Mental Health in the Tech Industry

Exploring Mental Health Conditions in the Tech Industry

By Olga Tsubiks [source]

About this dataset

This dataset contains survey responses from the tech industry about mental health, offering an insightful snapshot into the diagnoses, treatments, and attitudes of those in the field towards mental health. These data points allow people to understand more about how their peers in tech view mental health and can provide greater insight into how to better support those who work in this industry. This dataset includes questions on whether or not respondents have had a mental health disorder or sought treatment for a mental health issue in the past, if they currently have been diagnosed with a condition and what it is, their age group, location of work and residence as well as information on whether they are self-employed or working at a tech company with other questions. Additionally, this dataset also provides insight into respondents' attitudes towards speaking openly about their mental wellbeing versus physical wellbeing. To gain even more understanding of individual's experiences within their place of business overall employee count is included as well what role they fill within that organisation is related to technology/IT. This valuable data set may be used for medical research furthering our knowledge about workplace stressors effecting people seen within this particular field but also across multiple industries to help create support systems that reflect upon individual need rather than one-size fits all models previously employed by employers through out many parts globally

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Research Ideas

• Analyze the correlation between employment industry and mental health status, including self-identified diagnosis, use of mental health services and any history of mental illness in the family.
• Determine if there are differences in how people experience and speak out about their own mental health based on geographic location.
• Compare attitudes towards open conversations on physical vs mental health within different age groups both in the U.S. and abroad

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: Dataset copyright by authors - You are free to: - Share - copy and redistribute the material in any medium or format for any purpose, even commercially. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original. - Keep intact - all notices that refer to this license, including copyright notices.

Columns

File: OSMI_Survey_Data.csv | Column name | Description | |:-----------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------| | Are you selfemployed | Indicates whether the respondent is self-employed or not. (Boolean) | | How many employees does your company or organization have | Indicates the number of employees in the respondent's company or organization. (Numeric) | | Is your employer primarily a tech companyorganization | Indicates whether the respondent's employer is primarily a tech company or organization. (Boolean) | | Is your primary role within your company related to techIT | Indicates whether the respondent's primary role within their company is related to tech or IT. (Boolean) | | Do you have previous employers | Indicates whether the respondent has had previous employers. (Boolean) ...

The AI Training Dataset In Healthcare Market size was valued at USD 341.8 million in 2023 and is projected to reach USD 1464.13 million by 2032, exhibiting a CAGR of 23.1 % during the forecasts period. The growth is attributed to the rising adoption of AI in healthcare, increasing demand for accurate and reliable training datasets, government initiatives to promote AI in healthcare, and technological advancements in data collection and annotation. These factors are contributing to the expansion of the AI Training Dataset In Healthcare Market. Healthcare AI training data sets are vital for building effective algorithms, and enhancing patient care and diagnosis in the industry. These datasets include large volumes of Electronic Health Records, images such as X-ray and MRI scans, and genomics data which are thoroughly labeled. They help the AI systems to identify trends, forecast and even help in developing unique approaches to treating the disease. However, patient privacy and ethical use of a patient’s information is of the utmost importance, thus requiring high levels of anonymization and compliance with laws such as HIPAA. Ongoing expansion and variety of datasets are crucial to address existing bias and improve the efficiency of AI for different populations and diseases to provide safer solutions for global people’s health.

This is Health insurance Data to analyze Sales , internal operations and market size of a health insurance company . To analyze the sales, internal operations, and market size of a health insurance company, you would need access to relevant data. While I don't have real-time data, I can provide you with a general outline of the types of data you may need to analyze these aspects. Here are some key data points to consider:

Sales Analysis:

Monthly/quarterly/annual premium revenue Number of policies sold Premiums by product types (e.g., individual, family, group) Sales channels (e.g., agents, brokers, online) Internal Operations Analysis:

Claims data: Number of claims filed, paid, and denied Claim settlement time and ratios Customer service metrics (e.g., response time, satisfaction ratings) Underwriting metrics (e.g., policy acceptance rate, risk assessment) Market Analysis:

Market share: Percentage of the total health insurance market held by the company Competition analysis: Market share of competitors, their product offerings, and pricing Demographics: Age, income, location, and other relevant demographic information of policyholders Regulatory factors: Changes in regulations or laws affecting the health insurance industry Other data points that could be useful for analysis include customer retention rates, profitability analysis, marketing expenditure, and customer feedback.

Keep in mind that this is a general overview, and the specific data requirements may vary based on your company's unique goals and objectives. Additionally, it's important to handle and analyze this data in compliance with relevant privacy and data protection laws.

Discover the booming medical database software market! Learn about its $15 billion valuation in 2025, projected 12% CAGR to 2033, key drivers, regional trends, and leading companies. Explore EHR, HIM systems impacting healthcare.

Explore the Global OTC Health Products Market size, trends, key players, and forecast insights driving growth in self-care, wellness, and consumer healthcare.

Survey of advanced technology, development or production of medical devices for human health, by North American Industry Classification System (NAICS) and enterprise size for Canada and certain provinces, in 2014.

Description of the Dataset 1. Dataset Overview

Name: Wellness Technology Market Analysis Dataset Purpose: This dataset is designed to analyze various factors influencing the success of wellness technology companies. It aims to identify strategic opportunities and challenges in the wellness tech industry by evaluating market trends, customer behavior, and competitive dynamics. 2. Key Attributes

Company ID: A unique identifier for each wellness technology company. Company Name: The name of the company. Product Categories: Types of wellness products offered (e.g., wearables, fitness apps, mental health platforms). Market Share: Percentage of market share held by the company in different regions. Revenue: Annual revenue generated by the company (numerical, in USD). Customer Satisfaction Score: Average customer satisfaction ratings (numerical, e.g., 1 to 10 scale). Investment Amount: Total investment received by the company (numerical, in USD). Product Features: Key features of each product (categorical, e.g., heart rate monitoring, sleep tracking). Competitive Position: Assessment of the company’s position relative to competitors (categorical, e.g., leader, challenger, niche). Innovation Index: An index score representing the level of innovation in the company’s product offerings (numerical). Marketing Spend: Annual expenditure on marketing and promotional activities (numerical, in USD). User Demographics: Age, gender, and location of the users (categorical and numerical). 3. Data Collection Method

Sources: The data was collected from a combination of primary and secondary sources:

Industry Reports: Data was sourced from market research reports and industry analysis published by organizations like Gartner, IDC, and Statista.

Company Financial Statements: Financial information and market share data were obtained from public financial reports and investor relations sections of company websites.

Customer Reviews and Ratings: Customer satisfaction scores and feedback were collected from review platforms such as Trustpilot, Google Reviews, and app store ratings.

Surveys and Interviews: Direct surveys and interviews with industry experts, company executives, and customers were conducted to gather qualitative insights into product features and competitive positioning.

Market Analysis Tools: Tools like Google Trends and social media analytics were used to assess market trends and consumer sentiment.

Collection Tools and Techniques:

Web Scraping: Automated scripts were used to extract data from online reviews and financial websites. APIs: Data was pulled from APIs provided by financial databases and market analysis tools. Surveys: Surveys were administered using platforms like SurveyMonkey to gather direct feedback from stakeholders. Data Quality Assurance:

Data Cleaning: Involves handling missing values, correcting data inconsistencies, and ensuring accurate data entry. Validation: Data was cross-verified with multiple sources to ensure reliability and accuracy. 4. Dataset Size and Format

Size: The dataset comprises data from [number of companies, e.g., 50] wellness technology companies and covers [number of records, e.g., 500] individual data points. Format: The data is stored in [format, e.g., Excel spreadsheets, SQL database] for ease of analysis and integration with analytical tools. 5. Privacy and Compliance

Data Privacy: All data collected is anonymized to ensure the privacy of individuals and companies. Compliance: The data collection process adheres to relevant data protection regulations such as GDPR and CCPA, ensuring proper consent and secure handling of data.

Real World Evidence Solutions Market size was valued at USD 1.30 Billion in 2024 and is projected to reach USD 3.71 Billion by 2032, growing at a CAGR of 13.92% during the forecast period 2026-2032.Global Real World Evidence Solutions Market DriversThe market drivers for the Real World Evidence Solutions Market can be influenced by various factors. These may include:Growing Need for Evidence-Based Healthcare: Real-world evidence (RWE) is becoming more and more important in healthcare decision-making, according to stakeholders such as payers, providers, and regulators. In addition to traditional clinical trial data, RWE solutions offer important insights into the efficacy, safety, and value of healthcare interventions in real-world situations.Growing Use of RWE by Pharmaceutical Companies: RWE solutions are being used by pharmaceutical companies to assist with market entry, post-marketing surveillance, and drug development initiatives. Pharmaceutical businesses can find new indications for their current medications, improve clinical trial designs, and convince payers and providers of the worth of their products with the use of RWE.Increasing Priority for Value-Based Healthcare: The emphasis on proving the cost- and benefit-effectiveness of healthcare interventions in real-world settings is growing as value-based healthcare models gain traction. To assist value-based decision-making, RWE solutions are essential in evaluating the economic effect and real-world consequences of healthcare interventions.Technological and Data Analytics Advancements: RWE solutions are becoming more capable due to advances in machine learning, artificial intelligence, and big data analytics. With the use of these technologies, healthcare stakeholders can obtain actionable insights from the analysis of vast and varied datasets, including patient-generated data, claims data, and electronic health records.Regulatory Support for RWE Integration: RWE is being progressively integrated into regulatory decision-making processes by regulatory organisations including the European Medicines Agency (EMA) and the U.S. Food and Drug Administration (FDA). The FDA's Real-World Evidence Programme and the EMA's Adaptive Pathways and PRIority MEdicines (PRIME) programme are two examples of initiatives that are making it easier to incorporate RWE into regulatory submissions and drug development.Increasing Emphasis on Patient-Centric Healthcare: The value of patient-reported outcomes and real-world experiences in healthcare decision-making is becoming more widely acknowledged. RWE technologies facilitate the collection and examination of patient-centered data, offering valuable insights into treatment efficacy, patient inclinations, and quality of life consequences.Extension of RWE Use Cases: RWE solutions are being used in medication development, post-market surveillance, health economics and outcomes research (HEOR), comparative effectiveness research, and market access, among other healthcare fields. The necessity for a variety of RWE solutions catered to the needs of different stakeholders is being driven by the expansion of RWE use cases.

The AI Training Dataset in Healthcare market is poised for substantial growth, projected to reach an estimated market size of approximately $1,500 million by 2025, with a Compound Annual Growth Rate (CAGR) of around 25% anticipated through 2033. This robust expansion is fueled by the escalating demand for accurate and comprehensive datasets essential for training sophisticated AI models in healthcare applications. Key drivers include the increasing adoption of Electronic Health Records (EHRs), the growing sophistication of medical imaging analysis, and the proliferation of wearable devices that generate vast amounts of patient data. Furthermore, the rapid advancements in telemedicine, amplified by recent global health events, necessitate highly refined datasets to power remote diagnostics, personalized treatment plans, and predictive analytics. The market's dynamism is also evident in its segmentation; text-based data, encompassing clinical notes and research papers, currently holds a significant share due to its foundational role in natural language processing for healthcare. However, image/video data, crucial for medical imaging interpretation and surgical simulations, is expected to witness accelerated growth. The competitive landscape is characterized by the presence of major technology giants and specialized AI data providers, including Google, Microsoft, Amazon Web Services, and Scale AI, alongside niche players like Alegion and Appen Limited. These companies are actively investing in data annotation, curation, and synthetic data generation to address the unique challenges of healthcare data, such as privacy concerns (HIPAA compliance) and the need for domain expertise. Emerging trends like federated learning and explainable AI are further shaping the market, requiring new approaches to data training and validation. Restraints, such as stringent regulatory frameworks and the high cost of acquiring and annotating high-quality, diverse healthcare data, are being addressed through technological innovations and strategic partnerships. The Asia Pacific region, particularly China and India, is emerging as a significant growth hub due to the expanding digital health infrastructure and a growing focus on AI adoption in healthcare. This comprehensive report delves into the burgeoning AI Training Dataset market within the healthcare sector. Analyzing the period from 2019 to 2033, with a focus on the base year 2025, this study provides an in-depth understanding of market dynamics, key players, and future projections. The global market for AI training datasets in healthcare is projected to reach millions by 2025 and experience significant growth throughout the forecast period.

Healthcare Cloud Based Analytics Market Outlook

The global healthcare cloud based analytics market size was valued at approximately USD 14.8 billion in 2023, and it is anticipated to reach around USD 54.3 billion by 2032, growing at a compound annual growth rate (CAGR) of 15.7% from 2024 to 2032. One of the primary growth factors influencing this market is the increasing demand for data-driven decision-making processes in healthcare settings to enhance patient outcomes and operational efficiency.

One significant growth factor for the healthcare cloud based analytics market is the rapid digital transformation within the healthcare sector. The transition from paper-based systems to electronic health records (EHRs) and the adoption of telehealth services are driving the need for sophisticated analytics solutions that can process vast amounts of healthcare data. The accessibility and scalability offered by cloud-based solutions make them particularly attractive for healthcare providers looking to leverage patient data for better diagnostic and treatment outcomes.

Moreover, the rising focus on personalized medicine and the need for population health management are propelling the demand for healthcare cloud based analytics. Personalized medicine requires the analysis of large datasets to understand individual patient profiles and predict responses to treatments. Similarly, population health management aims to improve health outcomes by analyzing data to identify trends and intervene proactively. Cloud-based analytics platforms provide the necessary computational power and flexibility to handle these complex data requirements efficiently.

The cost-efficiency of cloud based solutions compared to traditional on-premises systems is another crucial growth driver. Healthcare organizations are under constant pressure to reduce operational costs while improving patient care quality. Cloud-based analytics solutions eliminate the need for significant upfront investments in hardware and software while offering the benefits of scalable resources and reduced IT maintenance costs. This financial advantage is particularly appealing to small and medium-sized healthcare providers who may have limited budgets for technology investments.

The integration of Business Intelligence in Healthcare is transforming the way data is utilized to improve patient care and streamline operations. By employing BI tools, healthcare organizations can analyze vast datasets to uncover insights that drive better decision-making. These tools enable healthcare providers to track patient outcomes, optimize resource allocation, and enhance overall operational efficiency. The ability to visualize data through dashboards and reports allows for a deeper understanding of patient trends and organizational performance, ultimately leading to improved healthcare delivery and patient satisfaction.

From a regional perspective, North America currently holds the largest market share in the healthcare cloud based analytics market, driven by advanced healthcare infrastructure and high adoption rates of digital healthcare technologies. However, regions like Asia Pacific are expected to witness the highest growth rates during the forecast period. Factors such as increasing healthcare expenditures, growing awareness about the benefits of healthcare analytics, and supportive government initiatives are contributing to the market expansion in these regions.

Component Analysis

The healthcare cloud based analytics market can be segmented by component into software and services. The software segment includes various analytics platforms and tools designed to process and analyze healthcare data. These software solutions are essential for enabling healthcare providers to harness the power of big data and derive actionable insights. As the volume of healthcare data continues to grow exponentially, the demand for robust and scalable analytics software solutions is expected to increase significantly. Innovations in artificial intelligence and machine learning are also enhancing the capabilities of these software solutions, making them more effective in predictive analytics and decision support.

Cloud Computing in Healthcare is revolutionizing the way healthcare data is stored, accessed, and analyzed. By leveraging cloud technology, healthcar

The Organisation for Economic Co-operation and Development (OECD) Health Statistics offers the most comprehensive source of comparable statistics on health and health systems across OECD countries. It is an essential tool for health researchers and policy advisors in governments, the private sector and the academic community, to carry out comparative analyses and draw lessons from international comparisons of diverse health care systems. Within UKDS.Stat the data are presented in the following databases:

Health status

This datasets presents internationally comparable statistics on morbidity and mortality with variables such as life expectancy, causes of mortality, maternal and infant mortality, potential years of life lost, perceived health status, infant health, dental health, communicable diseases, cancer, injuries, absence from work due to illness. The annual data begins in 2000.

Non-medical determinants of health

This dataset examines the non-medical determinants of health by comparing food, alcohol, tobacco consumption and body weight amongst countries. The data are expressed in different measures such as calories, grammes, kilo, gender, population. The data begins in 1960.

Healthcare resources

This dataset includes comparative tables analyzing various health care resources such as total health and social employment, physicians by age, gender, categories, midwives, nurses, caring personnel, personal care workers, dentists, pharmacists, physiotherapists, hospital employment, graduates, remuneration of health professionals, hospitals, hospital beds, medical technology with their respective subsets. The statistics are expressed in different units of measure such as number of persons, salaried, self-employed, per population. The annual data begins in 1960.

Healthcare utilisation

This dataset includes statistics comparing different countries’ level of health care utilisation in terms of prevention, immunisation, screening, diagnostics exams, consultations, in-patient utilisation, average length of stay, diagnostic categories, acute care, in-patient care, discharge rates, transplants, dialyses, ICD-9-CM. The data is comparable with respect to units of measures such as days, percentages, population, number per capita, procedures, and available beds.

Health Care Quality Indicators

This dataset includes comparative tables analyzing various health care quality indicators such as cancer care, care for acute exacerbation of chronic conditions, care for chronic conditions and care for mental disorders. The annual data begins in 1995.

Pharmaceutical market

This dataset focuses on the pharmaceutical market comparing countries in terms of pharmaceutical consumption, drugs, pharmaceutical sales, pharmaceutical market, revenues, statistics. The annual data begins in 1960.

Long-term care resources and utilisation

This dataset provides statistics comparing long-term care resources and utilisation by country in terms of workers, beds in nursing and residential care facilities and care recipients. In this table data is expressed in different measures such as gender, age and population. The annual data begins in 1960.

Health expenditure and financing

This dataset compares countries in terms of their current and total expenditures on health by comparing how they allocate their budget with respect to different health care functions while looking at different financing agents and providers. The data covers the years starting from 1960 extending until 2010. The countries covered are Australia, Austria, Belgium, Canada, Chile, Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Israel, Italy, Japan, Korea, Luxembourg, Mexico, Netherlands, New Zealand, Norway, Poland, Portugal, Slovak Republic, Slovenia, Spain, Sweden, Switzerland, Turkey, United Kingdom, and United States.

Social protection

This dataset introduces the different health care coverage systems such as the government/social health insurance and private health insurance. The statistics are expressed in percentage of the population covered or number of persons. The annual data begins in 1960.

Demographic references

This dataset provides statistics regarding general demographic references in terms of population, age structure, gender, but also in term of labour force. The annual data begins in 1960.

Economic references

This dataset presents main economic indicators such as GDP and Purchasing power parities (PPP) and compares countries in terms of those macroeconomic references as well as currency rates, average annual wages. The annual data begins in 1960.

These data were first provided by the UK Data Service in November 2014.

Electronic medical and electronic health records vendors (EMR/EHR vendors) provide services while regulatory requirements and persistent technological advancement impact their bottom line and their clients. Federal mandates like the HITECH have boosted adoption rates, making digital recordkeeping ubiquitous. As clients consolidate, so do EMR/HHR providers. The trend toward consolidation has defined much of the last decade, with two companies, Epic Systems Corporation and Oracle, controlling roughly half of the US market, presenting significant barriers to entry for new vendors. Interestingly, the industry has seen a marginal drop in overall revenue, partly resulting from healthcare organizations negotiating lower licensing fees and transitioning to more cost-effective cloud-based systems; nonetheless, profit have climbed. Efficiency gains from large-scale client portfolios, high switching costs and consolidation boost operational leverage for providers. Industry revenue has declined at a CAGR of 0.3% to reach $19.4 billion in 2025, with revenue growing 3.6% in 2025 alone and profit continuing to trend upwards. EMR/EHR platforms embrace advanced technologies (artificial intelligence and wearable integration). The explosion of data from devices like smartwatches, sensors and continuous glucose monitors is reshaping patient management and supporting the shift toward personalized, holistic care. EHRs now aggregate this real-time health data, granting clinicians and patients actionable insight into chronic and acute conditions. As wearables proliferate and consumers and healthcare professionals call for seamless data integration, EHR and EMR systems with AI will remain central to connected care delivery. The market is forecast to strengthen at a CAGR of 4.0% to reach $23.6 billion by 2030, with profit continuing upward. Consolidation and increased concentration provide economies of scale, allowing dominant vendors to spread costs, innovate and improve profit. Switching to a different provider is extremely challenging after a healthcare organization implements an EMR system because of the considerable expenses and complexities associated with migrating data and integrating new systems. These hurdles lock in vendors, resulting in persistent concentration. However, competitive pressure among the large incumbents and niche providers leads to competitive pricing battles and slower profit growth. The push to innovate from healthcare providers will be strong and supported by regulatory actions that require enhanced interoperability and data privacy. Overall, performance hinges on the healthcare industry's financial stability and the benefits of updating and expanding EMR/EHR systems.

Description

This comprehensive dataset provides a wealth of information about all countries worldwide, covering a wide range of indicators and attributes. It encompasses demographic statistics, economic indicators, environmental factors, healthcare metrics, education statistics, and much more. With every country represented, this dataset offers a complete global perspective on various aspects of nations, enabling in-depth analyses and cross-country comparisons.

Key Features

• Country: Name of the country.
• Density (P/Km2): Population density measured in persons per square kilometer.
• Abbreviation: Abbreviation or code representing the country.
• Agricultural Land (%): Percentage of land area used for agricultural purposes.
• Land Area (Km2): Total land area of the country in square kilometers.
• Armed Forces Size: Size of the armed forces in the country.
• Birth Rate: Number of births per 1,000 population per year.
• Calling Code: International calling code for the country.
• Capital/Major City: Name of the capital or major city.
• CO2 Emissions: Carbon dioxide emissions in tons.
• CPI: Consumer Price Index, a measure of inflation and purchasing power.
• CPI Change (%): Percentage change in the Consumer Price Index compared to the previous year.
• Currency_Code: Currency code used in the country.
• Fertility Rate: Average number of children born to a woman during her lifetime.
• Forested Area (%): Percentage of land area covered by forests.
• Gasoline_Price: Price of gasoline per liter in local currency.
• GDP: Gross Domestic Product, the total value of goods and services produced in the country.
• Gross Primary Education Enrollment (%): Gross enrollment ratio for primary education.
• Gross Tertiary Education Enrollment (%): Gross enrollment ratio for tertiary education.
• Infant Mortality: Number of deaths per 1,000 live births before reaching one year of age.
• Largest City: Name of the country's largest city.
• Life Expectancy: Average number of years a newborn is expected to live.
• Maternal Mortality Ratio: Number of maternal deaths per 100,000 live births.
• Minimum Wage: Minimum wage level in local currency.
• Official Language: Official language(s) spoken in the country.
• Out of Pocket Health Expenditure (%): Percentage of total health expenditure paid out-of-pocket by individuals.
• Physicians per Thousand: Number of physicians per thousand people.
• Population: Total population of the country.
• Population: Labor Force Participation (%): Percentage of the population that is part of the labor force.
• Tax Revenue (%): Tax revenue as a percentage of GDP.
• Total Tax Rate: Overall tax burden as a percentage of commercial profits.
• Unemployment Rate: Percentage of the labor force that is unemployed.
• Urban Population: Percentage of the population living in urban areas.
• Latitude: Latitude coordinate of the country's location.
• Longitude: Longitude coordinate of the country's location.

Potential Use Cases

• Analyze population density and land area to study spatial distribution patterns.
• Investigate the relationship between agricultural land and food security.
• Examine carbon dioxide emissions and their impact on climate change.
• Explore correlations between economic indicators such as GDP and various socio-economic factors.
• Investigate educational enrollment rates and their implications for human capital development.
• Analyze healthcare metrics such as infant mortality and life expectancy to assess overall well-being.
• Study labor market dynamics through indicators such as labor force participation and unemployment rates.
• Investigate the role of taxation and its impact on economic development.
• Explore urbanization trends and their social and environmental consequences.

Data Source: This dataset was compiled from multiple data sources

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The booming home healthcare software market, projected to reach $6.43 billion by 2033, is driven by aging populations and telehealth adoption. Explore key trends, leading companies (Axxess, WellSky, Homecare Homebase), and regional growth forecasts in this comprehensive market analysis.

The Clinical Healthcare IT market is booming, projected to reach $2.24 billion by 2033 with a 24.22% CAGR. Driven by EHR adoption, telehealth growth, and government initiatives, this market encompasses EHRs, LIMS, telehealth, and CPOE. Key players include Epic Systems, Cerner, and GE Healthcare. Explore market trends and growth projections in this in-depth analysis. Recent developments include: April 2024: The Union Health Ministry launched the innovative myCGHS app for iOS devices, aiming to boost access to EHR, information, and resources for the beneficiaries of the Central Government Health Scheme (CGHS)., March 2024: Emory Healthcare led the way in transforming how clinicians access patient health records with its deployment of the 15-inch MacBook Air and the launch of the new native Epic Hyperspace app. This marked the first time Epic was made available to clinicians on the Mac App Store.. Key drivers for this market are: Complex Healthcare Datasets and Implementation of AI and ML, Increase in Cloud-based Deployment. Potential restraints include: Complex Healthcare Datasets and Implementation of AI and ML, Increase in Cloud-based Deployment. Notable trends are: Electronic Health Record (EHR) is Expected to Witness Significant Growth.

Corrosion is a major threat in the aeronautic industry, both in terms of safety and cost. Ultrasonic Lamb Waves (LW) appear to be very efficient for corrosion monitoring and can be made cost effective and versatile when emitted and received by a sparse array of piezoelectric elements (PZT). A LW solution relying on a sparse PZT array and allowing to monitor corrosion pit growth on stainless 316L grade steel plate is here used to collect data during a controlled corrosion experiment. Experimentally, the corrosion pit size is electrochemically controlled by both the imposed electrical potential and the injection of a corrosive NaCl solution through a capillary located at the desired pit location. In parallel, the corrosion pit growth is monitored in-situ every 10 seconds by sending and measuring LW using a sparse array of 4 PZTs bonded to the back of the steel plate enduring corrosion. Two independent experiments were achieved in order to assess the repeatability of the proposed approach. If embedded in aeronautical structure, such an approach could be a versatile and cost-effective alternative to actual non-destructive maintenance procedures that are time and manpower consuming. This dataset can thus ease the development of associated SHM algorithms and methodologies and help filling the gap actually existing between research and industry in that domain. This dataset has been used for the article "In-situ monitoring of µm-sized electrochemically generated corrosion pits using Lamb Waves managed by a sparse array of piezoelectric transducers" published in open access in the "Ultrasonics" peer reviewed journal by the same authors as the dataset.

This dataset provides a comprehensive overview of pharmaceutical product pricing and availability across Pakistan. It contains information on over 4000 drug products from more than 50 pharmaceutical companies, including product names, company names, pack sizes, prices before and after discounts, and availability status.

Pakistan's pharmaceutical industry plays a vital role in the country's healthcare system, but faces challenges around drug pricing and accessibility. This dataset aims to provide transparency into the current pharmaceutical landscape in Pakistan. It was inspired by the need for consolidated, reliable data to support research, policy discussions and decision-making to improve the affordability and availability of essential medicines.

The data was collected from OpenData. Each record represents a unique drug product, pack size and price point. Prices are listed in Pakistani Rupees (Rs). The dataset can be used to analyze pricing trends, identify most common drugs and manufacturers, compare prices and discounts, assess product availability, and more. Potential applications include pharmaceutical industry analysis, public health research, government policy evaluation, and healthcare accessibility mapping.

I invite the Kaggle community to explore this dataset and derive valuable insights to address challenges in Pakistan's pharmaceutical sector and healthcare system. Please refer to the Data Dictionary for descriptions of each data field. I welcome your feedback, analyses and visualizations!

Background
The Annual Population Survey (APS) Household datasets are produced annually and are available from 2004 (Secure Access) and 2006 (End User Licence). They allow production of family and household labour market statistics at local areas and for small sub-groups of the population across the UK. The data comprise key variables from the Labour Force Survey (LFS) (held at the UK Data Archive under GN 33246) and the APS (person) datasets (held at the Data Archive under GN 33357). The former is a quarterly survey of households living at private addresses in the UK. The latter is created by combining individuals in waves one and five from four consecutive LFS quarters with the English, Welsh and Scottish Local Labour Force Surveys (LLFS). The APS Household datasets therefore contain results from four different sources.

The APS Household datasets include all the variables on the LFS and APS person datasets except for the income variables. They also include key family and household level derived variables. These variables allow for an analysis of the combined economic activity status of the family or household. In addition they also include more detailed geographical, industry, occupation, health and age variables.

For information on the main (person) APS datasets, for which EUL and Secure Access versions are available, please see GNs 33357 and 33427, respectively.

New reweighting policy
Following the new reweighting policy ONS has reviewed the latest population estimates made available during 2019 and have decided not to carry out a 2019 LFS and APS reweighting exercise. Therefore, the next reweighting exercise will take place in 2020. These will incorporate the 2019 Sub-National Population Projection data (published in May 2020) and 2019 Mid-Year Estimates (published in June 2020). It is expected that reweighted Labour Market aggregates and microdata will be published in 2021.

Secure Access APS Household data
Secure Access datasets for the APS Household survey include additional variables not included in the EUL versions (GN 33455). Extra variables that may be found in the Secure Access version but not in the EUL version relate to:

• geography (see 'Spatial Units' below)
• individual demographics, including age bands, day of birth, sex/marital status and detailed ethnicity
• main reason for coming to the UK
• number of bedrooms
• health problems, work-related health problems, sickness absence from work
• reasons why not in work, including health and other reasons, wage received when not in work, time away from job, and whether and when will work in the future
• type of benefit claimed
• education and training, including
  • vocational and work-related qualifications and training
  • class of first degree
  • qualifications from government schemes
  • number of O levels/GCSEs, etc held
  • qualifications held from UK and abroad
  • qualifications gained from school/home schooling
  • qualifications below highest level
  • other qualifications
  • time spent in taught courses
  • who paid for training
  • main place of education/training
  • length of training course
  • level of Welsh baccalaureate
• worst 30 local authorities based on Indices of Deprivation
• casual/holiday work
• disability, including learning difficulty/disability
• payment of own National Insurance and tax

Prospective users of the Secure Access version of an APS Household dataset will need to fulfil additional requirements, including completion of face-to-face training and agreement to Secure Access' User Agreement, in order to obtain permission to use that version (see 'Access' section below). The EUL version of the data, for which less stingent access conditions apply, may suffice for many users' research requirements. Further details and links to all APS studies can be found via the APS Key Data series webpage.

Documentation and coding frames
The APS is compiled from variables present in the LFS. For variable and value labelling and coding frames that are not included either in the data or in the current APS documentation (e.g. coding frames for education, industrial and geographic variables, which are held in LFS User Guide Vol.5, Classifications), users are advised to consult the latest versions of the LFS User Guides, which are available from the ONS Labour Force Survey - User Guidance webpages.

For the eleventh edition (September 2025), the data file covering January - December 2023 has been added.

The Recipe Nutrition Database market has emerged as a vital asset in the health and wellness sector, catering to a growing consumer demand for nutritional transparency and informed dietary choices. These databases provide comprehensive nutritional information for a variety of foods and recipes, enabling users-from h

This dataset provides information about Allegheny County vendors accepting WIC who participate in the Pennsylvania Department of Agriculture's Farmers Market Nutrition Program (FMNP). These markets provide the public, including WIC recipients, with fresh, nutritious, locally grown fruits, vegetables, and herbs from approved farmers in Pennsylvania.

Each row in the data includes details about location, days/hours of operation, and the length of the season. Additional directions and affiliations have also been provided when available.

Users may also be interested in the PA Department of Agriculture's new PA FMNP Market Locator app, a free mobile tool to help residents find markets closest to them across the entire state. The FMNP Market Locator app is available both in the Apple Store (https://apple.co/2KNJ4dk) and Google Play (http://bit.ly/2Z86Ytg).

Support for Health Equity datasets and tools provided by Amazon Web Services (AWS) through their Health Equity Initiative.