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 All CMS Data Feeds dataset is an expansive resource offering access to 118 unique report feeds, providing in-depth insights into various aspects of the U.S. healthcare system. 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 tools and platforms. This ensures that users can easily integrate the data into their existing wo...

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Population Health Management Market Size 2025-2029

The population health management market size is forecast to increase by USD 19.40 billion at a CAGR of 10.7% between 2024 and 2029.

The Population Health Management Market is experiencing significant growth, driven by the increasing adoption of healthcare IT solutions and the rising focus on personalized medicine. The implementation of electronic health records (EHRs) and other digital health technologies has enabled healthcare providers to collect and analyze large amounts of patient data, facilitating proactive care and population health management. Moreover, the trend towards personalized medicine, which aims to tailor healthcare treatments to individual patients based on their unique genetic makeup and health history, is further fueling the demand for PHM solutions. However, the high cost of installing and implementing these platforms poses a significant challenge for market growth.
Despite this, the potential benefits of PHM, including improved patient outcomes, reduced healthcare costs, and enhanced population health, make it an attractive area for investment and innovation. Companies seeking to capitalize on these opportunities must navigate the challenges of data privacy and security, interoperability, and integration with existing healthcare systems. By addressing these challenges and focusing on delivering actionable insights from patient data, PHM solution providers can help healthcare organizations optimize their resources, improve patient care, and ultimately, improve population health.

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

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The market is experiencing significant growth, driven by the increasing focus on accountable care organizations (ACOs) and payer organizations to improve health outcomes and reduce costs. Healthcare professionals are leveraging big data, data analytics services, and clinical data integration to develop personalized care plans and implement intervention strategies for various populations. Telehealth services have become essential in population health management, enabling care coordination, health promotion, and health navigation for patients. Health equity is a critical factor in population health management, with a growing emphasis on addressing disparities and ensuring equal access to care.
Data security and interoperability standards are essential in population health management, as healthcare providers exchange sensitive patient data for risk adjustment, care pathways, and quality reporting. Data mining and data visualization tools are used to identify health behavior changes and lifestyle modifications, leading to better health outcomes. Consumer health technology, such as patient engagement tools and wearable technology, are playing an increasingly important role in population health management. Health coaching and evidence-based medicine are intervention strategies used to prevent diseases and improve health outcomes. In summary, the market in the US is characterized by the adoption of precision medicine, health literacy, clinical guidelines, and personalized care plans.
The market is driven by the need for care coordination, data analytics, and patient engagement to improve health outcomes and reduce costs. The use of data security, data mining, and interoperability standards ensures the effective exchange and utilization of health data.

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
  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

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. Healthcare organizations are utilizing these solutions to effectively manage and enhance the health outcomes of diverse populations. The software component incorporates various tools that collect, analyze, and utilize health data for informed decision-making. Population health management platforms gather data from multiple sources, such as electronic health records, claims data, and patient-generated data. These platforms employ advanced analytics to generate valuable insi

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.

FAERS (FDA Adverse Events Reporting System) database is designed to support the FDA’s post-marketing safety surveillance program for drug and therapeutic biologic products. The "Patient Outcome" file contains information on patient outcomes for the event (0 or more).

The acute-care pathway (from the emergency department (ED) through acute medical units or ambulatory care and on to wards) is the most visible aspect of the hospital health-care system to most patients. Acute hospital admissions are increasing yearly and overcrowded emergency departments and high bed occupancy rates are associated with a range of adverse patient outcomes. Predicted growth in demand for acute care driven by an ageing population and increasing multimorbidity is likely to exacerbate these problems in the absence of innovation to improve the processes of care.

Key targets for Emergency Medicine services are changing, moving away from previous 4-hour targets. This will likely impact the assessment of patients admitted to hospital through Emergency Departments.

This data set provides highly granular patient level information, showing the day-to-day variation in case mix and acuity. The data includes detailed demography, co-morbidity, symptoms, longitudinal acuity scores, physiology and laboratory results, all investigations, prescriptions, diagnoses and outcomes. It could be used to develop new pathways or understand the prevalence or severity of specific disease presentations.

PIONEER geography: The West Midlands (WM) has a population of 5.9 million & includes a diverse ethnic & socio-economic mix.

Electronic Health Record: University Hospital Birmingham is one of the largest NHS Trusts in England, providing direct acute services & specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds & an expanded 250 ITU bed capacity during COVID. UHB runs a fully electronic healthcare record (EHR) (PICS; Birmingham Systems), a shared primary & secondary care record (Your Care Connected) & a patient portal “My Health”.

Scope: All patients with a medical emergency admitted to hospital, flowing through the acute medical unit. Longitudinal & individually linked, so that the preceding & subsequent health journey can be mapped & healthcare utilisation prior to & after admission understood. The dataset includes patient demographics, co-morbidities taken from ICD-10 & SNOMED-CT codes. Serial, structured data pertaining to process of care (timings, admissions, wards and readmissions), physiology readings (NEWS2 score and clinical frailty scale), Charlson comorbidity index and time dimensions.

Available supplementary data: Matched controls; ambulance data, OMOP data, synthetic data.

Available supplementary support: Analytics, Model build, validation & refinement; A.I.; Data partner support for ETL (extract, transform & load) process, Clinical expertise, Patient & end-user access, Purchaser access, Regulatory requirements, Data-driven trials, “fast screen” services.

Chronic kidney disease (CKD) affects over 13% of the population, totaling more than 800 million individuals worldwide. Timely identification and intervention are crucial to delay CKD progression and improve patient outcomes. This research focuses on developing a predictive model to classify diabetic patients showing signs of kidney function impairment based on their CKD development risk. Our model utilizes electronic medical record (EMR) data, specifically by incorporating patient demographics, laboratory results, chronic conditions, risk factors, and medication codes to predict the onset of CKD in diabetic patients six months in advance, achieving an average Area Under the Curve (AUC) of 0.88. We leverage aggregated EMR data to effectively capture relevant information within the observation year instead of using temporal EMR data. Furthermore, we identify the most significant features for predicting CKD onset, including mean, minimum, and first quartile of estimated glomerular filtration rate (eGFR) during the observation year, along with variables such as diagnosis age and duration of hypertension, osteoarthritis, and diabetes, as well as levels of hemoglobin and fasting blood glucose (FBG). We also explored a refined model utilizing only these most significant features, which yields a slightly lower AUC of 0.86. These variables are typically available in primary data, empowering physicians for real-time risk assessment. The proposed model’s ability to identify higher-risk patients is essential for timely intervention, personalized care, risk stratification, patient education, and potential cost savings. This research contributes valuable insights for healthcare practitioners seeking efficient tools for early CKD detection in diabetic populations.

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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

Patient Engagement Technology Market Outlook

The global patient engagement technology market size was valued at approximately $18 billion in 2023 and is projected to grow at a robust compound annual growth rate (CAGR) of 15.8% from 2024 to 2032, reaching an estimated market size of $52 billion by the end of the forecast period. Several growth factors are contributing to this impressive expansion, including the increasing demand for personalized healthcare solutions, advancements in digital health technologies, and a growing emphasis on patient-centered care. The push for improved healthcare outcomes and the need for efficient healthcare delivery systems are driving healthcare providers and payers to adopt these technologies extensively.

One of the primary growth drivers for the patient engagement technology market is the global shift towards value-based care. As healthcare systems around the world move away from fee-for-service models towards value-based care, there is a pressing need for technologies that can facilitate better patient engagement. Value-based care models prioritize patient outcomes and satisfaction, motivating healthcare providers to adopt technologies that enhance communication with patients, provide better access to medical information, and improve adherence to treatment plans. Additionally, the COVID-19 pandemic has accelerated the adoption of digital health technologies, including patient engagement platforms, as healthcare providers have had to find new ways to interact with and treat patients remotely.

Another significant factor contributing to the growth of this market is the increasing prevalence of chronic diseases and the rising geriatric population. As the number of individuals with chronic conditions continues to rise, there is a growing need for continuous patient monitoring and management. Patient engagement technologies, such as mobile health applications and remote monitoring tools, enable patients with chronic diseases to be actively involved in their healthcare management. These technologies not only facilitate better disease management but also empower patients by making them more informed about their health conditions. The aging population also demands more personalized and convenient healthcare solutions, further driving the adoption of patient engagement technologies.

The rapid advancements in technology, particularly in mobile and cloud computing, are also playing a crucial role in the growth of the patient engagement technology market. The widespread use of smartphones and the increasing penetration of the internet have made it easier for patients to access health information and communicate with healthcare providers. Cloud-based solutions, in particular, offer scalability, flexibility, and cost-effectiveness, making them an attractive option for healthcare providers of all sizes. Furthermore, the integration of artificial intelligence and machine learning into patient engagement platforms is enhancing their capabilities, allowing for more personalized and predictive healthcare services.

The integration of eConsent in healthcare is becoming increasingly vital as patient engagement technologies evolve. eConsent, or electronic consent, streamlines the process of obtaining patient consent for medical procedures, treatment plans, and participation in clinical trials. This digital approach not only enhances the efficiency of healthcare operations but also ensures that patients are fully informed about their healthcare choices. By providing a user-friendly platform for patients to review and sign consent forms electronically, healthcare providers can improve patient understanding and compliance. Moreover, eConsent systems often include features that allow patients to ask questions and receive clarifications in real-time, further enhancing the patient-provider relationship. As the healthcare industry continues to embrace digital transformation, the adoption of eConsent is expected to rise, offering a more transparent and efficient consent process.

Regionally, North America is expected to dominate the patient engagement technology market during the forecast period, owing to the presence of advanced healthcare infrastructure and a high adoption rate of digital health technologies. Europe, closely following, benefits from government initiatives supporting healthcare digitization. The Asia Pacific region is poised for significant growth due to increasing investments in healthcare IT and rising awareness about the benefits of patient engagement solutions. Meanwhile, Latin America and the Middle East

The Digital Patient Engagement (DPE) market is experiencing robust growth, driven by the increasing adoption of telehealth, remote patient monitoring, and the rising demand for personalized healthcare experiences. The market, estimated at $15 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033, reaching approximately $50 billion by 2033. This expansion is fueled by several key factors: a rising aging population requiring more intensive care, the increasing prevalence of chronic diseases necessitating ongoing monitoring and engagement, and the proactive push by healthcare providers to improve patient outcomes and reduce hospital readmissions. Technological advancements, such as the development of sophisticated mobile health (mHealth) apps and AI-powered patient portals, are further accelerating market growth. The software segment currently dominates the market, but the services segment is expected to witness significant growth due to the increasing need for technical support, integration services, and data analytics. Patient portals and mobile apps are the leading application segments, indicating a clear preference for convenient and accessible digital healthcare solutions. North America currently holds the largest market share, benefiting from early adoption and advanced technological infrastructure, but the Asia Pacific region is expected to witness the fastest growth due to increasing healthcare expenditure and rising smartphone penetration. However, the market also faces challenges. Data privacy and security concerns remain a significant restraint, particularly given the sensitive nature of patient health information. Furthermore, the digital literacy gap among certain patient demographics presents a barrier to widespread adoption. High implementation costs and the need for robust IT infrastructure also impede market penetration, especially in developing regions. To mitigate these challenges, stakeholders are increasingly focusing on robust cybersecurity measures, user-friendly interfaces, and customized solutions tailored to diverse patient needs and technological capabilities. The focus is shifting towards seamless integration of DPE solutions with existing Electronic Health Records (EHR) systems and the adoption of interoperable standards to facilitate data exchange and enhance care coordination. The competitive landscape is characterized by a mix of established technology giants like IBM, Google, and Microsoft, and specialized DPE companies such as Relatient and Vivify Health. These companies are constantly innovating to offer comprehensive and user-friendly solutions to cater to the evolving needs of both healthcare providers and patients.

Background. The Healthcare Safety Investigation Branch (HSIB) published a report in 2020 reviewing the need to have a better method of identifying and preventing medication errors. 237 million medications errors occur in England per year. 5% of hospital admissions are related to medication errors, side effects or drug/drug interactions. Older patients, those with multiple long-term conditions and polypharmacy are most likely to experience the worse outcomes from medicine related harm. This dataset provides highly detailed medicine prescribing, indication, administration and patient outcome data, focusing on hospitalised patients in acute care.

PIONEER geography. The West Midlands (WM) has a population of 5.9 million and includes a diverse ethnic and socio-economic mix.

EHR. UHB is one of the largest NHS Trusts in England, providing direct acute services and specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds and an expanded 250 ITU bed capacity during COVID. UHB runs a fully electronic healthcare record (EHR) (PICS; Birmingham Systems), a shared primary and secondary care record (Your Care Connected) and a patient portal “My Health”.

Scope: All hospitalised patients in UHB Acute Medicine (AMU) and Emergency Departments (ED) from November 2017 to October 2020, curated to focus on medicines reconciliation. Longitudinal and individually linked, so that the preceding and subsequent health journey can be mapped and healthcare utilisation prior to and after admission understood. The dataset includes highly granular patient demographics and co-morbidities taken from ICD-10. Serial, structured data pertaining to acute care process (timings and wards). Along with presenting complaints, physiology readings (NEWS 2 and SEWS score). Includes all prescribed treatments, drug history, medication history and pharmacy interventions.

Available supplementary data: Matched controls; ambulance, OMOP data, synthetic data.

Available supplementary support: Analytics, Model build, validation and refinement; A.I.; Data partner support for ETL (extract, transform & load) process, Clinical expertise, Patient & end-user access, Purchaser access, Regulatory requirements, Data-driven trials, “fast screen” services.

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

Patient Infotainment Terminal Market Outlook

According to our latest research, the global Patient Infotainment Terminal market size in 2024 stands at USD 1.56 billion, with a robust CAGR of 10.7% projected over the forecast period. This strong growth trajectory is anticipated to propel the market to reach approximately USD 3.87 billion by 2033. The surge in demand for advanced patient engagement solutions, coupled with the increasing adoption of digital healthcare infrastructure worldwide, is fueling this expansion. As per our most recent analysis, the market’s momentum is underpinned by the convergence of healthcare digitization, patient-centric care models, and the integration of multimedia technologies into clinical settings.

The growth of the Patient Infotainment Terminal market is significantly driven by the global shift towards digitized healthcare environments and patient-centered care delivery. Hospitals and healthcare providers are increasingly recognizing the importance of enhancing patient experiences, not only to improve clinical outcomes but also to boost patient satisfaction scores. Patient infotainment terminals serve as a pivotal interface, offering a blend of entertainment, communication, and medical information access at the bedside. The proliferation of electronic health records (EHRs) and the integration of real-time data sharing have further elevated the role of these terminals. They enable patients to access their health information, communicate with caregivers, and even control room settings, fostering a sense of empowerment and engagement during hospital stays. This trend is particularly pronounced in technologically advanced regions where investments in healthcare IT infrastructure are substantial.

Another key driver for the market is the rising focus on infection control and operational efficiency within healthcare facilities. Patient infotainment terminals, by offering touchless or minimal-contact interfaces, help reduce the need for physical paperwork and interactions, thereby supporting infection prevention protocols. The COVID-19 pandemic further accelerated the adoption of such solutions, as hospitals sought to minimize cross-contamination risks and maintain communication between patients and their families amid visitation restrictions. Moreover, the integration of telemedicine functionalities within these terminals allows for remote consultations and monitoring, a feature that has gained immense relevance in the post-pandemic landscape. As healthcare systems globally continue to adapt to new norms, the demand for versatile and hygienic patient engagement tools is expected to remain strong.

Additionally, the expanding geriatric population and the growing prevalence of chronic diseases are fueling the need for long-term care and rehabilitation facilities equipped with advanced infotainment systems. These patient demographics often require extended hospital stays and benefit greatly from enhanced bedside experiences. Patient infotainment terminals not only provide entertainment but also facilitate therapeutic and educational content tailored to individual patient needs. This capability helps in reducing anxiety, improving compliance with treatment regimens, and ultimately supporting better health outcomes. The market is also witnessing innovation in terms of device design, user interface, and content delivery, making these terminals more accessible and appealing to diverse patient groups, including pediatric and geriatric populations.

From a regional perspective, North America currently leads the Patient Infotainment Terminal market owing to its advanced healthcare infrastructure, high adoption of digital health technologies, and strong focus on patient-centric care. Europe follows closely, driven by government initiatives aimed at modernizing healthcare facilities and improving patient engagement metrics. The Asia Pacific region, meanwhile, is emerging as a high-growth market, fueled by rapid healthcare infrastructure development, increasing healthcare expenditure, and a rising middle-class population demanding better healthcare experiences. Latin America and the Middle East & Africa are also witnessing gradual adoption, supported by investments in healthcare modernization and the growing presence of multinational healthcare technology providers.

Healthcare Analytics Solutions Market Outlook

The global healthcare analytics solutions market size was valued at approximately USD 14.2 billion in 2023 and is poised to reach USD 74.1 billion by 2032, exhibiting a remarkable compound annual growth rate (CAGR) of 20.5% during the forecast period. This impressive growth trajectory can primarily be attributed to the increasing adoption of big data analytics in healthcare to enhance patient outcomes, optimize operational efficiency, and reduce costs. As the healthcare industry continues to transition towards a more data-driven approach, the demand for robust analytics solutions is expected to witness significant growth, driven by technological advancements and regulatory pressures to improve healthcare delivery.

One of the key growth factors for the healthcare analytics solutions market is the rising emphasis on personalized medicine, which requires extensive data analysis to tailor treatments to individual patients. Leveraging analytics allows healthcare providers to sift through vast amounts of patient data to identify patterns and predict outcomes, thereby enabling more precise and effective treatments. Furthermore, the integration of artificial intelligence (AI) and machine learning (ML) with healthcare analytics solutions is accelerating insights generation, further propelling market growth. These technologies are reducing the time required for data processing and improving the accuracy of predictive models, making analytics solutions indispensable for modern healthcare systems.

Another significant driver of the market is the escalating need for cost-effective healthcare solutions amid rising healthcare costs globally. Organizations are increasingly turning to analytics to streamline operations and improve financial performance by identifying inefficiencies and reducing wastage. Healthcare analytics enable providers to optimize resource allocation, manage supply chains more effectively, and enhance revenue cycle management, which are crucial for maintaining profitability in an increasingly competitive environment. Additionally, regulatory bodies are mandating the adoption of electronic health records (EHRs) and other digital tools, necessitating advanced analytics solutions to manage and utilize this influx of data effectively.

The growing prevalence of chronic diseases and aging populations is also contributing to the expansion of the healthcare analytics market. As these demographic changes result in increased demand for healthcare services, analytics solutions are becoming instrumental in managing population health. By analyzing data on patient demographics, disease prevalence, and treatment outcomes, healthcare providers can develop targeted interventions to manage chronic diseases and improve patient care. This proactive approach not only enhances patient outcomes but also helps in controlling costs by reducing hospital readmissions and emergency care utilization.

Healthcare Clinical Analytics is playing a pivotal role in transforming patient care by leveraging data to drive clinical decision-making. By analyzing clinical data, healthcare providers can gain insights into patient outcomes, treatment effectiveness, and potential areas for improvement. This data-driven approach enables a more personalized and precise treatment plan, ultimately enhancing patient outcomes and satisfaction. Furthermore, clinical analytics helps in identifying trends and patterns that can predict disease outbreaks or complications, allowing for proactive interventions. As healthcare systems increasingly focus on value-based care, the integration of clinical analytics is becoming essential for delivering high-quality, cost-effective care.

Regional disparities in healthcare infrastructure and technological adoption play a crucial role in shaping the market dynamics. North America currently holds the largest market share, attributed to its well-established healthcare system and high investment in healthcare IT. The region's strong focus on innovation and presence of key market players further strengthen its position. However, Asia Pacific is anticipated to register the highest growth rate over the forecast period, driven by rapid digitalization in emerging economies, increasing government initiatives to improve healthcare services, and a growing focus on implementing analytics solutions to address healthcare challenges. This regional outlook highlights the diverse opportunities and growth potential within the global healthcare analytics solutions market.

Electronic Medical Records Market Outlook

As per our latest research, the global Electronic Medical Records (EMR) market size reached USD 34.8 billion in 2024, reflecting robust adoption across healthcare systems worldwide. The market is poised for significant expansion with a projected CAGR of 7.3% from 2025 to 2033. By the end of 2033, the EMR market is forecasted to attain a value of approximately USD 65.8 billion. This impressive growth trajectory is primarily driven by the increasing digitalization of healthcare records, the need for improved patient care, and regulatory mandates for electronic data management in healthcare settings.

One of the most crucial growth factors propelling the Electronic Medical Records market is the global push towards healthcare modernization and interoperability. Governments and healthcare organizations are heavily investing in digital infrastructure to streamline patient data management and enhance care coordination. Initiatives such as the United States’ Health Information Technology for Economic and Clinical Health (HITECH) Act and similar policies in Europe and Asia Pacific have accelerated the adoption of EMR systems. These regulations not only incentivize healthcare providers to adopt electronic records but also impose penalties for non-compliance, further fueling market expansion. The growing emphasis on patient-centric care, reduction of medical errors, and the need for real-time access to patient information are compelling hospitals and clinics to transition from paper-based to electronic systems.

Another significant driver is the rapid advancement and integration of cutting-edge technologies within EMR platforms. Artificial Intelligence (AI), machine learning, and cloud computing are revolutionizing how patient data is captured, stored, and analyzed. These technologies are enabling predictive analytics, personalized medicine, and seamless data sharing across healthcare networks. The integration of telemedicine and remote patient monitoring solutions with EMR systems has also gained momentum, especially post-pandemic, as healthcare providers seek to offer virtual care without compromising on the quality or security of patient data. This technological evolution is not only enhancing the efficiency of healthcare delivery but is also making EMR solutions more scalable, secure, and user-friendly.

Furthermore, the rising prevalence of chronic diseases and the aging global population are contributing to the growing demand for comprehensive and accessible patient records. Chronic disease management requires continuous monitoring and long-term care coordination, both of which are facilitated by robust EMR systems. The ability to track patient histories, medication adherence, and clinical outcomes over time is invaluable for healthcare providers aiming to deliver value-based care. Additionally, the growing need for data-driven decision-making in healthcare, driven by the shift towards outcomes-based reimbursement models, is further accelerating the adoption of EMR platforms. These trends collectively underscore the critical role of EMRs in shaping the future of global healthcare delivery.

Regionally, North America continues to dominate the Electronic Medical Records market, accounting for the largest revenue share in 2024, followed closely by Europe and Asia Pacific. The United States remains at the forefront due to its advanced healthcare infrastructure, favorable government policies, and high adoption rate of digital health technologies. Europe is experiencing steady growth, propelled by stringent data protection regulations and increasing investments in healthcare IT. Meanwhile, the Asia Pacific region is emerging as a lucrative market, driven by expanding healthcare access, government-led digital health initiatives, and a burgeoning patient population. Latin America and Middle East & Africa are witnessing gradual adoption, supported by efforts to modernize healthcare systems and improve patient outcomes.

Component Analysis

The Electronic Medical Records market is segmented by component

About Dataset

Context:

This dataset simulates patient data from a hospital in the United Arab Emirates (UAE), focusing on diabetes-related diagnoses. It includes demographic information, visit details, and healthcare service times, along with intentional data quality issues such as missing values, duplicates, and inconsistencies. The dataset is designed to reflect real-world healthcare scenarios, making it suitable for practicing data cleaning, analysis, and predictive modeling.

Inspiration:

The dataset was inspired by the need for realistic healthcare data that can be used for training and testing in data science and machine learning. It aims to provide a comprehensive and challenging dataset for learners and professionals to explore healthcare analytics, predictive modeling, and data preprocessing techniques.

Dataset Information:

• Size: 100,000 rows and 13 columns.
• Columns:
  • Visit_Date: Date of the patient's visit (past 2 years).
  • Patient_ID: Unique identifier for each patient (with duplicates).
  • Age: Patient age (0–100 years).
  • Gender: Patient gender (Male, Female, Other, or missing).
  • Diagnosis: Diabetes-related diagnosis (Type 1, Type 2, Prediabetes, Gestational, or missing).
  • Has_Insurance: Insurance status (Yes, No, or missing).
  • Total_Cost: Total cost of the visit in AED (with some invalid negative values).
  • Region: Emirate where the patient is located (e.g., Abu Dhabi, Dubai).
  • Area: Specific location within the emirate (e.g., Al Ain, Palm Jumeirah).
  • Registration time: Time spent during registration (in minutes).
  • Nursing time: Time spent with nursing staff (in minutes).
  • Laboratory time: Time spent in the laboratory (in minutes).
  • Consultation time: Time spent in consultation (in minutes).
  • Pharmacy time: Time spent at the pharmacy (in minutes).

Usage Scenarios:

This dataset can be utilized for a wide range of purposes, including: - Developing and testing healthcare predictive models: Predict diabetes types or patient outcomes based on demographic and visit data. - Practicing data cleaning, transformation, and analysis techniques: Handle missing values, duplicates, and inconsistencies. - Creating data visualizations: Gain insights into healthcare trends, such as the distribution of diabetes types across regions or age groups. - Learning and teaching data science and machine learning concepts: Use the dataset to teach classification, regression, and clustering techniques in a healthcare context.

You can treat it as a Multi-Class Classification Problem and solve it for Diagnosis, which contains 4 categories: - Type 1 Diabetes - Type 2 Diabetes - Prediabetes - Gestational Diabetes

Acknowledgments:

This dataset was created synthetically to mimic real-world healthcare data. Special thanks to the UAE postal code and geographic information used to structure the Region and Area columns.

Image Credit:

Image by [Walid Barghout].

1) Data Introduction • The Healthcare Dataset is a synthetic dataset designed to mimic real-world healthcare data for data science, machine learning, and data analysis purposes. It includes patient information, medical conditions, admission details, and healthcare services provided. This dataset is ideal for developing and testing healthcare predictive models, practicing data manipulation techniques, and creating data visualizations.

2) Data Utilization (1) Healthcare data has characteristics that: • It includes detailed patient information such as age, gender, blood type, medical condition, and admission details. This information can be used to analyze healthcare trends, patient demographics, and the effectiveness of medical treatments. (2) Healthcare data can be used to: • Predictive Modeling: Helps in developing models to predict patient outcomes, treatment success rates, and disease progression. • Healthcare Analytics: Assists in analyzing patient data to identify patterns, improve patient care, and optimize resource allocation. • Educational Purposes: Supports learning and teaching data science concepts in a healthcare context, providing realistic data for experimentation and practice.

PIONEER: The impact of ethnicity and multi-morbidity on COVID-related outcomes; a primary care supplemented hospitalised dataset Dataset number 3.0

Coronavirus disease 2019 (COVID-19) was identified in January 2020. Currently, there have been more than 65million cases and more than 1.5 million deaths worldwide. Some individuals experience severe manifestations of infection, including viral pneumonia, adult respiratory distress syndrome (ARDS) and death. Evidence suggests that older patients, those from some ethnic minority groups and those with multiple long-term health conditions have worse outcomes. This secondary care COVID dataset contains granular demographic and morbidity data, supplemented from primary care records, to add to the understanding of patient factors on disease outcomes.

PIONEER geography The West Midlands (WM) has a population of 5.9 million & includes a diverse ethnic & socio-economic mix. There is a higher than average percentage of minority ethnic groups. WM has a large number of elderly residents but is the youngest population in the UK. Each day >100,000 people are treated in hospital, see their GP or are cared for by the NHS. The West Midlands was one of the hardest hit regions for COVID admissions in both wave 1 and 2.

EHR. University Hospitals Birmingham NHS Foundation Trust (UHB) is one of the largest NHS Trusts in England, providing direct acute services & specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds & 100 ITU beds. UHB runs a fully electronic healthcare record (EHR) (PICS; Birmingham Systems), a shared primary & secondary care record (Your Care Connected) & a patient portal “My Health”. UHB has cared for >5000 COVID admissions to date.

Scope: All COVID swab confirmed hospitalised patients to UHB from January – May 2020. The dataset includes highly granular patient demographics & co-morbidities taken from ICD-10 & SNOMED-CT codes but also primary care records and clinic letters. Serial, structured data pertaining to care process (timings, staff grades, specialty review, wards), presenting complaint, acuity, all physiology readings (pulse, blood pressure, respiratory rate, oxygen saturations), all blood results, microbiology, all prescribed & administered treatments (fluids, antibiotics, inotropes, vasopressors, organ support), all outcomes. Linked images available (radiographs, CT, MRI, ultrasound).

Available supplementary data: Health data preceding and following admission event. Matched “non-COVID” controls; ambulance, 111, 999 data, synthetic data.

Available supplementary support: Analytics, Model build, validation & refinement; A.I.; Data partner support for ETL (extract, transform & load) process, Clinical expertise, Patient & end-user access, Purchaser access, Regulatory requirements, Data-driven trials, “fast screen” services.

The Patient Engagement Services market is experiencing robust growth, projected to reach $14.82 billion in 2025 and exhibiting a Compound Annual Growth Rate (CAGR) of 4.5% from 2025 to 2033. This expansion is fueled by several key drivers. The increasing adoption of telehealth and remote patient monitoring technologies enhances patient access to care and improves treatment adherence. Furthermore, the rising prevalence of chronic diseases necessitates proactive patient engagement strategies to manage conditions effectively and reduce healthcare costs. Growing government initiatives focused on improving healthcare outcomes and patient satisfaction also contribute to market growth. The market is segmented by service type (Consulting Services, Implementation Services, Training & Education Services, and Other Services) and application (Individual, Government, and Others). The diverse service offerings cater to varying needs, ranging from strategic consulting and technological implementation to comprehensive training and educational programs. Different applications reflect the breadth of market penetration, from direct patient engagement to government initiatives focused on population health management. North America currently holds a significant market share, driven by advanced healthcare infrastructure and technology adoption. However, growing healthcare expenditure and increasing digital literacy in regions like Asia Pacific are poised to drive substantial future growth in these markets. The competitive landscape is marked by a mix of established players and emerging technology providers. Companies like Cerner, IBM, Epic Systems, and McKesson Corporation are leveraging their existing healthcare IT infrastructure and expertise to expand their patient engagement offerings. Smaller, more specialized companies are focusing on innovative technologies and niche services. The future success of companies within this sector will depend on their ability to innovate, adapt to evolving technological advancements, integrate seamlessly with existing Electronic Health Records (EHR) systems, and deliver demonstrably positive patient outcomes. The ongoing focus on data privacy and security is also a significant factor impacting market growth and shaping competitive strategies. This market demonstrates significant potential for continued growth as digital health tools continue to evolve and patient expectations for personalized, accessible care increase.

Chronic kidney disease (CKD) affects over 13% of the population, totaling more than 800 million individuals worldwide. Timely identification and intervention are crucial to delay CKD progression and improve patient outcomes. This research focuses on developing a predictive model to classify diabetic patients showing signs of kidney function impairment based on their CKD development risk. Our model utilizes electronic medical record (EMR) data, specifically by incorporating patient demographics, laboratory results, chronic conditions, risk factors, and medication codes to predict the onset of CKD in diabetic patients six months in advance, achieving an average Area Under the Curve (AUC) of 0.88. We leverage aggregated EMR data to effectively capture relevant information within the observation year instead of using temporal EMR data. Furthermore, we identify the most significant features for predicting CKD onset, including mean, minimum, and first quartile of estimated glomerular filtration rate (eGFR) during the observation year, along with variables such as diagnosis age and duration of hypertension, osteoarthritis, and diabetes, as well as levels of hemoglobin and fasting blood glucose (FBG). We also explored a refined model utilizing only these most significant features, which yields a slightly lower AUC of 0.86. These variables are typically available in primary data, empowering physicians for real-time risk assessment. The proposed model’s ability to identify higher-risk patients is essential for timely intervention, personalized care, risk stratification, patient education, and potential cost savings. This research contributes valuable insights for healthcare practitioners seeking efficient tools for early CKD detection in diabetic populations.