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

Abstract

The INSPECT dataset (Integrating Numerous Sources for Prognostic Evaluation of Clinical Timelines) contains de-identified longitudinal electronic health records (EHRs) from a large cohort of pulmonary embolism (PE) patients, along with ground truth labels for multiple outcomes. It includes 19,390 patients EHRs linked to 23,248 CTPA studies with paired radiology impressions.

Methodology

https://redivis.com/fileUploads/282601b3-2c4b-4de2-a84c-742037a916cd%3E" alt="inspect-logo.png">

1. Overview

INSPECT is a large-scale 3D multimodal medical imaging dataset:

• 19,390 patients
• 23,248 CT scans
• 225+ million clinical events
• 3 linked modalities

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2. CT Scans + Radiology Impression Notes

Imaging data are available for download from the Stanford AIMI Center.

3. EHR Data

EHR data is sourced from Stanford’s STARR-OMOP database. Data are standardized in the OMOP CDM schema and are fully de-identified. Complete technical details are included in the paper, but key highlights:

• Dates are jittered within patient to conceal real dates (but preserve deltas between dates)
• Data for patients %3E= 90 years old are removed
• Data for minors %3C18 are removed
• Unstructured text fields not mappable to OMOP standard concepts are redacted
• All clinical note text is redacted
• HIV test result are redacted.
• Provider names and NPIs are redacted

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Please see our Github repo to obtain code for loading the dataset, including a full data preprocessing pipeline for reproducibility, and running a set of pretrained baseline models

Usage

Access to the INSPECT dataset requires the following:

• Verified Affiliation (Academic, Government, Industry Research Lab). Please use your verified email address when applying, do not use gmail or personal emails.
• Encryption Verification / Attestation for Data Storage
• Signing the terms of the INSPECT Data Set License 1.0
• Providing a short description of your intended research use of INSPECT
• CITI Training

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**These data must remain on your encrypted machine. Redistribution of data is FORBIDDEN and will result in immediate termination of access privileges. **

IMPORTANT NOTES:

• Our policy on derived works aligns with PhysioNet's guidelines, requiring that these artifacts be hosted on Redivis. If you create derived research artifacts based on INSPECT EHR (such as additional annotations or synthetic data), please contact us to discuss hosting arrangements.
• Sending INSPECT data over a non-HIPAA-compliant API is a violation of the DUA.

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Please allow 7-10 business days to process applications.

Citation

@inproceedings{NEURIPS2023_39736af1,

author = {Huang, Shih-Cheng and Huo, Zepeng and Steinberg, Ethan and Chiang, Chia-Chun and Langlotz, Curtis and Lungren, Matthew and Yeung, Serena and Shah, Nigam and Fries, Jason},

booktitle = {Advances in Neural Information Processing Systems},

editor = {A. Oh and T. Naumann and A. Globerson and K. Saenko and M. Hardt and S. Levine},

pages = {17742--17772},

publisher = {Curran Associates, Inc.},

title = {INSPECT: A Multimodal Dataset for Patient Outcome Prediction of Pulmonary Embolisms},

url = {https://proceedings.neurips.cc/paper_files/paper/2023/file/39736af1b9d87a1fddad9f84a6bcf64c-Paper-Datasets_and_Benchmarks.pdf},

volume = {36},

year = {2023}

}

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Electronic Health Records (EHR) Market Outlook

As per our latest research, the global Electronic Health Records (EHR) market size reached USD 34.1 billion in 2024, demonstrating robust expansion driven by the ongoing digitalization of healthcare systems worldwide. The market is projected to grow at a CAGR of 7.8% from 2025 to 2033, reaching an estimated value of USD 67.1 billion by 2033. This growth trajectory is underpinned by increasing governmental mandates for digital record-keeping, rising demand for integrated healthcare solutions, and a surge in telehealth adoption, all of which are accelerating the global shift toward comprehensive EHR implementation.

One of the primary growth drivers for the Electronic Health Records (EHR) market is the intensifying emphasis on improving patient care outcomes and operational efficiency within healthcare systems. Hospitals, clinics, and other healthcare providers are increasingly leveraging EHR systems to streamline workflows, reduce medical errors, and enhance clinical decision-making. The integration of advanced technologies such as artificial intelligence, machine learning, and data analytics into EHR platforms further boosts their utility, enabling predictive analytics and personalized medicine. Additionally, the COVID-19 pandemic has significantly accelerated the adoption of EHRs, as healthcare organizations worldwide sought robust digital infrastructure to manage patient data efficiently and enable remote care delivery. This shift has not only highlighted the importance of EHRs in ensuring continuity of care but also catalyzed innovation in the sector.

Another significant factor propelling the EHR market is the increasing regulatory support and government-led initiatives promoting the digitization of healthcare records. In regions such as North America and Europe, stringent regulations like the Health Information Technology for Economic and Clinical Health (HITECH) Act and the General Data Protection Regulation (GDPR) have mandated the adoption of secure, interoperable EHR systems. Financial incentives, grants, and reimbursement policies have further encouraged healthcare providers to transition from paper-based systems to electronic platforms. Moreover, the growing need for population health management, value-based care, and seamless information exchange across healthcare networks is reinforcing the demand for EHR solutions that can integrate with other health IT systems, such as laboratory information management systems (LIMS) and radiology information systems (RIS).

The rapid advancements in cloud computing, cybersecurity, and mobile health technologies are also playing a pivotal role in shaping the future of the Electronic Health Records (EHR) market. Cloud-based EHR solutions are gaining substantial traction due to their scalability, cost-effectiveness, and ease of deployment, particularly among small and medium-sized healthcare facilities. Enhanced security protocols and compliance frameworks are addressing concerns related to data privacy and cyber threats, fostering greater confidence among stakeholders. Furthermore, the proliferation of mobile devices and telemedicine services is driving the demand for EHR platforms that offer remote access, real-time data synchronization, and patient engagement tools. As digital health ecosystems continue to evolve, EHR vendors are focusing on interoperability, user-friendly interfaces, and integration with wearable devices to deliver comprehensive, patient-centric care.

The concept of E-Health is becoming increasingly integral to the evolution of the Electronic Health Records market. As healthcare systems globally embrace digital transformation, E-Health initiatives are facilitating the integration of electronic health records with other digital health tools, such as telemedicine and mobile health applications. This integration is enhancing patient engagement, enabling remote monitoring, and supporting real-time data exchange between patients and healthcare providers. By leveraging E-Health technologies, healthcare organizations can improve care coordination, reduce costs, and achieve better health outcomes. The synergy between E-Health and EHR systems is driving innovation and creating new opportunities for healthcare providers to deliver more personalized, efficient, and accessible care.

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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.

The global medical database software market is experiencing robust growth, driven by the increasing adoption of electronic health records (EHRs) and health information management (HIM) systems across healthcare providers. The market size in 2025 is estimated at $15 billion, exhibiting a Compound Annual Growth Rate (CAGR) of 12% from 2025 to 2033. This significant expansion is fueled by several key factors. The rising prevalence of chronic diseases necessitates efficient data management for better patient care and research. Furthermore, government initiatives promoting digital healthcare and interoperability are accelerating the adoption of these systems. The shift towards value-based care models requires sophisticated data analytics capabilities offered by medical database software, further boosting market demand. Technological advancements, such as cloud-based solutions and artificial intelligence (AI) integration, are enhancing data security, accessibility, and analytical capabilities, driving market growth. The market segmentation reveals strong growth across both EHR and HIM systems, with EHR systems currently dominating due to broader adoption. Major players like NextGen Healthcare, Epic (implied based on industry knowledge), and Cerner (implied based on industry knowledge) are actively innovating and expanding their market share through strategic partnerships and acquisitions. Regional analysis shows North America currently holding the largest market share, followed by Europe and Asia Pacific, with emerging markets in Asia Pacific expected to demonstrate rapid growth in the coming years. The market is not without its challenges. Data security and privacy concerns remain a significant restraint, necessitating robust security measures and compliance with regulations like HIPAA. High implementation and maintenance costs can hinder adoption, especially for smaller healthcare providers. Integration complexities with existing legacy systems can also pose a challenge. However, the long-term benefits of improved patient care, enhanced operational efficiency, and valuable data-driven insights are likely to outweigh these challenges, ensuring continued market expansion throughout the forecast period. The market is expected to reach approximately $45 billion by 2033, driven by ongoing technological advancements, increasing regulatory pressures for digital health adoption, and a growing need for efficient and secure healthcare data management.

Snapshot img

Electronic Health Records Market Size 2025-2029

The electronic health records market size is forecast to increase by USD 49.41 billion, at a CAGR of 14.8% between 2024 and 2029. Benefits of EHR leading to rise in adoption will drive the electronic health records market.

Major Market Trends & Insights

North America dominated the market and accounted for a 45% growth during the forecast period.
By Deployment - On-premises segment was valued at USD 17.86 billion in 2023
By Component - Services segment accounted for the largest market revenue share in 2023

Market Size & Forecast

Market Opportunities: USD 269.86 billion
Market Future Opportunities: USD 49407.30 billion 
CAGR : 14.8%
North America: Largest market in 2023

Market Summary

The Electronic Health Records (EHR) Market is a dynamic and evolving sector that continues to shape the future of healthcare delivery. Core technologies, such as cloud computing and artificial intelligence, are revolutionizing the way healthcare providers manage patient data, leading to increased adoption rates. According to recent studies, the global EHR market is expected to reach a significant market share by 2026, growing at a steady pace due to the rising demand for self-medication and homecare medical devices. However, this growth is not without challenges. Data security and privacy concerns persist, with cyberattacks and breaches posing a significant threat to patient information.
Despite these challenges, opportunities abound, particularly in the areas of telemedicine and remote patient monitoring. As the market continues to unfold, it is essential to keep abreast of the latest trends and developments. Related markets such as telehealth and health information exchanges also play a crucial role in the EHR landscape.

What will be the Size of the Electronic Health Records Market during the forecast period?

Get Key Insights on Market Forecast (PDF) Request Free Sample

How is the Electronic Health Records Market Segmented and what are the key trends of market segmentation?

The electronic health records 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.

Deployment

  On-premises
  Cloud-based


Component

  Services
  Software
  Hardware


Business

  Licensed Software
  Technology Resale
  Subscriptions
  Professional Services
  Others
  Licensed Software
  Technology Resale
  Subscriptions
  Professional Services
  Others


Type

  Standalone
  Integrated
  Standalone
  Integrated


End-User

  Physician Offices
  Hospitals
  Others
  Physician Offices
  Hospitals
  Others


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    Italy
    Spain
    UK


  APAC

    China
    India
    Japan


  Rest of World (ROW)

By Deployment Insights

The on-premises segment is estimated to witness significant growth during the forecast period.

In the dynamic and evolving landscape of healthcare technology, Electronic Health Records (EHR) continue to play a pivotal role. According to recent reports, over 80% of US hospitals and 60% of physician offices currently use EHR systems, illustrating significant market penetration. Looking ahead, industry forecasts suggest that data security protocols, reporting and analytics, and population health management will drive future growth. Data security is a top priority, with 57% of healthcare organizations investing in advanced security measures. Remote patient monitoring and data interoperability are also gaining traction, with 30% of healthcare providers adopting these technologies. EHR company selection, health information exchange, and telehealth integration are essential components of a comprehensive EHR strategy.

Data governance policies, clinical documentation improvement, API integration, and system scalability are crucial for efficient EHR implementation. Population health management, clinical decision support, and disaster recovery planning are key areas of focus for improving patient care and operational efficiency. On-premise EHR systems offer physical control and long-term cost savings, but integration challenges persist. Approximately 20% of healthcare organizations still use on-premises EHR, citing benefits such as increased control and lower costs. However, these systems often require significant resources for implementation, maintenance, and customization. EHR implementation lifecycle, user access management, and audit trails are essential considerations for organizations implementing EHR systems.

Cloud-based EHR systems offer flexibility and scalability, with 70% of healthcare providers considering a cloud deployment. Data validation rules, patient portal access, and HL7 FHIR standard are ess

Objective: To evaluate the continuity and completeness of electronic health record (EHR) data, and the concordance of select clinical outcomes and baseline comorbidities between EHR and linked claims data, from three healthcare delivery systems in Taiwan.Methods: We identified oral hypoglycemic agent (OHA) users from the Integrated Medical Database of National Taiwan University Hospital (NTUH-iMD), which was linked to the National Health Insurance Research Database (NHIRD), from June 2011 to December 2016. A secondary evaluation involved two additional EHR databases. We created consecutive 90-day periods before and after the first recorded OHA prescription and defined patients as having continuous EHR data if there was at least one encounter or prescription in a 90-day interval. EHR data completeness was measured by dividing the number of encounters in the NTUH-iMD by the number of encounters in the NHIRD. We assessed the concordance between EHR and claims data on three clinical outcomes (cardiovascular events, nephropathy-related events, and heart failure admission). We used individual comorbidities that comprised the Charlson comorbidity index to examine the concordance of select baseline comorbidities between EHRs and claims.Results: We identified 39,268 OHA users in the NTUH-iMD. Thirty-one percent (n = 12,296) of these users contributed to the analysis that examined data continuity during the 6-month baseline and 24-month follow-up period; 31% (n = 3,845) of the 12,296 users had continuous data during this 30-month period and EHR data completeness was 52%. The concordance of major cardiovascular events, nephropathy-related events, and heart failure admission was moderate, with the NTU-iMD capturing 49–55% of the outcome events recorded in the NHIRD. The concordance of comorbidities was considerably different between the NTUH-iMD and NHIRD, with an absolute standardized difference >0.1 for most comorbidities examined. Across the three EHR databases studied, 29–55% of the OHA users had continuous records during the 6-month baseline and 24-month follow-up period.Conclusion: EHR data continuity and data completeness may be suboptimal. A thorough evaluation of data continuity and completeness is recommended before conducting clinical and translational research using EHR data in Taiwan.

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...

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.

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.

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.

Electronic health records (EHRs) are a rich source of information for medical research and public health monitoring. Information systems based on EHR data could also assist in patient care and hospital management. However, much of the data in EHRs is in the form of unstructured text, which is difficult to process for analysis. Natural language processing (NLP), a form of artificial intelligence, has the potential to enable automatic extraction of information from EHRs and several NLP tools adapted to the style of clinical writing have been developed for English and other major languages. In contrast, the development of NLP tools for less widely spoken languages such as Swedish has lagged behind. A major bottleneck in the development of NLP tools is the restricted access to EHRs due to legitimate patient privacy concerns. To overcome this issue we have generated a citizen science platform for collecting artificial Swedish EHRs with the help of Swedish physicians and medical students. These artificial EHRs describe imagined but plausible emergency care patients in a style that closely resembles EHRs used in emergency departments in Sweden. In the pilot phase, we collected a first batch of 50 artificial EHRs, which has passed review by an experienced Swedish emergency care physician. We make this dataset publicly available as OpenChart-SE corpus (version 1) under an open-source license for the NLP research community. The project is now open for general participation and Swedish physicians and medical students are invited to submit EHRs on the project website (https://github.com/Aitslab/openchart-se), where additional batches of quality-controlled EHRs will be released periodically.

Dataset content

OpenChart-SE, version 1 corpus (txt files and and dataset.csv)

The OpenChart-SE corpus, version 1, contains 50 artificial EHRs (note that the numbering starts with 5 as 1-4 were test cases that were not suitable for publication). The EHRs are available in two formats, structured as a .csv file and as separate textfiles for annotation. Note that flaws in the data were not cleaned up so that it simulates what could be encountered when working with data from different EHR systems. All charts have been checked for medical validity by a resident in Emergency Medicine at a Swedish hospital before publication.

Codebook.xlsx

The codebook contain information about each variable used. It is in XLSForm-format, which can be re-used in several different applications for data collection.

suppl_data_1_openchart-se_form.pdf

OpenChart-SE mock emergency care EHR form.

suppl_data_3_openchart-se_dataexploration.ipynb

This jupyter notebook contains the code and results from the analysis of the OpenChart-SE corpus.

More details about the project and information on the upcoming preprint accompanying the dataset can be found on the project website (https://github.com/Aitslab/openchart-se).

The global medical database software market is experiencing robust growth, driven by the increasing adoption of electronic health records (EHRs), the rising prevalence of chronic diseases, and the expanding demand for efficient healthcare management solutions. The market's value, estimated at $15 billion in 2025, is projected to witness a Compound Annual Growth Rate (CAGR) of 12% from 2025 to 2033, reaching approximately $45 billion by 2033. This expansion is fueled by several key trends, including the increasing integration of artificial intelligence (AI) and machine learning (ML) for improved diagnostics and personalized medicine, the growing adoption of cloud-based solutions for enhanced data accessibility and scalability, and the rising focus on data security and interoperability to comply with stringent regulations like HIPAA. The market is segmented by application (hospital management, clinical research, practice management) and type (cloud-based, on-premise), with cloud-based solutions rapidly gaining traction due to their cost-effectiveness and flexibility. Major players like Pabau, EHR Your Way, NextGen Healthcare, and others are driving innovation and market competition through continuous product development and strategic partnerships. Geographic expansion is another notable market driver, with North America currently holding the largest market share due to advanced healthcare infrastructure and high technological adoption. However, regions like Asia-Pacific are exhibiting rapid growth potential, driven by increasing healthcare expenditure and improving healthcare infrastructure. Despite the positive outlook, market restraints include concerns about data privacy and security, high implementation costs associated with some software solutions, and the need for extensive training for healthcare professionals to effectively use these systems. Furthermore, the heterogeneous nature of existing healthcare IT systems can pose integration challenges. To overcome these obstacles, vendors are focusing on developing user-friendly interfaces, robust security protocols, and cost-effective implementation strategies. The future of the medical database software market hinges on seamless integration, enhanced security features, and the ability to leverage data analytics for improved patient outcomes and operational efficiency.

This statistic displays the results of a survey on data that patients with electronic health records find most helpful to health management in England in 2016. Patients find their prescription medication history most helpful to health management at ** percent, followed by physician notes from visits/conditions at ** percent.

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.

The Medicare & Medicaid Electronic Health Record (EHR) Incentive Programs provide incentives to eligible ambulatory and inpatient providers to adopt electronic health records. This dataset provides the counts of health care providers that have reported a developer's product through participation in the Medicare EHR Incentive Program. The data are provided beginning in 2011. This dataset enables the tracking of trends in the adoption of healthIT by developer and by both office-based health care providers and non-federal acute-care hospitals. Filter the data by Program Year to get the most recent counts by health care provider type. The most recent data is available through the 2016 Program Year.