MIMIC-III is a large, freely-available database comprising deidentified health-related data associated with over forty thousand patients who stayed in critical care units of the Beth Israel Deaconess Medical Center between 2001 and 2012. The database includes information such as demographics, vital sign measurements made at the bedside (~1 data point per hour), laboratory test results, procedures, medications, caregiver notes, imaging reports, and mortality (including post-hospital discharge).MIMIC supports a diverse range of analytic studies spanning epidemiology, clinical decision-rule improvement, and electronic tool development. It is notable for three factors: it is freely available to researchers worldwide; it encompasses a diverse and very large population of ICU patients; and it contains highly granular data, including vital signs, laboratory results, and medications.

Overview: The goal of the project was to develop a unique database linking chronic disease clinical data from an electronic medical record (EMR) of a large academic healthcare system to multi-payer claims data. The longitudinal relational database can be used to study clinical effectiveness of many diagnostic and treatment interventions. The population of patients used consisted of those patients who were attributed to the University of Michigan Health System (UMHS) as continuing care patients, who are also in adjudicated and validated chronic disease registries. Data Access: These data are not available from ICPSR. The data are restricted to use by the principal investigator and cannot be shared.

The Medical Information Mart for Intensive Care (MIMIC)-IV database is comprised of deidentified electronic health records for patients admitted to the Beth Israel Deaconess Medical Center. Access to MIMIC-IV is limited to credentialed users. Here, we have provided an openly-available demo of MIMIC-IV containing a subset of 100 patients. The dataset includes similar content to MIMIC-IV, but excludes free-text clinical notes. The demo may be useful for running workshops and for assessing whether the MIMIC-IV is appropriate for a study before making an access request.

MIMIC-II documents a diverse and large population of intensive care unit patient stays and contains comprehensive and detailed clinical data, including physiological waveforms and minute-by-minute trends for a subset of records. It establishes a unique public-access resource for critical care research, supporting a diverse range of analytic studies spanning epidemiology, clinical decision-rule development, and electronic tool development. The MIMIC-II Clinical Database, although de-identified, still contains detailed information regarding the clinical care of patients, and must be treated with appropriate care and respect.

The National Database for Clinical Trials Related to Mental Illness (NDCT) is an extensible informatics platform for relevant data at all levels of biological and behavioral organization (molecules, genes, neural tissue, behavioral, social and environmental interactions) and for all data types (text, numeric, image, time series, etc.) related to clinical trials funded by the National Institute of Mental Health. Sharing data, associated tools, methodologies and results, rather than just summaries or interpretations, accelerates research progress. Community-wide sharing requires common data definitions and standards, as well as comprehensive and coherent informatics approaches for the sharing of de-identified human subject research data. Built on the National Database for Autism Research (NDAR) informatics platform, NDCT provides a comprehensive data sharing platform for NIMH grantees supporting clinical trials.

This data package contains datasets on clinical trials conducted in the United States. Diseases include cervical cancer, diabetes, acute respiratory infection as well as stress. This data package also includes clinical trials registry and results database.

A virtual database currently indexing clinical trials databases including EU Clinical Trials Register and Clinicaltrials.gov.

The Clinical Trials Registry and Results Database compiles information on publicly and privately supported clinical trial studies on a wide range of diseases and conditions. Its main goal is to provide an easy access to both privately and publicly funded clinical trials information for patients, their family members, healthcare professionals, researchers, and the public.

A database of Alzheimer's disease and dementia clinical trials currently in progress at centers throughout the U.S.

Health Canada's Clinical Trials Database is a listing of information about phase I, II and III clinical trials in patients. The database is managed by Health Canada and provides a source of information about Canadian clinical trials involving human pharmaceutical and biological drugs. Additional information on Health Canada’s CTD is available at: https://www.canada.ca/en/health-canada/services/drugs-health-products/drug-products/health-canada-clinical-trials-database/frequently-asked-questions.html

MIMIC-III is a large, freely-available database comprising deidentified health-related data associated with over 40,000 patients who stayed in critical care units of the Beth Israel Deaconess Medical Center between 2001 and 2012 [1]. The MIMIC-III Clinical Database is available on PhysioNet (doi: 10.13026/C2XW26). Though deidentified, MIMIC-III contains detailed information regarding the care of real patients, and as such requires credentialing before access. To allow researchers to ascertain whether the database is suitable for their work, we have manually curated a demo subset, which contains information for 100 patients also present in the MIMIC-III Clinical Database. Notably, the demo dataset does not include free-text notes.

Overview This dataset is at the core of a dementia research project focused on the exploration and diagnosis of dementia using advanced imaging technologies. It integrates data collected through Single-Photon Emission Computed Tomography (SPECT). Dataset Composition

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

This controlled data release focuses on CP-NET's initial Clinical Database which solely focused on children and youth, aged 2-18, with a confirmed diagnosis of hemiplegic cerebral palsy (CP). The Hemi-NET Clinical Database has data on 320 children and youth from across Ontario. The released data is organized around the following platforms: (1) Clinical Risk Factor Platform: clinically relevant neonatal and obstetric risk factors from obstetrical and neonatal health charts, (2) Genomics Platform: saliva samples acquired from the index child and both biological parent(s), (3) Neuroimaging Platform: standardized coding of clinically acquired neuroimaging, (4) Neurodevelopmental Platform: standardized assessments of gross motor, fine motor, language, cognitive, behavioural function, and self-reported quality of life.

Dataset III and dictionary III. Excel spreadsheet and Data Dictionary that contain information on tissue samples of suspected Melanoma cases including specimens such as presence of tumor, tissue source and other relevant tissue information relevant to genomic analysis.

The Medical Information Mart for Intensive Care III (MIMIC-III) dataset is a large, de-identified and publicly-available collection of medical records. Each record in the dataset includes ICD-9 codes, which identify diagnoses and procedures performed. Each code is partitioned into sub-codes, which often include specific circumstantial details. The dataset consists of 112,000 clinical reports records (average length 709.3 tokens) and 1,159 top-level ICD-9 codes. Each report is assigned to 7.6 codes, on average. Data includes vital signs, medications, laboratory measurements, observations and notes charted by care providers, fluid balance, procedure codes, diagnostic codes, imaging reports, hospital length of stay, survival data, and more.

The database supports applications including academic and industrial research, quality improvement initiatives, and higher education coursework.

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

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

Clinically annotated database of all patients seen in the Oncology Dept and/or presented at a Cancer Multidisciplinary meeting at SVHM. Details pertaining to the diseasse, treatment and outcome are included.

The advent of large, open access text databases has driven advances in state-of-the-art model performance in natural language processing (NLP). The relatively limited amount of clinical data available for NLP has been cited as a significant barrier to the field's progress. Here we describe MIMIC-IV-Note: a collection of deidentified free-text clinical notes for patients included in the MIMIC-IV clinical database. MIMIC-IV-Note contains 331,794 deidentified discharge summaries from 145,915 patients admitted to the hospital and emergency department at the Beth Israel Deaconess Medical Center in Boston, MA, USA. The database also contains 2,321,355 deidentified radiology reports for 237,427 patients. All notes have had protected health information removed in accordance with the Health Insurance Portability and Accountability Act (HIPAA) Safe Harbor provision. All notes are linkable to MIMIC-IV providing important context to the clinical data therein. The database is intended to stimulate research in clinical natural language processing and associated areas.

This dataset is the main file to construct the FDA (U.S. Food and Drug Administration) Postmarketing Requirements and Commitments searchable database. Postmarketing requirements refers to studies required to be conducted under statutes or regulations after product approval. Postmarketing commitments are not required studies that sponsors conduct. Official FDA's website has an available database to provide public detailed information on postmarketing requirements and commitments studies.