(Source: CMS Medicare Geographic Variation Public Use File, February 2021)

This data package shows information on Medicare enrollment level data for providers organized at the individual level, Phase III target list which revalidates all remaining providers, a list of due dates by which the provider/supplier's revalidation application must reach their MAC (Medicare Administrative Contractor) and the number of Medicare beneficiaries who use a health service area and a list of all providers and suppliers who have been mailed a revalidation notice.

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

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.

Files: mbsf_cc_summary (2006-2018)

The table Master Beneficiary Summary File - Chronic Conditions (CC27) is part of the dataset Medicare 20% [2006-2018] Enrollment/Summary (MBSF), available at https://stanford.redivis.com/datasets/e5h4-34n5ngp4r. It contains 142099691 rows across 84 variables.

This dataset tracks the updates made on the dataset "Medicare Beneficiary Enrollment and Demographics, Washington State and Counties, 2007-2018" as a repository for previous versions of the data and metadata.

United States Health Insurance: Enrollment: Medicare Supplement data was reported at 5.000 USD mn in 2023. This stayed constant from the previous number of 5.000 USD mn for 2022. United States Health Insurance: Enrollment: Medicare Supplement data is updated yearly, averaging 4.000 USD mn from Dec 2007 (Median) to 2023, with 17 observations. The data reached an all-time high of 5.000 USD mn in 2023 and a record low of 4.000 USD mn in 2018. United States Health Insurance: Enrollment: Medicare Supplement data remains active status in CEIC and is reported by National Association of Insurance Commissioners. The data is categorized under Global Database’s United States – Table US.RG022: Health Insurance: Operations by Lines of Business.

The CMS Program Statistics - Medicare Skilled Nursing Facility tables provide use and payment data for skilled nursing facilities. For additional information on enrollment, providers, and Medicare use and payment, visit the CMS Program Statistics page. Below is the list of tables: MDCR SNF 1. Medicare Skilled Nursing Facilities: Utilization, Program Payments, and Cost Sharing for Original Medicare Beneficiaries, by Type of Entitlement, Yearly Trend MDCR SNF 2. Medicare Skilled Nursing Facilities: Utilization, Program Payments, and Cost Sharing for Original Medicare Beneficiaries, by Demographic Characteristics and Medicare-Medicaid Enrollment Status MDCR SNF 3. Medicare Skilled Nursing Facilities: Utilization, Program Payments, and Cost Sharing for Original Medicare Beneficiaries, by Area of Residence MDCR SNF 4. Medicare Skilled Nursing Facilities: Utilization, Program Payments, and Cost Sharing for Original Medicare Beneficiaries, by Type of Entitlement and Covered Days of Care MDCR SNF 5. Medicare Skilled Nursing Facilities: Utilization, Program Payments, and Cost Sharing for Original Medicare Beneficiaries, by Type of Facility and Bedsize MDCR SNF 6. Medicare Skilled Nursing Facilities: Distribution of Medicare Covered Skilled Nursing Facility Days, by State of Provider and Major Resource Utilization Groups (RUG)-III (versions 2013-2018 only)

Files: mbsf_costuse (2006-2018)

The table Master Beneficiary Summary File (MBSF): Cost and Utilization is part of the dataset Medicare 20% [2006-2018] Enrollment/Summary (MBSF), available at https://stanford.redivis.com/datasets/e5h4-34n5ngp4r. It contains 142099691 rows across 83 variables.

The CMS Program Statistics - Medicare Skilled Nursing Facility tables provide use and payment data for skilled nursing facilities. 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 SNF 1. Medicare Skilled Nursing Facilities: Utilization, Program Payments, and Cost Sharing for Original Medicare Beneficiaries, by Type of Entitlement, Yearly Trend MDCR SNF 2. Medicare Skilled Nursing Facilities: Utilization, Program Payments, and Cost Sharing for Original Medicare Beneficiaries, by Demographic Characteristics and Medicare-Medicaid Enrollment Status MDCR SNF 3. Medicare Skilled Nursing Facilities: Utilization, Program Payments, and Cost Sharing for Original Medicare Beneficiaries, by Area of Residence MDCR SNF 4. Medicare Skilled Nursing Facilities: Utilization, Program Payments, and Cost Sharing for Original Medicare Beneficiaries, by Type of Entitlement and Covered Days of Care MDCR SNF 5. Medicare Skilled Nursing Facilities: Utilization, Program Payments, and Cost Sharing for Original Medicare Beneficiaries, by Type of Facility and Bedsize MDCR SNF 6. Medicare Skilled Nursing Facilities: Distribution of Medicare Covered Skilled Nursing Facility Days, by State of Provider and Major Resource Utilization Groups (RUG)-III (versions 2013-2018 only)

Over ** million Americans were estimated to be enrolled in the Medicaid program as of 2023. That is a significant increase from around ** million ten years earlier. Medicaid is basically a joint federal and state health program that provides medical coverage to low-income individuals and families. Currently, Medicaid is responsible for ** percent of the nation’s health care bill, making it the third-largest payer behind private insurances and Medicare. From the beginning to ObamacareMedicaid was implemented in 1965 and since then has become the largest source of medical services for Americans with low income and limited resources. The program has become particularly prominent since the introduction of President Obama’s health reform – the Patient Protection and Affordable Care Act - in 2010. Medicaid was largely impacted by this reform, for states now had the opportunity to expand Medicaid eligibility to larger parts of the uninsured population. Thus, the percentage of uninsured in the United States decreased from over ** percent in 2010 to *** percent in 2022. Who is enrolled in Medicaid?Medicaid enrollment is divided mainly into four groups of beneficiaries: children, adults under 65 years of age, seniors aged 65 years or older, and disabled people. Children are the largest group, with a share of approximately ** percent of enrollees. However, their share of Medicaid expenditures is relatively small, with around ** percent. Compared to that, disabled people, accounting for **** percent of total enrollment, were responsible for **** percent of total expenditures. Around half of total Medicaid spending goes to managed care and health plans.

Files: mbsf_oth_cc_summary (2006-2018)

The table MBSF: Other Chronic/Potentially Disabling Conditions is part of the dataset Medicare 20% [2006-2018] Enrollment/Summary (MBSF), available at https://stanford.redivis.com/datasets/e5h4-34n5ngp4r. It contains 142099691 rows across 84 variables.

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 Current Beneficiary Survey (MCBS) - Cost Supplement File Microdata Public Use File (PUF) dataset provides information on expenditures and payment sources for all services used by Medicare beneficiaries, including co-payments, deductibles, and non-covered services.This data is based on MCBS administrative data, but has been updated for public use, including the application of disclosure protections and de-identification of subjects. The file contains yearly survey results (2018-2022; txt, xpt, csv) as well as the corresponding MCBS, glossary (pdf), codebook (txt) and data user guide (pdf).

Medicare FFS Beneficiary Utilization and Costs

2007-2014 Washington State and Counties

By Health [source]

About this dataset

This dataset contains info on the number of Medicare Fee-for-Service Beneficiaries (FFS) receiving healthcare services from hospitals, physicians, and other providers, as well as their associated charges and payments. It provides in-depth, detailed demographics like age group, gender, all kinds of race/ethinicity data and geographical regions. This information can be used to better understand existing health disparities among Medicare FFS beneficiaries across the U.S., examine trends in utilization over time to identify areas where changes are needed within the system or research a wide range of policy issues in healthcare

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How to use the dataset

This dataset provides a look into Medicare Fee-for-Service beneficiaries in health services being utilized by those enrolled in the Medicare program. The information included can help to paint a picture of how Medicare recipients are using health services, such as hospital and physician visits, laboratory tests and procedures, prescription drugs and imaging services.

In order to make the most use of this data set for research or analysis purposes, there are several key pieces of information that should be taken into account. This includes examining both utilization data (such as the numbers of recommended specific procedures) as well as cost components (such as fee schedules). Specifics within this data set include the average estimated Submitted Charges for each procedure code from nationwide claims from 2011 to 2018.

When looking at utilization portion of this dataset it is important to consider: • Total number of services provided for each condition identified by ICD-9 or ICD10 code • Average total service minutes per beneficiary / patient with national average levels listed across five years throughout the period previously mentioned • Percentage change across accessed service types over time period wherein 2011 have been viewed versus more recent statistics • Top five provider specialty types who render service • Number of facilities providing care on annual basis along with percentages utilizing Rural Health Centers grouped together categories including but not exclusive not limited to metropolitan areas; counties; Congressional Districts ; Regions; states plus other geographic entities • Age groups who have used these facilities based on gender plus new acute admissions reported same time frame

A secondary component yet equally important component regarding fees associated with different medical therapies should be considered additionally when uses dataset  which includes:

• Amounts charged by certain facilities based upon current expenses related dates whether patient purchased generic version or brand-name medication due its additional costs relates most significantly towards said medication choices National level along with regional percentage splits relating drug alternatives utilized per given month Actual recharge associated calculated mechanism/formulae , sometimes may refer UPFS methodology Those charges represent sum total averages against whom paid expense examples include: Part B drugs recipients outpatient surgeries & facility visits Note future amounts collected depend upon patients Choice whether require certain distinct E&M codes sometimes need submit ancillary components( diagnoses codes ) separate selections meant cater both facility site & practitioner’s overall needs Sometimes technology assigns relative value unit ( RVU ) defining severity factors linked coding differing specialties so their respective fields well documented Finally analyzing any detail reporting requirements varying specialties

Research Ideas

• Analyze various patterns in health services utilization by Medicare beneficiaries to provide insight into the most commonly used services and ways to improve care.
• Track the number of Medicare beneficiaries using each type of health service in order to identify potential underserved populations or areas with high usage levels that necessitate additional coverage or resources.
• Identify regional differences in provider use rates and payment amounts for specific types of health services, which can help inform efforts to improve equity and access across different geographical regions

Acknowledgements

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

Files: mbsf_ab_summary (2006-2013), mbsf_abcd_summary (2014-2018)

2014-2015. This dataset is a de-identified summary table of vision and eye health data indicators from Medicare, stratified by all available combinations of age group, race/ethnicity, gender, and state. Medicare claims for VEHSS includes beneficiaries who were fully enrolled in Medicare Part B Fee-for-Service (FFS) for the duration of the year. Medicare claims provide a convenience sample that includes approximately 30 million individuals annually, which represents nearly 89% of the US population aged 65 and older and 3.3% of the US population younger than 65, including persons disabled due to blindness. Medicare data for VEHSS include Service Utilization and Medical Diagnoses indicators. Data were suppressed for de-identification to ensure protection of patient privacy. Data will be updated as it becomes available. Detailed information on VEHSS Medicare analyses can be found on the VEHSS Medicare webpage (link). Information on available Medicare claims data can be found on the ResDac website (www.resdac.org). The VEHSS Medicare dataset was last updated in June 2018.

The percentage of Americans covered by the Medicaid public health insurance plan decreased from **** percent in 2021 to around **** percent in 2024. However, the percentage of those insured through Medicaid remains lower than the peak of **** percent in 2015. The expansion of Medicaid The Affordable Care Act (ACA) provided the option for states to expand Medicaid eligibility to people whose income was below a particular threshold. The ACA’s major coverage expansion came into force in 2014, and the number of individuals estimated to be enrolled in Medicaid has since surpassed ** million. More than ** million children were enrolled in the program in 2018, representing ** percent of overall Medicaid enrollment. State Medicaid coverage Initially, the ACA mandated that all state Medicaid programs would have to be extended to provide medical coverage to nearly all low-income groups. However, the Supreme Court rejected that part of the act in 2012, leaving the door open for states to make their own decision on whether they expand their plans. As of September 2021, ** states plus the District of Columbia have adopted the Medicaid expansion.

The dataset explains different metrics or standard of measurement for Part C (Medicare private health plans) reported at the contract level with less than 500 enrolled. Medicare Part C is the part of Medicare policy that allows private health insurance companies to provide Medicare benefits. These Medicare private health plans, such as HMOs (Health Maintenance Organization) and PPOs (Preferred Provider Organization), are known as Medicare Advantage Plans.

BackgroundMost patients with wild-type transthyretin amyloid cardiomyopathy (ATTRwt-CM) are diagnosed noninvasively, using nuclear imaging and monoclonal protein testing. However, concerns have been raised that some may receive an ATTRwt-CM diagnosis based on incomplete evaluations (not based on current consensus recommendations). Using a cohort of US Medicare Fee-for-Service patients, we aimed to examine the frequency and cadence of diagnostic testing for ATTRwt-CM.MethodsIn this retrospective observational cross-sectional study, administrative de-identified claims data from 2018 to 2022 were derived from patients aged ≥65 years who had at least one claim for ATTRwt-CM and heart failure or cardiomyopathy, and ≥2 years of continuous Medicare enrollment before the first ATTRwt-CM diagnosis. Patients with claims for any other form of amyloidosis, multiple myeloma, or plasma cell dyscrasias were excluded.ResultsAmong 2,050 patients with ATTRwt-CM, mean (SD) age was 80.0 (6.9) years, and 75.5% were men. Annual new ATTRwt-CM diagnoses nearly tripled over the study period (2018, n = 198; 2022, n = 578). Technetium-99m pyrophosphate (PYP) scintigraphy use was performed in approximately half of diagnosed patients by the end of the study period (2018, 30%; 2022, 49%). Cardiac biopsy use declined from 14% in patients diagnosed in 2018 to 5% in those diagnosed in 2022. A small minority (14%) of patients underwent the recommended noninvasive diagnostic testing comprised of PYP scintigraphy and complete monoclonal protein testing.ConclusionsBased on Medicare claims data, most patients diagnosed with ATTRwt-CM have not been diagnosed following consensus-recommended pathways.