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.

Context:

This synthetic healthcare dataset has been created to serve as a valuable resource for data science, machine learning, and data analysis enthusiasts. It is designed to mimic real-world healthcare data, enabling users to practice, develop, and showcase their data manipulation and analysis skills in the context of the healthcare industry.

Inspiration:

The inspiration behind this dataset is rooted in the need for practical and diverse healthcare data for educational and research purposes. Healthcare data is often sensitive and subject to privacy regulations, making it challenging to access for learning and experimentation. To address this gap, I have leveraged Python's Faker library to generate a dataset that mirrors the structure and attributes commonly found in healthcare records. By providing this synthetic data, I hope to foster innovation, learning, and knowledge sharing in the healthcare analytics domain.

Dataset Information:

Each column provides specific information about the patient, their admission, and the healthcare services provided, making this dataset suitable for various data analysis and modeling tasks in the healthcare domain. Here's a brief explanation of each column in the dataset - - Name: This column represents the name of the patient associated with the healthcare record. - Age: The age of the patient at the time of admission, expressed in years. - Gender: Indicates the gender of the patient, either "Male" or "Female." - Blood Type: The patient's blood type, which can be one of the common blood types (e.g., "A+", "O-", etc.). - Medical Condition: This column specifies the primary medical condition or diagnosis associated with the patient, such as "Diabetes," "Hypertension," "Asthma," and more. - Date of Admission: The date on which the patient was admitted to the healthcare facility. - Doctor: The name of the doctor responsible for the patient's care during their admission. - Hospital: Identifies the healthcare facility or hospital where the patient was admitted. - Insurance Provider: This column indicates the patient's insurance provider, which can be one of several options, including "Aetna," "Blue Cross," "Cigna," "UnitedHealthcare," and "Medicare." - Billing Amount: The amount of money billed for the patient's healthcare services during their admission. This is expressed as a floating-point number. - Room Number: The room number where the patient was accommodated during their admission. - Admission Type: Specifies the type of admission, which can be "Emergency," "Elective," or "Urgent," reflecting the circumstances of the admission. - Discharge Date: The date on which the patient was discharged from the healthcare facility, based on the admission date and a random number of days within a realistic range. - Medication: Identifies a medication prescribed or administered to the patient during their admission. Examples include "Aspirin," "Ibuprofen," "Penicillin," "Paracetamol," and "Lipitor." - Test Results: Describes the results of a medical test conducted during the patient's admission. Possible values include "Normal," "Abnormal," or "Inconclusive," indicating the outcome of the test.

Usage Scenarios:

This dataset can be utilized for a wide range of purposes, including: - Developing and testing healthcare predictive models. - Practicing data cleaning, transformation, and analysis techniques. - Creating data visualizations to gain insights into healthcare trends. - Learning and teaching data science and machine learning concepts in a healthcare context. - You can treat it as a Multi-Class Classification Problem and solve it for Test Results which contains 3 categories(Normal, Abnormal, and Inconclusive).

Acknowledgments:

• I acknowledge the importance of healthcare data privacy and security and emphasize that this dataset is entirely synthetic. It does not contain any real patient information or violate any privacy regulations.
• I hope that this dataset contributes to the advancement of data science and healthcare analytics and inspires new ideas. Feel free to explore, analyze, and share your findings with the Kaggle community.

Image Credit:

Image by BC Y from Pixabay

HERO contains the key studies EPA uses to develop environmental risk assessments for the public. EPA uses risk assessments to characterize the nature and magnitude of health risks to humans and the ecosystem from pollutants and chemicals in the environment.

My HealtheVet (www.myhealth.va.gov) is a Personal Health Record portal designed to improve the delivery of health care services to Veterans, to promote health and wellness, and to engage Veterans as more active participants in their health care. The My HealtheVet portal enables Veterans to create and maintain a web-based PHR that provides access to patient health education information and resources, a comprehensive personal health journal, and electronic services such as online VA prescription refill requests and Secure Messaging. Veterans can visit the My HealtheVet website and self-register to create an account, although registration is not required to view the professionally-sponsored health education resources, including topics of special interest to the Veteran population. Once registered, Veterans can create a customized PHR that is accessible from any computer with Internet access.

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, ** percent of the patients in Iceland reported accessing their online health data to see their medication and/or renew their prescriptions.

Our highly-targeted consumer healthcare database includes:

🗸 Name 🗸 Postal Address, Email Address, Telephone Number 🗸 Age, Gender 🗸 Most likely to ask a Doctor About an Advertised Prescription Medicine 🗸 Most likely looked for Medical Information on the Web 🗸 Most Likely to Prefer Brand Name Medicines 🗸 Most Likely to Buy Prescriptions through the Mail

The dataset is available for purchase by US region: 🗸 New England (Connecticut, Maine, Massachusetts, New Hampshire, Rhode Island, and Vermont) 🗸 Middle Atlantic (New Jersey, New York, and Pennsylvania) 🗸 East North Central (Illinois, Indiana, Michigan, Ohio, and Wisconsin) 🗸 West North Central (Iowa, Kansas, Minnesota, Missouri, Nebraska, North Dakota, and South Dakota) 🗸 South Atlantic (Delaware; Florida; Georgia; Maryland; North Carolina; South Carolina; Virginia; Washington, D.C. and West Virginia) 🗸 East South Central (Alabama, Kentucky, Mississippi, and Tennessee) 🗸 West South Central (Arkansas, Louisiana, Oklahoma, and Texas) 🗸 Mountain (Arizona, Colorado, Idaho, Montana, Nevada, New Mexico, Utah, and Wyoming) 🗸 Pacific (Alaska, California, Hawaii, Oregon, and Washington)

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.

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.

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.

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.

This dataset appears to contain a variety of features related to text analysis, sentiment analysis, and psychological indicators, likely derived from posts or text data. Some features include readability indices such as Automated Readability Index (ARI), Coleman Liau Index, and Flesch-Kincaid Grade Level, as well as sentiment analysis scores like sentiment compound, negative, neutral, and positive scores. Additionally, there are features related to psychological aspects such as economic stress, isolation, substance use, and domestic stress. The dataset seems to cover a wide range of linguistic, psychological, and behavioural attributes, potentially suitable for analyzing mental health-related topics in online communities or text data.

Benefits of using this dataset:

• Insight into Mental Health: The dataset provides valuable insights into mental health by analyzing linguistic patterns, sentiment, and psychological indicators in text data. Researchers and data scientists can gain a better understanding of how mental health issues manifest in online communication.
• Predictive Modeling: With a wide range of features, including sentiment analysis scores and psychological indicators, the dataset offers opportunities for developing predictive models to identify or predict mental health outcomes based on textual data. This can be useful for early intervention and support.
• Community Engagement: Mental health is a topic of increasing importance, and this dataset can foster community engagement on platforms like Kaggle. Data enthusiasts, researchers, and mental health professionals can collaborate to analyze the data and develop solutions to address mental health challenges.
• Data-driven Insights: By analyzing the dataset, users can uncover correlations and patterns between linguistic features, sentiment, and mental health indicators. These insights can inform interventions, policies, and support systems aimed at promoting mental well-being.
• Educational Resource: The dataset can serve as a valuable educational resource for teaching and learning about mental health analytics, sentiment analysis, and text mining techniques. It provides a real-world dataset for students and practitioners to apply data science skills in a meaningful context.

Background: In Brazil, studies that map electronic healthcare databases in order to assess their suitability for use in pharmacoepidemiologic research are lacking. We aimed to identify, catalogue, and characterize Brazilian data sources for Drug Utilization Research (DUR).Methods: The present study is part of the project entitled, “Publicly Available Data Sources for Drug Utilization Research in Latin American (LatAm) Countries.” A network of Brazilian health experts was assembled to map secondary administrative data from healthcare organizations that might provide information related to medication use. A multi-phase approach including internet search of institutional government websites, traditional bibliographic databases, and experts’ input was used for mapping the data sources. The reviewers searched, screened and selected the data sources independently; disagreements were resolved by consensus. Data sources were grouped into the following categories: 1) automated databases; 2) Electronic Medical Records (EMR); 3) national surveys or datasets; 4) adverse event reporting systems; and 5) others. Each data source was characterized by accessibility, geographic granularity, setting, type of data (aggregate or individual-level), and years of coverage. We also searched for publications related to each data source.Results: A total of 62 data sources were identified and screened; 38 met the eligibility criteria for inclusion and were fully characterized. We grouped 23 (60%) as automated databases, four (11%) as adverse event reporting systems, four (11%) as EMRs, three (8%) as national surveys or datasets, and four (11%) as other types. Eighteen (47%) were classified as publicly and conveniently accessible online; providing information at national level. Most of them offered more than 5 years of comprehensive data coverage, and presented data at both the individual and aggregated levels. No information about population coverage was found. Drug coding is not uniform; each data source has its own coding system, depending on the purpose of the data. At least one scientific publication was found for each publicly available data source.Conclusions: There are several types of data sources for DUR in Brazil, but a uniform system for drug classification and data quality evaluation does not exist. The extent of population covered by year is unknown. Our comprehensive and structured inventory reveals a need for full characterization of these data sources.

Field studies have been performed for decades to analyze effects of different management practices on agricultural soils and crop yields, but these data have never been combined together in a way that can inform current and future cropland management. Here, we collected, extracted, and integrated a database of soil health measurements conducted in the field from sites across the globe. The database, named SoilHealthDB, currently focuses on four main conservation management methods: cover crops, no-tillage, agro-forestry systems, and organic farming. These studies represent 354 geographic sites (i.e., locations with unique latitudes and longitudes) in 42 countries around the world. The SoilHealthDB includes 42 soil health indicators and 46 background indicators that describe factors such as climate, elevation, and soil type. A primary goal of this effort is to enable the research community to perform comprehensive analyses, e.g., meta-analyses, of soil health changes related to cropland conservation management. The database also provides a common framework for sharing soil health, and the scientific research community is encouraged to contribute their own measurements. Resources in this dataset:Resource Title: Data Records - A database for global soil health assessment. File Name: Web Page, url: https://doi.org/10.1038/s41597-020-0356-3 The data and R code can be downloaded in figshare; there are two folders, named data and RScripts, when ‘SoilHealthDB.zip’ is unzipped. ‘SoilHealthDB_V1.xlsx’ in the data file currently includes 5,907 rows and 268 columns, which were retrieved from 321 papers (for the detailed reference list please refer to ‘References’ under ‘SoilHealthDB_V1.xlsx’). Each column corresponds to one data point of either background information or soil health indicator, and each row includes as many as 42 comparisons between treatments and controls (if all soil health indicators have data). The names, attributes, and descriptions of the background information and soil health indicators are presented in Online-only Tables 1 and 2. It should be noted that different measurements and/or units may be involved in the same soil health indicator (e.g., soil total nitrogen, soil organic nitrogen, or soil inorganic nitrogen are reported in different papers to represent the soil nitrogen indicator, ID 5 in Online-only Table 2); therefore, it is important that measurement objectives, units, and other detailed descriptions are recorded in the comments columns. It should also be noted that for some soil health indicators (e.g., CH4 and N2O emission), we were only able to extract limited numbers of comparisons, which may restrain the ability of those data to be used in further analyses. ‘SoilHealthDB_V1.csv’ is a simplified version of ‘SoilHealthDB_V1.xlsx’, with only soil health background and indicator information kept (e.g., all the description sheets were not kept). There are two R scripts in the ‘RScripts’ folder: the ‘SoilHealthDB_quality_check.R’ script includes code for quality check of the ‘SoilHealthDB’, and the ‘functions.R’ script defines several functions, including one to generate the location of the site in ‘SoilHealthDB’. The SoilHealthDB_V1.csv file is to be used when running the R codes.

As more patients begin to take control of their care, it is essential to know if these information technology options are meeting the expectation of effective communication between user and doctor to improving health outcomes patients. The business question here is, "does the utilization of online medical records meet patients' expectations to take control of their care?". The intent is to provide data for this area and compare it with nationwide studies and to establish if the online medical record is indeed living up to its intended goals or benefits. The findings will help make improvements in areas of concern to help move us further into patient-centered health care that enhances effective communication, empathy, and a feeling of partnership between caregivers and patients.

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.

A Web service that allows patient portals and electronic health record (EHR) systems to use existing code sets to link to relevant, authoritative health information from MedlinePlus.gov. Matches ICD-9-CM or SNOMED CT codes to related MedlinePlus consumer health information, LOINC codes to information on lab tests, and also matches NDC, RXCUI or text strings to patient medication information.

IntroductionData science training has the potential to propel environmental health research efforts into territories that remain untapped and holds immense promise to change our understanding of human health and the environment. Though data science training resources are expanding, they are still limited in terms of public accessibility, user friendliness, breadth of content, tangibility through real-world examples, and applicability to the field of environmental health science.MethodsTo fill this gap, we developed an environmental health data science training resource, the inTelligence And Machine lEarning (TAME) Toolkit, version 2.0 (TAME 2.0).ResultsTAME 2.0 is a publicly available website that includes training modules organized into seven chapters. Training topics were prioritized based upon ongoing engagement with trainees, professional colleague feedback, and emerging topics in the field of environmental health research (e.g., artificial intelligence and machine learning). TAME 2.0 is a significant expansion upon the original TAME training resource pilot. TAME 2.0 specifically includes training organized into the following chapters: (1) Data management to enable scientific collaborations; (2) Coding in R; (3) Basics of data analysis and visualizations; (4) Converting wet lab data into dry lab analyses; (5) Machine learning; (6) Applications in toxicology and exposure science; and (7) Environmental health database mining. Also new to TAME 2.0 are “Test Your Knowledge” activities at the end of each training module, in which participants are asked additional module-specific questions about the example datasets and apply skills introduced in the module to answer them. TAME 2.0 effectiveness was evaluated via participant surveys during graduate-level workshops and coursework, as well as undergraduate-level summer research training events, and suggested edits were incorporated while overall metrics of effectiveness were quantified.DiscussionCollectively, TAME 2.0 now serves as a valuable resource to address the growing demand of increased data science training in environmental health research. TAME 2.0 is publicly available at: https://uncsrp.github.io/TAME2/.

A database of federally funded biomedical research projects conducted at universities, hospitals, and other research institutions that provides a central point of access to reports, data, and analyses of NIH research. The RePORTER has replaced the CRISP database. The database, maintained by the Office of Extramural Research at the National Institutes of Health, includes projects funded by the National Institutes of Health (NIH), Substance Abuse and Mental Health Services (SAMHSA), Health Resources and Services Administration (HRSA), Food and Drug Administration (FDA), Centers for Disease Control and Prevention (CDCP), Agency for Health Care Research and Quality (AHRQ), and Office of Assistant Secretary of Health (OASH).

This dataset appears to contain a variety of features related to text analysis, sentiment analysis, and psychological indicators, likely derived from posts or text data. Some features include readability indices such as Automated Readability Index (ARI), Coleman Liau Index, and Flesch-Kincaid Grade Level, as well as sentiment analysis scores like sentiment compound, negative, neutral, and positive scores. Additionally, there are features related to psychological aspects such as economic stress, isolation, substance use, and domestic stress. The dataset seems to cover a wide range of linguistic, psychological, and behavioral attributes, potentially suitable for analyzing mental health-related topics in online communities or text data.

Benefits of using this dataset:

• Insight into Mental Health: The dataset provides valuable insights into mental health by analyzing linguistic patterns, sentiment, and psychological indicators in text data. Researchers and data scientists can gain a better understanding of how mental health issues manifest in online communication.
• Predictive Modeling: With a wide range of features, including sentiment analysis scores and psychological indicators, the dataset offers opportunities for developing predictive models to identify or predict mental health outcomes based on textual data. This can be useful for early intervention and support.
• Community Engagement: Mental health is a topic of increasing importance, and this dataset can foster community engagement on platforms like Kaggle. Data enthusiasts, researchers, and mental health professionals can collaborate to analyze the data and develop solutions to address mental health challenges.
• Data-driven Insights: By analyzing the dataset, users can uncover correlations and patterns between linguistic features, sentiment, and mental health indicators. These insights can inform interventions, policies, and support systems aimed at promoting mental well-being.
• Educational Resource: The dataset can serve as a valuable educational resource for teaching and learning about mental health analytics, sentiment analysis, and text mining techniques. It provides a real-world dataset for students and practitioners to apply data science skills in a meaningful context.

The online Health Profiles data has been updated for May 2016.

The online Health Profiles are now being updated quarterly at the same time as the Public Health Outcomes Framework (PHOF). The 4-page local authority PDF documents are updated annually and were last updated in July 2015. The PDFs will next be updated in September or October 2016.

The data are presented in an interactive tool that allows users to view them in a user-friendly format. The profiles provide a snapshot overview of health for each local authority in England. These profiles are intended to help local government and health services make plans to improve local people’s health and reduce health inequalities.

This quarterly update contains:

• more recent data for 5 indicators
• changes to 1 indicator to take account of revisions to the underlying data
• updates to 3 indicators that are currently in the PDF Health Profiles reports and are being included in the online interactive Health Profiles tool for the first time

http://fingertips.phe.org.uk/profile/health-profiles">View the online Health Profiles.