The MZG is a registration with which all non-psychiatric hospitals in Belgium must make their (anonymised) administrative, medical and nursing data available to the Federal Public Service (FPS) Public Health. The aim of the MZG is to support the government's health policy by

• Determining the needs for hospital facilities;
• Describing the qualitative and quantitative accreditation standards of hospitals and their services;
• Organising the financing of hospitals;
• Determining policy for the practice of medicine;
• To outline epidemiological policy.

The MZG aims also to support the health policy of hospitals by providing national and individual feedback so that a hospital can compare itself with other hospitals and adapt its internal policy.

All reports can be found here (in French/Dutch).

This page shares the technical validation datasets used to evaluate a Large Dataset of Annotated Incident Reports on Medication Errors and its machine annotator. The files contain in this repository include the IFMIR gold standard dataset (CrossValid_IFMIR_522.xlsx), randomly sampled labeled incident reports from 2010 – 2020 (InternalValid_JQ2010-20_40.xlsx), randomly sampled labeled incident reports from 2021 (ExternalValid_JQ2021_20.xlsx) and Error-free reports (Error_analysis.xlsx).

To use any of these datasets, one should also cite this original data source: Medical Adverse Event Information Collection Project [Iryō jiko jōhō shūshū-tō jigyō]　 Japan Council for Quality Health Care; 2022 [Available from: https://www.med-safe.jp/index.html.]

Electronic Health Legal Data

Exploring Laws and Regulations

By US Open Data Portal, data.gov [source]

About this dataset

This Electronic Health Information Legal Epidemiology dataset offers an extensive collection of legal and epidemiological data that can be used to understand the complexities of electronic health information. It contains a detailed balance of variables, including legal requirements, enforcement mechanisms, proprietary tools, access restrictions, privacy and security implications, data rights and responsibilities, user accounts and authentication systems. This powerful set provides researchers with real-world insights into the functioning of EHI law in order to assess its impact on patient safety and public health outcomes. With such data it is possible to gain a better understanding of current policies regarding the regulation of electronic health information as well as their potential for improvement in safeguarding patient confidentiality. Use this dataset to explore how these laws impact our healthcare system by exploring patterns across different groups over time or analyze changes leading up to new versions or updates. Make exciting discoveries with this comprehensive dataset!

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

• Start by familiarizing yourself with the different columns of the dataset. Examine each column closely and look up any unfamiliar terminology to get a better understanding of what the columns are referencing.

• Once you understand the data and what it is intended to represent, think about how you might want to use it in your analysis. You may want to create a research question, or narrower focus for your project surrounding legal epidemiology of electronic health information that can be answered with this data set.

• After creating your research plan, begin manipulating and cleaning up the data as needed in order to prepare it for analysis or visualization as specified in your project plan or research question/model design steps you have outlined .

4 .Next, perform exploratory data analysis (EDA) on relevant subsets of data from specific countries if needed on specific subsets based on targets of interests (e.g gender). Filter out irrelevant information necessary for drawing meaningful insights; analyze patterns and trends observed in your filtered datasets ; compare areas which have differing rates e-health related rules and regulations tying decisions made by elected officials strongly driven by demographics , socioeconomics factors ,ideology etc.. . Look out for correlations using statistical information as needed throughout all stages in process from filtering out dis-informative subgroups from full population set til generating visualizations(graphs/ diagrams) depicting valid insight leveraging descriptive / predictive models properly validate against reference datasets when available always keep openness principal during gathering info especially when needs requires contact external sources such validating multiple sources work best provide strong seals establishing validity accuracy facts statement representing humans case scenarios digital support suitably localized supporting local languages culture respectively while keeping secure datasets private visible limited particular users duly authorized access 5 Finally create concrete summaries reporting discoveries create share findings preferably infographics showcasing evidence observances providing overall assessment main conclusions protocols developed so far broader community indirectly related interested professionals able benefit those results ideas complete transparently freely adapted locally ported increase overall global society level enhancing potentiality range impact derive conditions allowing wider adoption increased usage diffusion capture wide spread change movement affect global e-health legal domain clear manner

Research Ideas

• Studying how technology affects public health policies and practice - Using the data, researchers can look at the various types of legal regulations related to electronic health information to examine any relations between technology and public health decisions in certain areas or regions.
• Evaluating trends in legal epidemiology – With this data, policymakers can identify patterns that help measure the evolution of electronic health information regulations over time and investigate why such rules are changing within different states or countries.
• Analysing possible impacts on healthcare costs – Looking at changes in laws, regulations, and standards relate...

Evaluation of data quality in large healthcare datasets.

abstract: Data quality and fitness for analysis are crucial if outputs of big data analyses should be trusted by the public and the research community. Here we analyze the output from a data quality tool called Achilles Heel as it was applied to 24 datasets across seven different organizations. We highlight 12 data quality rules that identified issues in at least 10 of the 24 datasets and provide a full set of 71 rules identified in at least one dataset. Achilles Heel is developed by Observational Health Data Sciences and Informatics (OHDSI) community and is a freely available software that provides a useful starter set of data quality rules. Our analysis represents the first data quality comparison of multiple datasets across several countries in America, Europe and Asia.

According to Cognitive Market Research, the global healthcare data storage market size is USD 5.4 billion in 2024 and will expand at a compound annual growth rate (CAGR) of 14.3% from 2024 to 2031. Market Dynamics of Healthcare Data Storage Market

Key Drivers for Healthcare Data Storage Market

Increasing amount of healthcare records- Healthcare data storage market is in high demand due to the increasing amount of healthcare data. Electronic health records (EHRs), medical imaging, wearable electronics, and health applications all contribute to the daily deluge of data generated and amassed by healthcare institutions. This data includes a wide range of information, including patients’ medical records, diagnostic pictures, treatment programs, health indicators in real-time, and more. Moreover, healthcare data storage systems are necessary for efficient management of such vast data sets because they can manage high volumes, provide fast retrieval, and keep data secure. Further, state-of-the-art storage systems are required for compliance with data retention and security regulations. Thus, in order to facilitate better patient care and operational efficiency, the ever-increasing volume of healthcare data is driving the use of advanced data storage technologies.
The market is being propelled by the demand for efficient and rapid access to patient data in order to enhance clinical decision-making and patient care.

Key Restraints for Healthcare Data Storage Market

Healthcare data storage market growth is hindered due to the high costs of implementation and upkeep.
The market expansion is being impeded by concerns about data breaches and data accessibility.

Introduction of the Healthcare Data Storage Market

Healthcare data storage describes the infrastructure and procedures put in place to keep and handle massive volumes of patient records safely. Complying with regulatory requirements while ensuring data integrity, confidentiality, and accessibility is essential for healthcare data storage solutions. The rising amount of digital data produced by healthcare companies, the convenience and speed with which cloud storage solutions can be implemented, and the increasing popularity of hybrid data storage solutions are the primary elements propelling the expansion of this market. Security concerns over cloud-based image processing and analytics, however, are limiting the company’s growth. Concerns about the security of cloud-based image processing and analytics are expected to dampen the worldwide healthcare data storage industry. Additionally, advancements in artificial intelligence, big data analytics, and cloud computing have greatly improved the efficiency and capacity of the healthcare data storage market.

2023

After May 3, 2024, this dataset and webpage will no longer be updated because hospitals are no longer required to report data on COVID-19 hospital admissions, and hospital capacity and occupancy data, to HHS through CDC’s National Healthcare Safety Network. Data voluntarily reported to NHSN after May 1, 2024, will be available starting May 10, 2024, at COVID Data Tracker Hospitalizations.

The following dataset provides facility-level data for hospital utilization aggregated on a weekly basis (Sunday to Saturday). 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 2020-11-15 means the average/sum/coverage of the elements captured from that given facility starting and including Sunday, November 15, 2020, and ending and including reports for Saturday, November 21, 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”.

A story page was created to display both corrected and raw datasets and can be accessed at this link: https://healthdata.gov/stories/s/nhgk-5gpv

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.

For recent updates to the dataset, scroll to the bottom of the dataset description.

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_

The Healthcare Cost and Utilization Project (HCUP) Nationwide Readmissions Database (NRD) is a unique and powerful database designed to support various types of analyses of national readmission rates for all payers and the uninsured. The NRD includes discharges for patients with and without repeat hospital visits in a year and those who have died in the hospital. Repeat stays may or may not be related. The criteria to determine the relationship between hospital admissions is left to the analyst using the NRD. This database addresses a large gap in health care data - the lack of nationally representative information on hospital readmissions for all ages. Outcomes of interest include national readmission rates, reasons for returning to the hospital for care, and the hospital costs for discharges with and without readmissions. Unweighted, the NRD contains data from approximately 18 million discharges each year. Weighted, it estimates roughly 35 million discharges. Developed through a Federal-State-Industry partnership sponsored by the Agency for Healthcare Research and Quality, HCUP data inform decision making at the national, State, and community levels. The NRD is drawn from HCUP State Inpatient Databases (SID) containing verified patient linkage numbers that can be used to track a person across hospitals within a State, while adhering to strict privacy guidelines. The NRD is not designed to support regional, State-, or hospital-specific readmission analyses. The NRD contains more than 100 clinical and non-clinical data elements provided in a hospital discharge abstract. Data elements include but are not limited to: diagnoses, procedures, patient demographics (e.g., sex, age), expected source of payer, regardless of expected payer, including but not limited to Medicare, Medicaid, private insurance, self-pay, or those billed as ‘no charge, discharge month, quarter, and year, total charges, length of stay, and data elements essential to readmission analyses. The NIS excludes data elements that could directly or indirectly identify individuals. Restricted access data files are available with a data use agreement and brief online security training.

The Healthcare Operational Data Flows (HODF): Acute Data Set provides an automated patient-based daily data collection to support NHS delivery plans for the recovery of elective care and emergency and urgent care.

Success.ai’s Healthcare Industry Leads Data and B2B Contact Data for US Healthcare Professionals offers an extensive and verified database tailored to connect businesses with key executives and administrators in the healthcare industry across the United States. With over 170M verified profiles, including work emails and direct phone numbers, this dataset enables precise targeting of decision-makers in hospitals, clinics, and healthcare organizations.

Backed by AI-driven validation technology for unmatched accuracy and reliability, this contact data empowers your marketing, sales, and recruitment strategies. Designed for industry professionals, our continuously updated profiles provide the actionable insights you need to grow your business in the competitive healthcare sector.

Key Features of Success.ai’s US Healthcare Contact Data:

• Comprehensive Healthcare Sector Coverage Access detailed contact information for professionals across the healthcare spectrum:

Hospital Executives: CEOs, CFOs, and COOs managing top-tier facilities. Healthcare Administrators: Decision-makers driving operational excellence. Medical Professionals: Physicians, specialists, and nurse practitioners. Clinic Managers: Leaders in small and mid-sized healthcare organizations.

• AI-Validated Accuracy and Updates

  99% Verified Accuracy: Our advanced AI technology ensures data reliability for optimal engagement. Real-Time Updates: Profiles are continuously refreshed to maintain relevance and accuracy. Minimized Bounce Rates: Save time and resources by reaching verified contacts.

• Customizable Delivery Options Choose how you access the data to match your business requirements:

API Integration: Connect our data directly to your CRM or sales platform. Flat File Delivery: Receive customized datasets in formats suited to your needs.

Why Choose Success.ai for Healthcare Data?

• Best Price Guarantee We ensure competitive pricing for our verified contact data, offering the most comprehensive and cost-effective solution in the market.

• Compliance-Driven and Ethical Data Our data collection adheres to strict global standards, including HIPAA, GDPR, and CCPA compliance, ensuring secure and ethical usage.

• Strategic Benefits for Your Business Success.ai’s US healthcare professional data unlocks numerous business opportunities:

Targeted Marketing: Develop tailored campaigns aimed at healthcare executives and decision-makers. Efficient Sales Outreach: Engage with key contacts to accelerate your sales process. Recruitment Optimization: Access verified profiles to identify and recruit top talent in the healthcare industry. Market Intelligence: Use detailed firmographic and demographic insights to guide strategic decisions. Partnership Development: Build valuable relationships within the healthcare ecosystem.

• Data Highlights 170M+ Verified Profiles 50M Direct Phone Numbers 700M Global Professional Profiles 70M Verified Company Profiles

Key APIs for Advanced Functionality

• Enrichment API Enhance your existing contact data with real-time updates, ensuring accuracy and relevance for your outreach initiatives.

• Lead Generation API Drive high-quality lead generation efforts by utilizing verified contact information, including work emails and direct phone numbers, for up to 860,000 API calls per day.

• Use Cases

1. Healthcare Marketing Campaigns Target verified executives and administrators to deliver personalized and impactful marketing campaigns.

2. Sales Enablement Connect with key decision-makers in healthcare organizations, ensuring higher conversion rates and shorter sales cycles.

3. Talent Acquisition Source and engage healthcare professionals and administrators with accurate, up-to-date contact information.

4. Strategic Partnerships Foster collaborations with healthcare institutions and professionals to expand your business network.

5. Industry Analysis Leverage enriched contact data to gain insights into the US healthcare market, helping you refine your strategies.

• What Sets Success.ai Apart?

Verified Accuracy: AI-driven technology ensures 99% reliability for all contact details. Comprehensive Reach: Covering healthcare professionals from large hospital systems to smaller clinics nationwide. Flexible Access: Customizable data delivery methods tailored to your business needs. Ethical Standards: Fully compliant with healthcare and data protection regulations.

Success.ai’s B2B Contact Data for US Healthcare Professionals is the ultimate solution for connecting with industry leaders, driving impactful marketing campaigns, and optimizing your recruitment strategies. Our commitment to quality, accuracy, and affordability ensures you achieve exceptional results while adhering to ethical and legal standards.

No one beats us on price. Period.

Abstract

👂💉 EHRSHOT is a dataset for benchmarking the few-shot performance of foundation models for clinical prediction tasks. EHRSHOT contains de-identified structured data (e.g., diagnosis and procedure codes, medications, lab values) from the electronic health records (EHRs) of 6,739 Stanford Medicine patients and includes 15 prediction tasks. Unlike MIMIC-III/IV and other popular EHR datasets, EHRSHOT is longitudinal and includes data beyond ICU and emergency department patients.

⚡️Quickstart 1. To recreate the original EHRSHOT paper, download the EHRSHOT_ASSETS.zip file from the "Files" tab 2. To work with OMOP CDM formatted data, download all the tables in the "Tables" tab

⚙️ Please see the "Methodology" section below for details on the dataset and downloadable files.

Methodology

1. 📖 Overview

EHRSHOT is a benchmark for evaluating models on few-shot learning for patient classification tasks. The dataset contains:

• **6,739 **patients
• 41.6 million clinical events
• 921,499 visits
• 15 prediction tasks

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2. 💽 Dataset

EHRSHOT is sourced from Stanford’s STARR-OMOP database.

• Data follows the OMOP CDM and is fully de-identified.
• Unlike most other EHR research datasets, EHRSHOT is not restricted to ED/ICU visits and instead includes longitudinal patient data for all hospital encounter types.
• EHRSHOT does not contain clinical notes or images.

%3C!-- --%3E

We provide two versions of the dataset:

• EHRSHOT-Original is the same exact dataset used in the original EHRSHOT paper.
• EHRSHOT-OMOP is a more complete version of the EHRSHOT dataset which includes all OMOP CDM tables and additional OMOP metadata.

%3C!-- --%3E

To access the raw data, please see the "Tables" and "Files"** **tabs above:

3. 💽 Data Files and Formats

We provide EHRSHOT in two file formats:

• OMOP CDM v5.4
• Medical Event Data Standard (MEDS)

%3C!-- --%3E

Within the "Tables" tab...

1. %3Cu%3EEHRSHOT-OMOP%3C/u%3E

* Dataset Version: EHRSHOT-OMOP

* Notes: Contains all OMOP CDM tables for the EHRSHOT patients. Note that this dataset is slightly different than the original EHRSHOT dataset, as these tables contain the full OMOP schema rather than a filtered subset.

Within the "Files" tab...

1. %3Cu%3EEHRSHOT_ASSETS.zip%3C/u%3E

* Dataset Version: EHRSHOT-Original

* Data Format: FEMR 0.1.16

* Notes: The original EHRSHOT dataset as detailed in the paper. Also includes model weights.

2. %3Cu%3EEHRSHOT_MEDS.zip%3C/u%3E

* Dataset Version: EHRSHOT-Original

* Data Format: MEDS 0.3.3

* Notes: The original EHRSHOT dataset as detailed in the paper. It does not include any models.

3. %3Cu%3EEHRSHOT_OMOP_MEDS.zip%3C/u%3E

* Dataset Version: EHRSHOT-OMOP

* Data Format: MEDS 0.3.3 + MEDS-ETL 0.3.8

* Notes: Converts the dataset from EHRSHOT-OMOP into MEDS format via the `meds_etl_omop`command from MEDS-ETL.

4. %3Cu%3EEHRSHOT_OMOP_MEDS_Reader.zip%3C/u%3E

* Dataset Version: EHRSHOT-OMOP

* Data Format: MEDS Reader 0.1.9 + MEDS 0.3.3 + MEDS-ETL 0.3.8

* Notes: Same data as EHRSHOT_OMOP_MEDS.zip, but converted into a MEDS-Reader database for faster reads.

4. 🤖 Model

We also release the full weights of **CLMBR-T-base, **a 141M parameter clinical foundation model pretrained on the structured EHR data of 2.57M patients. Please download from https://huggingface.co/StanfordShahLab/clmbr-t-base

**5. 🧑‍💻 Code **

Please see our Github repo to obtain code for loading the dataset and running a set of pretrained baseline models: https://github.com/som-shahlab/ehrshot-benchmark/

Usage

**NOTE: You must authenticate to Redivis using your formal affiliation's email address. If you use gmail or other personal email addresses, you will not be granted access. **

Access to the EHRSHOT dataset requires the following:

• Verified Affiliation with an **Academic, Government, **o

Big Data Analytics for Clinical Research Market Outlook

As per our latest research, the Big Data Analytics for Clinical Research market size reached USD 7.45 billion globally in 2024, reflecting a robust adoption pace driven by the increasing digitization of healthcare and clinical trial processes. The market is forecasted to grow at a CAGR of 17.2% from 2025 to 2033, reaching an estimated USD 25.54 billion by 2033. This significant growth is primarily attributed to the rising need for real-time data-driven decision-making, the proliferation of electronic health records (EHRs), and the growing emphasis on precision medicine and personalized healthcare solutions. The industry is experiencing rapid technological advancements, making big data analytics a cornerstone in transforming clinical research methodologies and outcomes.

Several key growth factors are propelling the expansion of the Big Data Analytics for Clinical Research market. One of the primary drivers is the exponential increase in clinical data volumes from diverse sources, including EHRs, wearable devices, genomics, and imaging. Healthcare providers and research organizations are leveraging big data analytics to extract actionable insights from these massive datasets, accelerating drug discovery, optimizing clinical trial design, and improving patient outcomes. The integration of artificial intelligence (AI) and machine learning (ML) algorithms with big data platforms has further enhanced the ability to identify patterns, predict patient responses, and streamline the entire research process. These technological advancements are reducing the time and cost associated with clinical research, making it more efficient and effective.

Another significant factor fueling market growth is the increasing collaboration between pharmaceutical & biotechnology companies and technology firms. These partnerships are fostering the development of advanced analytics solutions tailored specifically for clinical research applications. The demand for real-world evidence (RWE) and real-time patient monitoring is rising, particularly in the context of post-market surveillance and regulatory compliance. Big data analytics is enabling stakeholders to gain deeper insights into patient populations, treatment efficacy, and adverse event patterns, thereby supporting evidence-based decision-making. Furthermore, the shift towards decentralized and virtual clinical trials is creating new opportunities for leveraging big data to monitor patient engagement, adherence, and safety remotely.

The regulatory landscape is also evolving to accommodate the growing use of big data analytics in clinical research. Regulatory agencies such as the FDA and EMA are increasingly recognizing the value of data-driven approaches for enhancing the reliability and transparency of clinical trials. This has led to the establishment of guidelines and frameworks that encourage the adoption of big data technologies while ensuring data privacy and security. However, the implementation of stringent data protection regulations, such as GDPR and HIPAA, poses challenges related to data integration, interoperability, and compliance. Despite these challenges, the overall outlook for the Big Data Analytics for Clinical Research market remains highly positive, with sustained investments in digital health infrastructure and analytics capabilities.

From a regional perspective, North America currently dominates the Big Data Analytics for Clinical Research market, accounting for the largest share due to its advanced healthcare infrastructure, high adoption of digital technologies, and strong presence of leading pharmaceutical companies. Europe follows closely, driven by increasing government initiatives to promote health data interoperability and research collaborations. The Asia Pacific region is emerging as a high-growth market, supported by expanding healthcare IT investments, rising clinical trial activities, and growing awareness of data-driven healthcare solutions. Latin America and the Middle East & Africa are also witnessing gradual adoption, albeit at a slower pace, due to infrastructural and regulatory challenges. Overall, the global market is poised for substantial growth across all major regions over the forecast period.

Background:
The Millennium Cohort Study (MCS) is a large-scale, multi-purpose longitudinal dataset providing information about babies born at the beginning of the 21st century, their progress through life, and the families who are bringing them up, for the four countries of the United Kingdom. The original objectives of the first MCS survey, as laid down in the proposal to the Economic and Social Research Council (ESRC) in March 2000, were:

• to chart the initial conditions of social, economic and health advantages and disadvantages facing children born at the start of the 21st century, capturing information that the research community of the future will require
• to provide a basis for comparing patterns of development with the preceding cohorts (the National Child Development Study, held at the UK Data Archive under GN 33004, and the 1970 Birth Cohort Study, held under GN 33229)
• to collect information on previously neglected topics, such as fathers' involvement in children's care and development
• to focus on parents as the most immediate elements of the children's 'background', charting their experience as mothers and fathers of newborn babies in the year 2000, recording how they (and any other children in the family) adapted to the newcomer, and what their aspirations for her/his future may be
• to emphasise intergenerational links including those back to the parents' own childhood
• to investigate the wider social ecology of the family, including social networks, civic engagement and community facilities and services, splicing in geo-coded data when available

• to provide control cases for the national evaluation of Sure Start (a government programme intended to alleviate child poverty and social exclusion)
• to provide samples of adequate size to analyse and compare the smaller countries of the United Kingdom, and include disadvantaged areas of England

Further information about the MCS can be found on the Centre for Longitudinal Studies web pages.

The content of MCS studies, including questions, topics and variables can be explored via the CLOSER Discovery website.

• detailed sensitive variables not available under EUL. These have been grouped thematically and are held under SN 8753 (socio-economic, accommodation and occupational data), SN 8754 (self-reported health, behaviour and fertility), SN 8755 (demographics, language and religion) and SN 8756 (exact participation dates). These files replace previously available studies held under SNs 8456 and 8622-8627
  
• detailed geographical identifier files which are grouped by sweep held under SN 7758 (MCS1), SN 7759 (MCS2), SN 7760 (MCS3), SN 7761 (MCS4), SN 7762 (MCS5 2001 Census Boundaries), SN 7763 (MCS5 2011 Census Boundaries), SN 8231 (MCS6 2001 Census Boundaries), SN 8232 (MCS6 2011 Census Boundaries), SN 8757 (MCS7), SN 8758 (MCS7 2001 Census Boundaries) and SN 8759 (MCS7 2011 Census Boundaries). These files replace previously available files grouped by geography SN 7049 (Ward level), SN 7050 (Lower Super Output Area level), and SN 7051 (Output Area level)
• linked education administrative datasets for Key Stages 1, 2, 4 and 5 held under SN 8481 (England). This replaces previously available datasets for Key Stage 1 (SN 6862) and Key Stage 2 (SN 7712)
  
• linked education administrative datasets for Key Stage 1 held under SN 7414 (Scotland)
• linked education administrative dataset for Key Stages 1, 2, 3 and 4 under SN 9085 (Wales)
  
• linked NHS Patient Episode Database for Wales (PEDW) for MCS1 – MCS5 held under SN 8302
• linked Scottish Medical Records data held under SNs 8709, 8710, 8711, 8712, 8713 and 8714;
• Banded Distances to English Grammar Schools for MCS5 held under SN 8394
• linked Health Administrative Datasets (Hospital Episode Statistics) for England for years 2000-2019 held under SN 9030
• linked Health Administrative Datasets (SAIL) for Wales held under SN 9310
  
• linked Hospital of Birth data held under SN 5724.

The Millennium Cohort Study: Linked Health Administrative Data (Scottish Medical Records), Child Health Reviews, 2000-2015: Secure Access includes data files from the NHS Digital Hospital Episode Statistics database for those cohort members who provided consent to health data linkage in the Age 50 sweep, and had ever lived in Scotland. The Scottish Medical Records database contains information about all hospital admissions in Scotland. This study concerns the Child Health Reviews (CHR) from first visit to school reviews.

Other datasets are available from the Scottish Medical Records database, these include:

• Prescribing Information System (PIS) held under SN 8710
• Scottish Immunisation and Recall System (SIRS) held under SN 8711
• Scottish Birth Records (SMR11) held under SN 8712
• Inpatient and Day Care Attendance (SMR01) held under SN 8713
• Outpatient Attendance (SMR00) held under SN 8714

Users

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

This MS Excel files contains datasets and codebooks for Fofanah BD et al's Operational Research paper on AMR in Sierra Leone, Year 2022.

Federated Analytics for Hospital Benchmarking Market Outlook

As per our latest research, the global market size for Federated Analytics for Hospital Benchmarking reached USD 1.02 billion in 2024, driven by the surging demand for data-driven decision-making across the healthcare sector. The market is experiencing robust momentum, with a recorded CAGR of 21.8% during the forecast period. By 2033, the market is forecasted to attain a valuation of USD 7.51 billion, reflecting the accelerated adoption of federated analytics platforms to enhance hospital benchmarking, operational efficiency, and patient outcomes. The primary growth factor underpinning this trajectory is the increasing necessity for secure, collaborative analytics solutions that comply with stringent healthcare data privacy regulations while enabling comprehensive benchmarking at scale.

One of the principal drivers of growth in the Federated Analytics for Hospital Benchmarking market is the exponential rise in healthcare data generation and the critical need for its secure utilization. Hospitals and healthcare systems are under mounting pressure to improve clinical outcomes, operational efficiency, and financial performance. Traditional benchmarking approaches often face challenges due to data privacy constraints and interoperability issues. Federated analytics overcomes these barriers by allowing multiple institutions to collaborate on analytics projects without sharing sensitive patient-level data, ensuring compliance with HIPAA and GDPR regulations. This capability is particularly valuable as hospitals seek to benchmark performance against peers and industry standards, driving adoption of federated analytics platforms across both developed and emerging markets.

Another significant growth factor is the technological advancements in artificial intelligence, machine learning, and secure multi-party computation that underpin federated analytics solutions. These technologies enable real-time, distributed analysis of heterogeneous datasets from multiple hospitals, unlocking insights that were previously inaccessible due to data silos. The integration of federated analytics with electronic health records (EHRs), hospital information systems, and cloud infrastructure further enhances its appeal by streamlining workflows and reducing IT overhead. As healthcare organizations prioritize digital transformation initiatives, the demand for scalable, privacy-preserving analytics platforms is set to rise, fueling market expansion over the coming years.

Furthermore, the market is being propelled by growing governmental and regulatory support for healthcare quality improvement and transparency initiatives. Policymakers worldwide are encouraging hospitals to adopt advanced analytics for benchmarking and reporting purposes, with several countries launching national programs focused on performance measurement and value-based care. The COVID-19 pandemic has also accelerated the shift towards data-driven healthcare management, highlighting the importance of collaborative analytics in managing resources, monitoring outcomes, and responding to public health crises. As a result, federated analytics is gaining traction as a strategic tool for hospitals seeking to enhance their competitive position and deliver superior patient care.

From a regional perspective, North America currently dominates the Federated Analytics for Hospital Benchmarking market, accounting for the largest revenue share in 2024. This leadership is attributed to the presence of advanced healthcare infrastructure, high adoption of digital health technologies, and supportive regulatory frameworks. However, Asia Pacific is expected to exhibit the highest CAGR during the forecast period, driven by rapid healthcare modernization, increasing investments in health IT, and growing awareness of the benefits of benchmarking analytics. Europe also represents a significant market, supported by robust data protection laws and initiatives to promote cross-border healthcare collaboration. The Middle East & Africa and Latin America are gradually emerging as promising markets, fueled by healthcare reforms and the expansion of hospital networks.

Overview

New York, NY – May 02, 2025 – Global Healthcare Cloud Based Analytics Market size is expected to be worth around USD 223.5 Billion by 2033 from USD 38.6 Billion in 2023, growing at a CAGR of 19.2% during the forecast period from 2024 to 2033.

Healthcare cloud-based analytics refers to the use of cloud computing platforms to collect, store, process, and analyze healthcare data in real time. This technology enables healthcare providers, payers, and researchers to derive actionable insights from vast and complex datasets such as electronic health records (EHRs), patient outcomes, claims data, and clinical trials. By leveraging cloud infrastructure, healthcare organizations can access scalable, secure, and cost-efficient data analytics solutions without relying on traditional on-premise systems.

The demand for cloud-based analytics in healthcare is growing rapidly due to the increasing emphasis on value-based care, population health management, and predictive analytics. These solutions allow providers to identify trends, reduce costs, personalize treatment plans, and improve operational performance. They also play a critical role in disease surveillance, early diagnosis, and resource optimization.

Advanced capabilities, including artificial intelligence (AI), machine learning (ML), and natural language processing (NLP), further enhance the power of cloud analytics, making it easier to interpret unstructured data and support clinical decision-making. Additionally, cloud platforms offer enhanced data sharing, collaboration, and compliance with regulatory standards such as HIPAA and GDPR. As healthcare systems worldwide shift toward digital transformation, cloud-based analytics is emerging as a cornerstone of intelligent healthcare delivery, driving efficiency, innovation, and improved patient outcomes across the care continuum.

Objective

To construct and publicly release a set of medical concept embeddings for codes following the ICD-10 coding standard which explicitly incorporate hierarchical information from medical codes into the embedding formulation.

Materials and Methods

We trained concept embeddings using several new extensions to the Word2Vec algorithm using a dataset of approximately 600,000 patients from a major integrated healthcare organization in the Mid-Atlantic US. Our concept embeddings included additional entities to account for the medical categories assigned to codes by the Clinical Classification Software Revised (CCSR) dataset. We compare these results to sets of publicly-released pretrained embeddings and alternative training methodologies.

Results

We found that Word2Vec models which included hierarchical data outperformed ordinary Word2Vec alternatives on tasks which compared naïve clusters to canonical ones provided by CCSR. Our Skip-Gram model with both codes and categories achieved 61.4% Normalized Mutual Information with canonical labels in comparison to 57.5% with traditional Skip-Gram. In models operating on two different outcomes we found that including hierarchical embedding data improved classification performance 96.2% of the time. When controlling for all other variables, we found that co-training embeddings improved classification performance 66.7% of the time. We found that all models outperformed our competitive benchmarks.

Discussion

We found significant evidence that our proposed algorithms can express the hierarchical structure of medical codes more fully than ordinary Word2Vec models, and that this improvement carries forward into classification tasks. As part of this publication, we have released several sets of pretrained medical concept embeddings using the ICD-10 standard which significantly outperform other well-known pretrained vectors on our tested outcomes.

Methods This dataset includes trained medical concept embeddings for 5428 ICD-10 codes and 394 Clinical Classification Software (Revised) (CCSR) categories. We include several different sets of concept embeddings, each trained using a slightly different set of hyperparameters and algorithms.

To train our models, we employed data from the Kaiser Permanente Mid-Atlantic States (KPMAS) medical system. KPMAS is an integrated medical system serving approximately 780,000 members in Maryland, Virginia, and the District of Columbia. KPMAS has a comprehensive Electronic Medical Record system which includes data from all patient interactions with primary or specialty caregivers, from which all data is derived. Our embeddings training set included diagnoses allocated to all adult patients in calendar year 2019.

For each code, we also recovered an associated category, as assigned by the Clinical Classification Software (Revised).

We trained 12 sets of embeddings using classical Word2Vec models with settings differing across three parameters. Our first parameter was the selection of training algorithm, where we trained both CBOW and SG models. Each model was trained using dimension k of 10, 50, and 100. Furthermore, each model-dimension combination was trained with categories and codes trained separately and together (referred to hereafter as ‘co-trained embeddings’ or ‘co-embeddings’). Each model was trained for 10 iterations. We employed an arbitrarily large context window (100), since all codes necessarily occurred within a short period (1 year).

We also trained a set of validation embeddings only on ICD-10 codes using the Med2Vec architecture as a comparison. We trained the Med2Vec model on our data using its default settings, including the default vector size (200) and a training regime of 10 epochs. We grouped all codes occurring on the same calendar date as Med2Vec ‘visits.’ Our Med2Vec model benchmark did not include categorical entities or other novel innovations.

Word2Vec embeddings were generated using the GenSim package in Python. Med2Vec embeddings were generated using the Med2Vec code published by Choi. The JSON files used in this repository were generated using the JSON package in Python.

State and Local Public Health Departments in the United States Governmental public health departments are responsible for creating and maintaining conditions that keep people healthy. A local health department may be locally governed, part of a region or district, be an office or an administrative unit of the state health department, or a hybrid of these. Furthermore, each community has a unique "public health system" comprising individuals and public and private entities that are engaged in activities that affect the public's health. (Excerpted from the Operational Definition of a functional local health department, National Association of County and City Health Officials, November 2005) Please reference http://www.naccho.org/topics/infrastructure/accreditation/upload/OperationalDefinitionBrochure-2.pdf for more information. Facilities involved in direct patient care are intended to be excluded from this dataset; however, some of the entities represented in this dataset serve as both administrative and clinical locations. This dataset only includes the headquarters of Public Health Departments, not their satellite offices. Some health departments encompass multiple counties; therefore, not every county will be represented by an individual record. Also, some areas will appear to have over representation depending on the structure of the health departments in that particular region. Town health officers are included in Vermont and boards of health are included in Massachusetts. Both of these types of entities are elected or appointed to a term of office during which they make and enforce policies and regulations related to the protection of public health. Visiting nurses are represented in this dataset if they are contracted through the local government to fulfill the duties and responsibilities of the local health organization. Since many town health officers in Vermont work out of their personal homes, TechniGraphics represented these entities at the town hall. This is denoted in the [DIRECTIONS] field. Effort was made by TechniGraphics to verify whether or not each health department tracks statistics on communicable diseases. Records with "-DOD" appended to the end of the [NAME] value are located on a military base, as defined by the Defense Installation Spatial Data Infrastructure (DISDI) military installations and military range boundaries. "#" and "*" characters were automatically removed from standard HSIP fields populated by TechniGraphics. Double spaces were replaced by single spaces in these same fields. At the request of NGA, text fields in this dataset have been set to all upper case to facilitate consistent database engine search results. At the request of NGA, all diacritics (e.g., the German umlaut or the Spanish tilde) have been replaced with their closest equivalent English character to facilitate use with database systems that may not support diacritics. The currentness of this dataset is indicated by the [CONTDATE] field. Based on this field, the oldest record dates from 11/18/2009 and the newest record dates from 01/08/2010.

All content from the Directorate of Health published on Helsedirektoratet.no.

The data set includes: — normative products (national professional guidelines, national supervisors, supervisors to log and regulations, prioritisation supervisors, national academic councils, package courses and circulars) — LIS learning objectives (e.g. LIS 1), learning activities and specialties and more — statistics (e.g. quality indicators) — reports — grants — hearings — news — articles — conferences

The list is not exhaustive.

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Purpose: The dataset can be used to publish content in different channels, e.g. on websites, in mobile applications, and in various IT systems used by clinicians (e.g. electronic medical records, electronic curves and quality systems).