In 2020, around **** percent of the U.S. population had private health insurance coverage. This share slightly decreased to **** percent in 2024. Medicare and Medicaid together provided healthcare coverage to approximately ** percent of the population in the United States. U.S. population with and without health insurance In 2022, over half of the U.S. population had health insurance coverage through their place of employment, around 54.5 percent. Approximately 35 percent had coverage through some form of government plan in the same year. While still low, the U.S. population without health insurance has decreased slightly from the previous year. A large portion of those without health insurance are between 19 and 25 years of age. Approximately ** percent of adults in this age group did not have health insurance in 2021. Health expenditure The United States spent approximately ****** U.S. dollars per capita on health in 2022 while in comparison, the Canadian government expended some ***** U.S. dollars per capita in the same year. However, higher health spending did not equate to a better health system or outcomes and when ranked with other comparable high-income countries, the U.S. came in last on nearly all health performance categories from access of care to health outcomes.

As of 2024, nearly *** million people in the United States had some kind of health insurance, a significant increase from around *** million insured people in 2010. However, as of 2024, there were still approximately ** million people in the United States without any kind of health insurance. Insurance coverage The United States does not have universal health insurance, and so health care cost is mostly covered through different private and public insurance programs. In 2021, almost ** percent of the insured population of the United States were insured through employers, while **** percent of people were insured through Medicaid, and **** percent of people through Medicare. As of 2022, about *** percent of people were uninsured in the U.S., compared to ** percent in 2010. The Affordable Care Act The Affordable Care Act (ACA) significantly reduced the number of uninsured people in the United States, from **** million uninsured people in 2013 to **** million people in 2015. However, since the repeal of the individual mandate the number of people without health insurance has risen. Healthcare reform in the United States remains an ongoing political issue with public opinion on a Medicare-for-all plan consistently divided.

The percentage of people in the United States with health insurance has increased over the past decade with a noticeably sharp increase in 2014 when the Affordable Care Act (ACA) was enacted. As of 2024, around ** percent of people in the United States had some form of health insurance, compared to around ** percent in 2010. Despite the increases in the percentage of insured people in the U.S., there were still over ** million people in the United States without health insurance as of 2024. Insurance coverage Health insurance in the United States consists of different private and public insurance programs such as those provided by private employers or those provided publicly through Medicare and Medicaid. Almost half of the insured population in the United States were insured privately through an employer as of 2021, while **** percent of people were insured through Medicaid, and **** percent through Medicare . The Affordable Care Act The Affordable Care Act (ACA), enacted in 2014, has significantly reduced the number of uninsured people in the United States. In 2014, the percentage of U.S. individuals with health insurance increased to almost ** percent. Furthermore, the percentage of people without health insurance reached an all time low in 2022. Public opinion on healthcare reform in the United States remains an ongoing political issue with public opinion consistently divided.

In 2022, among people aged 65 years and above who had health coverage, **** percent non-Hispanic White Americans had private health insurance, while a further ** percent had Medicare Advantage. The majority of older adults in the U.S. were privately insured (with or without Medicare). This statistic illustrates the distribution of health insurance coverage among adults aged 65 and above in the U.S. in 2022, by race and coverage type.

Medicare is an important public health insurance scheme for U.S. adults aged 65 years and over. As of 2024, an estimated 19.1 percent of the U.S. population was covered by Medicare, an increase from the previous year. As of 2023, California, Florida, and Texas had the largest number of adults aged 65 years and older. The Medicare program Medicare has two primary parts: Medicare Part A covers hospital care and Medicare Part B covers medical and preventative services. Both parts of Medicare are available to those aged 65 years and older under certain conditions. Medicare premiums are variable and depend on the enrollee’s income. Despite a majority of the Medicare enrollees being above the federal poverty line, there are still several programs in place to help cover the costs of healthcare for the elderly. Opinions on elderly care in the U.S. It is estimated that about 23 percent of Medicare enrollees are in fair/poor health. But there are lots of questions about who should pay for or help with elderly care long-term. In a recent survey of U.S. adults, about half of the respondents said that health insurance companies should pay for elderly care. However, a majority of adults also supported a long-term government sponsored health plan like Medicaid. The issue is still hotly debated and politicized in the United States.

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..Beginning in 2017, selected variable categories were updated, including age-categories, income-to-poverty ratio (IPR) categories, and the age universe for certain employment and education variables. See user note entitled "Health Insurance Table Updates" for further details..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.

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.

The Medicare Fee-for-Service (FFS) Comprehensive Error Rate Testing (CERT) dataset provides information on a random sample of FFS claims to determine if they were paid properly under Medicare coverage, coding, and payment rules. The dataset contains information on type of FFS claim, Diagnosis Related Group (DRG) and Healthcare Common Procedure Coding System (HCPCS) codes, provider type, type of bill, review decision, and error code. Please note, each reporting year (RY) contains claims submitted July 1 two years before the report through June 30 one year before the report. For example, the 2024 data contains claims submitted July 1, 2022 through June 30, 2023.

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, 2022 American Community Survey 1-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..Beginning in 2017, selected variable categories were updated, including age-categories, income-to-poverty ratio (IPR) categories, and the age universe for certain employment and education variables. See user note entitled "Health Insurance Table Updates" for further details..The 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 delineations 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.

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, hospital capacity, or occupancy data to HHS through CDC’s National Healthcare Safety Network (NHSN). The related CDC COVID Data Tracker site was revised or retired on May 10, 2023.

This dataset represents weekly 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.

Note: 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.

According to cognitive market research, the global electronic health records market size was valued at USD xx billion in 2024 and is expected to reach USD xx billion at a CAGR of xx% during the forecast period.

An electronic health record (EHR), or electronic medical record (EMR), is the systematized collection of patient and population electronically stored health information in a digital format.
The cloud-based EHR segment led the market and accounted for more than xx% share of the global revenue in 2024.
Based on end-use, the market is classified into hospitals and ambulatory care. The hospitals segment held the largest market share in 2024.
The market was substantially driven by the integration of artificial intelligence in electronic health record solutions.
Medicare incentive payment system (IPPS) is available to acute care hospitals in the US that are covered by the Inpatient Prospective Payment System.
Healthcare professionals' use of EHRs is being driven by the need for contemporary healthcare facilities.
Globally, North America is estimated to hold the highest global Electronic Health Records market share.

Market Dynamics of the Electronic Health Records Market

Key Drivers of the Electronic Health Records Market

Increasing popularity of digital health applications to boost market growth

Electronic health records have demonstrated their efficacy in managing data and maintaining population health throughout the global COVID-19 pandemic. The worldwide electronic health record industry is seeing daily growth in EHR service providers due to increased product research and development, particularly in the area of cloud storage technologies. varying degrees of software development and technology improvement in the healthcare industry. Furthermore, the market for electronic health records will expand due to the advent of artificial intelligence. Healthcare professionals' use of EHRs is being driven by the need for contemporary healthcare facilities. Among the fundamental components of an EHR are clinical record systems, lab, radiography, pharmacy, administrative duties, and computerized physician order entry.

For instance, In May 2022, CPSI entered into a partnership agreement with Medicomp Systems to launch Quippe Clinical Lens. The new technology aims to empower EHR users with proper access to clinical information at PoC (Source:https://www.businesswire.com/news/home/20220519005390/en/CPSI-Pilots-Clinical-Lens-to-Ease-Provider-Data-Burdens )

Government incentives propelling the adoption of EHR systems across healthcare facilities

Several governments throughout the world offer incentives to healthcare providers that implement EHR systems. Throughout the forecast period, financial incentives from governments are anticipated to propel the global market for electronic health records. Through the Health Information Technology for Economic and Clinical Health (HITECH) Act of 2009, the US federal government promotes the widespread implementation of electronic health records (EHRs). CMS created the Medicare and Medicaid EHR incentive programs in 2011 to incentivize general practitioners (GPs), qualified hospitals, and physician offices/clinics to adopt, install, update, and demonstrate meaningful use of certified electronic health record technology (CEHRT). These initiatives are now known as the Medicare Interoperability Promotion Programme. The UK's Department of Health (DoH) has allotted over GBP 2 billion in funding as part of the NHS Digitization plan to support electronic patient records in all NHS trusts and assist over 500,000 individuals in using digital tools to manage their own homes by 2022.

For instance, in 2021, the Government of India launched a digital health initiative scheme called Ayushman Bharat Digital Mission (ABDM) that aims to provide easy access to treatment records, thereby enabling faster and more effective treatment for patients. (Source:https://www.india.gov.in/spotlight/ayushman-bharat-digital-mission-abdm )

Restraints of the Electronic Health Records Market

Critical security concerns to hinder market growth

Hackers can target any hardware or software-driven system. EHR systems are not impervious to data risks or cyberattacks, either. Targeting specific data sectors might result in patient privacy breaches since healthcare systems worldwide view patient healthcare information as one of their most vital as...

Cash-Flow-Per-Share Time Series for Cigna Corp. The Cigna Group, together with its subsidiaries, provides insurance and related products and services in the United States. Its Evernorth Health Services segment provides a range of coordinated and point solution health services, including pharmacy benefits, home delivery pharmacy, specialty pharmacy, distribution, and care delivery and management solutions to health plans, employers, government organizations, and health care providers. The company's Cigna Healthcare segment offers medical, pharmacy, behavioral health, dental, and other products and services for insured and self-insured customers; Medicare Advantage, Medicare Supplement, and Medicare Part D plans for seniors, as well as individual health insurance plans; and health care coverage in its international markets, as well as health care benefits for mobile individuals and employees of multinational organizations. In addition, it offers permanent insurance contracts sold to corporations to provide coverage on the lives of certain employees for financing employer-paid future benefit obligations and stop loss insurance. The company distributes its products and services through insurance brokers and consultants; directly to employers, unions and other groups, or individuals; and private and public exchanges. The company was formerly known as Cigna Corporation and changed its name to The Cigna Group in February 2023. The company was founded in 1792 and is headquartered in Bloomfield, Connecticut.

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.

Note: This dataset has been limited to show metrics for Ramsey County, Minnesota.

This dataset represents COVID-19 hospitalization data and metrics aggregated to county or county-equivalent, for all counties or county-equivalents (including territories) in the United States. 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

Calculation of county-level hospital metrics: County-level hospital data are derived using calculations performed at the Health Service Area (HSA) level. An HSA is defined by CDC’s National Center for Health Statistics as a geographic area containing at least one county which is self-contained with respect to the population’s provision of routine hospital care. Every county in the United States is assigned to an HSA, and each HSA must contain at least one hospital. Therefore, use of HSAs in the calculation of local hospital metrics allows for more accurate characterization of the relationship between health care utilization and health status at the local level. Data presented at the county-level represent admissions, hospital inpatient and ICU bed capacity and occupancy among hospitals within the selected HSA. Therefore, admissions, capacity, and occupancy are not limited to residents of the selected HSA. For all county-level hospital metrics listed below the values are calculated first for the entire HSA, and then the HSA-level value is then applied to each county within the HSA. For all county-level hospital metrics listed below the values are calculated first for the entire HSA, and then the HSA-level value is then applied to each county within the HSA.

Metric details: Time period: data for the previous MMWR week (Sunday-Saturday) will update weekly on Thursdays 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 hospital admissions (count): Total number of admissions of patients with laboratory-confirmed COVID-19 in the previous week (including both adult and pediatric admissions) in the entire jurisdiction New Hospital Admissions Rate Value (Admissions per 100k): Total number of new admissions of patients with laboratory-confirmed COVID-19 in the past week (including both adult and pediatric admissions) for the entire jurisdiction divided by 2019 intercensal population estimate for that jurisdiction multiplied by 100,000. (Note: This metric is used to determine each county’s COVID-19 Hospital Admissions Level for a given week). New COVID-19 Hospital Admissions Rate Level: qualitative value of new COVID-19 hospital admissions rate level [Low, Medium, High, Insufficient Data] New hospital admissions percent change from prior week: Percent change in the current weekly total new admissions of patients with laboratory-confirmed COVID-19 per 100,000 population compared with the prior week. New hospital admissions percent change from prior week level: Qualitative value of percent change in hospital admissions rate from prior week [Substantial decrease, Moderate decrease, Stable, Moderate increase, Substantial increase, Insufficient data] COVID-19 Inpatient Bed Occupancy Value: Percentage of all staffed inpatient beds occupied by patients with laboratory-confirmed COVID-19 (including both adult and pediatric patients) within the in the entire jurisdiction is calculated as an average of valid daily values within the past week (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 Level: Qualitative value of inpatient beds occupied by COVID-19 patients level [Minimal, Low, Moderate, Substantial, High, Insufficient data] COVID-19 Inpatient Bed Occupancy percent 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 average occupancy of patients with confirmed COVID-19 in staffed inpatient beds in the past week, compared with the prior week, in the entire jurisdiction. COVID-19 ICU Bed Occupancy Value: Percentage of all staffed inpatient beds occupied by adult patients with confirmed COVID-19 within the entire jurisdiction is calculated as an average of valid daily values within the past week (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 Level: Qualitative value of ICU beds occupied by COVID-19 patients level [Minimal, Low, Moderate, Substantial, High, Insufficient data] COVID-19 ICU Bed Occupancy percent 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 week, compared with the prior week, in the in the entire jurisdiction. For all metrics, if there are no data in the specified locality for a given week, the metric value is displayed as “insufficient data”.

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..Beginning in 2017, selected variable categories were updated, including age-categories, income-to-poverty ratio (IPR) categories, and the age universe for certain employment and education variables. See user note entitled "Health Insurance Table Updates" for further details..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.

The size of the Accountable Care Solutions Market was valued at USD 2.24 Million in 2023 and is projected to reach USD 4.63 Million by 2032, with an expected CAGR of 10.92% during the forecast period. 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: Data Security and Privacy Concerns, High Investments Required for Supporting Infrastructure. Notable trends are: Electronic Health/Medical Records Segment is Expected to Hold a Significant Market Share Over the Forecast Period.

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, 2022 American Community Survey 1-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..Beginning in 2017, selected variable categories were updated, including age-categories, income-to-poverty ratio (IPR) categories, and the age universe for certain employment and education variables. See user note entitled "Health Insurance Table Updates" for further details..The Hispanic origin and race codes were updated in 2020. For more information on the Hispanic origin and race code changes, please visit the American Community Survey Technical Documentation website..The 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 delineations 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.

Although more than half of all Americans were familiar with Medicare for All as of 2022, slightly less than half of the likely Republican voters surveyed were informed about the proposal. In contrast, approximately ** percent of those likely to vote for Democrats stated being familiar with the proposed reform. The least well-known healthcare reform proposal was Medicare buy-in, with only ** percent of both Democrat and Republican voters being aware of it.

The Healthcare Middleware Software Development Market is experiencing robust growth, projected to reach $3.15 billion in 2025 and maintain a Compound Annual Growth Rate (CAGR) of 9.87% from 2025 to 2033. This expansion is driven by several key factors. The increasing adoption of electronic health records (EHRs) and the growing need for interoperability between disparate healthcare systems are primary catalysts. Furthermore, the rising demand for cloud-based solutions, offering scalability and cost-effectiveness, is significantly fueling market growth. The market is segmented by type (Integration, Communication, Platform, and Others), deployment mode (Cloud, On-Premise, Hybrid), application (Clinical, Financial, Operational & Administrative), and end-user (Hospitals, Clinical Laboratories, and Others). The diverse applications of middleware across various healthcare settings, along with the increasing focus on data security and regulatory compliance, contribute to the market's positive outlook. North America currently holds a significant market share due to advanced healthcare infrastructure and high adoption rates of advanced technologies. However, Asia Pacific is expected to witness substantial growth in the coming years, driven by increasing healthcare expenditure and technological advancements in emerging economies like India and China. Competitive landscape includes established players like Informatica, Epic Systems Corporation, Microsoft, and others, all striving to provide innovative solutions to meet the evolving needs of the healthcare industry. The continued emphasis on improving patient care through data-driven insights and streamlined workflows will continue to drive demand for sophisticated middleware solutions. The integration of artificial intelligence (AI) and machine learning (ML) capabilities within middleware platforms is emerging as a significant trend, promising to enhance data analytics and predictive capabilities in healthcare. While challenges exist in terms of data security concerns and the complexity of integrating legacy systems, the overall market outlook remains exceptionally promising. The ongoing digital transformation within the healthcare sector, coupled with increasing government initiatives to promote interoperability and data exchange, will create ample opportunities for market players in the foreseeable future. Recent developments include: In March 2022, COPE Health Solutions and its Analytics for Risk Contracting (ARC) subsidiary have partnered with CareJourneyto provide one of the first health analytics platforms and solutions that integrate a health care organization's claims, electronic health records, lab, social determinants, and other data with CareJourney's suite of cost and utilization benchmarks derived on Medicare and Medicaid datasets., In April 2021, 3M Health Information Systems announced the launch of a new technology platform that can assist healthcare providers and payers in the prioritization of patient care and resource allocation.. Key drivers for this market are: Increasing Demand for the Advanced Devices and Its Usage in Healthcare, Increasing Research and Development Investments and Favorable Regulatory Infrastructure. Potential restraints include: Increasing Demand for the Advanced Devices and Its Usage in Healthcare, Increasing Research and Development Investments and Favorable Regulatory Infrastructure. Notable trends are: Cloud Based Software is Believed to Witness Significant Growth Over the Forecast Period.

Dividend-Per-Share Time Series for CNO Financial Group Inc. CNO Financial Group, Inc., through its subsidiaries, develops, markets, and administers health insurance, annuity, individual life insurance, insurance products, and financial services for middle-income pre-retiree and retired Americans in the United States. It offers Medicare supplement, supplemental health, and long-term care insurance policies; life insurance; and annuities, as well as Medicare advantage plans to individual consumers through phone, virtually, online, and face-to-face with agents. The company also focuses on sale of voluntary benefit life and health insurance products for businesses, associations, and other membership groups by interacting with customers at their place of employment. In addition, it provides fixed indexed annuities; fixed interest annuities, including fixed rate single and flexible premium deferred annuities; single premium immediate annuities; supplemental health products, such as specified disease, accident, and hospital indemnity products; and long-term care plans primarily to retirees, lesser degree, and older self-employed individuals in the middle-income market. Further, the company offers universal life and other interest-sensitive life products; and traditional life policies that include whole life, graded benefit life, term life, and single premium whole life products, as well as graded benefit life insurance products. It markets its products under the Bankers Life, Washington National, and Colonial Penn brand names. The company was founded in 1979 and is headquartered in Carmel, Indiana.