Healthcare Provider Population Health Management Software Market Outlook

According to our latest research, the global Healthcare Provider Population Health Management Software market size reached USD 15.2 billion in 2024. The market is projected to expand at a robust CAGR of 13.8% from 2025 to 2033, reaching approximately USD 47.2 billion by 2033. This impressive growth is primarily driven by the rising demand for value-based care, increasing healthcare data volumes, and the critical need for efficient patient management across diverse healthcare settings. The ongoing digital transformation in healthcare, coupled with regulatory mandates for data interoperability and quality reporting, continues to accelerate the adoption of advanced population health management solutions among providers worldwide.

One of the most significant growth factors propelling the Healthcare Provider Population Health Management Software market is the global shift from fee-for-service to value-based care models. Healthcare systems and providers are under increasing pressure to improve patient outcomes while controlling costs, necessitating robust tools for data aggregation, risk stratification, and care coordination. Population health management (PHM) software enables providers to analyze large datasets, identify at-risk populations, and proactively manage chronic diseases. The integration of electronic health records (EHRs), claims data, and social determinants of health into PHM platforms allows for a more holistic approach to patient care, driving better clinical and financial outcomes. Additionally, government initiatives and reforms, such as the Affordable Care Act in the United States and similar policies in Europe and Asia Pacific, are further incentivizing the adoption of PHM solutions by linking reimbursement to quality metrics and patient satisfaction.

Another critical driver is the rapid advancement of healthcare IT infrastructure and the proliferation of digital health technologies. The increasing adoption of cloud computing, artificial intelligence, and machine learning in healthcare is transforming the way providers manage patient populations. Modern PHM software platforms leverage these technologies to deliver predictive analytics, automate care management workflows, and facilitate real-time decision support. This technological evolution enables healthcare organizations to efficiently aggregate and analyze disparate data sources, streamline patient engagement, and optimize resource allocation. The growing emphasis on interoperability and data exchange standards, such as HL7 FHIR, is also fostering a more connected and integrated healthcare ecosystem, further enhancing the value proposition of PHM software.

Population Health Management is increasingly becoming a cornerstone in the healthcare industry, as it focuses on improving the health outcomes of entire populations. This approach involves the systematic collection and analysis of health-related data to identify patterns and trends that can inform healthcare strategies. By leveraging Population Health Management, providers can better understand the needs of their patient populations, tailor interventions to specific groups, and ultimately enhance the quality of care delivered. As healthcare systems worldwide continue to shift towards value-based care, the role of Population Health Management in driving efficiency and effectiveness in healthcare delivery is more critical than ever. This paradigm shift is not only improving patient outcomes but also helping to control rising healthcare costs by promoting preventive care and reducing unnecessary hospitalizations.

The COVID-19 pandemic has also played a pivotal role in accelerating the adoption of population health management solutions. The need for remote patient monitoring, telehealth, and coordinated care during the pandemic highlighted the importance of robust PHM platforms. Providers leveraged these tools to track disease outbreaks, manage high-risk patient cohorts, and allocate resources more effectively. As healthcare systems continue to adapt to the post-pandemic landscape, the focus on preventive care, chronic disease management, and population-level analytics is expected to remain strong, sustaining the long-term growth trajectory of the market. Furthermore, the increasing prevalence of chronic diseases, aging populations, and rising healthcare expenditures

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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?

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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 to take charge of their health. Welln

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Population Health Management Market Size 2025-2029

The population health management market size is valued to increase USD 19.40 billion, at a CAGR of 10.7% from 2024 to 2029. Rising adoption of healthcare IT will drive the population health management market.

Major Market Trends & Insights

North America dominated the market and accounted for a 68% growth during the forecast period.
By Component - Software segment was valued at USD 16.04 billion in 2023
By End-user - Large enterprises segment accounted for the largest market revenue share in 2023

Market Size & Forecast

Market Opportunities: USD 113.32 billion
Market Future Opportunities: USD 19.40 billion
CAGR : 10.7%
North America: Largest market in 2023

Market Summary

The market encompasses a continually evolving landscape of core technologies and applications, service types, and regulatory frameworks. With the rising adoption of healthcare IT solutions, population health management platforms are increasingly being adopted to improve patient outcomes and reduce costs. According to a recent study, The market is expected to witness a significant growth, with over 30% of healthcare organizations implementing these solutions by 2025. The focus on personalized medicine and the need to manage the rising cost of healthcare are major drivers for this trend. Core technologies such as data analytics, machine learning, and telehealth are transforming the way healthcare providers manage patient populations.
Despite these opportunities, challenges such as data privacy concerns, interoperability issues, and the high cost of implementation persist. The market is further shaped by regional differences in regulatory frameworks and healthcare infrastructure. For instance, in North America, the Affordable Care Act has fueled the adoption of population health management solutions, while in Europe, the European Medicines Agency's focus on personalized medicine is driving demand.

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

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How is the Population Health Management Market Segmented and what are the key trends of market segmentation?

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
  On-Premise
  Cloud-Based


End-Use

  Providers
  Payers
  Employer Groups
  Government Bodies
  Providers
  Payers
  Employer Groups


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 is experiencing significant growth, with the software segment playing a crucial role in this expansion. Currently, remote patient monitoring solutions are witnessing a 25% adoption rate, enabling healthcare providers to monitor patients' health in real-time and intervene promptly when necessary. Additionally, predictive modeling and risk stratification models are being utilized to identify high-risk patients and provide personalized care plans, contributing to a 21% increase in disease management efficiency. Furthermore, the integration of electronic health records, wellness programs, care coordination platforms, and value-based care models is fostering a data-driven approach to healthcare, leading to a 19% reduction in healthcare costs.

Health equity initiatives and healthcare data analytics are essential components of population health management, ensuring equitable access to care and improving healthcare quality metrics. Looking ahead, the market is expected to grow further, with utilization management and care management programs seeing a 27% increase in implementation. Preventive health programs and clinical decision support systems are also anticipated to experience a 24% surge in adoption, emphasizing the importance of proactive care and early intervention. Moreover, population health strategies are evolving to incorporate behavioral health integration, interoperability standards, and disease registry data to provide comprehensive care. The use of disease prevalence data and public health surveillance is becoming increasingly crucial in addressing population health challenges and improving overall health outcomes.

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The Software segment was valued at USD 16.04 billion in 2019 and showed a gradual increase during the forecast period.

In conclusion, the market is

IntroductionMany regions in the world are using the population health approach and require a means to measure the health of their population of interest. Population health frameworks provide a theoretical grounding for conceptualization of population health and therefore a logical basis for selection of indicators. The aim of this scoping review was to provide an overview and summary of the characteristics of existing population health frameworks that have been used to conceptualize the measurement of population health.MethodsWe used the Population, Concept and Context (PCC) framework to define eligibility criteria of frameworks. We were interested in frameworks applicable for general populations, that contained components of measurement of health with or without its antecedents and applied at the population level or used a population health approach. Eligible reports of eligible frameworks should include at least domains and subdomains, purpose, or indicators. We searched 5 databases (Pubmed, EMBASE, Web of Science, NYAM Grey Literature Report, and OpenGrey), governmental and organizational sites on Google and websites of selected organizations using keywords from the PCC framework. Characteristics of the frameworks were summarized descriptively and narratively.ResultsFifty-seven frameworks were included. The majority originated from the US (46%), Europe (23%) and Canada (19%). Apart from 1 framework developed for rural populations and 2 for indigenous populations, the rest were for general urban populations. The numbers of domains, subdomains and indicators were highly variable. Health status and social determinants of health were the most common domains across all frameworks. Different frameworks had different priorities and therefore focus on different domains.ConclusionKey domains common across frameworks other than health status were social determinants of health, health behaviours and healthcare system performance. The results in this review serve as a useful resource for governments and healthcare organizations for informing their population health measurement efforts.

Alignment between population health approach elements, in health and education.

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.

Population Health Management AI Market Outlook

According to our latest research, the global Population Health Management AI market size reached USD 7.2 billion in 2024, with a robust compound annual growth rate (CAGR) of 24.8% projected through the forecast period. By 2033, the market is expected to reach approximately USD 66.6 billion, driven by the rapid adoption of artificial intelligence technologies across healthcare systems worldwide. This remarkable growth is primarily fueled by the increasing need for data-driven decision-making in population health, the rising prevalence of chronic diseases, and the urgent demand for cost-effective healthcare solutions. As per our latest research, the integration of AI into population health management is fundamentally transforming how healthcare providers, payers, and governments approach patient care and resource allocation.

A key growth factor for the Population Health Management AI market is the exponential increase in healthcare data generation. Healthcare organizations are inundated with vast amounts of structured and unstructured data from electronic health records, wearable devices, insurance claims, and social determinants of health. The ability of AI-powered solutions to aggregate, analyze, and derive actionable insights from these datasets is revolutionizing population health management. Advanced machine learning algorithms and natural language processing technologies are enabling healthcare stakeholders to identify at-risk populations, predict disease outbreaks, and personalize interventions with unprecedented accuracy. This data-driven approach not only improves patient outcomes but also optimizes resource utilization and reduces operational costs, making AI an indispensable tool in modern healthcare ecosystems.

Another significant driver is the growing emphasis on value-based care models globally. Healthcare systems are shifting from traditional fee-for-service models to value-based frameworks that prioritize patient outcomes and cost efficiency. Population Health Management AI solutions play a pivotal role in this transition by enabling real-time risk stratification, effective care coordination, and proactive patient engagement. AI-driven analytics empower healthcare providers to identify gaps in care, monitor patient adherence, and intervene early to prevent complications. Moreover, payers and government organizations are leveraging AI to design targeted wellness programs, reduce hospital readmissions, and manage population health at scale. These factors collectively contribute to the sustained expansion of the Population Health Management AI market.

Furthermore, advancements in cloud computing and interoperability standards are accelerating the adoption of AI-powered population health platforms. The availability of scalable, secure, and cost-effective cloud infrastructure allows healthcare organizations to deploy sophisticated AI solutions without heavy upfront investments. Interoperability frameworks such as FHIR (Fast Healthcare Interoperability Resources) are facilitating seamless data exchange across disparate healthcare systems, enhancing the utility and reach of AI-driven population health management tools. As regulatory bodies increasingly mandate data transparency and patient privacy, AI vendors are integrating robust compliance features into their platforms, further boosting market growth. These technological and regulatory advancements are creating a fertile environment for innovation and expansion in the Population Health Management AI sector.

From a regional perspective, North America currently dominates the Population Health Management AI market, accounting for the largest share in 2024. This leadership is attributed to the region's advanced healthcare infrastructure, high adoption of digital health technologies, and supportive government initiatives. Europe follows closely, driven by strategic investments in healthcare digitization and AI research. The Asia Pacific region is anticipated to exhibit the fastest growth during the forecast period, propelled by rising healthcare expenditure, expanding patient populations, and increasing awareness of AI's potential in healthcare management. Latin America and the Middle East & Africa are also witnessing gradual adoption, supported by public-private partnerships and international collaborations aimed at improving population health outcomes.

Component Analysis

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Age-adjustment mortality rates are rates of deaths that are computed using a statistical method to create a metric based on the true death rate so that it can be compared over time for a single population (i.e. comparing 2006-2008 to 2010-2012), as well as enable comparisons across different populations with possibly different age distributions in their populations (i.e. comparing Hispanic residents to Asian residents). Age adjustment methods applied to Montgomery County rates are consistent with US Centers for Disease Control and Prevention (CDC), National Center for Health Statistics (NCHS) as well as Maryland Department of Health and Mental Hygiene’s Vital Statistics Administration (DHMH VSA). PHS Planning and Epidemiology receives an annual data file of Montgomery County resident deaths registered with Maryland Department of Health and Mental Hygiene’s Vital Statistics Administration (DHMH VSA). Using SAS analytic software, MCDHHS standardizes, aggregates, and calculates age-adjusted rates for each of the leading causes of death category consistent with state and national methods and by subgroups based on age, gender, race, and ethnicity combinations. Data are released in compliance with Data Use Agreements between DHMH VSA and MCDHHS. This dataset will be updated Annually.

Self-evaluation tool for clinical practices to identify opportunities for collaboration with public health. (XLSX 73 kb)

[Coursera] Major Depression in the Population: A Public Health Approach

Population Health Management Software Market Outlook

According to our latest research, the global population health management software market size reached USD 35.6 billion in 2024, demonstrating robust expansion driven by the increasing need for value-based care and digital transformation in the healthcare sector. The market is forecasted to grow at a CAGR of 13.7% from 2025 to 2033, reaching an estimated USD 110.2 billion by 2033. This remarkable growth is primarily attributed to the surge in chronic disease prevalence, rising healthcare costs, and the growing adoption of healthcare IT solutions worldwide.

A key growth factor fueling the population health management software market is the global shift toward value-based healthcare models. Healthcare systems are increasingly moving away from fee-for-service frameworks, instead prioritizing patient outcomes and cost-efficiency. Population health management software plays a pivotal role in this transition by aggregating and analyzing patient data from multiple sources, enabling healthcare providers to identify at-risk populations, optimize care delivery, and reduce unnecessary expenditures. The integration of advanced analytics and artificial intelligence within these platforms is further enhancing their ability to deliver actionable insights, improve patient engagement, and support preventive care strategies, which collectively drive market expansion.

Another significant driver for the market is the escalating burden of chronic diseases such as diabetes, cardiovascular disorders, and respiratory illnesses. With aging populations and lifestyle changes, the prevalence of these conditions is rising globally, necessitating proactive population health strategies. Population health management software empowers healthcare organizations to monitor patient cohorts, track disease progression, and implement targeted interventions. This not only improves clinical outcomes but also supports regulatory compliance and reimbursement requirements, particularly as governments and payers increasingly incentivize the management of chronic conditions through coordinated care and patient engagement initiatives.

The rapid digitalization of healthcare infrastructure, coupled with favorable government initiatives and funding, is also propelling the adoption of population health management software. Many countries are investing in electronic health records (EHRs), health information exchanges (HIEs), and interoperability standards to facilitate seamless data sharing across the care continuum. These developments are creating a fertile environment for the deployment of population health management solutions, which thrive on the availability of comprehensive, real-time patient data. Furthermore, the COVID-19 pandemic has accelerated the adoption of digital health tools, highlighting the critical importance of population health management in tracking outbreaks, managing resources, and improving public health outcomes.

From a regional perspective, North America continues to dominate the population health management software market, accounting for the largest share in 2024. This leadership is underpinned by advanced healthcare IT infrastructure, significant investments in digital health, and strong regulatory support for value-based care. However, the Asia Pacific region is expected to exhibit the fastest growth over the forecast period, driven by rising healthcare expenditure, expanding insurance coverage, and increasing focus on healthcare modernization in countries such as China, India, and Japan. Europe also remains a key market, benefiting from robust government initiatives aimed at improving healthcare quality and efficiency through digital solutions.

Component Analysis

The population health management software market is segmented by component into software and services, each playing a critical role in the overall ecosystem. The software segment encompasses a wide range of solutions, including data aggregation, analytics, care coordinatio

Background: Multisector collaboratives are increasingly popular strategies for improving population health. To be comprehensive, collaboratives must coordinate the activities of many organizations across a geographic region. Many policy-relevant models encourage creation and use of centralized hub organizations to do this work, yet there is little guidance on how to evaluate implementation of such hubs and track their network reach. We sought to demonstrate how social network analysis (SNA) could be used for this purpose.Methods: Through formative research, we defined and conceptualized key characteristics of a bridging hub network and identified a set of candidate measures—(1) network membership, (2) network interaction, (3) role and reach of the bridging hub, and (4) network collaboration—to evaluate its implementation within a pre-determined geographic region of Southeast Minnesota, USA. We then developed and administered a survey to assess outcomes as part of a SNA. We commented on the feasibility and usefulness of the methods.Results: The initial surveyed network consisted of 50 healthcare organizational sites and 50 community organizations representing sectors of public health, education, research, health promotion, social services, and long-term care and supports. Fifty-three of these organizations responded to the survey. The network's level of collaboration was “Cooperation” (level 2 of 5) and reported levels of collaboration varied by organization. Thirty-eight additional, unsurveyed organizations were identified as collaborators by respondents, pushing the theoretical network denominator up to 138 organizations. These additional organizations included grocery stores, ambulance services, and smaller, independent healthcare and community-based services focused on meeting the needs of underserved populations. The bridging hub organization had the highest betweenness centrality and was in good position to bridge healthcare and the community, although its organizational reach was estimated at only 51%. The SNA methods were feasible and useful for identifying opportunities and guiding implementation.Conclusions: Bridging hub organizations are not likely to link—or even be aware of—all relevant organizations in a geographic region at initial implementation. SNA may be a useful method for evaluating the value and reach of a bridging hub organization and guiding ongoing implementation efforts.Trial registration: http://ClinicalTrials.gov; #NCT03046498

U.S. Mortality and Health Indicators

Impact of Risk Factors on Population Health Outcomes

By Data Society [source]

About this dataset

This dataset provides county-level mortality and health indicators that are useful for measuring the impact of health policies in the United States. It includes data elements and values from over a dozen categories, including Demographics, Leading Causes of Death, Summary Measures of Health, Measures of Birth and Death, Relative Health Importance, Vulnerable Populations and Environmental Health, Preventive Services Use, Risk Factors and Access to Care. Additionally, this dataset offers Healthy People 2010 Targets and US Percentages or Rates for easy comparison across states. With comprehensive information for each county in each indicator domain available here at your fingertips could help you get insight into American population health from the local level like never before. Discover trends on disease outbreaks or immunizations that are unprecedentedly localized with insights from this dataset!

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How to use the dataset

This dataset contains various data elements related to the mortality and health of the US population at various levels such as county, state, etc. This dataset is an ideal source of information for researchers and policy makers who are interested in exploring patterns in the mortality and health of US citizens.

In order to use this dataset effectively, it is important to understand the different indicators included as well as how to interpret these indicators. In this guide we will look at each indicator domain separately so that users can easily identify which relevant data elements they need for their analysis.

Demographics: The Demographics indicator domain includes data elements related to demographic characteristics such as age composition, gender composition etc. These indicators can be used to explore trends across different parts of the country or identify disparities among populations.

Leading Causes of Death: The Leading Causes of Death indicator domain contains information on fatalities by cause over a set period of time -- either two years or five years depending on availability -- so that researchers can identify causes that pose major threats to public health overall or in more specific regions such as certain counties. It is important to note that these largely report figures based on death certificates which may not always tell an exact story due to reporting inaccuracies caused by both individual factors and registration biases across counties/states over time.

 **Summary Measures Of Health**: The Summary Measures Of Health Indicator Domain includes measures commonly used for gauging overall population health such as birth rates and death rates but also key quality-of-life considerations like prevalence rate physical activity rate . These can be used together with other data sources (such as income info) when analyzing population health outcomes from a broader perspective than individual diseases or conditions would allow for . 

 **Measures Of Birth And Death**: This category provides further insight into the important summary level figures mentioned earlier by providing observations about frequency , timing , type etc where available . Additionally , it offers valuable insights about trends related specifically (among others ) out - migration /in - migration mortality ratio changes/births outside hospitals marriage age / labor force participation trends etc – all essential ingredients when trying solve complex issues related improving public one's life expectancy positively  

 **Relative Health Importance & Vulnerable Populations And Environment Capacity :** This section covers two closely intertwined fields revealing how they interact – socioeconomic status disparities & environment quality – around boundaries & neighborhoods influencing risks factors (not only related medical matters ) aspects such disabilities insurance coverage alcohol use & smoking habits road fatalities veh

Research Ideas

• Using the Health Status Indicators as input features, machine learning models can be built to predict county-level mortality rate, which can then be used as an important indicator for health and medical resource allocation.
• The data can also be used to analyze the social determinants of health in different counties by combining with socioeconomic indicators such as poverty, population density and educational attainment levels.
• Additionally, the dataset could help assess th...

Healthcare Provider Population Health Management Software Market Outlook

As of 2023, the global market size for Healthcare Provider Population Health Management (PHM) Software was valued at approximately USD 12 billion and is projected to grow at a compound annual growth rate (CAGR) of 15% over the forecast period, reaching nearly USD 35 billion by 2032. This significant growth is driven by several factors, including the increasing prevalence of chronic diseases, the rising demand for cost-effective healthcare solutions, and the growing adoption of healthcare IT systems.

One of the primary growth factors for the Healthcare Provider Population Health Management Software market is the increasing burden of chronic diseases worldwide. Chronic diseases such as diabetes, heart disease, and cancer require continuous monitoring and management, which can be efficiently handled through PHM software. These tools allow healthcare providers to analyze large volumes of patient data to identify trends and make informed decisions, ultimately improving patient outcomes and reducing healthcare costs. Furthermore, the aging global population contributes to a higher prevalence of chronic conditions, further driving the demand for effective PHM solutions.

Another significant growth driver is the rising demand for cost-effective healthcare solutions. With healthcare costs continuing to rise, both providers and payers are looking for ways to deliver high-quality care more efficiently. PHM software enables healthcare providers to identify high-risk patient populations and proactively manage their care, thereby reducing hospital readmissions and emergency department visits. By focusing on prevention and early intervention, these tools help lower overall healthcare costs while improving patient health outcomes. Additionally, the shift towards value-based care models incentivizes healthcare organizations to invest in PHM solutions to meet performance metrics and achieve financial incentives.

The growing adoption of healthcare IT systems is also a crucial factor contributing to the market's growth. As healthcare organizations increasingly recognize the benefits of digital transformation, the implementation of electronic health records (EHRs), telehealth services, and other health IT solutions has become more widespread. PHM software integrates seamlessly with these systems, enabling healthcare providers to leverage data from various sources to create comprehensive patient profiles and deliver personalized care. The interoperability of PHM software with existing healthcare IT infrastructure enhances its appeal and drives market growth.

From a regional perspective, North America is expected to hold the largest market share for Healthcare Provider Population Health Management Software, driven by advanced healthcare infrastructure, high adoption of digital health technologies, and supportive government initiatives. The Asia Pacific region is anticipated to witness the highest growth rate during the forecast period, fueled by increasing healthcare investments, improving healthcare infrastructure, and rising awareness about the benefits of PHM solutions. Europe and Latin America are also expected to experience significant growth, albeit at a slightly slower pace, due to ongoing healthcare reforms and the increasing focus on population health management.

Component Analysis

The Healthcare Provider Population Health Management Software market can be segmented into Software and Services based on components. The software segment is expected to dominate the market, driven by the increasing need for advanced analytics, data integration, and interoperability solutions. PHM software provides healthcare providers with tools to analyze large datasets, identify trends, and make data-driven decisions to improve patient outcomes. The software segment includes various solutions such as care management software, patient engagement tools, and analytics platforms, all of which play a crucial role in population health management.

Within the software segment, care management software is particularly important as it enables healthcare providers to coordinate care for patients with chronic conditions or complex medical needs. These tools help identify high-risk patients, develop personalized care plans, and monitor patient progress. By facilitating better care coordination, care management software can reduce hospital readmissions and emergency department visits, ultimately improving patient outcomes and reducing healthcare costs. The growing emphasis on value-based care models further drives the demand for care m

Summary of key characteristics of included frameworks.

Objectives: To identify the validated and reliable indicators and tools to assess good governance for population health, wellbeing, and equity in urban settings, and assess processes of multisectoral action and civic engagement as reported by peer-reviewed articles.Methods: We conducted a systematic review searching six databases for observational studies reporting strategies of either urban health, multisectoral action or civic engagement for wellbeing, health, or equity.Results: Out of 8,154 studies initially identified we included 17. From the included studies, 14 presented information about high-income countries. The general population was the main target in most studies. Multisectoral action was the most frequently reported strategy (14 studies). Three studies used Urban Health Equity Assessment and Response Tool (Urban HEART). Health indicators were the most frequently represented (6 studies). Barriers and facilitators for the implementation of participatory health governance strategies were reported in 12 studies.Conclusion: Data on the implementation of participatory health governance strategies has been mainly reported in high-income countries. Updated and reliable data, measured repeatedly, is needed to closely monitor these processes and further develop indicators to assess their impact on population health, wellbeing, and equity.

Global Population Health Management Systems comes with the extensive industry analysis of development components, patterns, flows and sizes. The report also calculates present and past market values to forecast potential market management through the forecast period between 2024 - 2032. The report may be the best of what is a geographic area which expands the competitive landscape and industry perspective of the market.

Medical practitioners, trained to isolate health within and upon the body of the individual, are now challenged to negotiate research and population health theories that link health status to geographic location as evidence suggests a connection between place and health. This paper builds an integrated place-health model and structural competency analytical framework with nine domains and four levels of proficiency that is utilized to assess a community-based photovoice project’s ability to shift the practice of medicine by medical students from the surface of the body to the body within a place. Analysis of the medical student’s photovoice data demonstrated that the students achieved structural competency level 1 proficiency and came to understand how health might be connected to place represented by six of the nine domains of the structural competency framework. Results suggest that medical student’s engagement with place-health systemic, institutional and structural forces deepens when they co-create narratives of their lived experiences in a place with patients as community members during a community-based photovoice project. Given the importance of place-health theories to explain population health outcomes, a place-health model and structural competency analytical framework utilized during a community-based photovoice project could help medical students merge the image of patients as singular bodies into bodies set within a context.

Yearly citation counts for the publication titled "Comparison of Different Valuation Methods for Population Health Status Measured by the EQ-5D in Three European Countries".

ObjectiveSince Asians are particularly vulnerable to the risk of gestational diabetes mellitus (GDM), the lifecourse health implications of which are far beyond pregnancy, we aimed to summarize the literature to understand the research gaps on current GDM research among Asians.MethodsWe systematically searched the articles in PubMed, Web of Science, Embase, and Scopus by 30 June 2021 with keywords applied on three topics, namely “GDM prevalence in Asians”, “GDM and maternal health outcomes in Asians”, and “GDM and offspring health outcomes in Asians”.ResultsWe observed that Asian women (natives and immigrants) are at the highest risk of developing GDM and subsequent progression to type 2 diabetes among all populations. Children born to GDM-complicated pregnancies had a higher risk of macrosomia and congenital anomalies (i.e. heart, kidney and urinary tract) at birth and greater adiposity later in life.ConclusionThis review summarized various determinants underlying the conversion between GDM and long-term health outcomes in Asian women, and it might shed light on efforts to prevent GDM and improve the lifecourse health in Asians from a public health perspective.Systematic Review RegistrationProspero, CRD42021286075.