No ethnic/racial groups experienced better access to healthcare (across different access measures from health insurance to usual source of care) compared with non-Hispanic White or White people in 2017, 2018, or 2019. The exception is Asians, where they experienced better access than White population on 2 access measures (or 14 percent) but experienced worse access than White population on 4 measures (or 29 percent). The disparity was largest comparing Hispanic vs. non-Hispanic White population . This statistic depicts the percentage of healthcare access measures for which members of select ethnic groups had better or worse access to care than White population in the U.S. in 2017, 2018, or 2019.

The US Healthcare Visits Statistics dataset includes data about the frequency of healthcare visits to doctor offices, emergency departments, and home visits within the past 12 months in the United States by age, race, Hispanic origin, poverty level, health insurance status, geographic region and other characteristics between 1997 and 2016.

This is the current Medical Service Study Area. California Medical Service Study Areas are created by the California Department of Health Care Access and Information (HCAI).Check the Data Dictionary for field descriptions.Search for the Medical Service Study Area data on the CHHS Open Data Portal.Checkout the California Healthcare Atlas for more Medical Service Study Area information.This is an update to the MSSA geometries and demographics to reflect the new 2020 Census tract data. The Medical Service Study Area (MSSA) polygon layer represents the best fit mapping of all new 2020 California census tract boundaries to the original 2010 census tract boundaries used in the construction of the original 2010 MSSA file. Each of the state's new 9,129 census tracts was assigned to one of the previously established medical service study areas (excluding tracts with no land area), as identified in this data layer. The MSSA Census tract data is aggregated by HCAI, to create this MSSA data layer. This represents the final re-mapping of 2020 Census tracts to the original 2010 MSSA geometries. The 2010 MSSA were based on U.S. Census 2010 data and public meetings held throughout California.Source of update: American Community Survey 5-year 2006-2010 data for poverty. For source tables refer to InfoUSA update procedural documentation. The 2010 MSSA Detail layer was developed to update fields affected by population change. The American Community Survey 5-year 2006-2010 population data pertaining to total, in households, race, ethnicity, age, and poverty was used in the update. The 2010 MSSA Census Tract Detail map layer was developed to support geographic information systems (GIS) applications, representing 2010 census tract geography that is the foundation of 2010 medical service study area (MSSA) boundaries. ***This version is the finalized MSSA reconfiguration boundaries based on the US Census Bureau 2010 Census. In 1976 Garamendi Rural Health Services Act, required the development of a geographic framework for determining which parts of the state were rural and which were urban, and for determining which parts of counties and cities had adequate health care resources and which were "medically underserved". Thus, sub-city and sub-county geographic units called "medical service study areas [MSSAs]" were developed, using combinations of census-defined geographic units, established following General Rules promulgated by a statutory commission. After each subsequent census the MSSAs were revised. In the scheduled revisions that followed the 1990 census, community meetings of stakeholders (including county officials, and representatives of hospitals and community health centers) were held in larger metropolitan areas. The meetings were designed to develop consensus as how to draw the sub-city units so as to best display health care disparities. The importance of involving stakeholders was heightened in 1992 when the United States Department of Health and Human Services' Health and Resources Administration entered a formal agreement to recognize the state-determined MSSAs as "rational service areas" for federal recognition of "health professional shortage areas" and "medically underserved areas". After the 2000 census, two innovations transformed the process, and set the stage for GIS to emerge as a major factor in health care resource planning in California. First, the Office of Statewide Health Planning and Development [OSHPD], which organizes the community stakeholder meetings and provides the staff to administer the MSSAs, entered into an Enterprise GIS contract. Second, OSHPD authorized at least one community meeting to be held in each of the 58 counties, a significant number of which were wholly rural or frontier counties. For populous Los Angeles County, 11 community meetings were held. As a result, health resource data in California are collected and organized by 541 geographic units. The boundaries of these units were established by community healthcare experts, with the objective of maximizing their usefulness for needs assessment purposes. The most dramatic consequence was introducing a data simultaneously displayed in a GIS format. A two-person team, incorporating healthcare policy and GIS expertise, conducted the series of meetings, and supervised the development of the 2000-census configuration of the MSSAs.MSSA Configuration Guidelines (General Rules):- Each MSSA is composed of one or more complete census tracts.- As a general rule, MSSAs are deemed to be "rational service areas [RSAs]" for purposes of designating health professional shortage areas [HPSAs], medically underserved areas [MUAs] or medically underserved populations [MUPs].- MSSAs will not cross county lines.- To the extent practicable, all census-defined places within the MSSA are within 30 minutes travel time to the largest population center within the MSSA, except in those circumstances where meeting this criterion would require splitting a census tract.- To the extent practicable, areas that, standing alone, would meet both the definition of an MSSA and a Rural MSSA, should not be a part of an Urban MSSA.- Any Urban MSSA whose population exceeds 200,000 shall be divided into two or more Urban MSSA Subdivisions.- Urban MSSA Subdivisions should be within a population range of 75,000 to 125,000, but may not be smaller than five square miles in area. If removing any census tract on the perimeter of the Urban MSSA Subdivision would cause the area to fall below five square miles in area, then the population of the Urban MSSA may exceed 125,000. - To the extent practicable, Urban MSSA Subdivisions should reflect recognized community and neighborhood boundaries and take into account such demographic information as income level and ethnicity. Rural Definitions: A rural MSSA is an MSSA adopted by the Commission, which has a population density of less than 250 persons per square mile, and which has no census defined place within the area with a population in excess of 50,000. Only the population that is located within the MSSA is counted in determining the population of the census defined place. A frontier MSSA is a rural MSSA adopted by the Commission which has a population density of less than 11 persons per square mile. Any MSSA which is not a rural or frontier MSSA is an urban MSSA. Last updated December 6th 2024.

Multiple sclerosis (MS) results in an extensive use of the health care system, even within the first years of diagnosis. The effectiveness and accessibility of the health care system may affect patients' quality of life. The aim of the present study was to evaluate the health care resource use of MS patients under interferon beta-1b (EXTAVIA) treatment in Greece, the demographic or clinical factors that may affect this use and also patient satisfaction with the health care system. Structured interviews were conducted for data collection. In total, 204 patients (74.02% females, mean age (SD) 43.58 (11.42) years) were enrolled in the study. Analysis of the reported data revealed that during the previous year patients made extensive use of health services in particular neurologists (71.08% visited neurologists in public hospitals, 66.67% in private offices and 48.53% in insurance institutes) and physiotherapists. However, the majority of the patients (52.45%) chose as their treating doctor private practice neurologists, which may reflect accessibility barriers or low quality health services in the public health system. Patients seemed to be generally satisfied with the received health care, support and information on MS (84.81% were satisfied from the information provided to them). Patients' health status (as denoted by disease duration, disability status and hospitalization needs) and insurance institute were found to influence their visits to neurologists. Good adherence (up to 70.1%) to the study medication was reported. Patients' feedback on currently provided health services could direct these services towards the patients' expectations.

The dataset contains information on California’s Medical Service Study Areas (MSSA), at the census tract level for 2000. MSSAs are sub-city and sub-county geographical units used to organize and display population, demographic and physician data. MSSA areas are a geographic analysis unit defined by the California Office of Statewide Health Planning and Development. MSSA are a good foundation for needs assessment analysis, healthcare planning, and healthcare policy development.

The amount of global healthcare data is expected to increase dramatically by the year 2020. Early estimates from 2013 suggest that there were about 153 exabytes of healthcare data generated in that year. However, projections indicate that there could be as much as 2,314 exabytes of new data generated in 2020.

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Population Health Management Market Size and Forecast 2025-2029

The population health management market size estimates the market to reach by USD 19.40 billion, at a CAGR of 10.7% between 2024 and 2029. North America is expected to account for 68% of the growth contribution to the global market during this period. In 2019 the software segment was valued at USD 16.04 billion and has demonstrated steady growth since then.

The market is experiencing significant growth, driven by the increasing adoption of healthcare IT and the rising focus on personalized medicine. Healthcare providers are recognizing the value of population health management platforms in improving patient outcomes and reducing costs. The implementation of these systems enables proactive care management, disease prevention, and population health analysis. However, the market faces challenges as well. The cost of installing population health management platforms can be a significant barrier for smaller healthcare organizations. Additionally, ensuring data security and interoperability across various systems remains a major concern.
Effective data management and integration are essential for population health management to deliver its full potential. Companies seeking to capitalize on market opportunities must address these challenges and provide cost-effective, secure, and interoperable solutions. By focusing on these areas, they can help healthcare providers optimize their population health management initiatives and improve patient care.

What will be the Size of the Population Health Management Market during the forecast period?

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The market continues to evolve, driven by advancements in technology and a growing focus on value-based care. Risk adjustment models, which help account for the variability in health risks among patient populations, are increasingly being adopted to improve care coordination and health outcome measures. For instance, a leading healthcare organization implemented risk stratification models, resulting in a 20% reduction in hospital readmissions. Remote patient monitoring, public health surveillance, and disease outbreak response are crucial applications of population health management. These technologies enable real-time health data collection, allowing for early intervention and improved health equity initiatives. Chronic disease management, a significant focus area, benefits from electronic health records, care coordination models, and health information exchange.

Value-based care programs, predictive modeling healthcare, and telehealth platforms are transforming the landscape of healthcare delivery. Healthcare data analytics, interoperability standards, and population health dashboards facilitate data-driven decision-making, enhancing health intervention efficacy. Behavioral health integration and preventive health services are gaining prominence, with health literacy programs and clinical decision support tools supporting personalized medicine strategies. The market is expected to grow at a robust rate, with industry growth estimates reaching 15% annually. This growth is fueled by the ongoing need for healthcare cost reduction, quality improvement initiatives, and the integration of technology into healthcare delivery.

How is this Population Health Management Industry segmented?

The population health management industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Component

  Software
  Services


End-user

  Large enterprises
  SMEs


Delivery Mode

  On-Premise
  Cloud-Based
  Web-Based


End-Use

  Providers
  Payers
  Employer Groups
  Government Bodies


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    Italy
    UK


  APAC

    China
    India
    Japan
    South Korea


  Rest of World (ROW)

By Component Insights

The software segment is estimated to witness significant growth during the forecast period.

The market's software segment is experiencing significant growth and innovation, driven by various components that enhance healthcare organizations' capacity to manage and enhance the health outcomes of diverse populations. Population health management platforms aggregate and integrate data from multiple sources, including electronic health records, claims data, and patient-generated data. Advanced analytics are employed to generate valuable insights, enabling healthcare providers to identify at-risk populations, address chronic conditions, and improve overall patient outcomes. These platforms facilitate seamless data exchange between stakeholders, ensuring harmonious care coordination and enhancing the overall effectiveness of healthcare services.

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This statistic, biennative and censal in nature, allows, from the Free Insurance Entities operating in the C.A. of the Basque Country, to give information on the main magnitudes of insurance in health care (insured persons, premiums and type of insurance)

Department of State Hospitals Patient Population Demographic (Fiscal Effective Dates: 2010-2020)

Objective: To analyze the correlation between demographic and healthcare availability indicators with COVID-19 outcome among Indonesian provinces.Methods: We employed an ecological study design to study the correlation between demographics, healthcare availability, and COVID-19 indicators. Demographic and healthcare indicators were obtained from the Indonesian Health Profile of 2019 by the Ministry of Health while COVID-19 indicators were obtained from the Indonesian COVID-19 website in August 31st 2020. Non-parametric correlation and multivariate regression analyses were conducted with IBM SPSS 23.0.Results: We found the number of confirmed cases and case growth to be significantly correlated with demographic indicators, especially with distribution of age groups. Confirmed cases and case growth was significantly correlated (p < 0.05) with population density (correlation coefficient of 0.461 and 0.491) and proportion of young people (−0.377; −0.394). Incidence and incidence growth were correlated with ratios of GPs (0.426; 0.534), hospitals (0.376; 0.431), primary care clinics (0.423; 0.424), and hospital beds (0.472; 0.599) per capita. For mortality, case fatality rate (CFR) was correlated with population density (0.390) whereas mortality rate was correlated with ratio of hospital beds (0.387). Multivariate analyses found confirmed case independently associated with population density (β of 0.638) and demographic structure (−0.289). Case growth was independently associated with density (0.763). Incidence growth was independently associated with hospital bed ratio (0.486).Conclusion: Pre-existing inequality of healthcare availability correlates with current reported incidence and mortality rate of COVID-19. Lack of healthcare availability in some provinces may have resulted in artificially low numbers of cases being diagnosed, lower demands for COVID-19 tests, and eventually lower case-findings.

Population Health Management Market Outlook

According to our latest research, the global population health management market size reached USD 34.7 billion in 2024, reflecting a robust expansion driven by technological integration and evolving healthcare needs. The market is expected to grow at a CAGR of 12.8% from 2025 to 2033, reaching a projected value of USD 102.3 billion by 2033. This impressive growth rate is primarily attributed to the increasing prevalence of chronic diseases, the shift toward value-based care models, and the rising adoption of digital health solutions by healthcare providers and payers worldwide. As per our latest research, the market is witnessing a significant transformation, with a strong emphasis on data-driven decision-making and patient-centric care models.

One of the most significant growth factors propelling the population health management market is the surging incidence of chronic diseases such as diabetes, cardiovascular disorders, and respiratory illnesses. As populations age and lifestyle-related health risks escalate globally, healthcare systems are under mounting pressure to deliver more effective and coordinated care. Population health management solutions offer a holistic approach by integrating clinical, financial, and operational data, enabling healthcare stakeholders to identify at-risk populations, implement targeted interventions, and monitor health outcomes in real-time. This proactive approach not only reduces the overall cost of care but also improves patient outcomes, making it a critical component in the transition from fee-for-service to value-based care models.

Another crucial driver for the population health management market is the rapid advancement and adoption of digital health technologies. The proliferation of electronic health records (EHRs), wearable health devices, telemedicine platforms, and artificial intelligence-powered analytics tools has revolutionized how healthcare data is collected, shared, and analyzed. These technologies empower healthcare providers to gain deeper insights into population health trends, personalize care plans, and enhance patient engagement. Furthermore, government initiatives and regulatory mandates supporting interoperability and data sharing are accelerating the adoption of population health management software and services, especially in developed regions. The integration of advanced analytics and machine learning further amplifies the ability to predict disease outbreaks and manage resource allocation efficiently.

A third major growth factor is the increasing focus on preventive healthcare and wellness programs by both public and private sector stakeholders. Employers, insurers, and government bodies are investing heavily in population health management solutions to reduce long-term healthcare expenditures and improve workforce productivity. Preventive health initiatives, such as vaccination programs, health risk assessments, and wellness coaching, are being seamlessly integrated into population health platforms. These efforts are supported by favorable reimbursement policies and incentives for adopting value-based payment models, which reward healthcare organizations for improving population health metrics. As a result, the market is experiencing widespread adoption across various end-user segments, including healthcare providers, payers, employer groups, and government organizations.

From a regional perspective, North America continues to dominate the population health management market, accounting for the largest share in 2024. This dominance is driven by the presence of advanced healthcare infrastructure, high healthcare IT adoption rates, and supportive government policies such as the Affordable Care Act in the United States. Europe follows closely, benefiting from strong regulatory frameworks and increasing investments in digital health transformation. Meanwhile, the Asia Pacific region is emerging as a high-growth market, fueled by rising healthcare expenditure, expanding insurance coverage, and the growing burden of chronic diseases. Latin America and the Middle East & Africa are also witnessing gradual adoption, although challenges such as limited healthcare IT infrastructure and regulatory complexities persist. Overall, the global market landscape is characterized by rapid technological advancements, evolving care delivery models, and a growing emphasis on population health outcomes.

Introduction

Healthcare Staffing Statistics: Healthcare staffing is a crucial facet of the healthcare industry. Involves the recruitment, hiring, and management of qualified professionals to meet the ever-changing demands of patients and medical institutions.

This intricate process plays a pivotal role in ensuring high-quality patient care by matching individuals' skills and qualifications to specific roles, considering factors like patient load and location.

Effective healthcare staffing requires anticipating staffing needs, managing schedules, addressing turnover, and adhering to regulatory standards.

Inadequate staffing can jeopardize patient safety and care quality. Effective staffing enhances patient outcomes and experiences, making it a cornerstone of healthcare delivery.

In essence, healthcare staffing is a complex, indispensable process that directly impacts patient well-being and the overall success of healthcare organizations. Demanding meticulous planning and unwavering commitment to excellent patient care.

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.

An information system based on data from the healthcare sector and related areas. The online portal gives researchers the opportunity to research various health topics including population, socio-economic factors, health insurance, health laws.

• Laws

• Population

  • Births
  • Deaths
  • Population Numbers
  • other

• Pregnancy, Births

• Terminations of Pregnancy

• Households, Families, Living Communities

• Social Conditions

  • Education
  • Habitation
  • Employment
  • Unemployment
  • Poverty and Disparity
  • Single parent
  • Income
  • Social Welfare
  • other

• Training of the Health Personnel

• Economy

• Research

• Private Health Insurance

• Statutory Health Insurance

  • Statutory Health Insurance: Income, Expenditure
  • Statutory Health Insurance: Members, Insured Persons
  • Statutory Health Insurance: Services
  • Statutory Health Insurance: Services (Pharmaceuticals)
  • Statutory Health Insurance: other

• Nursing Care Insurance

• Pension Insurance

  • Insured Persons
  • Rehabilitation Services
  • Pensions because of old age
  • Ppensions because of diminution of ability to wrk
  • Pensions because of death (widow/widower pensions)
  • other

• Accident Insurance, employers' liability insurance association

Please note: This is a Synthetic data file, also known as a Dummy file - it is not real data. This synthetic file should not be used for purposes other than to develop an test computer programs that are to be submitted by remote access. Each record in the synthetic file matches the format and content parameters of the real Statistics Canada Master File with which it is associated, but the data themselves have been 'made up'. They do NOT represent responses from real individuals and should NOT be used for actual analysis. These data are provided solely for the purpose of testing statistical package 'code' (e.g. SPSS syntax, SAS programs, etc.) in preperation for analysis using the associated Master File in a Research Data Centre, by Remote Job Submission, or by some other means of secure access. If statistical analysis 'code' works with the synthetic data, researchers can have some confidence that the same code will run successfully against the Master File data in the Resource Data Centres. In the fall of 1991, the National Health Information Council recommended that an ongoing national survey of population health be conducted. This recommendation was based on consideration of the economic and fiscal pressures on the health care systems and the requirement for information with which to improve the health status of the population in Canada. Commencing in April 1992, Statistics Canada received funding for development of a National Population Health Survey (NPHS). The NPHS collects information related to the health of the Canadian population and related socio-demographic information to: aid in the development of public policy by providing measures of the level, trend and distribution of the health status of the population, provide data for analytic studies that will assist in understanding the determinants of health, and collect data on the economic, social, demographic, occupational and environmental correlates of health. In addition the NPHS seeks to increase the understanding of the relationship between health status and health care utilization, including alternative as well as traditional services, and also to allow the possibility of linking survey data to routinely collected administrative data such as vital statistics, environmental measures, community variables, and health services utilization. The NPHS collects information related to the health of the Canadian population and related socio-demographic information. It is composed of three components: the Households, the Health Institutions, and the North components. The Household component started in 1994/1995 and is conducted every two years. The first three cycles (1994/1995, 1996/1997, 1997/1998) were both cross-sectional and longitudinal. The NPHS longitudinal sample includes 17,276 persons from all ages in 1994/1995 and these same persons are to be interviewed every two years. Beginning in Cycle 4 (2000/2001) the survey became strictly longitudinal (collecting health information from the same individuals each cycle). The cross-sectional and longitudinal documentation of the Household component is presented separately as well as the documentation for the Health Institutions and North components. The cross-sectional component of the Population Health Survey Program has been taken over by the Canadian Community Health Survey (CCHS). With the introduction of the Canadian Community Health Survey (CCHS), there were many changes to the 2000-2001 National Population Health Survey - Household questionnaire. Since NPHS is strictly a longitudinal survey, some content was migrated to the CCHS (such as the two-week disability section and certain questions on place where health care was provided) or was dropped (e.g. certain chronic conditions), while the order of the questionnaire changed. As only the longitudinal respondent is now surveyed, it was no longer necessary to distinguish between the General questionnaire and the Health component. Health Canada, Public Health Agency of Canada and provincial ministries of health use NPHS longitudinal data to plan, implement and evaluate programs and health policies to improve health and the efficiency of health services. Non-profit health organizations and researchers in the academic fields use the information to move research ahead and to improve health.

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US Population Health Management (PHM) Market Size 2025-2029

The us population health management (phm) market size is forecast to increase by USD 6.04 billion at a CAGR of 7.4% between 2024 and 2029.

The Population Health Management (PHM) market in the US is experiencing significant growth, driven by the increasing adoption of healthcare IT solutions and analytics. These technologies enable healthcare providers to collect, analyze, and act on patient data to improve health outcomes and reduce costs. However, the high perceived costs associated with PHM solutions pose a challenge for some organizations, limiting their ability to fully implement and optimize these technologies. Despite this obstacle, the potential benefits of PHM, including improved patient care and population health, make it a strategic priority for many healthcare organizations. To capitalize on this opportunity, companies must focus on cost-effective solutions and innovative approaches to addressing the challenges of PHM implementation and optimization. By leveraging advanced analytics, cloud technologies, and strategic partnerships, organizations can overcome cost barriers and deliver better care to their patient populations.

What will be the size of the US Population Health Management (PHM) Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
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The Population Health Management (PHM) market in the US is experiencing significant advancements, integrating various elements to improve patient outcomes and reduce healthcare costs. Public health surveillance and data governance ensure accurate population health data, enabling healthcare leaders to identify health disparities and target interventions. Quality measures and health literacy initiatives promote transparency and patient activation, while data visualization and business intelligence facilitate data-driven decision-making. Behavioral health integration, substance abuse treatment, and mental health services address the growing need for holistic care, and outcome-based contracts incentivize providers to focus on patient outcomes. Health communication, community health workers, and patient portals enhance patient engagement, while wearable devices and mHealth technologies provide real-time data for personalized care plans. Precision medicine and predictive modeling leverage advanced analytics to tailor treatment approaches, and social service integration addresses the social determinants of health. Health data management, data storytelling, and healthcare innovation continue to drive market growth, transforming the industry and improving overall population health.

How is this market segmented?

The market research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments. ProductSoftwareServicesDeploymentCloudOn-premisesEnd-userHealthcare providersHealthcare payersEmployers and government bodiesGeographyNorth AmericaUS

By Product Insights

The software segment is estimated to witness significant growth during the forecast period.

Population Health Management (PHM) software in the US gathers patient data from healthcare systems and utilizes advanced analytics tools, including data visualization and business intelligence, to predict health conditions and improve patient care. PHM software aims to enhance healthcare efficiency, reduce costs, and ensure quality patient care. By analyzing accurate patient data, PHM software enables the identification of community health risks, leading to proactive interventions and better health outcomes. The adoption of PHM software is on the rise in the US due to the growing emphasis on value-based care and the increasing prevalence of chronic diseases. Machine learning, artificial intelligence, and predictive analytics are integral components of PHM software, enabling healthcare payers to develop personalized care plans and improve care coordination. Data integration and interoperability facilitate seamless data sharing among various healthcare stakeholders, while data visualization tools help in making informed decisions. Public health agencies and healthcare providers leverage PHM software for population health research, disease management programs, and quality improvement initiatives. Cloud computing and data warehousing provide the necessary infrastructure for storing and managing large volumes of population health data. Healthcare regulations mandate the adoption of PHM software to ensure compliance with data privacy and security standards. PHM software also supports care management services, patient engagement platforms, and remote patient monitoring, empowering patients

Medical Service Study Areas (MSSAs)As defined by California's Office of Statewide Health Planning and Development (OSHPD) in 2013, "MSSAs are sub-city and sub-county geographical units used to organize and display population, demographic and physician data" (Source). Each census tract in CA is assigned to a given MSSA. The most recent MSSA dataset (2014) was used. Spatial data are available via OSHPD at the California Open Data Portal. This information may be useful in studying health equity.Definitions:Race/Ethnicity: Race/ethnicity is categorized as: All races/ethnicities, Non-Hispanic (NH) White, NH Black, Asian/Pacific Islander, or Hispanic. "All races" includes all of the above, as well as other and unknown race/ethnicity and American Indian/Alaska Native. The latter two groups are not reported separately due to small numbers for many cancer sites.Racial/Ethnic Composition: Distribution of residents' race/ethnicity (e.g., % Hispanic, % non-Hispanic White, % non-Hispanic Black, % non-Hispanic Asian/Pacific Islander). (Source: US Census, 2010.)Rural: Percent of residents who reside in blocks that are designated as rural. (Source: US Census, 2010.)Foreign Born: Percent of residents who were born outside the United States. (Source: American Community Survey, 2008-2012.)Socioeconomic Status (Neighborhood Level): A composite measure of seven indicator variables created by principal component analysis; indicators include: education, blue-collar job, unemployment, household income, poverty, rent, and house value. Quintiles based on state distribution, with quintile 1 being the lowest SES and 5 being the highest. (Source: American Community Survey, 2008-2012.)Spatial extent: CaliforniaSpatial Unit: MSSACreated: n/aUpdated: n/aSource: California Health MapsContact Email: gbacr@ucsf.eduSource Link: https://www.californiahealthmaps.org/?areatype=mssa&address=&sex=Both&site=AllSite&race=&year=05yr&overlays=none&choropleth=Obesity

Demographic information for healthcare workers (n = 319).

Healthcare Big Data Analytics Market Outlook

The global healthcare big data analytics market size is projected to achieve a robust growth trajectory, with a valuation of approximately USD 32 billion in 2023. It is anticipated to soar to around USD 115 billion by 2032, reflecting an impressive compound annual growth rate (CAGR) of 15.4%. This remarkable growth can largely be attributed to the increasing demand for efficient data management systems in the healthcare sector, the rising need for data-driven decision-making, and the expanding adoption of analytics in diverse healthcare applications. The integration of artificial intelligence and machine learning in analytics, the emphasis on personalized medicine, and the growing importance of predictive analytics are further propelling the market forward.

One of the key growth drivers in the healthcare big data analytics market is the rising necessity for cost reduction and improved operational efficiency within the healthcare sector. Hospitals and clinics are increasingly recognizing the value of analytics in streamlining processes, reducing waste, and enhancing patient care. By leveraging big data analytics, healthcare providers can gain insights into patient care patterns, optimize resource allocation, and minimize unnecessary expenditures. This drive towards efficiency is further bolstered by government initiatives and policies aimed at improving healthcare delivery and reducing costs, creating a fertile ground for the adoption of advanced analytics solutions.

Another significant factor contributing to the market's expansion is the growing emphasis on personalized and precision medicine. As healthcare providers aim to offer more tailored treatment options, the analysis of vast datasets becomes crucial. Big data analytics facilitates the identification of patterns and trends in patient data, enabling healthcare providers to make informed decisions regarding personalized treatment plans. Moreover, the continuous advancements in genomics and biotechnology are generating immense volumes of data, necessitating robust analytics solutions to derive actionable insights. This trend towards personalized care is expected to drive substantial investments in big data analytics technologies in the coming years.

Additionally, the increasing prevalence of chronic diseases and the aging global population are driving the demand for effective population health management. Big data analytics plays a pivotal role in analyzing population health trends, identifying at-risk individuals, and devising preventive strategies. Governments and healthcare organizations are increasingly focusing on population health analytics to enhance public health outcomes and reduce the burden on healthcare infrastructure. This growing demand for comprehensive population health management solutions is expected to be a significant driving force for the healthcare big data analytics market over the forecast period.

Healthcare Analytics & Medical Analytics are becoming increasingly vital in the pursuit of personalized and precision medicine. By leveraging these analytics, healthcare providers can delve deeper into patient data to uncover insights that inform individualized treatment plans. This approach not only enhances patient outcomes but also optimizes the use of healthcare resources. As the demand for personalized care continues to rise, the role of healthcare analytics in tailoring treatments to individual patient needs is expected to grow exponentially. The integration of advanced analytics tools into healthcare systems is facilitating a shift towards more patient-centric care models, thereby driving the adoption of these technologies across the sector.

The regional outlook for the healthcare big data analytics market shows a diverse growth pattern across different geographies. North America currently holds a significant share of the market, driven by the presence of advanced healthcare infrastructure, a high level of digitalization, and a strong focus on research and development. Europe is also witnessing considerable growth, with countries like Germany and the United Kingdom leading the charge in the adoption of analytics solutions. Meanwhile, the Asia Pacific region is poised to experience the fastest growth, fueled by rapid technological advancements, increasing healthcare investments, and the need to address healthcare challenges in densely populated regions. Latin America and the Middle East & Africa are expected to show steady growth, driven by improving healthcare infrastruct

The dataset contains information on California’s Medical Service Study Areas (MSSA). MSSAs are sub-city and sub-county geographical units used to organize and display population, demographic and physician data for 2010. Medical Service Study Areas are a geographic analysis unit defined by the California Office of Statewide Health Planning and Development. MSSA are a good foundation for needs assessment analysis, healthcare planning, and healthcare policy development.