The CMS Program Statistics - Medicare Total Enrollment tables provide data on characteristics of the Medicare-covered populations. For additional information on enrollment, providers, and Medicare use and payment, visit the CMS Program Statistics page. These data do not exist in a machine-readable format, so the view data and API options are not available. Please use the download function to access the data. Below is the list of tables: MDCR ENROLL AB 1. Total Medicare Enrollment: Total, Original Medicare, and Medicare Advantage and Other Health Plan Enrollment, Yearly Trend MDCR ENROLL AB 2. Total Medicare Enrollment: Total, Original Medicare, Medicare Advantage and Other Health Plan Enrollment, and Resident Population, by Area of Residence MDCR ENROLL AB 3. Total Medicare Enrollment: Part A and/or Part B Total, Aged, and Disabled Enrollees, Yearly Trend MDCR ENROLL AB 4. Total Medicare Enrollment: Part A and/or Part B Enrollees, by Age Group, Yearly Trend MDCR ENROLL AB 5. Total Medicare Enrollment: Part A and/or Part B Enrollees, by Demographic Characteristics MDCR ENROLL AB 6. Total Medicare Enrollment: Part A and/or Part B Enrollees, by Type of Entitlement and Demographic Characteristics MDCR ENROLL AB 7. Total Medicare Enrollment: Part A and/or Part B Total, Aged, and Disabled Enrollees, by Area of Residence MDCR ENROLL AB 8. Total Medicare Enrollment: Part A and/or Part B Enrollees, by Type of Entitlement and Area of Residence

This series of files links two large population-based sources providing detailed data about Medicare beneficiaries with cancer. The SEER (Surveillance, Epidemiology, and End Results) program consists of clinical, demographic, and cause of death information collected from tumor registries beginning in January 1, 1973. The Medicare contribution includes all claims for covered health care services from beneficiaries’ time of eligibility until death. Linkage is processed biennially by SEER and Centers for Medicare and Medicaid Services (CMS) staff. 95% of individuals age 65 and older are included in the SEER files. Due to privacy concerns, access to this database requires an application, SEER-Medicare Data Use Agreement (DUA), and documentation of institutional review board approval. Additionally, the National Cancer Institute’s information technology contractor assesses a processing fee the amount of which is dependent upon the type and number of files requested.

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?

Context

This public dataset was created by the Centers for Medicare & Medicaid Services. The data summarize counts of enrollees who are dually-eligible for both Medicare and Medicaid program, including those in Medicare Savings Programs. “Duals” represent 20 percent of all Medicare beneficiaries, yet they account for 34 percent of all spending by the program, according to the Commonwealth Fund . As a representation of this high-needs, high-cost population, these data offer a view of regions ripe for more intensive care coordination that can address complex social and clinical needs. In addition to the high cost savings opportunity to deliver upstream clinical interventions, this population represents the county-by-county volume of patients who are eligible for both state level (Medicaid) and federal level (Medicare) reimbursements and potential funding streams to address unmet social needs across various programs, waivers, and other projects. The dataset includes eligibility type and enrollment by quarter, at both the state and county level. These data represent monthly snapshots submitted by states to the CMS, which are inherently lower than ever-enrolled counts (which include persons enrolled at any time during a calendar year.) For more information on dually eligible beneficiaries

Querying BigQuery tables

You can use the BigQuery Python client library to query tables in this dataset in Kernels. Note that methods available in Kernels are limited to querying data. Tables are at bigquery-public-data.sdoh_cms_dual_eligible_enrollment.

Sample Query

In what counties in Michigan has the number of dual-eligible individuals increased the most from 2015 to 2018? Find the counties in Michigan which have experienced the largest increase of dual enrollment households

duals_Jan_2015 AS ( SELECT Public_Total AS duals_2015, County_Name, FIPS FROM bigquery-public-data.sdoh_cms_dual_eligible_enrollment.dual_eligible_enrollment_by_county_and_program WHERE State_Abbr = "MI" AND Date = '2015-12-01' ),

duals_increase AS ( SELECT d18.FIPS, d18.County_Name, d15.duals_2015, d18.duals_2018, (d18.duals_2018 - d15.duals_2015) AS total_duals_diff FROM duals_Jan_2018 d18 JOIN duals_Jan_2015 d15 ON d18.FIPS = d15.FIPS )

SELECT * FROM duals_increase WHERE total_duals_diff IS NOT NULL ORDER BY total_duals_diff DESC

NCHS has linked data from various surveys with Medicare program enrollment and health care utilization and expenditure data from the Centers for Medicare & Medicaid Services (CMS). Linkage of the NCHS survey participants with the CMS Medicare data provides the opportunity to study changes in health status, health care utilization and costs, and prescription drug use among Medicare enrollees. Medicare is the federal health insurance program for people who are 65 or older, certain younger people with disabilities, and people with End-Stage Renal Disease.

Content

This crosswalk links the types of providers and suppliers who are eligible to apply for enrollment in the Medicare program with the appropriate Healthcare Provider Taxonomy Codes. This crosswalk includes the Medicare Specialty Codes for those provider/supplier types who have Medicare Specialty Codes. The Healthcare Provider Taxonomy Code Set is available from the Washington Publishing Company (www.wpc-edi.com) and is maintained by the National Uniform Claim Committee (www.nucc.org). The code set is updated twice a year, with the updates being effective April 1 and October 1 of each year. This document reflects Healthcare Provider Taxonomy Codes effective for use on April 2, 2018.

When changes are made to Medicare provider enrollment requirements, the Medicare Specialty Codes, or the Healthcare Provider Taxonomy Code Set, this document may need to be revised.

NOTE: This document does not alter existing Medicare claims preparation, processing, or payment instructions, nor does it alter existing Medicare provider enrollment requirements or policies.

Context

This is a dataset hosted by the Centers for Medicare & Medicaid Services (CMS). The organization has an open data platform found here and they update their information according the amount of data that is brought in. Explore CMS's Data using Kaggle and all of the data sources available through the CMS organization page!

• Update Frequency: This dataset is updated daily.

Acknowledgements

This dataset is maintained using Socrata's API and Kaggle's API. Socrata has assisted countless organizations with hosting their open data and has been an integral part of the process of bringing more data to the public.

Cover photo by Markus Spiske on Unsplash
Unsplash Images are distributed under a unique Unsplash License.

This dataset is distributed under NA

The Medicare Telehealth Trends dataset provides information about people with Medicare who used telehealth services between January 1, 2020 and June 30, 2024. The data were used to generate the Medicare Telehealth Trends Report.

Verify the accuracy of SSNs of all individual Medicare providers, owners, managing/directing employees, authorized representatives, ambulance service medical directors, ambulance crew members, technicians, chain organization administrators, Independent Diagnostic Test Facility (IDTF), supervising/directing physicians, and IDTF interpretation service providers. Also included in this Agreement are individual health care providers who apply for a National Provider Identification Number (NPI).

Content

The hospital readmission rate PUF presents nation-wide information about inpatient hospital stays that occurred within 30 days of a previous inpatient hospital stay (readmissions) for Medicare fee-for-service beneficiaries. The readmission rate equals the number of inpatient hospital stays classified as readmissions divided by the number of index stays for a given month. Index stays include all inpatient hospital stays except those where the primary diagnosis was cancer treatment or rehabilitation. Readmissions include stays where a beneficiary was admitted as an inpatient within 30 days of the discharge date following a previous index stay, except cases where a stay is considered always planned or potentially planned. Planned readmissions include admissions for organ transplant surgery, maintenance chemotherapy/immunotherapy, and rehabilitation.

This dataset has several limitations. Readmissions rates are unadjusted for age, health status or other factors. In addition, this dataset reports data for some months where claims are not yet final. Data published for the most recent six months is preliminary and subject to change. Final data will be published as they become available, although the difference between preliminary and final readmission rates for a given month is likely to be less than 0.1 percentage point.

Data Source: The primary data source for these data is the CMS Chronic Condition Data Warehouse (CCW), a database with 100% of Medicare enrollment and fee-for-service claims data. For complete information regarding data in the CCW, visit http://ccwdata.org/index.php. Study Population: Medicare fee-for-service beneficiaries with inpatient hospital stays.

Context

This is a dataset hosted by the Centers for Medicare & Medicaid Services (CMS). The organization has an open data platform found here and they update their information according the amount of data that is brought in. Explore CMS's Data using Kaggle and all of the data sources available through the CMS organization page!

• Update Frequency: This dataset is updated daily.

Acknowledgements

This dataset is maintained using Socrata's API and Kaggle's API. Socrata has assisted countless organizations with hosting their open data and has been an integral part of the process of bringing more data to the public.

Cover photo by Justyn Warner on Unsplash
Unsplash Images are distributed under a unique Unsplash License.

This dataset is distributed under NA

This dataset includes total enrollment in separate CHIP (S-CHIP) programs by month and state from April 2023 forward. Sources: T-MSIS Analytic Files (TAF) and state-submitted enrollment totals. The data notes indicate when a state’s monthly total was a state-submitted value, rather than from T-MSIS. Methods: Enrollment includes individuals enrolled in S-CHIP at any point during the coverage month, excluding those enrolled in dental-only coverage. The S-CHIP enrollment in this report also excludes enrollees covered by Medicaid expansion CHIP, a program in which a state receives federal funding to expand Medicaid eligibility to optional targeted low-income children that meets the requirements of section 2103 of the Social Security Act. If an individual is enrolled in both Medicaid or Medicaid-expansion CHIP and S-CHIP in a given month, TAF picks the program in which they were last enrolled. Unless S-CHIP enrollment counts are replaced with a state-submitted value, each state's monthly S-CHIP enrollment is equal to the number of unique people in TAF with a CHIP_CODE = 3 (S-CHIP) and ELGBLTY_GRP_CD not equal to ‘66’ (Children Eligible for Dental Only Supplemental Coverage). More information about TAF is available at https://www.medicaid.gov/medicaid/data-systems/macbis/medicaid-chip-research-files/transformed-medicaid-statistical-information-system-t-msis-analytic-files-taf/index.html. Note: A historic dataset with S-CHIP enrollment by month and state from April 2023 to June 2024 is also available at: https://data.medicaid.gov/dataset/d30cfc7c-4b32-4df1-b2bf-e0a850befd77. This historic dataset was created to fulfill reporting requirements under section 1902(tt)(1) of the Social Security Act, which was added by section 5131(b) of subtitle D of title V of division FF of the Consolidated Appropriations Act, 2023 (P.L. 117-328) (CAA, 2023). Please note that the methods used to count S-CHIP enrollees differ slightly between the two datasets; as a result, data users should exercise caution if comparing S-CHIP enrollment across the two datasets. State notes: Alaska, District of Columbia, Hawaii, New Hampshire, New Mexico, North Carolina, North Dakota, Ohio, South Carolina, Vermont, and Wyoming do not have S-CHIP programs. Maryland has an S-CHIP program for the from conception to end of pregnancy group that began in July 2023; April 2023 - June 2023 data for Maryland represents retroactive coverage. Oregon moved all its S-CHIP enrollees, other than those in the from conception to the end of pregnancy group, to a Medicaid-expansion CHIP program effective January 1, 2024. CHIP: Children's Health Insurance Program

Hospitals Registered with MedicareThis feature layer, utilizing data from the Centers of Medicare and Medicaid Services (CMS), depicts all hospitals that are currently registered with Medicare in the U.S. Per NIH, "Since the passage of Medicare legislation in 1965, Section 1861 of the Social Security Act has stated that hospitals participating in Medicare must meet certain requirements specified in the act and that the Secretary of the Department of Health, Education and Welfare (HEW) [now the Department of Health and Human Services (DHHS)] may impose additional requirements found necessary to ensure the health and safety of Medicare beneficiaries receiving services in hospitals. On this basis, the Conditions of Participation, a set of regulations setting minimum health and safety standards for hospitals participating in Medicare, were promulgated in 1966 and substantially revised in 1986."Ascension Columbia St Mary's HospitalData currency: 11/26/2024Data modification: This data was created using the geocoding process on the CSV file.Data downloaded from: Hospital General InformationFor more information: HospitalsSupport documentation: Data dictionaryFor feedback, please contact: ArcGIScomNationalMaps@esri.comCenters of Medicare and Medicaid ServicesPer USA.gov, "The Centers for Medicare and Medicaid Services (CMS) provides health coverage to more than 100 million people through Medicare, Medicaid, the Children’s Health Insurance Program, and the Health Insurance Marketplace. The CMS seeks to strengthen and modernize the Nation’s health care system, to provide access to high quality care and improved health at lower costs."

This historic dataset with total enrollment in separate CHIP programs by month and state was created to fulfill reporting requirements under section 1902(tt)(1) of the Social Security Act, which was added by section 5131(b) of subtitle D of title V of division FF of the Consolidated Appropriations Act, 2023 (P.L. 117-328) (CAA, 2023). For each month from April 1, 2023, through June 30, 2024, states were required to submit to CMS (on a timely basis), and CMS was required to make public, certain monthly data, including the total number of beneficiaries who were enrolled in a separate CHIP program. Accordingly, this historic dataset contains separate CHIP enrollment by month and state between April 2023 and June 2024. CMS will continue to publicly report separate CHIP enrollment by month and state (beyond the historic CAA/Unwinding period) in a new dataset, which is available at [link]. Please note that the methods used to count separate CHIP enrollees differ slightly between the two datasets; as a result, data users should exercise caution if comparing separate CHIP enrollment across the two datasets. Sources: T-MSIS Analytic Files (TAF) and state-submitted enrollment totals. The data notes indicate when a state’s monthly total was a state-submitted value, rather than from T-MSIS.TAF data were pulled as follows:April 2023 enrollment - TAF as of August 2023May 2023 enrollment - TAF as of August 2023June 2023 enrollment - TAF as of September 2023July 2023 enrollment - TAF as of October 2023August 2023 enrollment - TAF as of November 2023September 2023 enrollment - TAF as of December 2023October 2023 enrollment - TAF as of January 2024November 2023 enrollment - TAF as of February 2024December 2023 enrollment - TAF as of March 2024January 2024 enrollment - TAF as of April 2024February 2024 enrollment - TAF as of May 2024March 2024 enrollment - TAF as of June 2024April 2024 enrollment – TAF as of July 2024May 2024 enrollment – TAF as of August 2024June 2024 enrollment – TAF as of September 2024 TAF are produced one month after the T-MSIS submission month. For example, TAF as of August 2023 is based on July T-MSIS submissions. Notes: The separate CHIP enrollment in this report is not inclusive of enrollees covered by Medicaid expansion CHIP. Enrollment includes individuals enrolled in separate CHIP at any point during the month but excludes those enrolled in both Medicaid and separate CHIP during the month. See the Data Sources and Metrics Definitions Overview document for a full description of the data sources, metric definitions, and general data limitations.Alaska, District of Columbia, Hawaii, New Hampshire, New Mexico, North Carolina, North Dakota, Ohio, South Carolina, Vermont, and Wyoming do not have separate CHIP Programs. Maryland has a separate CHIP program that began in July 2023; April 2023 - June 2023 data for Maryland represents retroactive coverage. This document includes separate CHIP data submitted to CMS by states via T-MSIS or a separate collection form. These data include reporting metrics consistent with section 1902(tt)(1) of the Social Security Act.CHIP: Children's Health Insurance Program Data notes: (a) State-submitted value; data not from T-MSIS(b1) May 2023 enrollment pulled from TAF as of September 2023(b2) Data was restated using TAF as of October 2023(b3) Data was restated using TAF as of April 2024(b4) Data was restated using TAF as of July 2024(b5) Data was restated using TAF as of August 2024(c) Enrollment counts include postpartum women with coverage funded via a Health Services Initiative

analyze the basic stand alone medicare claims public use files (bsapufs) with r and monetdb the centers for medicare and medicaid services (cms) took the plunge. the famous medicare 5% sample has been released to the public, free of charge. jfyi - medicare is the u.s. government program that provides health insurance to 50 million elderly and disabled americans. the basic stand alone medicare claims public use files (bsapufs) contain either person- or event-level data on inpatient stays, durable medical equipment purchases, prescription drug fills, hospice users, doctor visits, home health provision , outpatient hospital procedures, skilled nursing facility short-term residents, as well as aggregated statistics for medicare beneficiaries with chronic conditions and medicare beneficiaries living in nursing homes. oh sorry, there's one catch: they only provide sas scripts to analyze everything. cue the villian music. that bored old game of monopoly ends today. the initial release of the 2008 bsapufs was accompanied by some major fanfare in the world of health policy , a big win for government transparency. unfortunately, the final files that cleared the confidentiality hurdles are heavily de-identified and obfuscated. prime examples: none of the files can be linked to any other file. not across years, not across expenditure categories costs are rounded to the nearest fifth or tenth dollar at lower values, nearest thousandth at higher values ages are categorized into five year bands so these files are baldly inferior to the unsquelched, linkable data only available through an expensive formal application process. any researcher with a budget flush enough to afford a sas license (the only statistical software mentioned in the cms official documentation) can probably also cough up the money to buy the identifiable data through resdac (resdac, btw, rocks). soapbox: cms released free public data sets that could only be analyzed with a software package costing thousands of dollars. so even though the actual data sets were free, researchers still needed deep pock ets to buy sas. meanwhile, the unsquelched and therefore superior data sets are also available for many thousands of dollars. researchers with funding would (reasonably) just buy the better data. researchers without any financial resources - the target audience of free, public data - were left out in the cold. no wonder these bsapufs haven't been used much. that ends now. using r, monetdb, and the personal computer you already own (mine cost $700 in 2009), researchers can, for the first time, seriously analyze these medicare public use files without spending another dime. woah. plus hey guess what all you researcher fat-cats with your federal grant streams and your proprietary software licenses: r + monetdb runs one heckuva lot faster than sas. woah^2. dump your sas license water wings and learn how to swim. the scripts below require monetdb . click here for step-by-step instructions of how to install it on windows and click here for speed tests. vroom. since the bsapufs comprise 5% of the medicare population, ya generally need to multiply any counts or sums by twenty. although the individuals represented in these claims are randomly sampled, this data should not be treated like a complex survey sample, meaning that the creation of a survey object is unnecessary. most bsapufs generalize to either the total or fee-for-service medicare population, but each file is different so give the documentation a hard stare before that eureka moment. this new github repository contains three scripts: 2008 - download all csv files.R loop through and download every zip file hosted by cms unzip the contents of each zipped file to the working directory 2008 - import all csv files into monetdb.R create the batch (.bat) file needed to initiate the monet database in the f uture loop through each csv file in the current working directory and import them into the monet database create a well-documented block of code to re-initiate the monetdb server in the future 2008 - replicate cms publications.R initiate the same monetdb server instance, unsing the same well-documented block of code as above replicate nine sets of statistics found in data tables provided by cms < a href="https://github.com/ajdamico/usgsd/tree/master/Basic%20Stand%20Alone%20Medicare%20Claims%20Public%20Use%20Files">click here to view these three scripts for more detail about the basic stand alone medicare claims public use files (bsapufs), visit: the centers for medicare and medicaid's bsapuf homepage a joint academyhealth webinar given by the organizations that partnered to create these files - cms, impaq, norc notes: the replication script has oodles of easily-modified syntax and should be viewed for analysis examples. if you know the name of the data table you want to examine, you can quickly modify these general monetdb analysis examples too. just run sql queries - sas users, that's "proc...

Enrollment includes both Medicaid-only and Medicare-Medicaid (“dual”) enrollees. For both types of enrollees, Medicaid covers LTSS. For dual enrollees, Medicaid may also cover Medicare cost-sharing for acute, primary care, and specialty services covered by Medicare, and other non-LTSS services that are not covered by Medicare. Some comprehensive managed care programs enroll beneficiaries who may be at risk of needing LTSS but do not receive any LTSS. These counts only include individuals that receive LTSS. Moreover, states differ in their ability to report individuals who use MLTSS versus those who are enrolled (and may or may not be using LTSS). This table reports MLTSS users unless otherwise noted. Comprehensive Managed Care Including LTSS does not include PACE programs. MLTSS Only programs cover LTSS under capitation; acute, primary, and specialty care services for these enrollees may be covered by another Medicaid MCO, Medicaid FFS, or by Medicare for dual enrollees. These data include states that provide MLTSS plus other benefits in a package that does not include inpatient medical care. The indicated territory was not able to supply data for this report. The Northern Mariana Islands reported that they have no Medicaid managed care enrollment, but they did not report total Medicaid enrollees. Enrollment and user counts include both Medicaid-only enrollees and dually eligible individuals. For both types of enrollees, Medicaid covers LTSS. For dually eligible individuals, Medicaid may also cover Medicare cost-sharing for acute, primary care, and specialty services covered by Medicare, and other non-LTSS services that are not covered by Medicare. The “Comprehensive Managed Care Including LTSS” column does not include PACE programs. Columns indicating the "Number of enrollees using LTSS" reflect what states reported. In addition to the three states that reported LTSS users (Arizona, New York and Wisconsin), California and Delaware also offer LTSS services in a stand-alone program. Note: "n/a" indicates that a state or territory was not able to report data or does not have a managed care program. "--" indicates that a state or territory does not operate programs of the type listed in the column heading. Enrollment and user counts include both Medicaid-only and dually eligible individuals. For both types of enrollees, Medicaid covers LTSS. For dually eligible individuals, Medicaid may also cover Medicare cost-sharing for acute, primary care, and specialty services covered by Medicare and other non-LTSS services that are not covered by Medicare. Columns indicating the "Number of enrollees using LTSS" reflect what states reported. In addition to the three states that reported LTSS users (Arizona, New York and Wisconsin), California and Delaware also offer LTSS services in a stand-alone program.

The dataset contains information about the prevalence of chronic conditions among Original Medicare beneficiaries as well as about the spending and co-occurring conditions for those with each condition. The data are available for California and for the rest of the United States, overall and by demographic and geographic groups. Additionally, the data are available for each of 19 California geographic regions overall and by demographic and geographic groups. The data represent Medicare beneficiaries who are in the Original Medicare program. Medicare offers health care coverage for older adults and certain individuals with disabilities. The Original Medicare program is Parts A and B of Medicare, administered by the U.S. Centers for Medicare & Medicaid Services. The analysis excludes enrollees of the Medicare Advantage program, administered by private insurers, because Medicare Advantage data are incomplete.

This data set accompanies the Profile of the California Medicare Population chartbook, published by the Office of Medicare Innovation and Integration in February 2022, and available at (https://www.dhcs.ca.gov/services/Documents/OMII-Medicare-Databook-February-18-2022.pdf). The three data files in this data set were analyzed from federal administrative data (the Medicare Master Beneficiary Summary File) for beneficiary characteristics as of March 2021. These datasets include: Medicare enrollment, Medicare Advantage enrollment (and its converse fee-for-service Medicare enrollment), dual Medi-Cal eligibility and enrollment (and its converse Medicare-only enrollment), by county. Medicare Savings Program enrollees were considered Medicare-only and not dually enrolled in Medi-Cal. All Medicare Part C beneficiaries, including PACE, Cal MediConnect and Special Needs Plans, were considered to have Medicare Advantage.

DHCS partnered with The SCAN Foundation and ATI Advisory in 2021 and 2022 to develop a series of chartbooks that provide information about Medicare beneficiaries in California. This work is supported by a grant from The SCAN Foundation to advance a coordinated and easily navigated system of high-quality services for older adults that preserve dignity and independence. For more information, visit www.TheSCANFoundation.org.

Centers for Medicare & Medicaid Services - Nursing HomesThis feature layer, utilizing data from the Centers for Medicare & Medicaid Services (CMS), displays the locations of nursing homes in the U.S. Nursing homes provide a type of residential care. They are a place of residence for people who require constant nursing care and have significant deficiencies with activities of daily living. Per CMS, "Nursing homes, which include Skilled Nursing Facilities (SNFs) and Nursing Facilities (NFs), are required to be in compliance with Federal requirements to receive payment under the Medicare or Medicaid programs. The Secretary of the United States Department of Health & Human Services has delegated to the CMS and the State Medicaid Agency the authority to impose enforcement remedies against a nursing home that does not meet Federal requirements." This layer includes currently active nursing homes, including number of certified beds, address, and other information.Bridgepoint Sub-Acute and Rehab Capitol HillData downloaded: August 1, 2024Data source: Provider InformationData modification: This dataset includes only those facilities with addresses that were appropriately geocoded.For more information: Nursing homes including rehab servicesFor feedback, please contact: ArcGIScomNationalMaps@esri.comCenters for Medicare & Medicaid ServicesPer USA.gov, "The Centers for Medicare and Medicaid Services (CMS) provides health coverage to more than 100 million people through Medicare, Medicaid, the Children’s Health Insurance Program, and the Health Insurance Marketplace. The CMS seeks to strengthen and modernize the Nation’s health care system, to provide access to high quality care and improved health at lower costs."

Data consist of CMS Medicare data files which are restricted access and cannot be released publicly. This dataset is not publicly accessible because: EPA cannot release personally identifiable information regarding living individuals, according to the Privacy Act and the Freedom of Information Act (FOIA). This dataset contains information about human research subjects. Because there is potential to identify individual participants and disclose personal information, either alone or in combination with other datasets, individual level data are not appropriate to post for public access. Restricted access may be granted to authorized persons by contacting the party listed. EPA cannot release CBI, or data protected by copyright, patent, or otherwise subject to trade secret restrictions. Request for access to CBI data may be directed to the dataset owner by an authorized person by contacting the party listed. It can be accessed through the following means: CMS Medicare data are available from: https://www.cms.gov/data-research/files-for-order/data-disclosures-and-data-use-agreements-duas/limited-data-set-lds with the requirement of a signed Data Use Agreement. . Weather data are available at https://prism.oregonstate.edu/. Format: The data that support the findings of this study are available from the Centers for Medicare and Medicaid Services (CMS). Restrictions apply to the availability of these data, which were provided under a Data Use Agreement specific to this study. Data are available from: https://www.cms.gov/data-research/files-for-order/data-disclosures-and-data-use-agreements-duas/limited-data-set-lds with the requirement of a signed Data Use Agreement. Data do not contain personally identifiable information but contain are classified as Limited Data Set files and their distribution require an agreement and between CMS and the requester and approval by CMS. Weather data are available at https://prism.oregonstate.edu/. Because the data do not contain identifiable private information and were not obtained through interaction or intervention with individuals, the Institutional Review Board for the University of North Carolina and the US Environmental Protection Agency Human Research Protocol Officer determined that use of this data does not constitute human subjects research. This dataset is associated with the following publication: Wade, T., and C. Herbert. Weather conditions and legionellosis: a nationwide case-crossover study among Medicare recipients. EPIDEMIOLOGY AND INFECTION. Cambridge University Press, Cambridge, UK, 152: E125, (2024).

Health Insurance Marketplaces

Rates, Benefits, Coverage and Networks

By Data Society [source]

About this dataset

Do you want to explore the complexities of Health Insurance Marketplace and uncover insights into plan rates, benefits, and networks? Look no further! With this dataset from the Centers for Medicare & Medicaid Services (CMS), you can investigate trends in plan rates, access coverage across states and zip codes, compare metal level plans (across years), as well as analyze benefit information all in one place.

We’ve provided six CSV files containing combined data from across all years: BenefitsCostSharing.csv provides details on benefits, BusinessRules.csv provides details about premium payment requirements for a plan or set of plans, Network.csv offers details about health plans’ networks of providers who offer services at different cost levels to members enrolled in a given plan or set of plans; PlanAttributes.csv gives attributes like age off dates for various plans; Rate.csv delivers information on rate changes; ServiceArea.csv reveals demographic characteristics related to each service area associated with a specific issuer and two CSV files that join data across years (Crosswalk2015 & Crosswalk2016).

So come on board and use your creativity to unlock the mysteries behind changes in benefits in relation to costs while exploring network providers within different regions!!!

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How to use the dataset

This dataset contains information about the health insurance plans offered in the US Health Insurance Marketplace. It includes data on plan benefits, cost-sharing, networks, rates and service areas for different states. The data can be used to compare and analyze plan characteristics across different states and ages which will help guide users decision making when purchasing a health insurance plan.

To begin using the dataset, you should start by looking at the columns available. These include State, Dental Plan, Multistate Plan (2015 & 2016), Metal Level (2015 & 2016), Child/Adult Only (2015 & 2016), FIPS Code, Zip Code Crosswalk Level, Reason for Crosswalk, Multistate Plan Ageoff (2016 & 2015) and MetalLevel Ageoff (2016 & 2015). These columns provide important information on each plan that can be used to compare them across states or between years.

Using this data you can explore several interesting questions such as: How do benefit levels vary among states? Are there any differences in network providers between states? What factors influence plan rates?

In order to answer these questions you should join together relevant tables from across years using Crosswalk 2015/2016 CSV files then organize your data accordingly so that it is easier to visualize differences in features between plans sold across different states or years. Once the information is organized it might be helpful to use visualizations such as line graphs or bar charts to view comparison between feature values of two plans versus one another more clearly in order differentiate variations of plans among Consumers.

By doing this you can gain a better understanding of how certain factors may affect rate changes over time or how certain benefit levels might differ by state which will allow Consumers make an informed choice when selecting their next health insurance plan

Research Ideas

• Analyzing the effectiveness of different plan benefits and how they affect premiums to determine a fair price point for different types of healthcare plans.
• Examining the variation in rates, benefits and coverage by state or zip code to identify potential trends or disparities in access to quality health care services across regions.
• Developing an algorithm that can predict premium prices based on certain factors such as age groups, type of plan (metal levels), multistate coverage, etc., to help consumers more easily understand the true cost of their health insurance plans before committing to purchase them

Acknowledgements

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

License

License: Dataset copyright by authors - You are free to: - Share - copy and redistribute the material in any medium or format for any purpose, even commercially. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit -...

The Medicaid Managed Care Enrollment Report profiles enrollment statistics on Medicaid managed care programs on a plan-specific level. The managed care enrollment statistics include enrollees receiving comprehensive benefits and limited benefits and are point-in-time counts. Because Medicaid beneficiaries may be enrolled concurrently in more than one type of managed care program (e.g., a Comprehensive MCO and a BHO), users should not sum enrollment across all program types, since the total would count individuals more than once and, in some states, exceed the actual number of Medicaid enrollees. Comprehensive MCOs cover acute, primary, and specialty medical care services; they may also cover behavioral health, long-term services and supports, and other benefits in some states. Limited benefit managed care programs, including PCCM, MLTSS only, BHO, Dental, Transportation, and Other cover a narrower set of services. The “Total Medicaid Enrollees” column represents an unduplicated count of all beneficiaries in FFS and any type of managed care, including Medicaid-only and dually eligible individuals receiving full Medicaid benefits or Medicaid cost sharing. "--" indicates states that do not operate programs of a given type. 0 signifies that a state operated a program of this type in 2014, but it ended before July 1, 2014, or began after that date.