical role in understanding eligibility, getting help in applying, and navigating required documentation for both initial enrollment and redetermination of eligibility. However, as more government functions have moved online, in-person office locations and/or staff may have been cut to reduce costs, and gentrification has shifted where minoritized, marginalized, and/or low-income populations live, it is unclear if this key local connection point between residents and Medicaid has been maintained. Our objective was to identify and geocode all Medicaid offices in the United States for pairing with other spatial data (e.g., demographics, Medicaid participation, health care use, health outcomes) to investigate policy-relevant research questions. Three coders identified Medicaid office addresses in all 50 states and the District of Columbia by searching state government websites (e.g., Department of Health and Human Services or analogous state agency) during late 2021 and early 2022 for the appropriate Medicaid agency and its office locations, which were then reviewed for accuracy by a fourth coder. Our corpus of Medicaid office addresses was then geocoded using the Census Geocoder from the US Census Bureau (https://geocoding.geo.census.gov/geocoder/) with unresolved addresses investigated and/or manually geocoded using Google Maps. The corpus was updated in August through December 2023 following the end of the COVID-19 public health emergency by a fifth coder as several states closed and/or combined offices during the pandemic. After deduplication (e.g., where multiple counties share a single office) and removal of mailing addresses (e.g., PO Boxes), our dataset includes 3,027 Medicaid office locations. 1 (December 19, 2023) – original version 2 (January 25, 2024) – added related publication (Data in Brief), corrected two records that were missing negative signs in longitude 3 (February 6, 2024) – corrected latitude and longitude for one office (1340 State Route 9, Lake George, NY 12845) 4 (March 4, 2024) – added one office for Vermont after contacting relevant state agency (280 State Road, Waterbury, VT 05671)

  4. g

    Health Reform Monitoring Survey, United States, Second Quarter 2013 -...

    • search.gesis.org
    + more versions
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    GESIS search, Health Reform Monitoring Survey, United States, Second Quarter 2013 - Version 2 [Dataset]. http://doi.org/10.3886/ICPSR35623.v2
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    Dataset provided by
    GESIS search
    Inter-University Consortium for Political and Social Research
    License

    https://search.gesis.org/research_data/datasearch-httpwww-da-ra-deoaip--oaioai-da-ra-de452028https://search.gesis.org/research_data/datasearch-httpwww-da-ra-deoaip--oaioai-da-ra-de452028

    Area covered
    United States
    Description

    Abstract (en): In January 2013, the Urban Institute launched the Health Reform Monitoring Survey (HRMS), a quarterly survey of the nonelderly population, to explore the value of cutting-edge, Internet-based survey methods to monitor the Affordable Care Act (ACA) before data from federal government surveys are available. Topics covered by the second round of the survey (second quarter 2013) include self-reported health status, type of and satisfaction with current health insurance coverage, access to and use of health care, health care affordability, whether the respondent considered purchasing or tried to purchase health insurance coverage directly from an insurance company, whether the respondent considered obtaining coverage through Medicaid or other government sponsored assistance plan based on income or disability, sources of information about health insurance, and the importance of various criteria in choosing a health insurance plan. Additional information collected by the survey includes age, education, race, Hispanic origin, gender, income, household size, housing type, marital status, employment status, number of employees at place of work, United States citizenship, smoking, internet access, home ownership, body mass index, sexual orientation, and whether the respondent reported an ambulatory care sensitive condition or a mental or behavioral condition. ICPSR data undergo a confidentiality review and are altered when necessary to limit the risk of disclosure. ICPSR also routinely creates ready-to-go data files along with setups in the major statistical software formats as well as standard codebooks to accompany the data. In addition to these procedures, ICPSR performed the following processing steps for this data collection: Checked for undocumented or out-of-range codes.. Response Rates: The HRMS response rate is roughly five percent each quarter. Datasets:DS0: Study-Level FilesDS1: Public-use DataDS2: Restricted-use Data Household population aged 18-64. Each quarterly HRMS sample is drawn from the KnowledgePanel, a probability-based, nationally representative Internet panel maintained by GfK Custom Research. Beginning with the second quarter of 2013, the HRMS includes oversamples of adults with family incomes at or below 138 percent of the federal poverty level and adults from selected state groups based on (1) the potential for gains in insurance coverage in the state under the ACA as estimated by the Urban Institute's microsimulation model and (2) states of specific interest to the HRMS funders. Additional funders have supported oversamples of adults from individual states or subgroups of interest (including children). However, ICPSR received data only for the adults in the general national sample and the income and state group oversamples. 2019-07-10 Variable Q7_F was removed from public dataset. An updated codebook excluding this variable was provided for public use. Current release will feature DS1 as public-use data only and DS2 as restricted-use data. Previous release included both public and restricted versions of DS1. Study title updated to include geographic information.2017-06-20 The principal investigators added a new weight variable to the data file and the technical documentation was updated accordingly.2015-03-23 The principal investigators deleted the multiple imputation variables _1_famsize, _2_famsize, _3_famsize, _4_famsize and _5_famsize. ICPSR revised the codebook accordingly and added to the collection a plain text version of the data with a Stata setup and record layout file. Funding institution(s): Ford Foundation. Urban Institute. Robert Wood Johnson Foundation (71390). web-based survey

  5. Healthcare Patient Satisfaction - Data Collection

    • kaggle.com
    Updated Sep 21, 2023
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    KagglePro (2023). Healthcare Patient Satisfaction - Data Collection [Dataset]. https://www.kaggle.com/datasets/kaggleprollc/healthcare-patient-satisfaction-data-collection
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Sep 21, 2023
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    KagglePro
    License

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

    Description

    In the U.S., every hospital that receives payments from Medicare and Medicaid is mandated to provide quality data to The Centers for Medicare and Medicaid Services (CMS) annually. This data helps gauge patient satisfaction levels across the country. While overall hospital scores can be influenced by the quality of customer services, there may also be variations in satisfaction based on the type of hospital or its location.

    Year: 2016 - 2020

    The Star Rating Program, implemented by The Centers for Medicare & Medicaid Services (CMS), employs a five-star grading system to evaluate the experiences of Medicare beneficiaries with their respective health plans and the overall healthcare system. Health plans receive scores ranging from 1 to 5 stars, with 5 stars denoting the highest quality.

    Benefits:

    Historical Analysis: With data spanning from 2016 to 2020, researchers and analysts can observe trends over time, understanding how patient satisfaction has evolved over these years.

    Benchmarking: Hospitals can compare their performance against national averages or against peer institutions to see where they stand.

    Identifying Areas for Improvement: By analyzing specific metrics and feedback, hospitals can pinpoint areas where their services may be lacking and need enhancement.

    Policy and Decision Making: Governments and healthcare administrators can use the data to make informed decisions about healthcare policies, funding allocations, and other strategic decisions.

    Research and Academic Purposes: Academics and researchers can use the dataset for various studies, including correlational studies, predictions, and more.

    Geographical Insights: The dataset may provide insights into regional variations in patient satisfaction, helping to identify areas or states with particularly high or low scores.

    Understanding Factors Affecting Satisfaction: By correlating satisfaction scores with other variables (e.g., hospital type, size, location), it might be possible to determine which factors play the most significant role in patient satisfaction.

    Performance Evaluation: Hospitals can use the data to evaluate the efficacy of any interventions or changes they've made over the years in terms of improving patient satisfaction.

    Enhancing Patient Trust: Demonstrating transparency and a commitment to improvement can enhance patient trust and loyalty.

    Informed Patients: By making such data publicly available, potential patients can make more informed decisions about where to seek care based on the satisfaction ratings of previous patients.

    Source: https://data.cms.gov/provider-data/archived-data/hospitals

  6. g

    Health Reform Monitoring Survey, United States, Third Quarter 2018 -...

    • search.gesis.org
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    Inter-University Consortium for Political and Social Research, Health Reform Monitoring Survey, United States, Third Quarter 2018 - Archival Version [Dataset]. http://doi.org/10.3886/ICPSR37487
    Explore at:
    Dataset provided by
    GESIS search
    Inter-University Consortium for Political and Social Research
    License

    https://search.gesis.org/research_data/datasearch-httpwww-da-ra-deoaip--oaioai-da-ra-de738519https://search.gesis.org/research_data/datasearch-httpwww-da-ra-deoaip--oaioai-da-ra-de738519

    Area covered
    United States
    Description

    Abstract (en): In January 2013, the Urban Institute launched the Health Reform Monitoring Survey (HRMS), a survey of the nonelderly population, to explore the value of cutting-edge, Internet-based survey methods to monitor the Affordable Care Act (ACA) before data from federal government surveys are available. Topics covered by the 16th round of the survey (third quarter 2018) include self-reported health status, health insurance coverage, access to and use of health care, out-of-pocket health care costs, health care affordability, work experience, awareness of Medicaid work requirements, experiences with health care and social service providers, and health plan choice. Additional information collected by the survey includes age, gender, sexual orientation, marital status, education, race, Hispanic origin, United States citizenship, housing type, home ownership, internet access, income, employment status, and employer size. This study was conducted to provide information on health insurance coverage, access to and use of health care, health care affordability, and self-reported health status, as well as timely data on important implementation issues under the Affordable Care Act (ACA). The Health Reform Monitoring Survey (HRMS) provides data on health insurance coverage, access to and use of health care, health care affordability, and self-reported health status. Beginning in the second quarter of 2013, each round of the HRMS also contains topical questions focusing on timely ACA policy issues. In the first quarter of 2015, the HRMS shifted from a quarterly fielding schedule to a semiannual schedule. The variables include original survey questions, household demographic profile data, and constructed variables which can be used to link panel members who participated in multiple rounds. ICPSR data undergo a confidentiality review and are altered when necessary to limit the risk of disclosure. ICPSR also routinely creates ready-to-go data files along with setups in the major statistical software formats as well as standard codebooks to accompany the data. In addition to these procedures, ICPSR performed the following processing steps for this data collection: Created variable labels and/or value labels.; Created online analysis version with question text.; Performed recodes and/or calculated derived variables.; Checked for undocumented or out-of-range codes.. Response Rates: The HRMS response rate is roughly five percent each round. Datasets:DS0: Study-Level FilesDS1: Public-Use DataDS2: Restricted-Use Data Household population aged 18-64 Smallest Geographic Unit: Census region For each HRMS round a stratified random sample of adults ages 18-64 is drawn from the KnowledgePanel, a probability-based, nationally represented Internet panel maintained by Ipsos. The approximately 55,000 adults in the panel include households with and without Internet access. Panel members are recruited from an address-based sample frame derived from the United States Postal Service Delivery Sequence File, which covers 97 percent of United States households. The HRMS sample includes a random sample of approximately 9,500 nonelderly adults per quarter, including oversamples of adults with family incomes at or below 138 percent of the federal poverty line. Additional funders have supported oversamples of adults from individual states or subgroups of interest. However, the data file only includes data for adults in the general national sample and the income oversample. web-based survey

  7. d

    NCCI Medically Unlikely Edits (MUEs).

    • datadiscoverystudio.org
    • data.amerigeoss.org
    • +1more
    csv, json, rdf, xml
    Updated Jun 8, 2018
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    (2018). NCCI Medically Unlikely Edits (MUEs). [Dataset]. http://datadiscoverystudio.org/geoportal/rest/metadata/item/55198abca4394dcd8e72f55276fed9fb/html
    Explore at:
    csv, json, xml, rdfAvailable download formats
    Dataset updated
    Jun 8, 2018
    Description

    description:

    Medically Unlikely Edits (MUEs) define for each HCPCS / CPT code the maximum units of service (UOS) that a provider would report under most circumstances for a single beneficiary on a single date of service.

    Practitioner services also refers to ambulatory surgical centers.
    DME refers to provider claims for durable medical equipment.

    The CMS National Correct Coding Initiative (NCCI) promotes national correct coding methodologies and reduces improper coding which may result in inappropriate payments of Medicare Part B claims and Medicaid claims. NCCI procedure-to-procedure (PTP) edits define pairs of Healthcare Common Procedure Coding System (HCPCS)/Current Procedural Terminology (CPT) codes that should not be reported together for a variety of reasons. The purpose of the PTP edits is to prevent improper payments when incorrect code combinations are reported. The edits in this dataset are active for the dates indicated within. This file should NOT be used by state Medicaid programs as their edit file. Current Procedural Terminology (CPT) codes, descriptions and other data only are copyright 2017 American Medical Association. All rights reserved. CPT is a registered trademark of the American Medical Association. Applicable FARSDFARS Restrictions Apply to Government Use. Fee schedules, relative value units, conversion factors and/or related components are not assigned by the AMA, are not part of CPT, and the AMA is not recommending their use. The AMA does not directly or indirectly practice medicine or dispense medical services. The AMA assumes no liability for the data contained or not contained herein.

    For more information, visit https://www.medicaid.gov/medicaid/program-integrity/ncci/index.html.

    ; abstract:

    Medically Unlikely Edits (MUEs) define for each HCPCS / CPT code the maximum units of service (UOS) that a provider would report under most circumstances for a single beneficiary on a single date of service.

    Practitioner services also refers to ambulatory surgical centers.
    DME refers to provider claims for durable medical equipment.

    The CMS National Correct Coding Initiative (NCCI) promotes national correct coding methodologies and reduces improper coding which may result in inappropriate payments of Medicare Part B claims and Medicaid claims. NCCI procedure-to-procedure (PTP) edits define pairs of Healthcare Common Procedure Coding System (HCPCS)/Current Procedural Terminology (CPT) codes that should not be reported together for a variety of reasons. The purpose of the PTP edits is to prevent improper payments when incorrect code combinations are reported. The edits in this dataset are active for the dates indicated within. This file should NOT be used by state Medicaid programs as their edit file. Current Procedural Terminology (CPT) codes, descriptions and other data only are copyright 2017 American Medical Association. All rights reserved. CPT is a registered trademark of the American Medical Association. Applicable FARSDFARS Restrictions Apply to Government Use. Fee schedules, relative value units, conversion factors and/or related components are not assigned by the AMA, are not part of CPT, and the AMA is not recommending their use. The AMA does not directly or indirectly practice medicine or dispense medical services. The AMA assumes no liability for the data contained or not contained herein.

    For more information, visit https://www.medicaid.gov/medicaid/program-integrity/ncci/index.html.

  8. Licensed and Certified Healthcare Facility Listing

    • data.chhs.ca.gov
    • data.ca.gov
    • +5more
    csv, pdf, tableau +2
    Updated Jun 25, 2025
    + more versions
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    California Department of Public Health (2025). Licensed and Certified Healthcare Facility Listing [Dataset]. https://data.chhs.ca.gov/dataset/healthcare-facility-locations
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    tableau, csv(793019), zip, pdf, pdf(95299), xlsx(16257), xlsx(11897), csv(7735675), xlsx(30428)Available download formats
    Dataset updated
    Jun 25, 2025
    Dataset authored and provided by
    California Department of Public Healthhttps://www.cdph.ca.gov/
    Description

    Note: This web page provides data on health facilities only. To file a complaint against a facility, please see: https://www.cdph.ca.gov/Programs/CHCQ/LCP/Pages/FileAComplaint.aspx

    The California Department of Public Health (CDPH), Center for Health Care Quality, Licensing and Certification (L&C) Program licenses and certifies more than 30 types of healthcare facilities. The Electronic Licensing Management System (ELMS) is a CDPH data system created to manage state licensing-related data and enforcement actions. This file includes California healthcare facilities that are operational and have a current license issued by the CDPH and/or a current U.S. Department of Health and Human Services’ Centers for Medicare and Medicaid Services (CMS) certification.

    To link the CDPH facility IDs with those from other Departments, like HCAI, please reference the "Licensed Facility Cross-Walk" Open Data table at https://data.chhs.ca.gov/dataset/licensed-facility-crosswalk. Facility geographic variables are updated monthly, if latitude/longitude information is missing at any point in time, it should be available when the next time the Open Data facility file is refreshed.

    Please note that the file contains the data from ELMS as of the 11th business day of the month. See DATA_DATE variable for the specific date of when the data was extracted.

    Map of all Health Care Facilities in California: https://go.cdii.ca.gov/cdph-facilities

  9. d

    NCCI Procedure to Procedure Edits (PTP).

    • datadiscoverystudio.org
    • data.wu.ac.at
    csv, json, rdf, xml
    Updated Jun 8, 2018
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    (2018). NCCI Procedure to Procedure Edits (PTP). [Dataset]. http://datadiscoverystudio.org/geoportal/rest/metadata/item/261ea8e526bc4a0c949c17595dc0ea96/html
    Explore at:
    rdf, xml, json, csvAvailable download formats
    Dataset updated
    Jun 8, 2018
    Description

    description:

    The CMS National Correct Coding Initiative (NCCI) promotes national correct coding methodologies and reduces improper coding which may result in inappropriate payments of Medicare Part B claims and Medicaid claims.

    NCCI procedure-to-procedure (PTP) edits define pairs of Healthcare Common Procedure Coding System (HCPCS)/Current Procedural Terminology (CPT) codes that should not be reported together for a variety of reasons. The purpose of the PTP edits is to prevent improper payments when incorrect code combinations are reported.

    Practitioner services also refers to ambulatory surgical centers.
    DME refers to provider claims for durable medical equipment.

    The CMS National Correct Coding Initiative (NCCI) promotes national correct coding methodologies and reduces improper coding which may result in inappropriate payments of Medicare Part B claims and Medicaid claims. NCCI procedure-to-procedure (PTP) edits define pairs of Healthcare Common Procedure Coding System (HCPCS)/Current Procedural Terminology (CPT) codes that should not be reported together for a variety of reasons. The purpose of the PTP edits is to prevent improper payments when incorrect code combinations are reported. The edits in this dataset are active for the dates indicated within. This file should NOT be used by state Medicaid programs as their edit file. Current Procedural Terminology (CPT) codes, descriptions and other data only are copyright 2017 American Medical Association. All rights reserved. CPT is a registered trademark of the American Medical Association. Applicable FARSDFARS Restrictions Apply to Government Use. Fee schedules, relative value units, conversion factors and/or related components are not assigned by the AMA, are not part of CPT, and the AMA is not recommending their use. The AMA does not directly or indirectly practice medicine or dispense medical services. The AMA assumes no liability for the data contained or not contained herein.

    For more information, visit https://www.medicaid.gov/medicaid/program-integrity/ncci/index.html.

    ; abstract:

    The CMS National Correct Coding Initiative (NCCI) promotes national correct coding methodologies and reduces improper coding which may result in inappropriate payments of Medicare Part B claims and Medicaid claims.

    NCCI procedure-to-procedure (PTP) edits define pairs of Healthcare Common Procedure Coding System (HCPCS)/Current Procedural Terminology (CPT) codes that should not be reported together for a variety of reasons. The purpose of the PTP edits is to prevent improper payments when incorrect code combinations are reported.

    Practitioner services also refers to ambulatory surgical centers.
    DME refers to provider claims for durable medical equipment.

    The CMS National Correct Coding Initiative (NCCI) promotes national correct coding methodologies and reduces improper coding which may result in inappropriate payments of Medicare Part B claims and Medicaid claims. NCCI procedure-to-procedure (PTP) edits define pairs of Healthcare Common Procedure Coding System (HCPCS)/Current Procedural Terminology (CPT) codes that should not be reported together for a variety of reasons. The purpose of the PTP edits is to prevent improper payments when incorrect code combinations are reported. The edits in this dataset are active for the dates indicated within. This file should NOT be used by state Medicaid programs as their edit file. Current Procedural Terminology (CPT) codes, descriptions and other data only are copyright 2017 American Medical Association. All rights reserved. CPT is a registered trademark of the American Medical Association. Applicable FARSDFARS Restrictions Apply to Government Use. Fee schedules, relative value units, conversion factors and/or related components are not assigned by the AMA, are not part of CPT, and the AMA is not recommending their use. The AMA does not directly or indirectly practice medicine or dispense medical services. The AMA assumes no liability for the data contained or not contained herein.

    For more information, visit https://www.medicaid.gov/medicaid/program-integrity/ncci/index.html.

  10. Not seeing a result you expected?
    Learn how you can add new datasets to our index.

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Centers for Medicare & Medicaid Services (2019). NPPES Plan and Provider Enumeration System [Dataset]. https://www.kaggle.com/cms/nppes
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NPPES Plan and Provider Enumeration System

The CMS National Plan and Provider Enumeration System Data (BigQuery Dataset)

Explore at:
zip(0 bytes)Available download formats
Dataset updated
Mar 20, 2019
Dataset authored and provided by
Centers for Medicare & Medicaid Services
License

https://creativecommons.org/publicdomain/zero/1.0/https://creativecommons.org/publicdomain/zero/1.0/

Description

Context

The CMS National Plan and Provider Enumeration System (NPPES) was developed as part of the Administrative Simplification provisions in the original HIPAA act. The primary purpose of NPPES was to develop a unique identifier for each physician that billed medicare and medicaid. This identifier is now known as the National Provider Identifier Standard (NPI) which is a required 10 digit number that is unique to an individual provider at the national level.

Once an NPI record is assigned to a healthcare provider, parts of the NPI record that have public relevance, including the provider’s name, speciality, and practice address are published in a searchable website as well as downloadable file of zipped data containing all of the FOIA disclosable health care provider data in NPPES and a separate PDF file of code values which documents and lists the descriptions for all of the codes found in the data file.

Content

The dataset contains the latest NPI downloadable file in an easy to query BigQuery table, npi_raw. In addition, there is a second table, npi_optimized which harnesses the power of Big Query’s next-generation columnar storage format to provide an analytical view of the NPI data containing description fields for the codes based on the mappings in Data Dissemination Public File - Code Values documentation as well as external lookups to the healthcare provider taxonomy codes . While this generates hundreds of columns, BigQuery makes it possible to process all this data effectively and have a convenient single lookup table for all provider information.

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Acknowledgements

https://bigquery.cloud.google.com/dataset/bigquery-public-data:nppes?_ga=2.117120578.-577194880.1523455401

https://console.cloud.google.com/marketplace/details/hhs/nppes?filter=category:science-research

Dataset Source: Center for Medicare and Medicaid Services. This dataset is publicly available for anyone to use under the following terms provided by the Dataset Source - http://www.data.gov/privacy-policy#data_policy — and is provided "AS IS" without any warranty, express or implied, from Google. Google disclaims all liability for any damages, direct or indirect, resulting from the use of the dataset.

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Inspiration

What are the top ten most common types of physicians in Mountain View?

What are the names and phone numbers of dentists in California who studied public health?

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