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

Description: This dataset contains 5 sample PDF Electronic Health Records (EHRs), generated as part of a synthetic healthcare data project. The purpose of this dataset is to assist with sales distribution, offering potential users and stakeholders a glimpse of how synthetic EHRs can look and function. These records have been crafted to mimic realistic admission data while ensuring privacy and compliance with all data protection regulations.

Key Features: 1. Synthetic Data: Entirely artificial data created for testing and demonstration purposes. 1. PDF Format: Records are presented in PDF format, commonly used in healthcare systems. 1. Diverse Use Cases: Useful for evaluating tools related to data parsing, machine learning in healthcare, or EHR management systems. 1. Rich Admission Details: Includes admission-related data that highlights the capabilities of synthetic EHR generation.

Potential Use Cases:

• Demonstrating EHR-related tools or services.
• Benchmarking data parsing models for PDF health records.
• Showcasing synthetic healthcare data in sales or marketing efforts.

Feel free to use this dataset for non-commercial testing and demonstration purposes. Feedback and suggestions for improvements are always welcome!

Get premium quality off-the-shelf EHR dataset to develop better performing machine learning models. Speak to our experts for Electronic Health Records data needs.

Electronic health records (EHR) are expected to boost the market value of smart hospitals. In 2021, the global smart hospital market was valued at **** billion U.S. dollars, from which *** billion were linked to EHR and the consequent clinical workflow. According to future estimations, this market was forecast to increase in value and reach nearly ** billion U.S. dollars in 2026. The use of electronic health records in hospitals EHR systems improve the quality and efficiency of healthcare delivery and enable patients more autonomy in their treatment. In 2020, over ** percent of surveyed European clinicians used electronic health records in their practice. According to the same survey, in countries such as the Netherlands or Denmark, nearly *** practicians used EHRs. The implementation of these medical records plays a central role in the emergence of smart hospitals. Data privacy and electronic health records Although the global EHR market is projected to steadily increase in the future, EHR use still raises some issues. Indeed, an electronic health record encompasses private information on a patient that can be shared across a range of healthcare settings. Thus, it presents challenges in terms of access control to ensure data privacy and confidentiality. These risks need to be addressed through legal frameworks, optimal access controls, quality training, and standards shared across all EHR users.

Electronic medical and electronic health records vendors (EMR/EHR vendors) provide services while regulatory requirements and persistent technological advancement impact their bottom line and their clients. Federal mandates like the HITECH have boosted adoption rates, making digital recordkeeping ubiquitous. As clients consolidate, so do EMR/HHR providers. The trend toward consolidation has defined much of the last decade, with two companies, Epic Systems Corporation and Oracle, controlling roughly half of the US market, presenting significant barriers to entry for new vendors. Interestingly, the industry has seen a marginal drop in overall revenue, partly resulting from healthcare organizations negotiating lower licensing fees and transitioning to more cost-effective cloud-based systems; nonetheless, profit have climbed. Efficiency gains from large-scale client portfolios, high switching costs and consolidation boost operational leverage for providers. Industry revenue has declined at a CAGR of 0.3% to reach $19.4 billion in 2025, with revenue growing 3.6% in 2025 alone and profit continuing to trend upwards. EMR/EHR platforms embrace advanced technologies (artificial intelligence and wearable integration). The explosion of data from devices like smartwatches, sensors and continuous glucose monitors is reshaping patient management and supporting the shift toward personalized, holistic care. EHRs now aggregate this real-time health data, granting clinicians and patients actionable insight into chronic and acute conditions. As wearables proliferate and consumers and healthcare professionals call for seamless data integration, EHR and EMR systems with AI will remain central to connected care delivery. The market is forecast to strengthen at a CAGR of 4.0% to reach $23.6 billion by 2030, with profit continuing upward. Consolidation and increased concentration provide economies of scale, allowing dominant vendors to spread costs, innovate and improve profit. Switching to a different provider is extremely challenging after a healthcare organization implements an EMR system because of the considerable expenses and complexities associated with migrating data and integrating new systems. These hurdles lock in vendors, resulting in persistent concentration. However, competitive pressure among the large incumbents and niche providers leads to competitive pricing battles and slower profit growth. The push to innovate from healthcare providers will be strong and supported by regulatory actions that require enhanced interoperability and data privacy. Overall, performance hinges on the healthcare industry's financial stability and the benefits of updating and expanding EMR/EHR systems.

Electronic Health Records Market Outlook

According to our latest research, the global Electronic Health Records (EHR) market size stood at USD 34.9 billion in 2024, reflecting robust adoption across healthcare systems worldwide. The market is anticipated to progress at a CAGR of 7.3% from 2025 to 2033, reaching an estimated USD 66.1 billion by 2033. This growth is primarily driven by the increasing demand for digital solutions to streamline healthcare delivery, rising government initiatives for health IT infrastructure, and the expanding need for data-driven patient care management.

One of the central growth factors for the Electronic Health Records market is the global push towards digital transformation in healthcare. As healthcare providers strive to improve patient outcomes and operational efficiency, EHR systems have become indispensable for storing, accessing, and analyzing patient data. The integration of advanced technologies such as artificial intelligence, machine learning, and interoperability standards has further accelerated EHR adoption. Governments in developed economies continue to mandate EHR usage, incentivizing providers through funding and regulatory frameworks, which in turn boosts the marketÂ’s expansion. Moreover, the COVID-19 pandemic underscored the importance of accessible digital records, further reinforcing the necessity of robust EHR systems.

Another significant driver of the EHR market is the increasing prevalence of chronic diseases and the aging global population. As the number of patients requiring long-term and coordinated care rises, healthcare providers are leveraging EHR solutions to enhance care coordination, reduce medical errors, and ensure continuity of care. The ability to share patient information seamlessly across different care settings is especially vital for managing complex cases. Additionally, the growing focus on value-based care and patient-centric models has led to higher investments in EHR platforms, which facilitate comprehensive data analytics, population health management, and personalized treatment plans.

Furthermore, the rapid proliferation of cloud computing and mobile health technologies is reshaping the Electronic Health Records market. Cloud-based EHR solutions offer scalability, cost-effectiveness, and remote accessibility, making them particularly attractive to small and medium-sized healthcare providers. These solutions enable real-time data sharing, telemedicine integration, and disaster recovery capabilities, all of which are crucial in todayÂ’s dynamic healthcare landscape. The shift towards interoperable and user-friendly EHR platforms is also fostering innovation, with vendors introducing customizable solutions tailored to the unique needs of various healthcare settings.

Regionally, North America continues to dominate the Electronic Health Records market, accounting for the largest share in 2024 due to the presence of advanced healthcare infrastructure, favorable government policies, and high EHR adoption rates. However, the Asia Pacific region is poised for the fastest growth, driven by rapid digitalization, increasing healthcare investments, and supportive regulatory initiatives. Europe follows closely, with strong emphasis on data privacy and cross-border health data exchange. Emerging markets in Latin America and the Middle East & Africa are also witnessing increased EHR adoption, albeit at a slower pace due to infrastructural and regulatory challenges.

Electronic Medical Records (EMRs) have become a cornerstone of modern healthcare, offering a digital alternative to traditional paper records. These systems are designed to store comprehensive patient information, including medical history, diagnoses, medications, treatment plans, immunization dates, allergies, radiology images, and laboratory test results. The transition to EMRs has facilitated improved patient care by enabling quick access to patient records, reducing the risk of errors, and enhancing the ability to coordinate care across different healthcare providers. Moreover, EMRs support healthcare providers in making informed decisions by providing access to a patient's complete medical history, which is crucial for accurate diagnosis and effective treatment planning.

The openEHR specifications are designed to support implementation of flexible and interoperable Electronic Health Record (EHR) systems. Despite the increasing number of solutions based on the openEHR specifications, it is difficult to find publicly available healthcare datasets in the openEHR format that can be used to test, compare and validate different data persistence mechanisms for openEHR. To foster research on openEHR servers, we present the openEHR Benchmark Dataset, ORBDA, a very large healthcare benchmark dataset encoded using the openEHR formalism. To construct ORBDA, we extracted and cleaned a de-identified dataset from the Brazilian National Healthcare System (SUS) containing hospitalisation and high complexity procedures information and formalised it using a set of openEHR archetypes and templates. Then, we implemented a tool to enrich the raw relational data and convert it into the openEHR model using the openEHR Java reference model library. The ORBDA dataset is available in composition, versioned composition and EHR openEHR representations in XML and JSON formats. In total, the dataset contains more than 150 million composition records. We describe the dataset and provide means to access it. Additionally, we demonstrate the usage of ORBDA for evaluating inserting throughput and query latency performances of some NoSQL database management systems. We believe that ORBDA is a valuable asset for assessing storage models for openEHR-based information systems during the software engineering process. It may also be a suitable component in future standardised benchmarking of available openEHR storage platforms.

Discover the booming medical database software market! Learn about its $15 billion valuation in 2025, projected 12% CAGR to 2033, key drivers, regional trends, and leading companies. Explore EHR, HIM systems impacting healthcare.

We conducted our experiments on de-identified EHR data from MIMIC-III. This data set contains various clinical data relating to patient admission to ICU, such as disease diagnoses in the form of International Classification of Diseases (ICD)-9 codes, and lab test results as detailed in Supplementary Materials. We collected data for 5,956 patients, extracting lab tests every hour from admission. There are a total of 409 unique lab tests and 3,387 unique disease diagnoses observed. The diagnoses were obtained as ICD-9 codes and they were represented using one-hot encoding where one represents patients with disease and zero indicates those without. We binned the lab test events into 6, 12, 24, and 48 hours prior to patient death or discharge from ICU. From these data, we performed mortality predictions that are 10-fold, cross validated.

Discover the booming medical database software market, projected to reach $45 billion by 2033, with a CAGR of 12%. This analysis explores key drivers, trends, restraints, and regional insights for EHR and HIM systems, featuring leading companies like NextGen and Epic. Learn more about this rapidly evolving sector.

Electronic Health Records (EHRs) are integral for storing comprehensive patient medical records, combining structured data (e.g., medications) with detailed clinical notes (e.g., physician notes). These elements are essential for straightforward data retrieval and provide deep, contextual insights into patient care. However, they often suffer from discrepancies due to unintuitive EHR system designs and human errors, posing serious risks to patient safety. To address this, we developed EHRCon, a new dataset and task specifically designed to ensure data consistency between structured tables and unstructured notes in EHRs. EHRCon was crafted in collaboration with healthcare professionals using the MIMIC-III EHR dataset, and includes manual annotations of 4,101 entities across 105 clinical notes checked against database entries for consistency. EHRCon has two versions, one using the original MIMIC-III schema, and another using the OMOP CDM schema, in order to increase its applicability and generalizability.

Introduction

EHR Industry Statistics: Electronic Health Records (EHRs) are digital versions of patient paper charts, revolutionizing healthcare by providing instant, secure access to comprehensive medical information.

They include details like medical history, diagnoses, medications, and test results, consolidating data from various sources into one accessible record.

EHRs enhance patient care by supporting better coordination among healthcare providers, improving efficiency through reduced paperwork, and enabling patient engagement via access to their records.

Challenges include high implementation costs, interoperability issues between different systems, and concerns about data privacy.

Looking ahead, advancements aim to improve interoperability, enhance data analytics, and integrate with telemedicine for more efficient and personalized healthcare delivery.

Global electronic health records market size was valued $37.92 Billion in 2024 and is predicted to $56.97 Billion by 2034, a CAGR of 4.3% between 2025 and 2034.

As of 2024, all 27 EU countries made identification and personal information available in electronic health record summary data, the highest across all categories. Meanwhile, medical devices and implants were the least commonly available category, recorded in only ** countries.

The dataset is Electronic Health Record Predicting collected from a private Hospital in Indonesia. It contains the patients laboratory test results used to determine next patient treatment whether in care or out care patient. The task embedded to the dataset is classification prediction.

Objective: To develop a clinical informatics pipeline designed to capture large-scale structured EHR data for a national patient registry.

Materials and Methods: The EHR-R-REDCap pipeline is implemented using R-statistical software to remap and import structured EHR data into the REDCap-based multi-institutional Merkel Cell Carcinoma (MCC) Patient Registry using an adaptable data dictionary.

Results: Clinical laboratory data were extracted from EPIC Clarity across several participating institutions. Labs were transformed, remapped and imported into the MCC registry using the EHR labs abstraction (eLAB) pipeline. Forty-nine clinical tests encompassing 482,450 results were imported into the registry for 1,109 enrolled MCC patients. Data-quality assessment revealed highly accurate, valid labs. Univariate modeling was performed for labs at baseline on overall survival (N=176) using this clinical informatics pipeline.

Conclusion: We demonstrate feasibility of the facile eLAB workflow. EHR data is successfully transformed, and bulk-loaded/imported into a REDCap-based national registry to execute real-world data analysis and interoperability.

Methods eLAB Development and Source Code (R statistical software):

eLAB is written in R (version 4.0.3), and utilizes the following packages for processing: DescTools, REDCapR, reshape2, splitstackshape, readxl, survival, survminer, and tidyverse. Source code for eLAB can be downloaded directly (https://github.com/TheMillerLab/eLAB).

eLAB reformats EHR data abstracted for an identified population of patients (e.g. medical record numbers (MRN)/name list) under an Institutional Review Board (IRB)-approved protocol. The MCCPR does not host MRNs/names and eLAB converts these to MCCPR assigned record identification numbers (record_id) before import for de-identification.

Functions were written to remap EHR bulk lab data pulls/queries from several sources including Clarity/Crystal reports or institutional EDW including Research Patient Data Registry (RPDR) at MGB. The input, a csv/delimited file of labs for user-defined patients, may vary. Thus, users may need to adapt the initial data wrangling script based on the data input format. However, the downstream transformation, code-lab lookup tables, outcomes analysis, and LOINC remapping are standard for use with the provided REDCap Data Dictionary, DataDictionary_eLAB.csv. The available R-markdown ((https://github.com/TheMillerLab/eLAB) provides suggestions and instructions on where or when upfront script modifications may be necessary to accommodate input variability.

The eLAB pipeline takes several inputs. For example, the input for use with the ‘ehr_format(dt)’ single-line command is non-tabular data assigned as R object ‘dt’ with 4 columns: 1) Patient Name (MRN), 2) Collection Date, 3) Collection Time, and 4) Lab Results wherein several lab panels are in one data frame cell. A mock dataset in this ‘untidy-format’ is provided for demonstration purposes (https://github.com/TheMillerLab/eLAB).

Bulk lab data pulls often result in subtypes of the same lab. For example, potassium labs are reported as “Potassium,” “Potassium-External,” “Potassium(POC),” “Potassium,whole-bld,” “Potassium-Level-External,” “Potassium,venous,” and “Potassium-whole-bld/plasma.” eLAB utilizes a key-value lookup table with ~300 lab subtypes for remapping labs to the Data Dictionary (DD) code. eLAB reformats/accepts only those lab units pre-defined by the registry DD. The lab lookup table is provided for direct use or may be re-configured/updated to meet end-user specifications. eLAB is designed to remap, transform, and filter/adjust value units of semi-structured/structured bulk laboratory values data pulls from the EHR to align with the pre-defined code of the DD.

Data Dictionary (DD)

EHR clinical laboratory data is captured in REDCap using the ‘Labs’ repeating instrument (Supplemental Figures 1-2). The DD is provided for use by researchers at REDCap-participating institutions and is optimized to accommodate the same lab-type captured more than once on the same day for the same patient. The instrument captures 35 clinical lab types. The DD serves several major purposes in the eLAB pipeline. First, it defines every lab type of interest and associated lab unit of interest with a set field/variable name. It also restricts/defines the type of data allowed for entry for each data field, such as a string or numerics. The DD is uploaded into REDCap by every participating site/collaborator and ensures each site collects and codes the data the same way. Automation pipelines, such as eLAB, are designed to remap/clean and reformat data/units utilizing key-value look-up tables that filter and select only the labs/units of interest. eLAB ensures the data pulled from the EHR contains the correct unit and format pre-configured by the DD. The use of the same DD at every participating site ensures that the data field code, format, and relationships in the database are uniform across each site to allow for the simple aggregation of the multi-site data. For example, since every site in the MCCPR uses the same DD, aggregation is efficient and different site csv files are simply combined.

Study Cohort

This study was approved by the MGB IRB. Search of the EHR was performed to identify patients diagnosed with MCC between 1975-2021 (N=1,109) for inclusion in the MCCPR. Subjects diagnosed with primary cutaneous MCC between 2016-2019 (N= 176) were included in the test cohort for exploratory studies of lab result associations with overall survival (OS) using eLAB.

Statistical Analysis

OS is defined as the time from date of MCC diagnosis to date of death. Data was censored at the date of the last follow-up visit if no death event occurred. Univariable Cox proportional hazard modeling was performed among all lab predictors. Due to the hypothesis-generating nature of the work, p-values were exploratory and Bonferroni corrections were not applied.

Discover the booming medical database software market! This comprehensive analysis reveals a $15 billion market projected to reach $45 billion by 2033, driven by EHR adoption, AI integration, and cloud solutions. Explore key trends, leading companies, and regional growth opportunities.

Secure patient health records and medical data management

The Medicare Electronic Health Record (EHR) Incentive Program provides incentives to eligible clinicians and hospitals to adopt electronic health records. This dataset combines meaningful use attestations from the Medicare EHR Incentive Program and certified health IT product data from the ONC Certified Health IT Product List (CHPL) to identify the unique vendors, products, and product types of each certified health IT product used to attest to meaningful use. The dataset also includes important provider-specific data, related to the provider's participation and status in the program, unique provider identifiers, and other characteristics unique to each provider, like geography and provider type.

According to cognitive market research, the global electronic health records market size was valued at USD xx billion in 2024 and is expected to reach USD xx billion at a CAGR of xx% during the forecast period.

An electronic health record (EHR), or electronic medical record (EMR), is the systematized collection of patient and population electronically stored health information in a digital format.
The cloud-based EHR segment led the market and accounted for more than xx% share of the global revenue in 2024.
Based on end-use, the market is classified into hospitals and ambulatory care. The hospitals segment held the largest market share in 2024.
The market was substantially driven by the integration of artificial intelligence in electronic health record solutions.
Medicare incentive payment system (IPPS) is available to acute care hospitals in the US that are covered by the Inpatient Prospective Payment System.
Healthcare professionals' use of EHRs is being driven by the need for contemporary healthcare facilities.
Globally, North America is estimated to hold the highest global Electronic Health Records market share.

Market Dynamics of the Electronic Health Records Market

Key Drivers of the Electronic Health Records Market

Increasing popularity of digital health applications to boost market growth

Electronic health records have demonstrated their efficacy in managing data and maintaining population health throughout the global COVID-19 pandemic. The worldwide electronic health record industry is seeing daily growth in EHR service providers due to increased product research and development, particularly in the area of cloud storage technologies. varying degrees of software development and technology improvement in the healthcare industry. Furthermore, the market for electronic health records will expand due to the advent of artificial intelligence. Healthcare professionals' use of EHRs is being driven by the need for contemporary healthcare facilities. Among the fundamental components of an EHR are clinical record systems, lab, radiography, pharmacy, administrative duties, and computerized physician order entry.

For instance, In May 2022, CPSI entered into a partnership agreement with Medicomp Systems to launch Quippe Clinical Lens. The new technology aims to empower EHR users with proper access to clinical information at PoC (Source:https://www.businesswire.com/news/home/20220519005390/en/CPSI-Pilots-Clinical-Lens-to-Ease-Provider-Data-Burdens )

Government incentives propelling the adoption of EHR systems across healthcare facilities

Several governments throughout the world offer incentives to healthcare providers that implement EHR systems. Throughout the forecast period, financial incentives from governments are anticipated to propel the global market for electronic health records. Through the Health Information Technology for Economic and Clinical Health (HITECH) Act of 2009, the US federal government promotes the widespread implementation of electronic health records (EHRs). CMS created the Medicare and Medicaid EHR incentive programs in 2011 to incentivize general practitioners (GPs), qualified hospitals, and physician offices/clinics to adopt, install, update, and demonstrate meaningful use of certified electronic health record technology (CEHRT). These initiatives are now known as the Medicare Interoperability Promotion Programme. The UK's Department of Health (DoH) has allotted over GBP 2 billion in funding as part of the NHS Digitization plan to support electronic patient records in all NHS trusts and assist over 500,000 individuals in using digital tools to manage their own homes by 2022.

For instance, in 2021, the Government of India launched a digital health initiative scheme called Ayushman Bharat Digital Mission (ABDM) that aims to provide easy access to treatment records, thereby enabling faster and more effective treatment for patients. (Source:https://www.india.gov.in/spotlight/ayushman-bharat-digital-mission-abdm )

Restraints of the Electronic Health Records Market

Critical security concerns to hinder market growth

Hackers can target any hardware or software-driven system. EHR systems are not impervious to data risks or cyberattacks, either. Targeting specific data sectors might result in patient privacy breaches since healthcare systems worldwide view patient healthcare information as one of their most vital as...