Context

Part of Janatahack Hackathon in Analytics Vidhya

Content

The healthcare sector has long been an early adopter of and benefited greatly from technological advances. These days, machine learning plays a key role in many health-related realms, including the development of new medical procedures, the handling of patient data, health camps and records, and the treatment of chronic diseases.

MedCamp organizes health camps in several cities with low work life balance. They reach out to working people and ask them to register for these health camps. For those who attend, MedCamp provides them facility to undergo health checks or increase awareness by visiting various stalls (depending on the format of camp).

MedCamp has conducted 65 such events over a period of 4 years and they see a high drop off between “Registration” and number of people taking tests at the Camps. In last 4 years, they have stored data of ~110,000 registrations they have done.

One of the huge costs in arranging these camps is the amount of inventory you need to carry. If you carry more than required inventory, you incur unnecessarily high costs. On the other hand, if you carry less than required inventory for conducting these medical checks, people end up having bad experience.

The Process:

MedCamp employees / volunteers reach out to people and drive registrations.
During the camp, People who “ShowUp” either undergo the medical tests or visit stalls depending on the format of health camp.

Other things to note:

Since this is a completely voluntary activity for the working professionals, MedCamp usually has little profile information about these people.
For a few camps, there was hardware failure, so some information about date and time of registration is lost.
MedCamp runs 3 formats of these camps. The first and second format provides people with an instantaneous health score. The third format provides  
information about several health issues through various awareness stalls.

Favorable outcome:

For the first 2 formats, a favourable outcome is defined as getting a health_score, while in the third format it is defined as visiting at least a stall.
You need to predict the chances (probability) of having a favourable outcome.

Train / Test split:

Camps started on or before 31st March 2006 are considered in Train
Test data is for all camps conducted on or after 1st April 2006.

Acknowledgements

Credits to AV

Inspiration

To share with the data science community to jump start their journey in Healthcare Analytics

Big Data Analytics In Healthcare Market size is estimated at USD 37.22 Billion in 2024 and is projected to reach USD 74.82 Billion by 2032, growing at a CAGR of 9.12% from 2026 to 2032.

Big Data Analytics In Healthcare Market: Definition/ Overview

Big Data Analytics in Healthcare, often referred to as health analytics, is the process of collecting, analyzing, and interpreting large volumes of complex health-related data to derive meaningful insights that can enhance healthcare delivery and decision-making. This field encompasses various data types, including electronic health records (EHRs), genomic data, and real-time patient information, allowing healthcare providers to identify patterns, predict outcomes, and improve patient care.

This statistic shows the size of the global big data analytics services market related to healthcare in 2016 and a forecast for 2025, by application. It is predicted that by 2025 the market for health-related financial analytics services using big data will increase to over 13 billion U.S. dollars.

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

Snapshot img

Healthcare Analytics Market Size 2025-2029

The healthcare analytics market size is forecast to increase by USD 81.28 billion, at a CAGR of 25% between 2024 and 2029.

The market is experiencing significant growth due to several key trends. The integration of big data with healthcare analytics is a major growth factor, enabling healthcare providers to make data-driven decisions and improve patient outcomes.
Another trend is the increasing use of Internet-enabled mobile devices in healthcare services, allowing for remote monitoring and real-time data access. However, data security and privacy concerns remain a challenge, with the need for strong security measures to protect sensitive patient information. These trends are shaping the future of patient engagement and driving growth in the global healthcare analytics market as well.

What will be the Size of the Healthcare Analytics Market During the Forecast Period?

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The market is experiencing significant growth due to the increasing adoption of digital solutions for improving patient care and reducing treatment costs. Healthcare organizations are leveraging descriptive analytics to gain insights from clinical data, while predictive and prescriptive analytics enable the development of personalized treatment plans and optimal therapeutic strategies. Financial analytics help manage healthcare expenses, ensuring cost-effective patient care. The National Institutes of Health (NIH) and other research institutions are driving innovation in health data analytics, leading to advancements in areas such as patient compliance, medication selection, and disease management. Industry leaders are utilizing artificial intelligence and machine learning to enhance clinical care, outreach, and disease management, ultimately leading to better treatment consistency and optimal outcomes for patients.

How is this Healthcare Analytics Industry segmented and which is the largest segment?

The healthcare analytics 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

  Services
  Software
  Hardware


Deployment

  On-premise
  Cloud-based


Type

  Descriptive Analysis
  Predictive Analysis
  Prescriptive and Diagnostics


Application

  Financial Analytics
  Clinical Analytics
  Operations and Administrative Analytics
  Population Health Analytics


End-User

  Insurance Company
  Government Agencies
  Healthcare Providers
  Pharmaceutical and Medical Device Companies


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    Italy
    UK


  APAC

    China
    India
    Japan
    South Korea


  South America



  Middle East and Africa

By Component Insights

The services segment is estimated to witness significant growth during the forecast period. Healthcare analytics services encompass consulting, learning and training, development and integration, hardware maintenance and support, IT management, process management, and software support. The consulting and software support segments are experiencing significant growth due to the increasing demand for advanced healthcare delivery systems and cost-effective models. The healthcare sector's ongoing transition from on-premises to cloud-based software and IT infrastructure deployment is another growth driver. This shift is expected to increase the demand for IT education and training services. End-users of these services range from individual doctor offices to full-service hospitals and multi-location clinics, including large hospitals and tissue and blood processing organizations.

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The services segment was valued at USD 6.7 billion in 2019 and showed a gradual increase during the forecast period.

Regional Analysis

APAC is estimated to contribute 36% to the growth of the global market during the forecast period. Technavio's analysts have elaborately explained the regional trends and drivers that shape the market during the forecast period.

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The North American market is driven by the increasing demand for secure data access and effective patient information management. The US and Canada are the primary contributors to this market due to their early adoption of advanced technologies, such as machine learning, predictive analytics, and quantum computing, across various industries. These technologies enable the healthcare sector to optimize patient compliance, medication selection, and therapeutic strategies and, ultimately, achieve optimal outcomes. Major companies in this market provide solutions to help healthcare organizations manage and

The big data in healthcare market size is estimated to grow from USD 78 billion in 2024 to USD 540 billion by 2035, representing a CAGR of 19.20% till 2035

Clinical Data Analytics Market Outlook

As per our latest research, the global clinical data analytics market size reached USD 12.8 billion in 2024, reflecting robust momentum driven by the increasing adoption of digital health technologies and the growing emphasis on data-driven decision-making in healthcare. The market is expected to expand at a CAGR of 24.1% from 2025 to 2033, with the forecasted market size projected to reach USD 86.7 billion by 2033. This remarkable growth trajectory is primarily fueled by the rising need for advanced analytics to improve patient outcomes, optimize operational efficiency, and comply with stringent regulatory requirements. The integration of artificial intelligence and machine learning into clinical data analytics platforms is further enhancing the market’s value proposition, making it an indispensable tool for modern healthcare organizations globally.

A key growth driver for the clinical data analytics market is the exponential increase in healthcare data generation, stemming from widespread adoption of electronic health records (EHRs), wearable devices, and connected health systems. Healthcare institutions are increasingly leveraging clinical data analytics solutions to extract actionable insights from these vast data pools, enabling more accurate diagnoses, personalized treatment plans, and proactive disease management. The need to reduce healthcare costs while maintaining high standards of patient care is compelling providers to adopt analytics-driven approaches. Clinical data analytics helps identify inefficiencies, detect patterns in patient care, and predict adverse events, which collectively contribute to improved clinical outcomes and operational savings.

Another significant growth factor is the rising prevalence of chronic diseases and the aging global population, which are placing unprecedented pressure on healthcare systems worldwide. Clinical data analytics empowers providers to stratify patient populations, monitor disease progression, and implement targeted interventions for high-risk groups. The ability to harness predictive analytics for early detection and prevention of complications is especially valuable in managing chronic conditions such as diabetes, cardiovascular diseases, and cancer. Moreover, the growing focus on value-based care models is incentivizing healthcare organizations to invest in analytics platforms that can demonstrate measurable improvements in quality and efficiency, further propelling market expansion.

The increasing regulatory scrutiny and demand for compliance with healthcare standards such as HIPAA, GDPR, and other regional data protection laws are also accelerating market growth. Clinical data analytics platforms are being designed with robust security and privacy features to ensure the safe handling of sensitive patient information. This not only helps organizations avoid costly penalties but also builds trust among patients, clinicians, and stakeholders. Additionally, the ongoing digital transformation in healthcare, supported by government initiatives and funding programs, is creating a favorable environment for the adoption of advanced analytics solutions across hospitals, clinics, research organizations, and pharmaceutical companies.

Regionally, North America continues to dominate the clinical data analytics market, accounting for the largest share due to its advanced healthcare infrastructure, high adoption of digital technologies, and supportive regulatory landscape. Europe follows closely, driven by strong government support for digital health initiatives and increasing investments in healthcare IT. The Asia Pacific region is emerging as a high-growth market, fueled by rapid healthcare modernization, rising healthcare expenditures, and growing awareness of the benefits of analytics. Latin America and the Middle East & Africa are also witnessing steady growth, albeit from a smaller base, as healthcare providers in these regions increasingly recognize the value of data-driven decision-making.

Component Analysis

Big Data in Healthcare Market Outlook

According to our latest research, the global Big Data in Healthcare market size reached USD 41.2 billion in 2024, demonstrating robust expansion driven by the increasing adoption of advanced analytics and data-driven decision-making in the healthcare sector. The market is projected to grow at a CAGR of 17.4% from 2025 to 2033, reaching an estimated value of USD 154.1 billion by 2033. This significant growth is primarily attributed to the surging volume of healthcare data, advancements in artificial intelligence and machine learning, and the increasing focus on improving patient outcomes and operational efficiency across healthcare institutions worldwide.

One of the primary growth factors fueling the Big Data in Healthcare market is the exponential rise in healthcare data generation, driven by the widespread adoption of electronic health records (EHRs), wearable devices, and connected medical equipment. As healthcare organizations seek to harness actionable insights from this data deluge, the demand for advanced analytics solutions has surged. The integration of big data analytics enables providers to enhance clinical decision-making, reduce medical errors, and optimize treatment protocols, thereby improving patient care and safety. Furthermore, the growing emphasis on value-based care models has compelled healthcare stakeholders to invest in robust data analytics platforms that can support population health management and evidence-based medicine, further accelerating market expansion.

Another key driver of the Big Data in Healthcare market is the growing need for cost containment and operational efficiency within healthcare organizations. Rising healthcare costs, resource constraints, and the increasing complexity of healthcare delivery have prompted providers and payers to leverage big data analytics to streamline operations, reduce redundancies, and enhance resource allocation. Financial analytics applications, in particular, are witnessing substantial uptake as organizations strive to identify cost-saving opportunities, detect fraudulent claims, and improve revenue cycle management. Additionally, operational analytics solutions are being deployed to optimize supply chain management, workforce planning, and facility utilization, resulting in enhanced productivity and reduced overheads.

The rapid advancement of artificial intelligence (AI), machine learning, and cloud computing technologies has also played a pivotal role in propelling the Big Data in Healthcare market forward. AI-driven analytics platforms are enabling healthcare providers to uncover hidden patterns in patient data, predict disease outbreaks, and personalize treatment plans based on individual patient profiles. The proliferation of cloud-based solutions has further democratized access to advanced analytics tools, allowing even small and medium-sized healthcare organizations to leverage big data capabilities without significant upfront investments in IT infrastructure. This technological evolution is expected to continue driving innovation and adoption across the global healthcare landscape.

From a regional perspective, North America continues to dominate the Big Data in Healthcare market, accounting for the largest revenue share in 2024, followed by Europe and Asia Pacific. The region's leadership is underpinned by robust healthcare IT infrastructure, high adoption rates of electronic health records, and strong government initiatives promoting data interoperability and healthcare digitization. Meanwhile, Asia Pacific is poised for the fastest growth during the forecast period, fueled by rapid healthcare modernization, expanding digital health initiatives, and increasing investments in healthcare analytics by both public and private sectors. As healthcare systems worldwide continue to prioritize data-driven transformation, the market's regional landscape is expected to evolve, with emerging economies playing an increasingly prominent role in shaping future growth trajectories.

The size of the Analytics in Healthcare Industry market was valued at USD 46.50 Million in 2023 and is projected to reach USD 197.16 Million by 2032, with an expected CAGR of 22.92% during the forecast period. The Analytics in Healthcare Industry refers to the use of data analysis, predictive modeling, and statistical methods to derive insights and support decision-making in healthcare. Healthcare analytics enables organizations to improve patient care, optimize operations, reduce costs, and enhance overall efficiency. The rise of big data, artificial intelligence (AI), machine learning (ML), and cloud computing has transformed the way healthcare providers, payers, and pharmaceutical companies manage and analyze data. The widespread implementation of EHRs has led to an enormous amount of patient data being collected. Healthcare analytics tools help in extracting valuable insights from this data to improve patient outcomes and operational efficiency.Increased emphasis on personalized healthcare: Analytics enable healthcare providers to tailor treatments based on individual patient data.Cost optimization: Analytics help healthcare organizations optimize costs by identifying areas for improvement and reducing operational inefficiencies.Improved patient outcomes: By analyzing patient data, healthcare providers can identify risk factors and develop early intervention strategies.Enhanced research and development: Analytics empower researchers to analyze vast amounts of data to identify new patterns and develop innovative therapies. Recent developments include: August 2022: Syntellis Performance Solutions acquired Stratasan Healthcare Solutions, a healthcare market intelligence and data analytics company. Through the acquisition, Syntellis expanded its solutions for healthcare organizations with data and intelligence solutions to improve operational, financial, and strategic growth planning., June 2022: Oracle Corporation acquired Cerner Corporation to combine the clinical capabilities of Cerner with Oracle's enterprise platform analytics and automation expertise., January 2022: IBM and Francisco Partners signed a definitive agreement under which Francisco Partners will acquire healthcare data and analytics assets from IBM that are currently part of the Watson Health business.. Key drivers for this market are: Technological Advancements and Favorable Governemnt Initiatives, Emergence of Big Data in the Healthcare Industry. Potential restraints include: Cost and Complexity of Software, Data Integrity and Privacy Concerns; Lack of Proper Skilled Labors. Notable trends are: The Predictive Analytics Segment is Expected to Witness High Growth Over the Forecast Period.

Health indicators are quantifiable characteristics of a population which researchers use as supporting evidence for describing the health of a population. The researchers use a survey methodology to gather information about certain people, use statistics in an attempt to generalize the information collected to the entire population, then use the statistical analysis to make a statement about the health of a population. Health indicators are often used by governments to guide health care policy.

Big Data Analytics in Healthcare Market by Component (Software, Hardware, Services [Descriptive, Prescriptive, Diagnostic]), Deployment (On-premise, Cloud), Application (Clinical, Financial, Operational, Population Health), and End User - Global Forecast to 2032

Healthcare Data Analytics Market Outlook

The global healthcare data analytics market size was valued at approximately USD 24.5 billion in 2023 and is projected to reach around USD 95.5 billion by 2032, growing at a robust CAGR of 16.5% during the forecast period. The market's growth is largely driven by the increasing adoption of electronic health records (EHRs) and the rising demand for data-driven decision-making in healthcare.

One of the primary growth factors for the healthcare data analytics market is the escalating volume of healthcare data generated from various sources such as clinical trials, patient records, and medical devices. The integration of big data analytics in healthcare facilitates enhanced patient outcomes and operational efficiency by enabling predictive analytics, personalized medicine, and real-time decision-making. Moreover, the adoption of advanced technologies such as artificial intelligence (AI) and machine learning (ML) further drives the need for sophisticated analytics tools to manage and interpret the vast amounts of data.

Another significant driver of market growth is the increasing emphasis on value-based care and the need to reduce healthcare costs. Healthcare providers and payers are increasingly leveraging data analytics to identify cost-saving opportunities, optimize resource allocation, and improve care quality. Analytics tools help in identifying patterns and trends, thereby enabling healthcare organizations to adopt preventive measures and reduce the incidence of chronic diseases. Furthermore, government initiatives promoting the use of healthcare IT solutions and the implementation of stringent regulations for data management and security are contributing to market expansion.

The growing popularity of telemedicine and remote patient monitoring also contributes to the expansion of the healthcare data analytics market. The COVID-19 pandemic has accelerated the adoption of telehealth services, leading to a surge in data generated from remote consultations and wearable devices. This data needs to be effectively analyzed to provide actionable insights, improve patient care, and streamline healthcare operations. Additionally, the rising focus on population health management and the need to address healthcare disparities are driving the adoption of analytics solutions to better understand and address the health needs of diverse populations.

The emergence of Healthcare BI Platform solutions is transforming the landscape of healthcare data analytics. These platforms provide healthcare organizations with powerful tools to aggregate, analyze, and visualize data from multiple sources, enabling more informed decision-making. By integrating data from electronic health records, financial systems, and operational databases, Healthcare BI Platforms offer a comprehensive view of organizational performance. This holistic approach not only aids in improving patient outcomes but also enhances operational efficiency by identifying areas for cost reduction and resource optimization. As healthcare systems continue to evolve, the role of BI platforms in facilitating data-driven strategies becomes increasingly vital, supporting the shift towards value-based care and personalized medicine.

Regionally, North America holds the largest share of the healthcare data analytics market due to the high adoption of advanced healthcare technologies and the presence of key market players. The Asia Pacific region is expected to witness the highest growth rate during the forecast period, fueled by the increasing healthcare expenditure, growing awareness about the benefits of data analytics, and the rapid digital transformation of the healthcare sector in countries like China and India.

Component Analysis

The healthcare data analytics market is segmented by component into software, hardware, and services. The software segment holds the largest market share, driven by the growing demand for advanced analytics solutions that can handle large volumes of healthcare data. Software tools for data visualization, predictive analytics, and machine learning are increasingly being adopted to derive meaningful insights from complex datasets and improve clinical and operational outcomes. Organizations are investing heavily in upgrading their software infrastructure to keep pace with the evolving healthcare landscape.

The hardware segment, although smaller in comparison to software, plays a crucial role i

The HCUP Summary Trend Tables include monthly information on hospital utilization derived from the HCUP State Inpatient Databases (SID) and HCUP State Emergency Department Databases (SEDD). Information on emergency department (ED) utilization is dependent on availability of HCUP data; not all HCUP Partners participate in the SEDD. The HCUP Summary Trend Tables include downloadable Microsoft® Excel tables with information on the following topics: Overview of monthly trends in inpatient and emergency department utilization All inpatient encounter types Inpatient stays by priority conditions -COVID-19 -Influenza -Other acute or viral respiratory infection Inpatient encounter type -Normal newborns -Deliveries -Non-elective inpatient stays, admitted through the ED -Non-elective inpatient stays, not admitted through the ED -Elective inpatient stays Inpatient service line -Maternal and neonatal conditions -Mental health and substance use disorders -Injuries -Surgeries -Other medical conditions Emergency department treat-and-release visits Emergency department treat-and-release visits by priority conditions -COVID-19 -Influenza -Other acute or viral respiratory infection Description of the data source, methodology, and clinical criteria

The summary statistics by North American Industry Classification System (NAICS) which include: operating revenue (dollars x 1,000,000), operating expenses (dollars x 1,000,000), salaries wages and benefits (dollars x 1,000,000), and operating profit margin (by percent), for home health care services (NAICS 621610) and services for the elderly and persons with disabilities (NAICS 624120), annual, Canada.

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

This release presents experimental statistics from the Mental Health Services Data Set (MHSDS), using final submissions for March 2016. This is the fourth monthly release from the dataset, which replaces the Mental Health and Learning Disabilities Dataset (MHLDDS). As well as analysis of waiting times, first published in March 2016, this release includes elements of the reports that were previously included in monthly reports produced from final MHLDDS submissions. It also includes some new measures. Because of the scope of the changes to the dataset (resulting in the name change to MHSDS and the new name for these monthly reports) it will take time to re-introduce all possible measures that were previously part of the MHLDS Monthly Reports. Additional measures will be added to this report in the coming months. Further details about these changes and the consultation that informed were announced in November. From January 2016 the release includes information on people in children and young people's mental health services, including CAMHS, for the first time. Learning disabilities services have been included since September 2014. The expansion in the scope of the dataset means that many of the basic measures in this release now cover a wider set of services. We have introduced service level breakdowns for some measures to provide new information to users, but also, importantly, to provide comparability with key measures that were part of the previous monthly release. This release of final data for March 2016 comprises: - An Executive Summary, which presents national-level analysis across the whole dataset and also for some specific service areas and age groups - Data tables about access and waiting times in mental health services for the based on final data for the period 1 January 2016 to 31 March 2016. In addition to National and Provider level, Clinical Commissioning Group (CCG) level statistics are included in this release for the first time. - A monthly data file which presents 90 measures at National, Provider and Clinical Commissioning Group (CCG) level - A Currency and Payments (CAP) data file, containing three measures relating to people assigned to Adult Mental Health Care Clusters. Further measures will be added in future releases. - Exploratory analysis of the coverage and completeness of information regarding people in contact with perinatal mental health services, and of the use of SNOMED CT within MHSDS. - A set of provider level data quality measures. - A metadata file, which provide contextual information for each measure, including a full description, current uses, method used for analysis and some notes on usage. We will release the reports as experimental statistics until the characteristics of data flowed using the new data standard are understood. A correction has been made to this publication on 10 September 2018. This amendment relates to statistics in the monthly CSV data file; the specific measures effected are listed in the “Corrected Measures” CSV. All listed measures have now been corrected. NHS Digital apologises for any inconvenience caused.

Technical notes and documentation on the common data model of the project CONCEPT-DM2.

This publication corresponds to the Common Data Model (CDM) specification of the CONCEPT-DM2 project for the implementation of a federated network analysis of the healthcare pathway of type 2 diabetes.

Aims of the CONCEPT-DM2 project:

General aim: To analyse chronic care effectiveness and efficiency of care pathways in diabetes, assuming the relevance of care pathways as independent factors of health outcomes using data from real life world (RWD) from five Spanish Regional Health Systems.

Main specific aims:

• To characterize the care pathways in patients with diabetes through the whole care system in terms of process indicators and pharmacologic recommendations
• To compare these observed care pathways with the theoretical clinical pathways derived from the clinical practice guidelines
• To assess if the adherence to clinical guidelines influence on important health outcomes, such as cardiovascular hospitalizations.
• To compare the traditional analytical methods with process mining methods in terms of modeling quality, prediction performance and information provided.

Study Design: It is a population-based retrospective observational study centered on all T2D patients diagnosed in five Regional Health Services within the Spanish National Health Service. We will include all the contacts of these patients with the health services using the electronic medical record systems including Primary Care data, Specialized Care data, Hospitalizations, Urgent Care data, Pharmacy Claims, and also other registers such as the mortality and the population register.

Cohort definition: All patients with code of Type 2 Diabetes in the clinical health records

• Inclusion criteria: patients that, at 01/01/2017 or during the follow-up from 01/01/2017 to 31/12/2022 had active health card (active TIS - tarjeta sanitaria activa) and code of type 2 diabetes (T2D, DM2 in spanish) in the clinical records of primary care (CIAP2 T90 in case of using CIAP code system)
• Exclusion criteria:
  • patients with no contact with the health system from 01/01/2017 to 31/12/2022
  • patients that had a T1D (DM1) code opened after the T2D code during the follow-up.
• Study period. From 01/01/2017 to 31/12/2022

Files included in this publication:

• Datamodel_CONCEPT_DM2_diagram.png
• Common data model specification (Datamodel_CONCEPT_DM2_v.0.1.0.xlsx)
• Synthetic datasets (Datamodel_CONCEPT_DM2_sample_data)
  • sample_data1_dm_patient.csv
  • sample_data2_dm_param.csv
  • sample_data3_dm_patient.csv
  • sample_data4_dm_param.csv
  • sample_data5_dm_patient.csv
  • sample_data6_dm_param.csv
  • sample_data7_dm_param.csv
  • sample_data8_dm_param.csv
• Datamodel_CONCEPT_DM2_explanation.pptx

The global Big Data Analytics in Healthcare market is experiencing robust growth, projected to reach $10.37 billion in 2025 and maintain a Compound Annual Growth Rate (CAGR) of 12.4% from 2025 to 2033. This expansion is driven by several key factors. The increasing volume of healthcare data generated through electronic health records (EHRs), wearable devices, and medical imaging is fueling the demand for sophisticated analytics solutions to improve patient care, operational efficiency, and research outcomes. Furthermore, the rising prevalence of chronic diseases necessitates predictive analytics for early diagnosis and personalized treatment plans, further boosting market growth. Government initiatives promoting the adoption of health information technology and the increasing focus on value-based care models are also significant contributors. Key market segments include software and services, with hospitals and clinics representing the largest application area, followed by finance & insurance agencies and research organizations. Leading players like Cisco, IBM, and McKesson are leveraging their expertise to develop advanced solutions that meet the evolving needs of this dynamic market. The market's segmentation reflects the diverse applications of big data analytics in healthcare. Software solutions offer advanced capabilities for data processing, analysis, and visualization, while service offerings provide consulting, implementation, and support. North America currently holds a significant market share, driven by high technology adoption rates and a strong focus on data-driven healthcare initiatives. However, other regions, particularly Asia Pacific and Europe, are also exhibiting significant growth potential, fueled by expanding healthcare infrastructure and increasing government investments. While the market faces challenges such as data privacy concerns and the need for robust data security measures, the overall trajectory remains positive, indicating considerable opportunities for innovation and expansion in the years to come. The continued convergence of big data, artificial intelligence, and cloud computing will further accelerate market growth, leading to more sophisticated and impactful analytics solutions.

The "COVID-19 Reported Patient Impact and Hospital Capacity by Facility" dataset from the U.S. Department of Health & Human Services, filtered for Connecticut. View the full dataset and detailed metadata here: https://healthdata.gov/Hospital/COVID-19-Reported-Patient-Impact-and-Hospital-Capa/anag-cw7u

The following dataset provides facility-level data for hospital utilization aggregated on a weekly basis (Friday to Thursday). These are derived from reports with facility-level granularity across two main sources: (1) HHS TeleTracking, and (2) reporting provided directly to HHS Protect by state/territorial health departments on behalf of their healthcare facilities.

The hospital population includes all hospitals registered with Centers for Medicare & Medicaid Services (CMS) as of June 1, 2020. It includes non-CMS hospitals that have reported since July 15, 2020. It does not include psychiatric, rehabilitation, Indian Health Service (IHS) facilities, U.S. Department of Veterans Affairs (VA) facilities, Defense Health Agency (DHA) facilities, and religious non-medical facilities.

For a given entry, the term “collection_week” signifies the start of the period that is aggregated. For example, a “collection_week” of 2020-11-20 means the average/sum/coverage of the elements captured from that given facility starting and including Friday, November 20, 2020, and ending and including reports for Thursday, November 26, 2020.

Reported elements include an append of either “_coverage”, “_sum”, or “_avg”.

A “_coverage” append denotes how many times the facility reported that element during that collection week.

A “_sum” append denotes the sum of the reports provided for that facility for that element during that collection week.

A “_avg” append is the average of the reports provided for that facility for that element during that collection week.

The file will be updated weekly. No statistical analysis is applied to impute non-response. For averages, calculations are based on the number of values collected for a given hospital in that collection week. Suppression is applied to the file for sums and averages less than four (4). In these cases, the field will be replaced with “-999,999”.

This data is preliminary and subject to change as more data become available. Data is available starting on July 31, 2020.

Sometimes, reports for a given facility will be provided to both HHS TeleTracking and HHS Protect. When this occurs, to ensure that there are not duplicate reports, deduplication is applied according to prioritization rules within HHS Protect.

For influenza fields listed in the file, the current HHS guidance marks these fields as optional. As a result, coverage of these elements are varied.

On May 3, 2021, the following fields have been added to this data set. hhs_ids previous_day_admission_adult_covid_confirmed_7_day_coverage previous_day_admission_pediatric_covid_confirmed_7_day_coverage previous_day_admission_adult_covid_suspected_7_day_coverage previous_day_admission_pediatric_covid_suspected_7_day_coverage previous_week_personnel_covid_vaccinated_doses_administered_7_day_sum total_personnel_covid_vaccinated_doses_none_7_day_sum total_personnel_covid_vaccinated_doses_one_7_day_sum total_personnel_covid_vaccinated_doses_all_7_day_sum previous_week_patients_covid_vaccinated_doses_one_7_day_sum previous_week_patients_covid_vaccinated_doses_all_7_day_sum

On May 8, 2021, this data set has been converted to a corrected data set. The corrections applied to this data set are to smooth out data anomalies caused by keyed in data errors. To help determine which records have had corrections made to it. An additional Boolean field called is_corrected has been added. To see the numbers as reported by the facilities, go to: https://healthdata.gov/Hospital/COVID-19-Reported-Patient-Impact-and-Hospital-Capa/uqq2-txqb

On May 13, 2021 Changed vaccination fields from sum to max or min fields. This reflects the maximum or minimum number reported for that metric in a given week.

On June 7, 2021 Changed vaccination fields from max or min fields to Wednesday collected fields only. This reflects that these fields are only reported on Wednesdays in a given week.

On 9/20/2021, the following has been updated: The use of analytic dataset as a source.

Snapshot img

Healthcare Information Systems Market Size 2024-2028

The healthcare information systems market size is forecast to increase by USD 126.2 billion at a CAGR of 9.5% between 2023 and 2028.

The market is experiencing significant growth due to the increasing demand for efficient medical care and disease management. Key features of HIS, such as medical device integration and ease of use, are driving this growth. Remote patient monitoring and disease management are becoming increasingly important, enabling healthcare providers to deliver better patient care and financial savings through improved efficiency. However, technical considerations, including data security and privacy, remain challenges that must be addressed to ensure the successful implementation and adoption of HIS. The market is witnessing a high demand for electronic health record (EHR) solutions and an increasing number of mergers and acquisitions. Despite these opportunities, it is crucial for providers to carefully consider the technical aspects of HIS implementation to ensure seamless integration and optimal performance.

What will be the Size of the Market During the Forecast Period?

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The healthcare industry is undergoing a significant transformation, driven by advancements in technology and the increasing demand for efficient, patient-centric care. The market is witnessing substantial growth as healthcare organizations seek to optimize their operations, improve patient outcomes, and reduce costs. Healthcare data management is a critical component of this transformation. The ability to collect, store, and analyze large volumes of patient data is essential for delivering personalized and precise medical care. Healthcare data analytics is playing an increasingly important role in this regard, enabling healthcare providers to gain valuable insights from patient data and make informed decisions.
In addition, another key trend in the market is healthcare data security. With the increasing digitization of healthcare data, ensuring its security and privacy is a top priority. Healthcare organizations are investing in advanced cybersecurity solutions to protect sensitive patient information from cyber threats. Mobile technology is also transforming the healthcare landscape. Mobile health apps, telehealth platforms, and wearable technology are enabling remote patient monitoring, teleconsultations, and other innovative healthcare services. These technologies are improving patient engagement, enhancing the patient experience, and reducing the need for in-person visits. Cloud-based healthcare systems are another area of growth in the market.

How is this market segmented and which is the largest segment?

The market research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

Application

  Revenue cycle management
  Hospital information system
  Medical imaging information system
  Pharmacy information systems
  Laboratory information systems


Geography

  North America

    Canada
    US


  Europe

    Germany
    UK
    France
    Italy


  Asia

    China
    India
    Japan
    South Korea


  Rest of World (ROW)

By Application Insights

The revenue cycle management segment is estimated to witness significant growth during the forecast period.

The healthcare industry's shift towards digitalization is driving the adoption of Healthcare Information Systems (HCIS), particularly in patient engagement and managing patient-related data. Chronic diseases, which account for a significant portion of healthcare expenditures, necessitate effective data management and analysis. HCIS product lines, including hardware and healthcare IT solutions, enable healthcare facilities to streamline operations, reduce costs, and enhance patient care. As the US population ages and the prevalence of chronic diseases increases, the need for advanced healthcare data analytics becomes more critical. HCIS solutions help manage complex billing processes, ensuring accuracy and compliance with regulations such as HIPAA and FDCPA.

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The revenue cycle management segment was valued at USD 81.10 billion in 2018 and showed a gradual increase during the forecast period.

Regional Analysis

North America is estimated to contribute 47% to the growth of the global market during the forecast period.

Technavio's analysts have elaborately explained the regional trends and drivers that shape the market during the forecast period.

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In North America, the market is among the most advanced, driven by substantial investments in healthcare and government initiativ