In 2022, Medicare and Medicaid national health expenditures reached 944 billion U.S. dollars and 805 billion U.S. dollars, respectively. The largest expense category for both healthcare care programs was hospital care. Long-term care solutions Medicaid’s second-largest expense category was other health care, which includes programs that provide alternatives to long-term institutional services. The use of home- and community-based services can substantially reduce expenditures for enrollees who would otherwise have to receive care in an institutional setting, such as a nursing home. In recent decades, there has been a significant shift in the distribution of Medicaid’s long-term care services expenditures. Medicaid’s federal-state partnership Medicare is a health insurance program solely funded by the federal government, whereas Medicaid plays an important role in both federal and state budgets. The federal government establishes certain parameters for all states to follow, but states can decide who gets coverage and what gets covered in its version of Medicaid. In 2021, California was the state with the highest Medicaid expenditure.

2023 saw the largest expenditures on Medicaid in U.S. history. At that time about 894 billion U.S. dollars were expended on the Medicaid public health insurance program that aims to provide affordable health care options to low income residents and people with disabilities. Medicaid was signed into law in 1965. By 1975 around 13 billion U.S. dollars were spent on the program. Groups covered by Medicaid There are several components of the Medicaid health insurance program. The Children’s Health Insurance Program (CHIP) was started in 1997 to provide health coverage to families and children that could not afford care. As of 2021, children represented the largest distribution of Medicaid enrollees. Despite having the largest proportion of enrollees, those that were enrolled in Medicaid as children had the lowest spending per enrollee. As of 2021, disabled Medicaid enrollees had the highest spending per enrollee. Medicaid expenditures Currently, Medicaid accounts for 19 percent of all health care expenditure in the United States. Expenditures on Medicaid programs vary among the U.S. states and depend heavily on whether Medicaid expansion was accepted after the Affordable Care Act was enacted. California and New York are the top states with the highest Medicaid expenditures. It is projected that Medicaid expenditure will continue to increase at both the state and federal levels.

Total Medicaid spending surpassed 804 billion U.S. dollars in 2022. The state of California had the highest expenditure throughout the year, followed by New York and Texas.

Federal government helps poorer states Both the federal and state governments fund the Medicaid health care program, but at least 50 percent of the costs incurred by states are matched by the federal government. The exact percentage varies by state because the matching rate was designed so that poorer states receive a larger share of program costs from the federal government. The states of Wyoming, South Dakota, North Dakota, spent the least on Medicaid costs in 2021.

Funding share of states set to increase Under the Affordable Care Act, states have the choice to expand their Medicaid programs to cover nearly all low-income Americans under age 65. For states that implemented the expansion, the federal government paid 100 percent of the state costs for all newly eligible adults from 2014 to 2016. The new matching rate has slowly declined since and reached 90 percent in 2020, which means states have to pick up ten percent of the bill. Governors are concerned about the rise in costs, and state expenditure is projected to increase by 50 percent between 2020 and 2027.

National Average Drug Acquisition Cost (NADAC) weekly reference data for the calendar year.

In 2022, health spending in the United States reached approximately 4.6 trillion U.S. dollars, and private insurance accounted for around 29 percent of that figure. However, public health insurance, which includes the Medicare and Medicaid programs, combined for a share of around 39 percent. The rising costs of health care coverage U.S. national health expenditure continues to increase and is projected to exceed four trillion U.S. dollars in 2021. Hospital care and physician services have been the leading spending categories for several years and combined for more than half of all health spending in 2021. In the same year, federal and state governments made up 61 percent of national health expenditures, with the federal government’s share accounting for 27 percent. The differences between Medicare and Medicaid Medicare and Medicaid were both signed into U.S. law by President Johnson in 1965. Medicare is a health insurance program solely funded by the federal government. The plan was primarily created for all Americans aged 65 and older, regardless of their income. Medicaid is administered at a state level in accordance with some core federal requirements, but both fund the program. The plan provides health care to millions of Americans, and some states have expanded the Medicaid program to cover nearly all low-income adults under the age of 65.

Medicaid continues to provide comprehensive long-term care in the United States. In 2023, the program was estimated to have paid for **** percent of all home health care and nearly ** percent of nursing home care. In addition, Medicaid covered over ** percent of other health, residential, and personal care, which includes payments for intermediate care facilities and other home- and community-based services. Health care spending in the U.S. Medicaid expenditure accounted for around ** percent of all U.S. health expenditures in 2021. Overall, health spending in the United States totaled *** trillion U.S. dollars in 2020 – hospital care continues to be the largest spending category. Around *** trillion U.S. dollars was spent on hospital care in 2020, and expenditures are projected to continue on an upward trajectory. The high price of hospital care Medicare and Medicaid spend significant amounts of money on national health services, and for both programs, hospital care is the largest expense category. Hospital care spending by both Medicare and Medicaid grew by around ** percent between 2013 and 2019. During the same period, private health insurance spending in this service category accelerated, rising by approximately ** billion U.S. dollars.

During a public health emergency in the Families First Coronavirus Response Act (FFCRA), a new optional Medicaid eligibility group was added called COVID-19 testing eligibility group. States reported these expenditures under sections 6004 and 6008 through the Medicaid Budget and Expenditure System (MBES) on the Form CMS-64. The data in these reports constitute summary level preliminary expenditure information related to these FFCRA provisions for each state Notes: 1. The Families First Coronavirus Response Act (FFCRA), enacted on March 18, 2020, provided a temporary FMAP increase to states and territories meeting certain qualifications and added a new optional Medicaid eligibility group for uninsured individuals during a public health emergency in section 1902(a)(10)(A)(ii)(XXIII) of the Act, referred to as the “COVID - 19 Testing Group.” 2. FFCRA Section 6008 provides a temporary 6.2 percentage point FMAP increase to each qualifying state and territory's FMAP under section 1905(b) of the Act, beginning January 1, 2020 and lasting through the end of the quarter in which the public health emergency (PHE) declared by the Secretary for COVID-19 ends, including any extensions. 3. FFCRA Section 6004 provides a 100 percent match rate for individuals eligible under the new optional Medicaid eligibility group in section 1902(a)(10)(A)(ii)(XXIII) of the Act, beginning no earlier than March 18, 2020 and lasting through the end of the PHE for COVID-19. 4. States that have reported “0” either have no expenditures for that reporting category or have not yet reported expenditures for that category. 5. This report is a cumulative summary report that includes current and prior period adjustment expenditures that apply to this quarter 6. For the Quarter ending 03/31/2020: Delaware has Negative Total Computable Expenditures and Total Federal Share Expenditures due to the reporting of prior period adjustments during this period. 7. For the Quarter ending 09/30/2020: Colorado has Negative Total Computable Section 6004 Covid 19 Expenditures and Total Federal Share Section 6004 Covid 19 Expenditures due to the reporting of prior period adjustments during this period. 8. For the Quarter ending 03/31/2021: California has Negative Total Computable Section 6004 Covid 19 Expenditures and Total Federal Share Section 6004 Covid 19 Expenditures due to the reporting of prior period adjustments during this period. This corrected FY 2020 Q4 expenditures for Treatment services that are not allowed for Section 6004 100% FMAP match. 9. For the Quarter ending 03/31/2021: Utah has Negative Total Computable Section 6004 Covid 19 Expenditures and Total Federal Share Section 6004 Covid 19 Expenditures due to the reporting of prior period adjustments during this period. 10. For the Quarter ending 12/31/2022: California has Negative Total Computable Section 6004 Covid 19 Expenditures and Total Federal Share Section 6004 Covid 19 Expenditures due to the reporting of prior period adjustments during this period. 11. For the Quarter ending 12/31/2022: Connecticut has Negative Total Computable Section 6004 Covid 19 Expenditures and Total Federal Share Section 6004 Covid 19 Expenditures due to the reporting of prior period adjustments during this period. 12. For the Quarter ending 09/30/2023: Connecticut has Negative Total Computable Section 6004 Covid 19 Expenditures and Total Federal Share Section 6004 Covid 19 Expenditures due to the reporting of prior period adjustments during this period. 13. For the Quarter ending 09/30/2023: Illinois has Negative Total Computable Section 6004 Covid 19 Expenditures and Total Federal Share Section 6004 Covid 19 Expenditures due to the reporting of prior period adjustments during this period. 14. For the Quarter ending 09/30/2023: Minnesota has Negative Total Computable Section 6004 Covid 19 Expenditures and Total Federal Share Section 6004 Covid

Analysis of ‘Managed Long Term Services and Supports (MLTSS) Enrollees’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/8195f999-29f7-49a1-b180-8c67737b71a2 on 26 January 2022.

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

1. 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.
2. 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.
3. Comprehensive Managed Care Including LTSS does not include PACE programs.
4. 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.
5. 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.

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

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 of housing units for states and counties..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..Source: U.S. Census Bureau, 2018-2022 American Community Survey 5-Year Estimates.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..Logical coverage edits applying a rules-based assignment of Medicaid, Medicare and military health coverage were added as of 2009 -- please see https://www.census.gov/library/working-papers/2010/demo/coverage_edits_final.html for more details. Select geographies of 2008 data comparable to the 2009 and later tables are available at https://www.census.gov/data/tables/time-series/acs/1-year-re-run-health-insurance.html. The health insurance coverage category names were modified in 2010. See https://www.census.gov/topics/health/health-insurance/about/glossary.html#par_textimage_18 for a list of the insurance type definitions..When information is missing or inconsistent, the Census Bureau logically assigns an acceptable value using the response to a related question or questions. If a logical assignment is not possible, data are filled using a statistical process called allocation, which uses a similar individual or household to provide a donor value. The "Allocated" section is the number of respondents who received an allocated value for a particular subject..The 2018-2022 American Community Survey (ACS) data generally reflect the March 2020 Office of Management and Budget (OMB) delineations of metropolitan and micropolitan statistical areas. In certain instances, the names, codes, and boundaries of the principal cities shown in ACS tables may differ from the OMB delineation lists due to differences in the effective dates of the geographic entities..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..Explanation of Symbols:- The estimate could not be computed because there were an insufficient number of sample observations. For a ratio of medians estimate, one or both of the median estimates falls in the lowest interval or highest interval of an open-ended distribution. For a 5-year median estimate, the margin of error associated with a median was larger than the median itself.N The estimate or margin of error cannot be displayed because there were an insufficient number of sample cases in the selected geographic area. (X) The estimate or margin of error is not applicable or not available.median- The median falls in the lowest interval of an open-ended distribution (for example "2,500-")median+ The median falls in the highest interval of an open-ended distribution (for example "250,000+").** The margin of error could not be computed because there were an insufficient number of sample observations.*** The margin of error could not be computed because the median falls in the lowest interval or highest interval of an open-ended distribution.***** A margin of error is not appropriate because the corresponding estimate is controlled to an independent population or housing estimate. Effectively, the corresponding estimate has no sampling error and the margin of error may be treated as zero.

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.

In FY2022, from the 3,124 hospitals Medicare assessed, 764 were penalized for high rates of hospital-acquired conditions (HAC). This is lower than the highest number to date of 800 hospitals recorded in 2019.

The hospital-acquired conditions reduction program (HACRP) was created as part of Affordable Care Act's (ACA's) payment and delivery system reform, to focus on quality rather than quantity of care. Hospitals are ranked by the rates of HACs such as infections, bed sores and post-operative blood clots. The Centers for Medicare & Medicaid Services penalizes the lowest performing 25 percent of all hospitals each year one percent of their Medicare hospital payments. Critics have voiced that the program will always punish the lowest performing quartile regardless if they made any improvement from the previous year and that teaching hospitals, where infections are more rigorously tested for, are more often punished by the program. This statistic presents the number of hospitals in the United States that Medicare punished for high rates of hospital-acquired conditions from FY2015 to FY2022.

The Accountable Care Solutions (ACS) market is experiencing robust growth, projected to reach $2.24 billion in 2025 and exhibiting a Compound Annual Growth Rate (CAGR) of 10.92% from 2025 to 2033. This expansion is driven by several key factors. Increasing prevalence of chronic diseases necessitates efficient and cost-effective healthcare delivery models, making ACS a vital solution. Government initiatives promoting value-based care and reimbursement models further incentivize the adoption of ACS technologies and services. The rising adoption of electronic health records (EHRs) and healthcare analytics provides a robust foundation for the implementation of ACS programs. Furthermore, technological advancements such as cloud computing and artificial intelligence are enhancing the capabilities and efficiency of ACS platforms, leading to wider acceptance among healthcare providers and payers. The market segmentation reveals a significant share held by Electronic Health/Medical Records (EHR/EMR) solutions, followed by Healthcare Analytics and Revenue Cycle Management (RCM) solutions. Cloud-based deployments are gaining traction due to their scalability and accessibility. Key players like Aetna, Allscripts, and Epic Systems are actively shaping market growth through innovation and strategic partnerships. Geographic analysis indicates that North America currently holds the largest market share, but the Asia-Pacific region is anticipated to witness significant growth driven by increasing healthcare spending and technological advancements. The continued growth of the ACS market is expected to be fueled by several factors. The increasing focus on population health management and improved patient outcomes will drive the adoption of comprehensive ACS solutions. The ongoing shift toward value-based care will place a greater emphasis on data analytics and coordinated care, strengthening the demand for sophisticated ACS platforms. Competition among vendors is likely to intensify, leading to product innovation and pricing pressure. However, challenges such as data interoperability issues, concerns regarding data privacy and security, and the need for skilled professionals to effectively implement and manage ACS programs could pose potential restraints to market growth. Despite these challenges, the long-term prospects for the ACS market remain positive, driven by the overarching need for a more efficient, effective, and cost-conscious healthcare system. Recent developments include: In March 2022, Collaborative Health Systems, a population health management organization, and Community Care Alliance, an accountable care organization, entered into a venture., In March 2022, The Center for Medicare and Medicaid Services introduced a new accountable care model, REACH (Realizing Equity, Access, and Community Health), which was developed by NAACOS, the National Association of ACOs (CMMI). The Global and Professional Direct Contracting (GPDC) model will be replaced by the REACH model.. Key drivers for this market are: Emergence of Big Data in Healthcare, Government Regulations and Initiatives to Promote Patient-Centric Care; Increasing Demand to Curtail Healthcare Costs. Potential restraints include: Emergence of Big Data in Healthcare, Government Regulations and Initiatives to Promote Patient-Centric Care; Increasing Demand to Curtail Healthcare Costs. Notable trends are: Electronic Health/Medical Records Segment is Expected to Hold a Significant Market Share Over the Forecast Period.

Analysis of ‘Health Insurance Coverage’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/hhs/health-insurance on 28 January 2022.

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

Context

The Affordable Care Act (ACA) is the name for the comprehensive health care reform law and its amendments which addresses health insurance coverage, health care costs, and preventive care. The law was enacted in two parts: The Patient Protection and Affordable Care Act was signed into law on March 23, 2010 by President Barack Obama and was amended by the Health Care and Education Reconciliation Act on March 30, 2010.

Content

This dataset provides health insurance coverage data for each state and the nation as a whole, including variables such as the uninsured rates before and after Obamacare, estimates of individuals covered by employer and marketplace healthcare plans, and enrollment in Medicare and Medicaid programs.

Acknowledgements

The health insurance coverage data was compiled from the US Department of Health and Human Services and US Census Bureau.

Inspiration

How has the Affordable Care Act changed the rate of citizens with health insurance coverage? Which states observed the greatest decline in their uninsured rate? Did those states expand Medicaid program coverage and/or implement a health insurance marketplace? What do you predict will happen to the nationwide uninsured rate in the next five years?

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

The Healthcare Descriptive Analysis market is experiencing robust growth, projected to reach $18.36 million in 2025 and exhibiting a remarkable Compound Annual Growth Rate (CAGR) of 23.50%. This expansion is fueled by several key drivers. The increasing volume of healthcare data generated from electronic health records (EHRs), medical devices, and wearable sensors necessitates sophisticated analytical tools for efficient management and insightful interpretation. Furthermore, the rising demand for improved patient outcomes, operational efficiency within healthcare organizations, and the ability to conduct proactive, data-driven research are significantly contributing to market growth. The adoption of cloud-based solutions is accelerating, offering scalability and cost-effectiveness compared to on-premise deployments. Clinics and hospitals are leading the adoption, followed by other private organizations. The market is segmented across various applications (clinical, financial, administrative, and research data analytics) and components (software, hardware, and services). Software solutions dominate the market share, leveraging advanced algorithms for data mining, visualization, and predictive modeling. The market's growth trajectory is expected to continue throughout the forecast period (2025-2033). While specific regional market shares are not provided, North America is anticipated to maintain a substantial market share due to early adoption of advanced analytics and robust healthcare infrastructure. The Asia Pacific region, however, is poised for significant growth driven by increasing healthcare expenditure and technological advancements. Competitive pressures are intense, with established players like SAS Institute, Oracle, and IBM competing with specialized healthcare analytics providers such as MedeAnalytics and Health Catalyst. The market faces challenges such as data privacy concerns, the need for skilled data analysts, and the high cost of implementation and maintenance of advanced analytics solutions. However, ongoing technological advancements and increasing government initiatives to improve healthcare data management are expected to mitigate these challenges and drive further market expansion. This comprehensive report provides a detailed analysis of the Healthcare Descriptive Analysis Market, offering invaluable insights for stakeholders across the healthcare IT landscape. With a study period spanning 2019-2033, a base year of 2025, and a forecast period of 2025-2033, this report utilizes extensive data analysis to illuminate market trends, growth drivers, and potential challenges. The market is projected to reach significant values in the millions. Recent developments include: In November 2022, Ursa Health updated Ursa Studio, its healthcare analytics development platform, to help organizations meet the requirements of the Centers for Medicare and Medicaid Services (CMS)., In November 2022, Hartford HealthCare entered a long-term partnership with Google Cloud to advance the healthcare digital transformation, improve data analytics, and enhance care delivery and access.. Key drivers for this market are: Need for Comprehensive Analytics, Integration of Big Data into Healthcare. Potential restraints include: Data Privacy and Security Concerns. Notable trends are: Cloud-based Segment Expected to Hold a Significant Share of the Market During the Forecast Period.

Analysis of ‘WA-APCD Quality and Cost Summary Report: Practice Quality’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/10d4ddee-0987-4f16-a780-430181a47bf2 on 28 January 2022.

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

WA-APCD - Washington All-Payer Claims Database

The WA-APCD is the state’s most complete source of health care eligibility, medical claims, pharmacy claims, and dental claims insurance data. It contains claims from more than 50 data suppliers, spanning commercial, Medicaid, and Medicare managed care. The WA-APCD has historical claims data for five years (2013-2017), with ongoing refreshes scheduled quarterly. Workers' compensation data from the Washington Department of Labor & Industries will be added in fall 2018.

Download the attachment for the data dictionary and more information about WA-APCD and the data.

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

Description2019 - 2022, county-level U.S. stroke hospitalization rates. Dataset developed by the Centers for Disease Control and Prevention, Division for Heart Disease and Stroke Prevention.Create maps of U.S. stroke hospitalization rates among Medicare fee-for-service beneficiaries aged 65 and older, by county. Data can be stratified by race/ethnicity and sex.Visit the CDC Atlas of Heart Disease and Stroke for additional data and maps. Atlas of Heart Disease and StrokeData SourceHospitalization data were obtained from the Centers for Medicare and Medicaid Services Medicare Provider Analysis and Review (MEDPAR) file, Part A and the Master Beneficiary Summary File (MBSF). International Classification of Diseases, 10th Revision, Clinical Modification (ICD-10-CM) codes: I60-I69; principle (i.e., first-listed) diagnosis. Medicare fee-for-service beneficiaries 65 and older were included. Visit the Atlas of Heart Disease and Stroke Statistical Methods pages for more detailed Medicare data inclusion criteria.Data DictionaryData for counties with small populations are not displayed when a reliable rate could not be generated. These counties are represented in the data with values of '-1.' CDC excludes these values when classifying the data on a map, indicating those counties as 'Insufficient Data.'Data field names and descriptionsstcty_fips: state FIPS code + county FIPS codeOther fields use the following format: RRR_S_aaaa (e.g., API_M_35UP)  RRR: 3 digits represent race/ethnicity    All - Overall   BLK - Black, non-Hispanic    HIS - Hispanic    WHT - White, non-Hispanic  S: 1 digit represents sex    A - All    F - Female    M - Male  aaaa: 4 digits represent age. The first 2 digits are the lower bound for age and the last 2 digits are the upper bound for age. 'UP' indicates the data includes the maximum age available and 'LT' indicates ages less than the upper bound. Example: The column 'BLK_M_65UP' displays rates per 1,000 black Medicare beneficiaries aged 65 years and older.MethodologyRates are calculated using a 3-year average and are age-standardized in 10-year age groups using the 2000 U.S. Standard Population. Rates are calculated and displayed per 1,000 Medicare beneficiaries. Rates were spatially smoothed using a Local Empirical Bayes algorithm to stabilize risk by borrowing information from neighboring geographic areas, making estimates more statistically robust and stable for counties with small populations. Data for counties with small populations are coded as '-1' when a reliable rate could not be generated. County-level rates were generated when the following criteria were met over a 3-year time period within each of the filters (e.g., age, race, and sex).At least one of the following 3 criteria:At least 20 events occurred within the county and its adjacent neighbors.ORAt least 16 events occurred within the county.ORAt least 5,000 population years within the county.AND all 3 of the following criteria:At least 6 population years for each age group used for age adjustment if that age group had 1 or more event.The number of population years in an age group was greater than the number of events.At least 100 population years within the county.More Questions?Interactive Atlas of Heart Disease and StrokeData SourcesStatistical Methods

Of the *** billion U.S. dollars that the home and community based services (HCBS) spending reported in 2022, roughly ** percent came from Medicaid payments. Meanwhile, Medicare does not generally cover long-term care services.

The Healthcare Descriptive Analytics Market is experiencing robust growth, projected to reach $18.36 billion in 2025 and maintain a Compound Annual Growth Rate (CAGR) of 23.50% from 2025 to 2033. This expansion is driven by several key factors. The increasing adoption of electronic health records (EHRs) generates massive datasets ripe for analysis, leading to improved patient care, operational efficiency, and more effective research. Furthermore, advancements in big data technologies and artificial intelligence (AI) are enabling sophisticated analytical capabilities, allowing healthcare providers and organizations to extract valuable insights from complex healthcare data. The demand for data-driven decision-making in areas like precision medicine, population health management, and risk stratification is further fueling market growth. Strong government initiatives promoting healthcare data interoperability and the rising need for improved healthcare outcomes also contribute significantly to the market's expansion. Market segmentation reveals strong performance across various applications. Clinical data analytics, focused on improving diagnoses and treatment, holds a significant share, followed by financial data analytics used for optimizing revenue cycle management and reducing costs. The software component dominates the market due to its versatility and scalability, complemented by robust growth in cloud-based deployment models, owing to their cost-effectiveness, accessibility, and enhanced security features. Private organizations, especially hospitals and clinics, are leading end-users, driving adoption across various segments. Geographically, North America is currently the largest market, fueled by advanced healthcare infrastructure and early adoption of analytical technologies. However, the Asia-Pacific region is poised for substantial growth, driven by increasing healthcare spending and technological advancements. The competitive landscape is dynamic, with established players like IBM, Oracle, and McKesson alongside specialized healthcare analytics firms, all vying for market share through innovative solutions and strategic partnerships. Recent developments include: In November 2022, Ursa Health updated Ursa Studio, its healthcare analytics development platform, to help organizations meet the requirements of the Centers for Medicare and Medicaid Services (CMS)., In November 2022, Hartford HealthCare entered a long-term partnership with Google Cloud to advance the healthcare digital transformation, improve data analytics, and enhance care delivery and access.. Key drivers for this market are: Need for Comprehensive Analytics, Integration of Big Data into Healthcare. Potential restraints include: Need for Comprehensive Analytics, Integration of Big Data into Healthcare. Notable trends are: Cloud-based Segment Expected to Hold a Significant Share of the Market During the Forecast Period.

Note: After May 3, 2024, this dataset will no longer be updated because hospitals are no longer required to report data on COVID-19 hospital admissions, and hospital capacity and occupancy data, to HHS through CDC’s National Healthcare Safety Network. The related CDC COVID Data Tracker site was revised or retired on May 10, 2023.

This dataset represents daily COVID-19 hospitalization data and metrics aggregated to national, state/territory, and regional levels. COVID-19 hospitalization data are reported to CDC’s National Healthcare Safety Network, which monitors national and local trends in healthcare system stress, capacity, and community disease levels for approximately 6,000 hospitals in the United States. Data reported by hospitals to NHSN and included in this dataset represent aggregated counts and include metrics capturing information specific to COVID-19 hospital admissions, and inpatient and ICU bed capacity occupancy.

Reporting information:

• As of December 15, 2022, COVID-19 hospital data are required to be reported to NHSN, which monitors national and local trends in healthcare system stress, capacity, and community disease levels for approximately 6,000 hospitals in the United States. Data reported by hospitals to NHSN represent aggregated counts and include metrics capturing information specific to hospital capacity, occupancy, hospitalizations, and admissions. Prior to December 15, 2022, hospitals reported data directly to the U.S. Department of Health and Human Services (HHS) or via a state submission for collection in the HHS Unified Hospital Data Surveillance System (UHDSS).
• While CDC reviews these data for errors and corrects those found, some reporting errors might still exist within the data. To minimize errors and inconsistencies in data reported, CDC removes outliers before calculating the metrics. CDC and partners work with reporters to correct these errors and update the data in subsequent weeks.
• Many hospital subtypes, including acute care and critical access hospitals, as well as Veterans Administration, Defense Health Agency, and Indian Health Service hospitals, are included in the metric calculations provided in this report. Psychiatric, rehabilitation, and religious non-medical hospital types are excluded from calculations.
• Data are aggregated and displayed for hospitals with the same Centers for Medicare and Medicaid Services (CMS) Certification Number (CCN), which are assigned by CMS to counties based on the CMS Provider of Services files.
• Full details on COVID-19 hospital data reporting guidance can be found here: https://www.hhs.gov/sites/default/files/covid-19-faqs-hospitals-hospital-laboratory-acute-care-facility-data-reporting.pdf

Metric details:

• Time Period: timeseries data will update weekly on Mondays as soon as they are reviewed and verified, usually before 8 pm ET. Updates will occur the following day when reporting coincides with a federal holiday. Note: Weekly updates might be delayed due to delays in reporting. All data are provisional. Because these provisional counts are subject to change, including updates to data reported previously, adjustments can occur. Data may be updated since original publication due to delays in reporting (to account for data received after a given Thursday publication) or data quality corrections.
• New COVID-19 Hospital Admissions (count): Number of new admissions of patients with laboratory-confirmed COVID-19 in the previous week (including both adult and pediatric admissions) in the entire jurisdiction.
• New COVID-19 Hospital Admissions (7-Day Average): 7-day average of new admissions of patients with laboratory-confirmed COVID-19 in the previous week (including both adult and pediatric admissions) in the entire jurisdiction.
• Cumulative COVID-19 Hospital Admissions: Cumulative total number of admissions of patients with laboratory-confirmed COVID-19 (including both adult and pediatric admissions) in the entire jurisdiction since August 1, 2020.
• Cumulative COVID-19 Hospital Admissions Rate: Cumulative total number of admissions of patients with laboratory-confirmed COVID-19 (including both adult and pediatric admissions) in the entire jurisdiction since August 1, 2020 divided by 2019 intercensal population estimate for that jurisdiction multiplied by 100,000.
• New COVID-19 Hospital Admissions Rate (7-day average) percent change from prior week: Percent change in the 7-day average new admissions of patients with laboratory-confirmed COVID-19 per 100,000 population compared with the prior week.
• New COVID-19 Hospital Admissions (7-Day Total): 7-day total number of new admissions of patients with laboratory-confirmed COVID-19 (including both adult and pediatric admissions) in the entire jurisdiction.
• New COVID-19 Hospital Admissions Rate (7-Day Total): 7-day total number of new admissions of patients with laboratory-confirmed COVID-19 (including both adult and pediatric admissions) for the entire jurisdiction divided by 2019 intercensal population estimate for that jurisdiction multiplied by 100,000.
• Total Hospitalized COVID-19 Patients: 7-day total number of patients currently hospitalized with laboratory-confirmed COVID-19 (including both adult and pediatric patients) for the entire jurisdiction.
• Total Hospitalized COVID-19 Patients (7-Day Average): 7-day average of the number of patients currently hospitalized with laboratory-confirmed COVID-19 (including both adult and pediatric patients) for the entire jurisdiction.
• COVID-19 Inpatient Bed Occupancy (7-Day Average): Percentage of all staffed inpatient beds occupied by patients with laboratory-confirmed COVID-19 (including both adult and pediatric patients) within the entire jurisdiction is calculated as an average of valid daily values within the past 7 days (e.g., if only three valid values, the average of those three is taken). Averages are separately calculated for the daily numerators (patients hospitalized with confirmed COVID-19) and denominators (staffed inpatient beds). The average percentage can then be taken as the ratio of these two values for the entire jurisdiction.
• COVID-19 Inpatient Bed Occupancy absolute change from prior week: The absolute change in the percent of staffed inpatient beds occupied by patients with laboratory-confirmed COVID-19 represents the week-over-week absolute difference between the 7-day average occupancy of patients with confirmed COVID-19 in staffed inpatient beds in the past 7 days, compared with the prior week, in the entire jurisdiction.
• COVID-19 ICU Bed Occupancy (7-Day Average): Percentage of all staffed inpatient beds occupied by adult patients with confirmed COVID-19 within the entire jurisdiction is calculated as a 7-day average of valid daily values within the past 7 days (e.g., if only three valid values, the average of those three is taken). Averages are separately calculated for the daily numerators (adult patients hospitalized with confirmed COVID-19) and denominators (staffed adult ICU beds). The average percentage can then be taken as the ratio of these two values for the entire jurisdiction.
• COVID-19 ICU Bed Occupancy absolute change from prior week: The absolute change in the percent of staffed ICU beds occupied by patients with laboratory-confirmed COVID-19 represents the week-over-week absolute difference between the average occupancy of patients with confirmed COVID-19 in staffed adult ICU beds for the past 7 days, compared with the prior week, in the in the entire jurisdiction.

Notes: October 27, 2023: Due to a data processing error, reported values for avg_percent_inpatient_beds_occupied_covid_confirmed will appear lower than previously reported values by an average difference of less than 1%. Therefore, previously reported values for avg_percent_inpatient_beds_occupied_covid_confirmed may have been overestimated and should be interpreted with caution.

October 27, 2023: Due to a data processing error, reported values for abs_chg_avg_percent_inpatient_beds_occupied_covid_confirmed will differ from previously reported values by an average absolute difference of less than 1%. Therefore, previously reported values for abs_chg_avg_percent_inpatient_beds_occupied_covid_confirmed should be interpreted with caution.

December 29, 2023: Hospitalization data reported to CDC’s National Healthcare Safety Network (NHSN) through December 23, 2023, should be interpreted with caution due to potential reporting delays that are impacted by Christmas and New Years holidays. As a result, metrics including new hospital admissions for COVID-19 and influenza and hospital occupancy may be underestimated for the week ending December 23, 2023.

January 5, 2024: Hospitalization data reported to CDC’s National Healthcare Safety Network (NHSN) through December 30, 2023 should be interpreted with caution due to potential reporting delays that are impacted by Christmas and New Years holidays. As a result, metrics including new hospital admissions for COVID-19 and influenza and hospital occupancy may be underestimated for the week ending December 30, 2023.

The global healthcare analytics market, valued at $13.44 billion in 2025, is projected to experience robust growth, driven by a compound annual growth rate (CAGR) of 20.96% from 2025 to 2033. This expansion is fueled by several key factors. The increasing adoption of electronic health records (EHRs) generates massive datasets ripe for analysis, enabling providers to improve patient care, reduce costs, and enhance operational efficiency. Furthermore, the rising prevalence of chronic diseases necessitates more sophisticated predictive analytics to manage patient populations effectively and prevent costly hospital readmissions. Government initiatives promoting value-based care and data interoperability are also significantly boosting market growth, as are advancements in artificial intelligence (AI) and machine learning (ML) which are revolutionizing diagnostic accuracy, treatment planning, and drug discovery. Competition is fierce, with established players like Allscripts, Cerner, IBM, and McKesson vying for market share alongside emerging innovative companies specializing in niche areas such as predictive modeling and clinical decision support. The market is segmented by various factors including technology (predictive analytics, descriptive analytics, etc.), deployment mode (cloud, on-premise), application (risk management, population health management etc.), and end-user (hospitals, pharmaceutical companies etc.). Despite the significant growth potential, the market faces certain challenges. Data security and privacy concerns remain paramount, requiring robust cybersecurity measures and adherence to strict regulatory guidelines like HIPAA. The complexity of integrating diverse data sources from various healthcare systems can also hinder adoption. Moreover, the high cost of implementing and maintaining advanced analytics solutions can pose a barrier for smaller healthcare providers. However, these challenges are being addressed through improved data security technologies, cloud-based solutions that offer scalability and cost-effectiveness, and increased government support for data integration and interoperability initiatives. The future of healthcare analytics is bright, with its potential to transform healthcare delivery and improve patient outcomes globally. Further growth is anticipated as AI-driven solutions become more sophisticated and accessible. Recent developments include: In November 2022, Ursa Health has introduced new capabilities to Ursa Studio, its healthcare analytics development platform, to help organizations meet the requirements of the Centers for Medicare and Medicaid Services (CMS) ACO REACH (Accountable Care Organization Realizing Equity, Access, and Community Health) Model. Ursa Studio spans the full breadth of healthcare data work in one no-code platform, including raw data ingestion and integration, data modeling, analytics development, and business intelligence., In March 2022, Databricks launched its new lakehouse platform, the Databricks Lakehouse for Healthcare and Life Sciences. Lakehouse is a single platform that brings together all data and analytics workloads to enable transformative innovations in patient care and drug research and development.. Key drivers for this market are: High Adoption Rate of E-health Record System, Technological Advancements in Data Analytics; Favorable Government Initiatives. Potential restraints include: High Adoption Rate of E-health Record System, Technological Advancements in Data Analytics; Favorable Government Initiatives. Notable trends are: Predictive Analytics Sub-Segment is Expected to Witness Significant Growth Over the Forecast Period.