The global medical database software market is experiencing robust growth, driven by the increasing adoption of electronic health records (EHRs) and the rising need for efficient health information management (HIM) systems. The market, estimated at $15 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 12% from 2025 to 2033, reaching an estimated $45 billion by 2033. This expansion is fueled by several key factors: the increasing digitization of healthcare, the growing demand for data-driven insights to improve patient care and operational efficiency, and the expanding adoption of cloud-based solutions offering scalability and accessibility. Pharmaceutical companies and academic/research institutions are significant drivers, leveraging these systems for drug discovery, clinical trials management, and advanced research initiatives. However, challenges such as data security concerns, high implementation costs, and the need for robust interoperability between different systems pose restraints to market growth. The market is segmented by software type (EHR, HIM) and application (pharmaceutical companies, academic institutions, others), providing diverse opportunities for specialized vendors. Geographic expansion continues, with North America and Europe currently holding significant market share, but growth is anticipated across Asia-Pacific and other regions as healthcare infrastructure modernizes. The competitive landscape is dynamic, with established players like NextGen Healthcare and emerging companies like Pabau and EHR Your Way vying for market share. The success of individual vendors depends on factors including the scalability of their solutions, the depth of their data analytics capabilities, and the strength of their customer support network. The market's trajectory is heavily influenced by government regulations regarding data privacy and interoperability, the ongoing evolution of healthcare technology, and the increasing focus on personalized medicine. Further growth is likely to be seen in areas such as AI-powered diagnostics, predictive analytics, and advanced data visualization tools integrated within medical databases.

This project used national databases to describe the incidence and distribution of fatal and nonfatal police shootings and to develop an empirically based typology of legal intervention homicides. To accomplish this, the study team evaluated the comprehensiveness of the National Violent Death Reporting System (NVDRS) for fatal police shootings along with various open-source databases. The study team also explained the variation across states in fatal police shootings using a validated national database (Washington Post "Fatal Force Database") and is currently examining the variation in fatal police shooting across urban vs. rural areas.

AHRQ's database on Social Determinants of Health (SDOH) was created under a project funded by the Patient Centered Outcomes Research (PCOR) Trust Fund. The purpose of this project is to create easy to use, easily linkable SDOH-focused data to use in PCOR research, inform approaches to address emerging health issues, and ultimately contribute to improved health outcomes.The database was developed to make it easier to find a range of well documented, readily linkable SDOH variables across domains without having to access multiple source files, facilitating SDOH research and analysis.Variables in the files correspond to five key SDOH domains: social context (e.g., age, race/ethnicity, veteran status), economic context (e.g., income, unemployment rate), education, physical infrastructure (e.g, housing, crime, transportation), and healthcare context (e.g., health insurance). The files can be linked to other data by geography (county, ZIP Code, and census tract). The database includes data files and codebooks by year at three levels of geography, as well as a documentation file.The data contained in the SDOH database are drawn from multiple sources and variables may have differing availability, patterns of missing, and methodological considerations across sources, geographies, and years. Users should refer to the data source documentation and codebooks, as well as the original data sources, to help identify these patterns

The Washington State Department of Health presents this information as a service to the public. This includes information on the work status, practice characteristics, education, and demographics of healthcare providers, provided in response to the Washington Health Workforce Survey.

This is a complete set of data across all of the responding professions. The data dictionary identifies questions that are specific to an individual profession and aren't common to all surveys. The dataset is provided without identifying information for the responding providers.

More information on the Washington Health Workforce Survey can be found at www.doh.wa.gov/workforcesurvey

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

Health, United States is the report on the health status of the country. Every year, the report presents an overview of national health trends organized around four subject areas: health status and determinants, utilization of health resources, health care resources, and health care expenditures and payers.

ONC uses the SK&A Office-based Provider Database to calculate the counts of medical doctors, doctors of osteopathy, nurse practitioners, and physician assistants at the state and count level from 2011 through 2013. These counts are grouped as a total, as well as segmented by each provider type and separately as counts of primary care providers.

The National Health Data System (SNDS) will make it possible to link:

• health insurance data (SNIIRAM database);
• hospital data (PMSI database);
• the medical causes of death (base of the CépiDC of Inserm);
• disability-related data (from MDPH - CNSA data);
• a sample of data from complementary health insurance organisations.

The first two categories of data are already available and constitute the first version of the SNDS. The medical causes of death should feed the SNDS from the second half of 2017. The first data from the CNSA will arrive from 2018 and the sample of complementary organizations in 2019.

The purpose of the SNDS is to make these data available in order to promote studies, research or evaluations of a nature in the public interest and contributing to one of the following purposes:

• health information;
• the implementation of health policies;
• knowledge of health expenditure;
• informing professionals and establishments about their activities;
• innovation in the fields of health and medico-social care;
• monitoring, surveillance and health security.

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.

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The National Patient Care Database (NPCD), located at the Austin Information Technology Center, is part of the National Medical Information Systems (NMIS). The NPCD collects integrated patient care data from all Veterans Health Information Systems and Technology Architecture (VistA) IT systems. Data recorded in the VistA Patient Care Encounter (PCE) package, which captures clinical data resulting from ambulatory care patient encounters is transmitted to the NPCD using the Ambulatory Care Reporting (ACR) Module of the VistA Patient Information Management System (PIMS) package. The Ambulatory Care Reporting Module provides necessary information on patient treatment, what services were rendered to patients, who provided the services, and whether services reported were synchronized with the VA medical center database. Directive 2006-026 (05/05/2006) required the inclusion to patient care data capture requirements the capture of inpatient encounters for patients seen in outpatient clinics and inpatient billable professional services.Additionally, NPCD includes VistA Spinal Cord Dysfunction (SCD) package and Primary Care Management Module (PCMM) data. The SCD central registry in NPCD is used to provide VA-wide review of patient demographics, clinical aspects of injury and disease, and resource utilization involved in providing care to patients. As of October 2010, data for the Spinal Cord Dysfunction is being maintained in the Spinal Cord Injury and Disorders Outcomes (SCIDO) database; current SCD data in NPCD is residual data only. The data load and extraction process for SCD data in NPCD will be discontinued in FY12. The PCMM data in NPCD includes primary care patient to provider assignments and provider utilization data.The NPCD is used by Veterans Health Administration (VHA) program offices for a wide variety of tasks to include research and budget allocation to medical centers.

The National Patient Care Database (NPCD), located at the Austin Information Technology Center, is part of the National Medical Information Systems (NMIS). The NPCD collects integrated patient care data from all Veterans Health Information Systems and Technology Architecture (VistA) IT systems. Data recorded in the VistA Patient Care Encounter (PCE) package, which captures clinical data resulting from ambulatory care patient encounters is transmitted to the NPCD using the Ambulatory Care Reporting (ACR) Module of the VistA Patient Information Management System (PIMS) package. The Ambulatory Care Reporting Module provides necessary information on patient treatment, what services were rendered to patients, who provided the services, and whether services reported were synchronized with the VA medical center database. Directive 2006-026 (05/05/2006) required the inclusion to patient care data capture requirements the capture of inpatient encounters for patients seen in outpatient clinics and inpatient billable professional services.Additionally, NPCD includes VistA Spinal Cord Dysfunction (SCD) package and Primary Care Management Module (PCMM) data. The SCD central registry in NPCD is used to provide VA-wide review of patient demographics, clinical aspects of injury and disease, and resource utilization involved in providing care to patients. As of October 2010, data for the Spinal Cord Dysfunction is being maintained in the Spinal Cord Injury and Disorders Outcomes (SCIDO) database; current SCD data in NPCD is residual data only. The data load and extraction process for SCD data in NPCD will be discontinued in FY12. The PCMM data in NPCD includes primary care patient to provider assignments and provider utilization data.The NPCD is used by Veterans Health Administration (VHA) program offices for a wide variety of tasks to include research and budget allocation to medical centers.

This dataset contains statistically weighted estimates of initial education levels, highest education levels, and initial education locations for 43 key health workforce professions actively licensed in California as of July 1st, 2023. These metrics can be compared by workforce category, license type, time since license issue date (in years), race & ethnicity group, assigned sex at birth, and CHIS region.

Health Data Archiving Market size is expected to be worth around USD 3.4 Billion by 2033 from USD 1.4 Billion in 2023

Imputed employer-sponsored health insurance coverage data which when linked to the March Annual Social and Economic Supplement to the Current Population Survey (March CPS), generates estimates of the number of individuals with different types of insurance coverage.

The Big Data in Healthcare Market Report is Segmented by Component (Software, Services), Deployment (On-Premise, Cloud), Analytics Type (Descriptive Analytics, Predictive Analytics, Prescriptive Analytics), Application (Financial Analytics, and More), End User (Healthcare Providers, and More), and Geography (North America, Europe, Asia-Pacific, and More). The Market Forecasts are Provided in Terms of Value (USD).

De-identified Health Data Market holds a forecasted revenue of US$ 8.21 Bn in 2025 and is likely to cross US$ 15.31 Bn by 2032.

Databases searched.

There is an increasing prevalence of cancer in Africa with approximately 80% of cancers diagnosed at an advanced stage. High out-of-pocket healthcare costs and overstretched health systems lead to heavy reliance on informal carers for cancer care. This study aims to explore the roles and experiences of informal carers including the impact of cancer care on individuals and communities and support available for carers. We carried out a systematic review following PRISMA reporting guidelines and used critical interpretative synthesis to identify themes and develop an informal carers’ experience framework. We searched nine databases and screened 8,123 articles from which 31 studies were included in the review. Most studies were from Sub-Saharan Africa (29/31, 94%), particularly Uganda (9, 29%). Carers were mostly women, aged 30–40 years, and siblings, spouses, or children. Caring roles included care coordination, fundraising, and emotional support. Caring was time-consuming with some carers reporting 121 hours/week of caring, associated with the inability to pursue paid work and depression. Four themes demonstrated carers’ experiences: 1) intrapersonal factors: strong sense of familial obligation, and grappling with gender roles, 2) interpersonal factors: impact of a cancer diagnosis on households, changing social and sexual relationships, 3) community factors: navigating cultural norms on nature and location of care, and 4) health system influences: barriers to accessing healthcare services, and tensions between traditional and biomedical medicine. These themes aligned with Bronfenbrenner’s social ecological model which aided our development of a framework for understanding informal carers’ experiences’. Our review highlights multifaceted roles and experiences of informal carers in Africa, amidst cultural and community impacts. Carers experience a strong obligation and willingly undertake the role of carer, but at the expense of their social, economic, and psychological wellbeing. Support for carers, including flexible working hours/ carers’ allowance, should be incorporated as part of universal health coverage.

A 10,000-patient database that contains in total 10,000 virtual patients, 36,143 admissions, and 10,726,505 lab observations.

Introduction: The objective of this study was to evaluate the complementarity of the French national health database (Système national des données de Santé, SNDS) and the Dijon Stroke Registry for the epidemiology of stroke patients with anticoagulated atrial fibrillation (AF). Methods: The SNDS collects healthcare prescriptions and procedures reimbursed by the French national health insurance for almost all of the 66 million individuals living in France. A previously published algorithm was used to identify AF newly treated with oral anticoagulants. The Dijon Stroke Registry is a population-based study covering the residents of the city of Dijon since 1985 and records all stroke cases of the area. We compared the proportions of stroke patients with anticoagulated AF in the city of Dijon identified in SNDS databases to those registered in the Dijon Stroke Registry. Results: For the period 2013–2017 in the city of Dijon, 1,146 strokes were identified in the SNDS and 1,188 in the registry. The proportion of strokes with anticoagulated AF was 13.4% in the SNDS and 20.3% in the Dijon Stroke Registry. Very similar characteristics were found between patients identified through the 2 databases. The overall prevalence of AF in stroke patients could be estimated only in the Dijon stroke registry and was 30.4% for the study period. Discussion/Conclusion: If administrative health databases can be a useful tool to study the epidemiology of anticoagulated AF in stroke patients, population-based stroke registries as the Dijon Stroke Registry remain essential to fully study the epidemiology of strokes with anticoagulated AF.

These data are modelled using the OMOP Common Data Model v5.3.Correlated Data SourceNG tube vocabulariesGeneration RulesThe patient’s age should be between 18 and 100 at the moment of the visit.Ethnicity data is using 2021 census data in England and Wales (Census in England and Wales 2021) .Gender is equally distributed between Male and Female (50% each).Every person in the record has a link in procedure_occurrence with the concept “Checking the position of nasogastric tube using X-ray”2% of person records have a link in procedure_occurrence with the concept of “Plain chest X-ray”60% of visit_occurrence has visit concept “Inpatient Visit”, while 40% have “Emergency Room Visit”NotesVersion 0Generated by man-made rule/story generatorStructural correct, all tables linked with the relationshipWe used national ethnicity data to generate a realistic distribution (see below)2011 Race Census figure in England and WalesEthnic Group : Population(%)Asian or Asian British: Bangladeshi - 1.1Asian or Asian British: Chinese - 0.7Asian or Asian British: Indian - 3.1Asian or Asian British: Pakistani - 2.7Asian or Asian British: any other Asian background -1.6Black or African or Caribbean or Black British: African - 2.5Black or African or Caribbean or Black British: Caribbean - 1Black or African or Caribbean or Black British: other Black or African or Caribbean background - 0.5Mixed multiple ethnic groups: White and Asian - 0.8Mixed multiple ethnic groups: White and Black African - 0.4Mixed multiple ethnic groups: White and Black Caribbean - 0.9Mixed multiple ethnic groups: any other Mixed or multiple ethnic background - 0.8White: English or Welsh or Scottish or Northern Irish or British - 74.4White: Irish - 0.9White: Gypsy or Irish Traveller - 0.1White: any other White background - 6.4Other ethnic group: any other ethnic group - 1.6Other ethnic group: Arab - 0.6