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

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

The data used in the chronic condition reports are based upon CMS administrative enrollment and claims data for Medicare beneficiaries enrolled in the fee-for-service program. These data are available from the CMS Chronic Condition Data Warehouse (CCW), a database with 100 percent of Medicare enrollment and fee-for-service claims data. The Medicare beneficiary population is limited to fee-for-service beneficiaries. We excluded Medicare beneficiaries with any Medicare Advantage enrollment during the year since claims data are not available for these beneficiaries. Also, we excluded beneficiaries who were enrolled at any time in the year in Part A only or Part B only, since their utilization and spending cannot be compared directly to beneficiaries enrolled in both Part A and Part B. Beneficiaries who died during the year are included up to their date of death if they meet the other inclusion criteria.

Background Our aim was to compare access to effective care among elderly Medicare patients in a Staff Model and Group Model HMO and in Fee-for-Service (FFS) care.

   Methods
   We used a retrospective cohort study design, using claims and automated medical record data to compare achievement on quality indicators for elderly Medicare recipients. Secondary data were collected from 1) HMO data sets and 2) Medicare claims files for the time period 1994–95. All subjects were Medicare enrollees in a defined area of New England: those enrolled in two divisions of a managed care plan with different physician payment arrangements: a staff model, and a group model; and the Medicare FFS population. We abstracted information on indicators covering several domains: preventive, diagnosis-specific, and chronic disease care.


   Results
   On the indicators we created and tested, access in the single managed care plan under study was comparable to or better than FFS care in the same geographic region. Percent of Medicare recipients with breast cancer screening was 36 percentage points higher in the staff model versus FFS (95% confidence interval 34–38 percentage points). Follow up after hospitalization for myocardial infarction was 20 percentage points higher in the group model than in FFS (95% confidence interval 14–26 percentage points).


   Conclusion
   According to indicators developed for use in both claims and automated medical record data, access to care for elderly Medicare beneficiaries in one large managed care organization was as good as or better than that in FFS care in the same geographic area.

Overview The Dartmouth Institute for Health Policy and Clinical Practice (TDI) has created a publicly available source of data that provides researchers, payers, regulators, and innovators with metrics that quantify temporal and regional patterns of health care spending and utilization in the United States. Using CMS Medicare claims data (mostly for age >64 enrollees), Atlas researchers built cohorts (“denominators”) and numerous measures or events (“numerators”) which were then used to calculate rates either by geography or for patients assigned to specific hospitals. These rates, which are calculated consistently across time and place, provide researchers with opportunities to evaluate spatial and temporal variation/trends. This entry contains Dartmouth Atlas mortality rate data for the 65+ Medicare population, including overall mortality rates, mortality rates for beneficiaries with health maintenance organization (HMO) coverage, and mortality rates for beneficiaries without HMO coverage. Rates are provided at the state, hospital referral region (HRR), and hospital service area (HSA) levels, and all rates have been adjusted for age, sex, and race. Users downloading data should review the methods sections of Dartmouth Atlas reports for context as well as for information about any temporal changes in methods. All reports in the Dartmouth Atlas of Health Care series are available from the National Library of Medicine https://www.ncbi.nlm.nih.gov/books/NBK584737/ Note that for the general Dartmouth Atlas rate datasets, which span multiple decades, the author list includes all Dartmouth staff (programmers, analysts, supervisors, etc.) involved in generating all types of Atlas rates across all years. We do not attempt to assign individuals to specific datasets or years.

This map shows where people have Medicaid or means-tested healthcare coverage in the US (ages under 65). This is shown by State, County, and Census Tract, and uses the most current ACS 5-year estimates.

The dataset gives information on Coronary Angiography rates of Medicare beneficiaries at Hospital Referral Regions (HRR) for the year 2012. Hospitalization rates represent the counts of the number of discharges that occurred in a definitive time period (the numerator) for a specific population (the denominator).

The included document uses GIS to investigate and compare Medicare and Medicaid provider infrastructure in Massachusetts. Provider addresses were geocoded and then compared to the geospatial locations of each insurance programs' eligible patient populations (percent of population of each census tract over 65 for Medicare and percent population for each census tract below the Federal Poverty Line for Medicaid). Massachusetts (MA) was picked for the comparison because Medicaid provider data, unlike Medicare provider data, is only available on cms.gov's website going back to 2011 and 2010, before the ACA was implemented in most states. However, MA had enacted "An Act Providing Access to Affordable, Quality, Accountable Health Care" in 2006, which had similar provisions to the subsequent ACA. The included maps used direct comparisons, buffers, and kernel density. Provider addresses obtained from: CMS' MAX Provider Characteristics and Provider of Services Current Files.

This dataset contains the number and percent of Medicare population enrolled in the Stand Alone Prescription Drug Plan (PDP) and average premium for prescription drug plan charged by state. It also contains information on the number of plans per contract for Prescription Drug Plan organizations offered by region and number of prescription drug plans that are eligible for low income subsidy.

This dataset presents the footprint of the percentage of general practitioner (GP) attendances and associated Medicare benefits expenditure per person. GP attendance has been calculated with the total services from eligible claims (excluding any bulk-billed incentive items or other top-up items), divided by the Estimated Resident Population (ERP) as at 30 June 2016. GP expenditure has been calculated with the total benefit paid for eligible claims, divided by the ERP as at 30 June 2016. The data spans the financial years of 2013-2017 and is aggregated to 2015 Department of Health Primary Health Network (PHN) areas, based on the 2011 Australian Statistical Geography Standard (ASGS).

The data is sourced from the Medicare Benefits Schedule (MBS) claims data, which are administered by the Australian Government Department of Health. These claims data are derived from administrative information on services that qualify for a Medicare benefit under the Health Insurance Act 1973 and for which a claim is processed by the Department of Human Services.

For further information about this dataset visit the data source: Australian Institute of Health and Welfare - Medicare Benefits Schedule GP and Specialist Attendances and Expenditure in 2016-17 Data Tables.

Please note:

• AURIN has spatially enabled the original data using the Department of Health - PHN Areas.

• MBS claims data do not include services that were provided free of charge to public patients in hospitals or were subsidised by the Department of Veterans' Affairs, compensation arrangements or through other publicly funded programs including jurisdictional salaried GP services provided in remote outreach clinics.

• GP attendances are Medicare benefit-funded patient/doctor encounters, such as visits and consultations, for which the patient has not been referred by another doctor. GP attendances do not include services provided by practice nurses and Aboriginal and Torres Strait Islander health practitioners on a GP's behalf.

• GP after-hours attendances are Medicare benefit-funded after-hours patient/doctor encounters, such as visits and consultations, for which the patient has not been referred by another doctor. They include urgent and non-urgent attendances.

• Expenditure on GP/specialist attendances comprises MBS funding for patient/doctor encounters. Expenditure is reported unadjusted for inflation.

• Bulk-billing is an arrangement in which a medical practitioner sends the bill directly to Medicare, so the patient pays nothing. Also known as direct billing.

• Age-standardisation allows fairer comparisons to be made between areas by accounting for variation in the age of populations within each area. This adjustment is important because the rates of many health conditions and health service use vary with age.

Metrics from individual Marketplaces during the current reporting period. The report includes data for the states using HealthCare.gov. Sources: HealthCare.gov application and policy data through October 6, 2024, HealthCare.gov inbound account transfer data through November 7, 2024, and T-MSIS Analytic Files (TAF) through July 2024 (TAF version 7.1). The table includes states that use HealthCare.gov. Notes: This table includes Marketplace consumers who submitted a HealthCare.gov application from March 6, 2023 - October 6, 2024 or who had an inbound account transfer from April 3, 2023 - November 7, 2024, who can be linked to an enrollment record in TAF that shows a last day of Medicaid or CHIP enrollment from March 31, 2023 - July 31, 2024. Beneficiaries with a leaving event may have continuous coverage through another coverage source, including Medicaid or CHIP coverage in another state. However, a beneficiary that lost Medicaid or CHIP coverage and regained coverage in the same state must have a gap of at least 31 days or a full calendar month. This table includes Medicaid or CHIP beneficiaries with full benefits in the month they left Medicaid or CHIP coverage. ‘Account Transfer Consumers Whose Medicaid or CHIP Coverage was Terminated’ are consumers 1) whose full benefit Medicaid or CHIP coverage was terminated and 2) were sent by a state Medicaid or CHIP agency via secure electronic file to the HealthCare.gov Marketplace in a process referred to as an inbound account transfer either 2 months before or 4 months after they left Medicaid or CHIP. 'Marketplace Consumers Not on Account Transfer Whose Medicaid or CHIP Coverage was Terminated' are consumers 1) who applied at the HealthCare.gov Marketplace and 2) were not sent by a state Medicaid or CHIP agency via an inbound account transfer either 2 months before or 4 months after they left Medicaid or CHIP. Marketplace consumers counts are based on the month Medicaid or CHIP coverage was terminated for a beneficiary. Counts include all recent Marketplace activity. HealthCare.gov data are organized by week. Reporting months start on the first Monday of the month and end on the first Sunday of the next month when the last day of the reporting month is not a Sunday. HealthCare.gov data are through Sunday, October 6. Data are preliminary and will be restated over time to reflect consumers most recent HealthCare.gov status. Data may change as states resubmit T-MSIS data or data quality issues are identified. See the data and methodology documentation for a full description of the data sources, measure definitions, and general data limitations. Data notes: The percentages for the 'Marketplace Consumers Not on Account Transfer whose Medicaid or CHIP Coverage was Terminated' data record group are marked as not available (NA) because the full population of consumers without an account transfer was not available for this report. Virginia operated a Federally Facilitated Exchange (FFE) on the HealthCare.gov platform during 2023. In 2024, the state started operating a State Based Marketplace (SBM) platform. This table only includes data about 2023 applications and policies obtained through the HealthCare.gov Marketplace. Due to limited Marketplace activity on the HealthCare.gov platform in November 2023, data from November 2023 onward are excluded. The cumulative count and percentage for Virginia and the HealthCare.gov total reflect Virginia data from April 2023 through October 2023. APTC: Advance Premium Tax Credit; CHIP: Children's Health Insurance Program; QHP: Qualified Health Plan; NA: Not Available

This dataset presents the footprint of the percentage of patients with costs, the total out-of-pocket cost per patient at the 25th, 50th, 75th and 90th percentile and various statistics for all patients. The data spans the financial year of 2016-2017 and is aggregated to Statistical Area Level 3 (SA3) geographic areas from the 2016 Australian Statistical Geography Standard (ASGS). The data is sourced from the Medicare Benefits Schedule (MBS) claims data, which are administered by the Australian Government Department of Health. The claims data are derived from administrative information on services that qualify for a Medicare benefit under the Health Insurance Act 1973 and for which a claim has been processed by the Department of Human Services. Data are reported for claims processed between 1 July 2016 and 30 June 2017. The data also contains the results from the ABS 2016-17 Patient Experience Survey, collected between 1 July 2016 and 30 June 2017. The Patient Experience Survey is conducted annually by the Australian Bureau of Statistics (ABS) and collects information from a representative sample of the Australian population. The Patient Experience Survey is one of several components of the Multipurpose Household Survey, as a supplement to the monthly Labour Force Survey. The Patients' spending on Medicare Services data accompanies the Patients' out-of-pocket spending on Medicare services 2016-17 Report. For further information about this dataset, visit the data source:Australian Institute of Health and Welfare - Patients' out-of-pocket spending on Medicare services Data Tables.

This dataset presents the footprint of the percentage of people who did not claim a general practitioner (GP) attendance. This has been calculated with the number of people who did not claim a GP attendance, divided by the Estimated Resident Population (ERP) as at 30 June 2016, and multiplied by 100. The data spans the financial year of 2016-2017 and is aggregated to 2015 Department of Health Primary Health Network (PHN) areas, based on the 2011 Australian Statistical Geography Standard (ASGS). The data is sourced from the Medicare Benefits Schedule (MBS) claims data, which are administered by the Australian Government Department of Health. These claims data are derived from administrative information on services that qualify for a Medicare benefit under the Health Insurance Act 1973 and for which a claim is processed by the Department of Human Services. For further information about this dataset visit the data source: Australian Institute of Health and Welfare - Medicare Benefits Schedule GP and Specialist Attendances and Expenditure in 2016-17 Data Tables. Please note: AURIN has spatially enabled the original data using the Department of Health - PHN Areas.

This dataset presents the footprint of the percentage of patients with imaging costs, and out-of-pocket cost per diagnostic imaging service attendance at the 25th, 50th, 75th and 90th percentile. The data spans the financial year of 2016-2017 and is aggregated to 2015 Department of Health Primary Health Network (PHN) areas, based on the 2011 Australian Statistical Geography Standard (ASGS). The data is sourced from the Medicare Benefits Schedule (MBS) claims data, which are administered by the Australian Government Department of Health. The claims data are derived from administrative information on services that qualify for a Medicare benefit under the Health Insurance Act 1973 and for which a claim has been processed by the Department of Human Services. Data are reported for claims processed between 1 July 2016 and 30 June 2017. The data also contains the results from the ABS 2016-17 Patient Experience Survey, collected between 1 July 2016 and 30 June 2017. The Patient Experience Survey is conducted annually by the Australian Bureau of Statistics (ABS) and collects information from a representative sample of the Australian population. The Patient Experience Survey is one of several components of the Multipurpose Household Survey, as a supplement to the monthly Labour Force Survey. The Patients' spending on Medicare Services data accompanies the Patients' out-of-pocket spending on Medicare services 2016-17 Report. For further information about this dataset, visit the data source:Australian Institute of Health and Welfare - Patients' out-of-pocket spending on Medicare services Data Tables. Please note:

AURIN has spatially enabled the original data using the Department of Health - PHN Areas.

Diagnostic imaging services are Medicare-subsidised diagnostic imaging procedures such as x-rays, computerised tomography scans, ultrasound scans, magnetic resonance imaging scans and nuclear medicine scans.

Out-of-pocket cost per service is the net cost to the patient of a health service, after deducting the Medicare benefit paid. The measure is calculated per patient (patients' annual out-of-pocket cost for eligible attendances, divided by the number of eligible attendances that the patient claimed in the year), for patients with out-of-pocket costs.

The data is based on the patient's Medicare enrolment postcode, not where they received the health care service. Most peoples' Medicare enrolment postcode will be their residential postcode.

If a service was flagged as bulk-billed, then the fee charged was set to equal the benefit paid (so there was no out-of-pocket cost for that service).

Patients were excluded if the sum of eligible services in the year was less than one, or if their annual out-of-pocket expenditure on the eligible services was equal to or less than zero.

Includes non-hospital Medicare-subsidised services only.

NP - Not available for publication. The estimate is considered to be unreliable. Values assigned to NP in the original data have been set to null.

Key Table Information.Table Title.Medicare Coverage by Sex by Age.Table ID.ACSDT1Y2024.C27006.Survey/Program.American Community Survey.Year.2024.Dataset.ACS 1-Year Estimates Detailed Tables.Source.U.S. Census Bureau, 2024 American Community Survey, 1-Year Estimates.Dataset Universe.The dataset universe of the American Community Survey (ACS) is the U.S. resident population and housing. For more information about ACS residence rules, see the ACS Design and Methodology Report. Note that each table describes the specific universe of interest for that set of estimates..Methodology.Unit(s) of Observation.American Community Survey (ACS) data are collected from individuals living in housing units and group quarters, and about housing units whether occupied or vacant. For more information about ACS sampling and data collection, see the ACS Design and Methodology Report..Geography Coverage.ACS data generally reflect the geographic boundaries of legal and statistical areas as of January 1 of the estimate year. For more information, see Geography Boundaries by Year.Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on 2020 Census data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Sampling.The ACS consists of two separate samples: housing unit addresses and group quarters facilities. Independent housing unit address samples are selected for each county or county-equivalent in the U.S. and Puerto Rico, with sampling rates depending on a measure of size for the area. For more information on sampling in the ACS, see the Accuracy of the Data document..Confidentiality.The Census Bureau has modified or suppressed some estimates in ACS data products to protect respondents' confidentiality. Title 13 United States Code, Section 9, prohibits the Census Bureau from publishing results in which an individual's data can be identified. For more information on confidentiality protection in the ACS, see the Accuracy of the Data document..Technical Documentation/Methodology.Information about the American Community Survey (ACS) can be found on the ACS website. Supporting documentation including code lists, subject definitions, data accuracy, and statistical testing, and a full list of ACS tables and table shells (without estimates) can be found on the Technical Documentation section of the ACS website.Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section.Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see ACS Technical Documentation). The effect of nonsampling error is not represented in these tables.Users must consider potential differences in geographic boundaries, questionnaire content or coding, or other methodological issues when comparing ACS data from different years. Statistically significant differences shown in ACS Comparison Profiles, or in data users' own analysis, may be the result of these differences and thus might not necessarily reflect changes to the social, economic, housing, or demographic characteristics being compared. For more information, see Comparing ACS Data..Weights.ACS estimates are obtained from a raking ratio estimation procedure that results in the assignment of two sets of weights: a weight to each sample person record and a weight to each sample housing unit record. Estimates of person characteristics are based on the person weight. Estimates of family, household, and housing unit characteristics are based on the housing unit weight. For any given geographic area, a characteristic total is estimated by summing the weights assigned to the persons, households, families or housing units possessing the characteristic in the geographic area. For more information on weighting and estimation in the ACS, see the Accuracy of the Data document.Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, the decennial census is the official source of population totals for April 1st of each decennial year. In between censuses, the Census Bureau's Population Estimates Program produces and disseminates the official estimates of the population for the nation, states, counties, cities, and towns and estimates ...

The dataset contains information on the non-PACE (Programs of All-Inclusive Care for the Elderly) risk scores 2016-2020. Risk scores used in the ratebooks are calculated using the model to be used in the payment year. The beneficiaries included in the state-level risk scores in this dataset are those enrolled in fee-for-service (FFS) population.

Information on provider-level measure rates for the Agency for Healthcare Research and Quality (AHRQ) Patient Safety Indicator 11 (PSI-11) Postoperative Respiratory Failure measure.The Agency for Healthcare Research and Quality (AHRQ) Patient Safety Indicator 11 (PSI-11) Measure Rates dataset provides information on provider-level measure rates regarding one preventable complication (postoperative respiratory failure) for Medicare fee-for-service discharges. The PSI-11 measure data is solely reported for providers’ information and quality improvement purposes and are not a part of the Deficit Reduction Act (DRA) Hospital-Acquired Condition (HAC) Payment Provision or HAC Reduction Program.Q: What is the history of the PSI-11 measure reporting?In August 2015, CMS calculated and publicly reported the Agency for Healthcare Research and Quality (AHRQ) Patient Safety Indicator (PSI) 11 – Postoperative Respiratory Failure Rate on data.cms.gov. CMS publicly reported the same PSI-11 measure again in August 2016. CMS reports the AHRQ PSI-11 – Postoperative Respiratory Failure Rate measure for information and quality improvement purposes only; PSI-11 is not a part of the Deficit Reduction Act (DRA) Hospital-Acquired Condition (HAC) Payment Provision or HAC Reduction Program.Q: How do the PSI-11 results being posted in August 2016 differ from the PSI-11 results from August 2015?CMS made the following changes since the previous reporting of the PSI-11 measure:Updated time period for measures calculation — CMS updated the time period used for the PSI-11 measure calculations to include discharges from July 1, 2013 through June 30, 2015 (as opposed to July 1, 2011 through June 30, 2013).Updated and recalibrated AHRQ PSI software for PSI-11 — CMS calculated the PSI-11 measure using recalibrated version 5.0.1 of the AHRQ PSI software, as opposed to version 4.5a. In general, CMS recalibrated the risk-adjustment coefficients, signal variance, smoothing target, and composite weights based on the Medicare Fee-for-Service (FFS) population rather than the Healthcare Cost and Utilization Project (HCUP) population.Inclusion of Maryland hospitals – CMS will include Maryland hospitals in the calculation of the PSI-11 measure for the first time in August 2016 because Maryland hospitals were required to start reporting POA Indicators, a field on an inpatient claim necessary for the PSI-11 measure calculations, as of October 1, 2013. Q: Why is CMS reporting the PSI-11 measure rate?In addition to researcher and stakeholder interest, CMS is publicly reporting the PSI-11 measure rate to identify complications and undesirable conditions that patients experience in hospital settings which can reasonably be prevented by changes at the hospital level. Improving patient safety is one of the ultimate goals of quality improvement. The PSI-11 measure remains an important aspect of CMS’s commitment to patient safety.Q: Which hospitals are included in the PSI-11 measure calculations?The PSI-11 measure depends on complete and accurate coding of POA Indicator fields. Hospitals participating in the IPPS program and Maryland hospitals must submit complete POA coding, although other types of hospitals can and will report these codes. To avoid any bias against exempt hospitals that are not reporting POA indicators, the PSI-11 measure is only calculated for IPPS and Maryland hospitals.A list of hospital types exempt from POA reporting is provided on the CMS Hospital-Acquired Conditions webpage under the link for Affected Hospitals located at the following website: http://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/HospitalAcqCond/AffectedHospitals.htmlQ: How is the PSI-11 measure rate calculated?CMS calculates the PSI-11 measure rate using claims for Medicare Fee-for-Service (FFS) discharges.The PSI-11 measure rate is reported as a smoothed rate. The measure uses the count of actual occurrences identified at a hospital (numerator) divided by the eligible number of discharges at that hospital (denominator). This ratio is then risk-adjusted to account for the hospital’s case mix and reliability-adjusted (or “smoothed”) to account for statistical uncertainty.Q: Is the PSI-11 measure adjusted for our hospital’s patient case-mix?The PSI-11 measure is risk and reliability-adjusted, according to AHRQ’s specifications.Q: How ar

This dataset presents the footprint of the percentage of patients with GP costs, and out-of-pocket cost per GP attendance at the 25th, 50th, 75th and 90th percentile. The data spans the financial year of 2016-2017 and is aggregated to 2015 Department of Health Primary Health Network (PHN) areas, based on the 2011 Australian Statistical Geography Standard (ASGS). The data is sourced from the Medicare Benefits Schedule (MBS) claims data, which are administered by the Australian Government Department of Health. The claims data are derived from administrative information on services that qualify for a Medicare benefit under the Health Insurance Act 1973 and for which a claim has been processed by the Department of Human Services. Data are reported for claims processed between 1 July 2016 and 30 June 2017. The data also contains the results from the ABS 2016-17 Patient Experience Survey, collected between 1 July 2016 and 30 June 2017. The Patient Experience Survey is conducted annually by the Australian Bureau of Statistics (ABS) and collects information from a representative sample of the Australian population. The Patient Experience Survey is one of several components of the Multipurpose Household Survey, as a supplement to the monthly Labour Force Survey. The Patients' spending on Medicare Services data accompanies the Patients' out-of-pocket spending on Medicare services 2016-17 Report. For further information about this dataset, visit the data source:Australian Institute of Health and Welfare - Patients' out-of-pocket spending on Medicare services Data Tables. Please note: AURIN has spatially enabled the original data using the Department of Health - PHN Areas.

Background: The dialysis patient population in the United States continues to grow. Trends in rates of death and hospitalization among dialysis patients have important consequences for outpatient dialysis capacity and Medicare spending. Objectives: To estimate contemporary trends in rates of death and hospitalization among dialysis patients in the United States, overall and within subgroups. Methods: We used Medicare Limited Data Sets (100% sample) in 2014–2017 to estimate trends in rates of death and hospitalization among dialysis patients with Medicare Parts A and B enrollment. We used seasonal autoregressive integrated moving average models to identify secular trends in the incidence of outcomes. Results: There were 631,075 unique patients; 222,924 deaths; and 1,876,779 hospital admissions. Weekly risks of both death and hospitalization exhibited strong seasonality. However, overall weekly risks of death were 34.9, 35.4, 35.2, and 35.7 deaths per 10,000 patients in 2014–2017, respectively (p = 0.47, from a likelihood ratio test of secular trend). The overall weekly risk of hospitalization was 3.08, 3.05, 3.11, and 3.11% in 2014, 2015, 2016, and 2017, respectively (p = 0.30). There were significant secular trends in risk of death in subgroups defined by black race and residency in South Atlantic states (p < 0.05). There were also secular trends in risk of hospitalization in subgroups defined by age 20–44 years, concurrent enrollment in Medicaid, and residency in South Central states. Conclusion: For the first time since the beginning of this century, rates of both death and hospitalization among dialysis patients with Medicare fee-for-service coverage have stagnated. The reasons for this change are unknown and require detailed assessment. Persistent lack of change in clinical outcomes may alter the future expectations about dialysis patient population growth.

This dataset presents the footprint of the percentage of people who delayed or did not see a GP when needed due to cost in the last 12 months. The data spans the financial years of 2013-2017 and is aggregated to 2015 Department of Health Primary Health Network (PHN) areas, based on the 2011 Australian Statistical Geography Standard (ASGS). The data is sourced from the Medicare Benefits Schedule (MBS) claims data, which are administered by the Australian Government Department of Health. The claims data are derived from administrative information on services that qualify for a Medicare benefit under the Health Insurance Act 1973 and for which a claim has been processed by the Department of Human Services. Data are reported for claims processed between 1 July 2016 and 30 June 2017. The data also contains the results from the ABS 2016-17 Patient Experience Survey, collected between 1 July 2016 and 30 June 2017. The Patient Experience Survey is conducted annually by the Australian Bureau of Statistics (ABS) and collects information from a representative sample of the Australian population. The Patient Experience Survey is one of several components of the Multipurpose Household Survey, as a supplement to the monthly Labour Force Survey. The Patients' spending on Medicare Services data accompanies the Patients' out-of-pocket spending on Medicare services 2016-17 Report. For further information about this dataset, visit the data source:Australian Institute of Health and Welfare - Patients' out-of-pocket spending on Medicare services Data Tables. Please note: AURIN has spatially enabled the original data using the Department of Health - PHN Areas. The data is based on the patient's Medicare enrolment postcode, not where they received the health care service. Most peoples' Medicare enrolment postcode will be their residential postcode.