Baseline characteristics of the participants including their demographics, co-morbidities, COVID exposure and duration of work.

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

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.

Abbreviations: AVP, arginine vasopressin; CSF, cerebrospinal fluid; ALL, acute lymphoblastic leukemia; AML, acute myeloblastic leukemia1indicates CSF collected from lumbar puncture2indicates CSF collected from left ventricle3indicates CSF collected from the cisterna magna.Patient demographics and medical characteristics.

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.

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.

The All CMS Data Feeds dataset is an expansive resource offering access to 119 unique report feeds, providing in-depth insights into various aspects of the U.S. healthcare system including nursing facility owners and accountable care organization participants contact data. With over 25.8 billion rows of data meticulously collected since 2007, this dataset is invaluable for healthcare professionals, analysts, researchers, and businesses seeking to understand and analyze healthcare trends, performance metrics, and demographic shifts over time. The dataset is updated monthly, ensuring that users always have access to the most current and relevant data available.

Dataset Overview:

118 Report Feeds: - The dataset includes a wide array of report feeds, each providing unique insights into different dimensions of healthcare. These topics range from Medicare and Medicaid service metrics, patient demographics, provider information, financial data, and much more. The breadth of information ensures that users can find relevant data for nearly any healthcare-related analysis. - As CMS releases new report feeds, they are automatically added to this dataset, keeping it current and expanding its utility for users.

25.8 Billion Rows of Data:

• With over 25.8 billion rows of data, this dataset provides a comprehensive view of the U.S. healthcare system. This extensive volume of data allows for granular analysis, enabling users to uncover insights that might be missed in smaller datasets. The data is also meticulously cleaned and aligned, ensuring accuracy and ease of use.

Historical Data Since 2007: - The dataset spans from 2007 to the present, offering a rich historical perspective that is essential for tracking long-term trends and changes in healthcare delivery, policy impacts, and patient outcomes. This historical data is particularly valuable for conducting longitudinal studies and evaluating the effects of various healthcare interventions over time.

Monthly Updates:

• To ensure that users have access to the most current information, the dataset is updated monthly. These updates include new reports as well as revisions to existing data, making the dataset a continuously evolving resource that stays relevant and accurate.

Data Sourced from CMS:

• The data in this dataset is sourced directly from the Centers for Medicare & Medicaid Services (CMS). After collection, the data is meticulously cleaned and its attributes are aligned, ensuring consistency, accuracy, and ease of use for any application. Furthermore, any new updates or releases from CMS are automatically integrated into the dataset, keeping it comprehensive and current.

Use Cases:

Market Analysis:

• The dataset is ideal for market analysts who need to understand the dynamics of the healthcare industry. The extensive historical data allows for detailed segmentation and analysis, helping users identify trends, market shifts, and growth opportunities. The comprehensive nature of the data enables users to perform in-depth analyses of specific market segments, making it a valuable tool for strategic decision-making.

Healthcare Research:

• Researchers will find the All CMS Data Feeds dataset to be a robust foundation for academic and commercial research. The historical data, combined with the breadth of coverage across various healthcare metrics, supports rigorous, in-depth analysis. Researchers can explore the effects of healthcare policies, study patient outcomes, analyze provider performance, and more, all within a single, comprehensive dataset.

Performance Tracking:

• Healthcare providers and organizations can use the dataset to track performance metrics over time. By comparing data across different periods, organizations can identify areas for improvement, monitor the effectiveness of initiatives, and ensure compliance with regulatory standards. The dataset provides the detailed, reliable data needed to track and analyze key performance indicators.

Compliance and Regulatory Reporting:

• The dataset is also an essential tool for compliance officers and those involved in regulatory reporting. With detailed data on provider performance, patient outcomes, and healthcare utilization, the dataset helps organizations meet regulatory requirements, prepare for audits, and ensure adherence to best practices. The accuracy and comprehensiveness of the data make it a trusted resource for regulatory compliance.

Data Quality and Reliability:

The All CMS Data Feeds dataset is designed with a strong emphasis on data quality and reliability. Each row of data is meticulously cleaned and aligned, ensuring that it is both accurate and consistent. This attention to detail makes the dataset a trusted resource for high-stakes applications, where data quality is critical.

Integration and Usability:

Ease of Integration:

• The dataset is provided in a CSV format, which is widely compatible with most data analysis too...

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.

This dataset presents a summary of healthcare workforce distribution and demographic indicators across various provinces in Indonesia. It includes data on the number of nurses, midwives, and doctors, as well as key population statistics such as total population, population density, and life expectancy.

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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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Introduction

According to Healthcare Staffing Statistics, Healthcare staffing is a crucial facet of the healthcare industry, involving 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, while 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.

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.

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

BackgroundThe prevalence of diabetes in West Africa is increasing, posing a major public health threat. An estimated 24 million Africans have diabetes, with rates in West Africa around 2–6% and projected to rise 129% by 2045 according to the WHO. Over 90% of cases are Type 2 diabetes (IDF, World Bank). As diabetes is ambulatory care sensitive, good primary care is crucial to reduce complications and mortality. However, research on factors influencing diabetes primary care access, utilisation and quality in West Africa remains limited despite growing disease burden. While research has emphasised diabetes prevalence and risk factors in West Africa, there remains limited evidence on contextual influences on primary care. This scoping review aims to address these evidence gaps.Methods and analysisUsing the established methodology by Arksey and O’Malley, this scoping review will undergo six stages. The review will adopt the Preferred Reporting Items for Systematic Reviews and Meta-Analysis Extension for Scoping Review (PRISMA-ScR) guidelines to ensure methodological rigour. We will search four electronic databases and search through grey literature sources to thoroughly explore the topic. The identified articles will undergo thorough screening. We will collect data using a standardised data extraction form that covers study characteristics, population demographics, and study methods. The study will identify key themes and sub-themes related to primary healthcare access, utilisation, and quality. We will then analyse and summarise the data using a narrative synthesis approach.ResultsThe findings and conclusive report will be finished and sent to a peer-reviewed publication within six months.ConclusionThis review protocol aims to systematically examine and assess the factors that impact the access, utilisation, and standard of primary healthcare services for diabetes in West Africa.

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.

The Health Statistics and Health Research Database is Estonian largest set of health-related statistics and survey results administrated by National Institute for Health Development. Use of the database is free of charge.

The database consists of eight main areas divided into sub-areas. The data tables included in the sub-areas are assigned unique codes. The data tables presented in the database can be both viewed in the Internet environment, and downloaded using different file formats (.px, .xlsx, .csv, .json). You can download the detailed database user manual here (.pdf).

The database is constantly updated with new data. Dates of updating the existing data tables and adding new data are provided in the release calendar. The date of the last update to each table is provided after the title of the table in the list of data tables.

A contact person for each sub-area is provided under the "Definitions and Methodology" link of each sub-area, so you can ask additional information about the data published in the database. Contact this person for any further questions and data requests.

Read more about publication of health statistics by National Institute for Health Development in Health Statistics Dissemination Principles.

The dataset contains counts of inpatient visits leading to a discharge to hospice care. Inpatient visits included in the counts consist of individuals aged 18 or over with a discharge disposition leading to home or facility hospice care. The total counts per each individual year can be viewed based on different patient characteristics, including patient age groups, individual counties of residence, primary payer type, diagnosis category, and patient sex/race/ethnicity. The disease categories include circulatory conditions, diabetes, malignant/benign neoplasms, malnutrition, neurodegenerative disease, renal failure or other kidney diagnoses, respiratory conditions and circulatory conditions. The categories represent common groupings of diagnoses seen in other studies related to hospice care and were created by grouping together relevant medical MSDRG codes in the HCAI inpatient data.

The National Medical Expenditure Survey (NMES) series provides information on health expenditures by or on behalf of families and individuals, the financing of these expenditures, and each person's use of services. The Survey of American Indians and Alaska Natives (SAIAN) was designed in collaboration with the Indian Health Service (IHS), and used the same data collection instruments, interview procedures, and time frame as the NMES Household Survey component. However, the SAIAN differed from the Household Survey in several respects. The SAIAN sample was interviewed only three times and was not given the supplements on long-term care, caregiving, and care-receiving. Also, SAIAN respondents were asked additional questions on topics such as use of IHS facilities and traditional medicine, and were given a modified self-administered questionnaire with separate versions for adults and children. Interviewers for the SAIAN were mainly American Indians or Alaska Natives, and about 20 percent of the interviews were not conducted entirely in English. Of these, approximately 40 percent were conducted entirely in the native language of the respondent. Part 1 of this collection contains information on formal home care providers for each eligible person in the SAIAN who reported receiving home health services, including date the provider was seen, provider's length of stay, type of agency the provider worked for, and kind of help performed by the provider. Demographic information on the recipient (race, age, and sex), and household-reported medical conditions associated with the use of home health care is also included. Part 2 contains information on medical items purchased, rented, or otherwise obtained. Demographic variables similar to those in Part 1 are provided, along with medical conditions and dates that items were obtained. Part 3 contains variables on the type of traditional practitioner seen by respondents, as well as demographic and medical condition variables.

Population Health Management Systems Market Outlook

The global population health management systems market has been witnessing significant growth, with a market size valued at approximately USD 34.8 billion in 2023. Projections indicate that this market is expected to experience a robust CAGR of 13.5% from 2024 to 2032, reaching an estimated market size of USD 104.5 billion by 2032. The primary growth factors driving this optimistic forecast include the increasing demand for efficient healthcare delivery systems, the need for cost reduction in healthcare services, and the growing emphasis on patient-centered care. As the global healthcare sector transitions towards value-based care models, population health management systems are becoming instrumental in facilitating the shift by enabling healthcare providers to manage, analyze, and optimize the health of entire populations.

One of the major growth drivers in the population health management systems market is the rising prevalence of chronic diseases and the aging population worldwide. The increasing incidence of conditions such as diabetes, cardiovascular diseases, and respiratory disorders necessitates comprehensive health management strategies that can effectively track and manage patient health data. Population health management systems enable healthcare providers to integrate and analyze this data, leading to improved patient outcomes and more efficient use of healthcare resources. Additionally, the aging population presents a unique challenge as older adults generally require more frequent and intensive healthcare services, further driving the demand for robust health management solutions.

Another significant growth factor is the ongoing advancements in healthcare IT and data analytics technologies, which are critical enablers of population health management systems. The integration of advanced analytics, artificial intelligence, and machine learning technologies into these systems allows for more precise and predictive insights, enabling healthcare providers to proactively manage patient health and identify potential health risks before they escalate into severe conditions. The adoption of electronic health records (EHRs) and interoperability standards is also contributing to the seamless exchange of health data across various healthcare settings, enhancing the effectiveness of population health management initiatives.

The push towards value-based healthcare models is also fueling the growth of the population health management systems market. As healthcare systems worldwide shift from fee-for-service to value-based care, there is an increased need for solutions that can help healthcare providers meet quality metrics while controlling costs. Population health management systems offer the tools necessary to align healthcare delivery with these new reimbursement models by facilitating the coordination of care, improving patient engagement, and ensuring compliance with regulatory requirements. Moreover, government initiatives aimed at improving healthcare access and quality, particularly in developing regions, are expected to further boost the adoption of these systems.

In terms of regional outlook, North America currently dominates the market, largely due to the presence of a well-established healthcare infrastructure, high adoption of advanced healthcare technologies, and favorable government initiatives promoting value-based care. However, other regions, particularly the Asia Pacific, are expected to witness significant growth during the forecast period. Factors such as the increasing healthcare expenditure, rising awareness about population health management, and the burgeoning demand for healthcare IT solutions in countries like China and India are driving this growth. Additionally, Europe and Latin America are also anticipated to contribute to market expansion owing to the increasing focus on improving healthcare delivery and the rising prevalence of chronic diseases.

Component Analysis

The population health management systems market is segmented by component into software and services, each playing a crucial role in the overall functioning and effectiveness of these systems. The software segment encompasses a wide range of applications, including data analytics, care management, and patient engagement platforms, which are essential for collecting, analyzing, and utilizing healthcare data to improve patient outcomes. These software solutions are being constantly upgraded with advanced features such as predictive analytics and artificial intelligence to provide deeper insights into patient health trends and facilitate proactive interventions.

These datasets are for a cohort of n=1540 anonymised hospitalised COVID-19 patients, and the data provide information on outcomes (i.e. patient death or discharge), demographics and biomarker measurements for two New York hospitals: State University of New York (SUNY) Downstate Health Sciences University and Maimonides Medical Center.

The file "demographics_both_hospitals.csv" contains the ultimate outcomes of hospitalisation (whether a patient was discharged or died), demographic information and known comorbidities for each of the patients.

The file "dynamics_clean_both_hospitals.csv" contains cleaned dynamic biomarker measurements for the n=1233 patients where this information was available and the data passed our various checks (see https://doi.org/10.1101/2021.11.12.21266248 for information of these checks and the cleaning process). Patients can be matched to demographic data via the "id" column.

Study approval and data collection

Study approval was obtained from the State University of New York (SUNY) Downstate Health Sciences University Institutional Review Board (IRB#1595271-1) and Maimonides Medical Center Institutional Review Board/Research Committee (IRB#2020-05-07). A retrospective query was performed among the patients who were admitted to SUNY Downstate Medical Center and Maimonides Medical Center with COVID-19-related symptoms, which was subsequently confirmed by RT PCR, from the beginning of February 2020 until the end of May 2020. Stratified randomization was used to select at least 500 patients who were discharged and 500 patients who died due to the complications of COVID-19. Patient outcome was recorded as a binary choice of “discharged” versus “COVID-19 related mortality”. Patients whose outcome was unknown were excluded. Demographic, clinical history and laboratory data was extracted from the hospital’s electronic health records.