THIS RESOURCE IS NO LONGER IN SERVICE, documented July 15, 2016. Database containing location and descriptive information about a wide variety of information resources including organizations, research resources, projects, and databases concerned with health and biomedicine. This information may not be readily available in bibliographic databases. Each record may contain information on the publications, holdings, and services provided. These information resources fall into many categories including federal, state, and local government agencies; information and referral centers; professional societies; self-help groups and voluntary associations; academic and research institutions and their programs; information systems and research facilities. Topics include HIV/AIDS, maternal and child health, most diseases and conditions including genetic and other rare diseases, health services research and technology assessment. DIRLINE can be searched using subject words (such as disease or condition) including Medical Subject Headings (MeSH) or for the name or location of a resource. It now offers an A to Z list of over 8,500 organizations.

In 2023, at least three quarters of patients in Denmark and Finland said they accessed their online health data to view their test results. Meanwhile, 73 percent of the patients in Iceland reported accessing their online health data to see their medication and/or renew their prescriptions.

As of 2023, ** percent of adults in Finland agreed they had sufficient access to their digital healthcare data online. In contrast, ** percent of surveyed adults in Iceland said they were satisfied with their access to digital health data.

Disability and Health Data System (DHDS) is an online source of state-level data on adults with disabilities. Users can access information on six functional disability types: cognitive (serious difficulty concentrating, remembering or making decisions), hearing (serious difficulty hearing or deaf), mobility (serious difficulty walking or climbing stairs), vision (serious difficulty seeing), self-care (difficulty dressing or bathing) and independent living (difficulty doing errands alone).

Since the mid-1980s, Member States of the WHO European Region have been reporting essential health-related statistics to the Health for All (HFA) family of databases, making it one of WHO’s oldest sources of data. As it is based on reported data, rather than estimates, the HFA family of databases is also particularly valuable.

HFA databases bring together the indicators that are part of major monitoring frameworks relevant to the Region, such as Health 2020 and the Sustainable Development Goals. The indicators cover basic demographics, health status, health determinants and risk factors, as well as health care resources, expenditures and more.

HFA databases allow access to regional, national and some subnational indicators and metadata, which are visualized through interactive online tools in the Health for All explorer. Data, metadata, graphs and maps can be exported or shared online and on social media.

A search-based Web service that provides access to disease, condition and wellness information via MedlinePlus health topic data in XML format. The service accepts keyword searches as requests and returns relevant MedlinePlus health topics in ranked order. The service also returns health topics summaries, search result snippets and other associated data.

Global Personal Health Record Software Market size was $8.21 Bn in 2022 and has the potential hit $15.55 Bn by 2030 with a CAGR of 8.30%

Healthcare Data Storage Market size was valued at USD 3.97 Billion in 2024 and is projected to reach USD 10.27 Billion by 2032, growing at a CAGR of 13.90% during the forecast period 2026-2032.Global Healthcare Data Storage Market DriversThe market drivers for the Healthcare Data Storage Market can be influenced by various factors. These may include:Growing volume of healthcare data: The amount of data produced by healthcare providers has increased dramatically as a result of the digitalization of medical records. This covers genomic information, medical imaging, electronic health records (EHRs), and more. To handle this data, healthcare institutions need effective and safe storage options.Severe laws and compliance requirements: HIPAA (Health Insurance Portability and Accountability Act) in the US and GDPR (General Data Protection Regulation) in Europe are two examples of the severe laws that apply to healthcare data. In order to protect patient information, these requirements mandate that healthcare organisations employ secure data storage solutions.Cloud storage is becoming more and more popular since it is affordable, flexible, and scalable, which appeals to healthcare institutions. Adoption is accelerated by cloud storage companies' provision of specialised healthcare cloud solutions that meet legal and regulatory standards.Technological developments: Artificial intelligence (AI), machine learning (ML), and big data analytics are some of the technologies that are revolutionising healthcare. To handle the massive volumes of data collected and analysed, these technologies need reliable data storage systems.Growing need for data interoperability: In order to enhance patient care coordination and results, healthcare providers are placing a greater emphasis on interoperability. This calls for the smooth transfer of medical data between various systems, which calls for trustworthy data storage options.Escalating healthcare expenses: There is pressure on healthcare institutions to save expenses without sacrificing care quality. Healthcare data management and storage operations can be made more cost-effective with the use of efficient data storage solutions.Growing comprehension of data security's significance Healthcare data breaches may result in severe repercussions, such as monetary losses and reputational harm. To safeguard patient data from online dangers, healthcare institutions are investing in secure data storage solutions.

The Health Statistics and Health Research Database is Estonian largest set of health-related statistics and survey results administrated by National Institute for Health Development. Use of the database is free of charge.

The database consists of eight main areas divided into sub-areas. The data tables included in the sub-areas are assigned unique codes. The data tables presented in the database can be both viewed in the Internet environment, and downloaded using different file formats (.px, .xlsx, .csv, .json). You can download the detailed database user manual here (.pdf).

The database is constantly updated with new data. Dates of updating the existing data tables and adding new data are provided in the release calendar. The date of the last update to each table is provided after the title of the table in the list of data tables.

A contact person for each sub-area is provided under the "Definitions and Methodology" link of each sub-area, so you can ask additional information about the data published in the database. Contact this person for any further questions and data requests.

Read more about publication of health statistics by National Institute for Health Development in Health Statistics Dissemination Principles.

The Research and Development Survey (RANDS) is a platform designed for conducting survey question evaluation and statistical research. RANDS is an ongoing series of surveys from probability-sampled commercial survey panels used for methodological research at the National Center for Health Statistics (NCHS). RANDS estimates are generated using an experimental approach that differs from the survey design approaches generally used by NCHS, including possible biases from different response patterns and sampling frames as well as increased variability from lower sample sizes. Use of the RANDS platform allows NCHS to produce more timely data than would be possible using traditional data collection methods. RANDS is not designed to replace NCHS’ higher quality, core data collections. Below are experimental estimates of reduced access to healthcare for three rounds of RANDS during COVID-19. Data collection for the three rounds of RANDS during COVID-19 occurred between June 9, 2020 and July 6, 2020, August 3, 2020 and August 20, 2020, and May 17, 2021 and June 30, 2021. Information needed to interpret these estimates can be found in the Technical Notes. RANDS during COVID-19 included questions about unmet care in the last 2 months during the coronavirus pandemic. Unmet needs for health care are often the result of cost-related barriers. The National Health Interview Survey, conducted by NCHS, is the source for high-quality data to monitor cost-related health care access problems in the United States. For example, in 2018, 7.3% of persons of all ages reported delaying medical care due to cost and 4.8% reported needing medical care but not getting it due to cost in the past year. However, cost is not the only reason someone might delay or not receive needed medical care. As a result of the coronavirus pandemic, people also may not get needed medical care due to cancelled appointments, cutbacks in transportation options, fear of going to the emergency room, or an altruistic desire to not be a burden on the health care system, among other reasons. The Household Pulse Survey (https://www.cdc.gov/nchs/covid19/pulse/reduced-access-to-care.htm), an online survey conducted in response to the COVID-19 pandemic by the Census Bureau in partnership with other federal agencies including NCHS, also reports estimates of reduced access to care during the pandemic (beginning in Phase 1, which started on April 23, 2020). The Household Pulse Survey reports the percentage of adults who delayed medical care in the last 4 weeks or who needed medical care at any time in the last 4 weeks for something other than coronavirus but did not get it because of the pandemic. The experimental estimates on this page are derived from RANDS during COVID-19 and show the percentage of U.S. adults who were unable to receive medical care (including urgent care, surgery, screening tests, ongoing treatment, regular checkups, prescriptions, dental care, vision care, and hearing care) in the last 2 months. Technical Notes: https://www.cdc.gov/nchs/covid19/rands/reduced-access-to-care.htm#limitations

Abstract

The NIS is the largest publicly available all-payer inpatient healthcare database designed to produce U.S. regional and national estimates of inpatient utilization, access, cost, quality, and outcomes. Unweighted, it contains data from around 7 million hospital stays each year. Weighted, it estimates around 35 million hospitalizations nationally. Developed through a Federal-State-Industry partnership sponsored by the Agency for Healthcare Research and Quality (AHRQ), HCUP data inform decision making at the national, State, and community levels.

Its large sample size is ideal for developing national and regional estimates and enables analyses of rare conditions, uncommon treatments, and special populations.

Usage

IMPORTANT NOTE: Some records are missing from the Severity Measures table for 2017 & 2018, but none are missing from any of the other 2012-2020 data. We are in the process of trying to recover the missing records, and will update this note when we have done so.

Also %3Cu%3EDO NOT%3C/u%3E

use this data without referring to the NIS Database Documentation, which includes:

• Description of NIS Database
• Restrictions on Use

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• Data Elements
• Additional Resources for Data Elements
• ICD-10-CM/PCS Data Included in the NIS Starting with 2015 (More details about this transition available here.)
• Known Data Issues
• NIS Supplemental Files
• HCUP Tools: Labels and Formats
• Obtaining HCUP Data

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Before Manuscript Submission

All manuscripts (and other items you'd like to publish) must be submitted to

phsdatacore@stanford.edu for approval prior to journal submission.

We will check your cell sizes and citations.

For more information about how to cite PHS and PHS datasets, please visit:

https:/phsdocs.developerhub.io/need-help/citing-phs-data-core

HCUP Online Tutorials

For additional assistance, AHRQ has created the HCUP Online Tutorial Series, a series of free, interactive courses which provide training on technical methods for conducting research with HCUP data. Topics include an HCUP Overview Course and these tutorials:

• The HCUP Sampling Design tutorial is designed to help users learn how to account for sample design in their work with HCUP national (nationwide) databases. • The Producing National HCUP Estimates tutorial is designed to help users understand how the three national (nationwide) databases – the NIS, Nationwide Emergency Department Sample (NEDS), and Kids' Inpatient Database (KID) – can be used to produce national and regional estimates. HCUP 2020 NIS (8/22/22) 14 Introduction • The Calculating Standard Errors tutorial shows how to accurately determine the precision of the estimates produced from the HCUP nationwide databases. Users will learn two methods for calculating standard errors for estimates produced from the HCUP national (nationwide) databases. • The HCUP Multi-year Analysis tutorial presents solutions that may be necessary when conducting analyses that span multiple years of HCUP data. • The HCUP Software Tools Tutorial provides instructions on how to apply the AHRQ software tools to HCUP or other administrative databases.

New tutorials are added periodically, and existing tutorials are updated when necessary. The Online Tutorial Series is located on the HCUP-US website at www.hcupus.ahrq.gov/tech_assist/tutorials.jsp.

Important notes about the 2015 data

In 2015, AHRQ restructured the data as described here:

https://hcup-us.ahrq.gov/db/nation/nis/2015HCUPNationalInpatientSample.pdf

Some key points:

• For the 2015 data, all diagnosis and procedure data elements, including any data elements derived from diagnoses and procedures, were moved out of the Core File and into the Diagnosis and Procedure Groups Files.
• Prior to 2015, and for Q1-3 of 2015, the DX1-30 and PR1-15 variables (which use ICD-9 codes) variables were used, but starting in Q4 of 2015, the I10_DX1-30 and I10_PR1-I10-15 (which use ICD-10 codes) were used. The best way to identify discharges for quarter 1-3 or quarter 4 is based on the value of the diagnosis version (DXVER); For quarters 1-3, DXVER has a value of 9; while for quarter 4, DXVER has a value of 10.
• Some other variables also transitioned in Q4 of 2015. Please refer to the link above for more details.
• Starting in 2016, the diagnosis and procedure information returned to the Core file. Additional details about the data in 2016 are available here: https://hcup-us.ahrq.gov/db/nation/nis/NISChangesBeginningDataYr2016.pdf

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NIS Areas of Research and HCUP Publications

Healthcare Data for use with GIS mapping software, databases, and web applications are from Caliper Corporation and contain point geographic files of healthcare organizations, providers, and hospitals and an boundary file of Primary Care Service Areas.

In 2023, ** percent of adults in Finland strongly agreed with the statement that they knew where they could access their digital healthcare data online. In contrast, only ** percent of the population in both Iceland and Sweden responded with a strong agreement to the statement.

Introduction

Global Big Data in Healthcare Market size is expected to be worth around USD 145.8 Billion by 2033 from USD 42.2 Billion in 2023, growing at a CAGR of 13.2% during the forecast period from 2024 to 2033.

Big data in healthcare encompasses vast amounts of diverse, unstructured data sourced from medical journals, biometric sensors, electronic medical records (EMRs), Internet of Medical Things (IoMT), social media platforms, payer records, omics research, and data repositories. Integrating this unstructured data into traditional systems presents considerable challenges, primarily in data structuring and standardization. Effective data structuring is essential for ensuring compatibility across systems and enabling robust analytical processes.

However, advancements in big data analytics, artificial intelligence, and machine learning have significantly enhanced the ability to convert complex healthcare data into actionable insights. These advancements have transformed healthcare, driving informed decision-making, enabling early and accurate diagnostics, facilitating precision medicine, and enhancing patient engagement through digital self-service platforms, including online portals, mobile applications, and wearable health devices.

The role of big data in pharmaceutical R&D has become increasingly central, as analytics tools streamline drug discovery, accelerate clinical trial processes, and identify potential therapeutic targets more efficiently. The demand for business intelligence solutions within healthcare is rising, fueled by the surge of unstructured data and the focus on developing tailored treatment protocols. As a result, the global market for big data in healthcare is projected to grow steadily during the forecast period.

The OECD Health database is an online database with comparative information on health policies and health care systems across the OECD countries (OECD). The purpose of the database is to give a broad overview of health care in the member countries. It offers a series of reports, which contain diverse cross-national policy data, on issues like long-term care for older people, high-performing health systems and private health insurance. Here we focus on policy data.

Database of the nation''s substance abuse and mental health research data providing public use data files, file documentation, and access to restricted-use data files to support a better understanding of this critical area of public health. The goal is to increase the use of the data to most accurately understand and assess substance abuse and mental health problems and the impact of related treatment systems. The data include the U.S. general and special populations, annual series, and designs that produce nationally representative estimates. Some of the data acquired and archived have never before been publicly distributed. Each collection includes survey instruments (when provided), a bibliography of related literature, and related Web site links. All data may be downloaded free of charge in SPSS, SAS, STATA, and ASCII formats and most studies are available for use with the online data analysis system. This system allows users to conduct analyses ranging from cross-tabulation to regression without downloading data or relying on other software. Another feature, Quick Tables, provides the ability to select variables from drop down menus to produce cross-tabulations and graphs that may be customized and cut and pasted into documents. Documentation files, such as codebooks and questionnaires, can be downloaded and viewed online.

BackgroundIncreasingly, researchers need to demonstrate the impact of their research to their sponsors, funders, and fellow academics. However, the most appropriate way of measuring the impact of healthcare research is subject to debate. We aimed to identify the existing methodological frameworks used to measure healthcare research impact and to summarise the common themes and metrics in an impact matrix.Methods and findingsTwo independent investigators systematically searched the Medical Literature Analysis and Retrieval System Online (MEDLINE), the Excerpta Medica Database (EMBASE), the Cumulative Index to Nursing and Allied Health Literature (CINAHL+), the Health Management Information Consortium, and the Journal of Research Evaluation from inception until May 2017 for publications that presented a methodological framework for research impact. We then summarised the common concepts and themes across methodological frameworks and identified the metrics used to evaluate differing forms of impact. Twenty-four unique methodological frameworks were identified, addressing 5 broad categories of impact: (1) ‘primary research-related impact’, (2) ‘influence on policy making’, (3) ‘health and health systems impact’, (4) ‘health-related and societal impact’, and (5) ‘broader economic impact’. These categories were subdivided into 16 common impact subgroups. Authors of the included publications proposed 80 different metrics aimed at measuring impact in these areas. The main limitation of the study was the potential exclusion of relevant articles, as a consequence of the poor indexing of the databases searched.ConclusionsThe measurement of research impact is an essential exercise to help direct the allocation of limited research resources, to maximise research benefit, and to help minimise research waste. This review provides a collective summary of existing methodological frameworks for research impact, which funders may use to inform the measurement of research impact and researchers may use to inform study design decisions aimed at maximising the short-, medium-, and long-term impact of their research.

The data extract is a series of compressed ASCII text files of the full data set contained in the Canada Vigilance Adverse Reaction Online Database. It is intended for users who are familiar with database structures and setting up their own queries. Find details on the data structure required for the data file in the Canada Vigilance Adverse Reaction Online Database - Data Structure. In order to use the data, the file must be loaded into an existing database or information system provided by the user. The Canada Vigilance Adverse Reaction Online Database contains information about suspected adverse reactions (also known as side effects) to health products, captured from adverse reaction reports submitted to Health Canada by consumers and health professionals, who submit reports voluntarily, as well as by market authorization holders (manufacturers and distributors), who are required to submit reports according to the Food and Drugs Regulations. Information concerning vaccines used for immunization have only been included in the database since January 1, 2011. Indication data has recently been added to the data extract files and the Detailed Adverse Reaction Report. Indication refers to the particular condition for which a health product was taken. For example, diabetes is an indication for insulin. Health products are often authorised for use in treating more than one indication. Note: The database cannot be used on its own to evaluate a health product's safety profile. It does not provide conclusive information on the safety of health products, and is not a substitute for medical advice. Should you have an issue of medical concern, consult a qualified health professional.

Project Overview This project explores the ethical, social, and economic implications of online health platforms that promote participatory models of data collection and utilization. These platforms, often heralded for democratizing healthcare, also raise significant concerns about privacy, data commodification, and exploitation. By using qualitative interviews and reviewing the literature, this project examined how such platforms influence healthcare dynamics. While these platforms fostered user empowerment, they simultaneously created environments where new forms of surveillance may emerge. This project emphasized the importance of addressing these concerns through rigorous technology assessment (TA), particularly in regions governed by strict data protection regulations such as the General Data Protection Regulation (GDPR) in Europe. By examining the socio-ethical challenges these platforms introduce, the study suggests pathways for ensuring that the benefits of participatory platforms are distributed equitably and ethically. Data and Data Collection Overview This study uses a qualitative research design, combining interviews with active users of participatory health platforms and a critical literature review. The research was conducted from 2019 to 2021, with interviews focusing on users of the PatientsLikeMe (PLM) platform, a global health community that allows users to share health experiences, treatment data, and personal stories. Twenty participants were recruited through PatientsLikeMe between March 2019 and May 2021. The interviews, each lasting between 60 and 90 minutes, were conducted via video conferencing platforms due to the restrictions imposed by the COVID-19 pandemic. Interview questions focused on participants’ experiences with data sharing, their perceptions of privacy, and their understanding of how their health data were used by the platform. Participants were also asked about their motivations for participating in the platform and any concerns they had about the commodification of their personal health data. The interviews were pseudonymized to protect participants’ identities, and all personal data were stored securely in accordance with GDPR regulations. Given the sensitive nature of health data, particular attention was paid to data security, both during the interviews and in the subsequent analysis. The interview data were analyzed using a grounded theory approach informed by a constructivist approach, which focuses on the co-production of meaning between participants and researcher. This involved coding the transcripts for key themes and patterns. The coding process was iterative, with themes such as empowerment, privacy concerns, and the commodification of health data emerging from the data. A literature review complemented the interview findings by contextualizing these themes within broader ethical discussions surrounding participatory health platforms. Particular attention was paid to how GDPR, and similar regulations in other regions, influence users’ experiences and perceptions of privacy on these platforms. Selection and Organization of Shared Data The key data file shared here summarizes observational notes from interviews with all 20 participants in the study, detailing contextual insights gathered during interviews, which contribute to a deeper understanding of participant perspectives. The documentation files shared consist of the original informed consent used, a thematic codebook developed during analysis, this Data Narrative and an administrative README file.

Maternal health data is of great importance to the understanding and improvement of maternal healthcare services. However, the availability and quality of this data remain scarce for most researchers around the world. In many regions, obtaining detailed and reliable maternal health data is very challenging due to the lack of comprehensive data collection and inconsistent records. This has created critical gaps in data availability to support and enhance maternal health research and services. To bridge this gap, we created a curated dataset of maternal healthcare questions and answers with the oversight of medical experts. The dataset, titled "MOTHER: Maternal Online Technology for Health Care," provides time-relevant information for maternal health questions. The data was collected in 2023 and includes questions and answers related to various aspects of maternal health. This dataset aims to provide informative responses to common questions that pregnant women or those using family planning health services might have regarding their health. The questions and answers are designed to address various concerns, symptoms, and conditions associated with pregnancy. This work was funded by the Makerere University Research and Innovation Fund, RIF