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

Data collection tools, administration, sample size and analytical techniques.

The global healthcare data collection and labeling market size was valued at USD 1.11 billion in 2024 and is likely to cross USD 21.94 billion by 2037, expanding at more than 25.8% CAGR during the forecast period i.e., between 2025-2037. North America industry is estimated to account for largest revenue share of 37.8% by 2037, owing to utilizing state-of-the-art tools such as artificial intelligence (AI) and machine learning to improve efficiency and accuracy in data labeling and annotation.

Patients often provide untruthful information about their health to avoid embarrassment, evade treatment, or prevent financial loss. Privacy disclosures (e.g. HIPAA) intended to dissuade privacy concerns may actually increase patient lying. We used new mouse tracking-based technology to detect lies through mouse movement (distance and time to response) and patient answer adjustment in an online controlled study of 611 potential patients, randomly assigned to one of six treatments. Treatments differed in the notices patients received before health information was requested, including notices about privacy, benefits of truthful disclosure, and risks of inaccurate disclosure. Increased time or distance of device mouse movement and greater adjustment of answers indicate less truthfulness. Mouse tracking revealed a significant overall effect (p < 0.001) by treatment on the time to reach their final choice. The control took the least time indicating greater truthfulness and the privacy + risk group took the longest indicating the least truthfulness. Privacy, risk, and benefit disclosure statements led to greater lying. These differences were moderated by gender. Mouse tracking results largely confirmed the answer adjustment lie detection method with an overall treatment effect (p < .0001) and gender differences (p < .0001) on truthfulness. Privacy notices led to decreased patient honesty. Privacy notices should perhaps be administered well before personal health disclosure is requested to minimize patient untruthfulness. Mouse tracking and answer adjustment appear to be healthcare lie-detection methods to enhance optimal diagnosis and treatment. Methods The data were collected as part of a controlled experiment using Amazon Mechanical Turk, Qualtrics, and JavaScript-based mouse tracking technology.

The global healthcare data collection and labeling market is experiencing robust growth, driven by the increasing adoption of artificial intelligence (AI) and machine learning (ML) in healthcare. The rising volume of patient data generated through electronic health records (EHRs), wearable devices, and medical imaging necessitates efficient and accurate data labeling for training sophisticated AI algorithms. This demand fuels the market's expansion. While precise market sizing figures require further details, a reasonable estimate, considering the current growth trajectory of related AI and healthcare sectors, would place the 2025 market value at approximately $2 billion, with a Compound Annual Growth Rate (CAGR) of 15-20% projected through 2033. Key drivers include the need for improved diagnostic accuracy, personalized medicine, and drug discovery, all heavily reliant on high-quality labeled datasets. Furthermore, regulatory compliance mandates around data privacy and security are indirectly driving the adoption of specialized data collection and labeling services, ensuring data integrity and patient confidentiality. The market is segmented based on data type (imaging, text, sensor data), labeling method (supervised, unsupervised, semi-supervised), service type (data annotation, data augmentation, model training), and end-user (hospitals, pharmaceutical companies, research institutions). Companies like Alegion, Appen, and iMerit are key players, offering a range of services to meet diverse healthcare data needs. However, challenges remain, including data heterogeneity, scalability concerns related to large datasets, and the potential for bias in labeled data. Addressing these challenges requires continuous innovation in data collection methodologies, advanced labeling techniques, and the development of robust quality control measures. Future market growth will hinge on the successful integration of advanced technologies like synthetic data generation and automated labeling tools, aiming to reduce costs and accelerate the development of AI-powered healthcare solutions.

Big Data Analytics in Healthcare Market Outlook

The global market size for Big Data Analytics in Healthcare was valued at approximately USD 34 billion in 2023 and is anticipated to grow at a robust CAGR of 11.9%, reaching an estimated USD 90 billion by 2032. This remarkable growth is driven by the increasing adoption of data-driven decision-making processes within the healthcare sector, spurred by the mounting pressure to enhance operational efficiencies, improve patient outcomes, and reduce overall healthcare costs. The integration of big data analytics within healthcare systems is enabling organizations to leverage vast amounts of data, leading to enhanced patient care and streamlined operations.

A significant growth factor fueling the expansion of the big data analytics market in healthcare is the ever-increasing volume of data generated by healthcare systems. With the surge of electronic health records, wearable health devices, and various other digital health technologies, the volume of data being generated is unprecedented. This data, if analyzed correctly, holds the potential to transform healthcare delivery models, allowing for more precise diagnostics, personalized treatment plans, and proactive disease management strategies. Consequently, healthcare organizations are increasingly investing in big data analytics tools to harness this data for clinical and operational improvements.

Another key driver of market growth is the growing emphasis on value-based care and the need for healthcare providers to demonstrate high-quality patient outcomes. Value-based care models require providers to focus on the quality rather than the quantity of care delivered, inherently demanding the use of advanced analytics to derive actionable insights from patient data. Big data analytics facilitates the identification of patterns and trends that can lead to improved treatment effectiveness and patient satisfaction. This shift in care models is prompting healthcare organizations to integrate sophisticated analytics solutions that help in predictive modeling, trend analysis, and real-time decision-making, further propelling market expansion.

Additionally, the increasing incidence of chronic diseases worldwide is driving the need for more efficient healthcare services. Big data analytics in healthcare can play a critical role in managing chronic diseases by enabling preventive care and personalized treatment plans. By analyzing patient data, including historical health records, genetic information, and lifestyle choices, healthcare providers can predict potential health issues and intervene early, thereby improving patient outcomes and reducing healthcare costs. This capability is essential in managing the global burden of chronic diseases, thereby boosting the adoption of big data analytics solutions in the healthcare sector.

Regionally, North America dominates the market due to the presence of advanced healthcare infrastructure, the availability of technologically advanced products, and the high adoption rate of healthcare IT solutions. The region's robust regulatory environment and substantial investments in healthcare IT make it a fertile ground for the growth of big data analytics solutions. However, the Asia Pacific region is expected to exhibit the highest growth rate during the forecast period, driven by increasing government initiatives supporting the digitization of healthcare, burgeoning healthcare infrastructure, and a growing focus on precision medicine. The integration of big data analytics in healthcare across diverse regions is indicative of its global importance in optimizing healthcare delivery and patient care.

Component Analysis

In the realm of big data analytics in healthcare, the component segment is vitally instrumental to the market's evolution and includes software and services. Software solutions are the backbone of big data analytics, providing healthcare organizations with the necessary tools to collect, process, and analyze vast datasets. These solutions encompass data management and analytical platforms, which are indispensable for extracting actionable insights from disparate data sources. The software component is continually evolving with advancements in artificial intelligence and machine learning, which enhance data analytics capabilities. Moreover, the increasing demand for user-friendly, customizable software solutions is driving innovation and growth within this segment.

The services component, on the other hand, plays a critical role in the implementation and maintenance of big data analytics solutions. This component includes cons

Data Collection Software Market Outlook

The global data collection software market size is anticipated to significantly expand from USD 1.8 billion in 2023 to USD 4.2 billion by 2032, exhibiting a CAGR of 10.1% during the forecast period. This remarkable growth is fueled by the increasing demand for data-driven decision-making solutions across various industries. As organizations continue to recognize the strategic value of harnessing vast amounts of data, the need for sophisticated data collection tools becomes more pressing. The growing integration of artificial intelligence and machine learning within software solutions is also a critical factor propelling the market forward, enabling more accurate and real-time data insights.

One major growth factor for the data collection software market is the rising importance of real-time analytics. In an era where time-sensitive decisions can define business success, the capability to gather and analyze data in real-time is invaluable. This trend is particularly evident in sectors like healthcare, where prompt data collection can impact patient care, and in retail, where immediate insights into consumer behavior can enhance customer experience and drive sales. Additionally, the proliferation of the Internet of Things (IoT) has further accelerated the demand for data collection software, as connected devices produce a continuous stream of data that organizations must manage efficiently.

The digital transformation sweeping across industries is another crucial driver of market growth. As businesses endeavor to modernize their operations and customer interactions, there is a heightened demand for robust data collection solutions that can seamlessly integrate with existing systems and infrastructure. Companies are increasingly investing in cloud-based data collection software to improve scalability, flexibility, and accessibility. This shift towards cloud solutions is not only enabling organizations to reduce IT costs but also to enhance collaboration by making data more readily available across different departments and geographies.

The intensified focus on regulatory compliance and data protection is also shaping the data collection software market. With the introduction of stringent data privacy regulations such as the General Data Protection Regulation (GDPR) in Europe and the California Consumer Privacy Act (CCPA) in the United States, organizations are compelled to adopt data collection practices that ensure compliance and protect customer information. This necessitates the use of sophisticated software capable of managing data responsibly and transparently, thereby fueling market growth. Moreover, the increasing awareness among businesses about the potential financial and reputational risks associated with data breaches is prompting the adoption of secure data collection solutions.

Component Analysis

The data collection software market can be segmented into software and services, each playing a pivotal role in the ecosystem. The software component remains the bedrock of this market, providing the essential tools and platforms that enable organizations to collect, store, and analyze data effectively. The software solutions offered vary in complexity and functionality, catering to different organizational needs ranging from basic data entry applications to advanced analytics platforms that incorporate AI and machine learning capabilities. The demand for such sophisticated solutions is on the rise as organizations seek to harness data not just for operational purposes but for strategic insights as well.

The services segment encompasses various offerings that support the deployment and optimization of data collection software. These services include consulting, implementation, training, and maintenance, all crucial for ensuring that the software operates efficiently and meets the evolving needs of the user. As the market evolves, there is an increasing emphasis on offering customized services that address specific industry requirements, thereby enhancing the overall value proposition for clients. The services segment is expected to grow steadily as businesses continue to seek external expertise to complement their internal capabilities, particularly in areas such as data analytics and cybersecurity.

Integration services have become particularly important as organizations strive to create seamless workflows that incorporate new data collection solutions with existing IT infrastructure. This need for integration is driven by the growing complexity of enterprise IT environments, where disparate systems and applications must wo

Electronic health records (EHRs) are a rich source of information for medical research and public health monitoring. Information systems based on EHR data could also assist in patient care and hospital management. However, much of the data in EHRs is in the form of unstructured text, which is difficult to process for analysis. Natural language processing (NLP), a form of artificial intelligence, has the potential to enable automatic extraction of information from EHRs and several NLP tools adapted to the style of clinical writing have been developed for English and other major languages. In contrast, the development of NLP tools for less widely spoken languages such as Swedish has lagged behind. A major bottleneck in the development of NLP tools is the restricted access to EHRs due to legitimate patient privacy concerns. To overcome this issue we have generated a citizen science platform for collecting artificial Swedish EHRs with the help of Swedish physicians and medical students. These artificial EHRs describe imagined but plausible emergency care patients in a style that closely resembles EHRs used in emergency departments in Sweden. In the pilot phase, we collected a first batch of 50 artificial EHRs, which has passed review by an experienced Swedish emergency care physician. We make this dataset publicly available as OpenChart-SE corpus (version 1) under an open-source license for the NLP research community. The project is now open for general participation and Swedish physicians and medical students are invited to submit EHRs on the project website (https://github.com/Aitslab/openchart-se), where additional batches of quality-controlled EHRs will be released periodically.

Dataset content

OpenChart-SE, version 1 corpus (txt files and and dataset.csv)

The OpenChart-SE corpus, version 1, contains 50 artificial EHRs (note that the numbering starts with 5 as 1-4 were test cases that were not suitable for publication). The EHRs are available in two formats, structured as a .csv file and as separate textfiles for annotation. Note that flaws in the data were not cleaned up so that it simulates what could be encountered when working with data from different EHR systems. All charts have been checked for medical validity by a resident in Emergency Medicine at a Swedish hospital before publication.

Codebook.xlsx

The codebook contain information about each variable used. It is in XLSForm-format, which can be re-used in several different applications for data collection.

suppl_data_1_openchart-se_form.pdf

OpenChart-SE mock emergency care EHR form.

suppl_data_3_openchart-se_dataexploration.ipynb

This jupyter notebook contains the code and results from the analysis of the OpenChart-SE corpus.

More details about the project and information on the upcoming preprint accompanying the dataset can be found on the project website (https://github.com/Aitslab/openchart-se).

BackgroundReducing medication-related harm is a global priority; however, impetus for improvement is impeded as routine medication safety data are seldom available. Therefore, the Medication Safety Thermometer was developed within England’s National Health Service. This study aimed to explore the implementation of the tool into routine practice from users’ perspectives.MethodFifteen semi-structured interviews were conducted with purposely sampled National Health Service staff from primary and secondary care settings. Interview data were analysed using an initial thematic analysis, and subsequent analysis using Normalisation Process Theory.ResultsSecondary care staff understood that the Medication Safety Thermometer’s purpose was to measure medication safety and improvement. However, other uses were reported, such as pinpointing poor practice. Confusion about its purpose existed in primary care, despite further training, suggesting unsuitability of the tool. Decreased engagement was displayed by staff less involved with medication use, who displayed less ownership. Nonetheless, these advocates often lacked support from management and frontline levels, leading to an overall lack of engagement. Many participants reported efforts to drive scale-up of the use of the tool, for example, by securing funding, despite uncertainty around how to use data. Successful improvement was often at ward-level and went unrecognised within the wider organisation. There was mixed feedback regarding the value of the tool, often due to a perceived lack of “capacity”. However, participants demonstrated interest in learning how to use their data and unexpected applications of data were reported.ConclusionRoutine medication safety data collection is complex, but achievable and facilitates improvements. However, collected data must be analysed, understood and used for further work to achieve improvement, which often does not happen. The national roll-out of the tool has accelerated shared learning; however, a number of difficulties still exist, particularly in primary care settings, where a different approach is likely to be required.

Clinical Trial Management Tool Market Outlook

The global clinical trial management tool market size was valued at approximately USD 1.2 billion in 2023 and is projected to reach USD 2.8 billion by 2032, growing at a compound annual growth rate (CAGR) of 9.8% over the forecast period. The growth of this market is driven by the increasing complexity of clinical trials, rising demand for efficient data management, and the need for better compliance with regulatory requirements.

One of the primary growth factors in the clinical trial management tool market is the escalating complexity of clinical trials. With the advent of personalized medicine and more stringent regulatory requirements, the need for comprehensive and integrated solutions has surged. Clinical trials now often require the handling of multiple data points across various stages of the trial, from patient recruitment to data analysis and reporting. This complexity necessitates sophisticated management tools that can streamline processes, reduce errors, and ensure data integrity. Consequently, the demand for advanced clinical trial management tools is expected to rise significantly.

Another crucial factor contributing to market growth is the increasing adoption of digital technology within the healthcare sector. The shift towards electronic health records (EHRs) and digital data collection methods has created a conducive environment for the adoption of clinical trial management tools. These tools offer seamless integration with existing digital infrastructures, enabling a more efficient and effective management of clinical trial data. Furthermore, the COVID-19 pandemic has accelerated the adoption of digital solutions, highlighting the need for remote monitoring and decentralized trials, which are well-supported by advanced management tools.

Moreover, the need for compliance with regulatory standards and the growing emphasis on patient safety are driving the adoption of clinical trial management tools. Regulatory bodies like the FDA and EMA have stringent guidelines for clinical trials, necessitating meticulous data management and reporting. Clinical trial management tools help organizations stay compliant by providing a centralized platform that ensures all data is collected, stored, and reported in accordance with regulatory requirements. This not only reduces the risk of non-compliance but also streamlines the overall trial process, making it more efficient and cost-effective.

Regionally, North America holds the largest share in the clinical trial management tool market, followed by Europe and Asia Pacific. The dominance of North America can be attributed to the high concentration of pharmaceutical and biotechnology companies, advanced healthcare infrastructure, and favorable regulatory frameworks. Europe also represents a significant market due to the presence of major clinical research organizations and increasing government support for clinical trials. Asia Pacific is expected to witness the highest growth rate, driven by the expanding healthcare sector, increasing clinical trial activities, and rising investments in healthcare technology.

Component Analysis

The clinical trial management tool market is segmented into software and services based on the component. The software segment is further divided into enterprise-based and site-based solutions. Enterprise-based solutions are designed for large-scale organizations that manage multiple clinical trials simultaneously, offering comprehensive functionalities such as project management, data analysis, and reporting. These solutions are highly scalable and customizable, making them suitable for complex trial operations. On the other hand, site-based solutions are tailored for individual trial sites or smaller organizations, providing essential functionalities to manage trial activities efficiently.

Within the software segment, the increasing demand for integrated solutions is a significant growth driver. Integrated clinical trial management systems (CTMS) combine various functionalities such as patient recruitment, data management, and regulatory compliance into a single platform. This integration enhances operational efficiency, reduces duplication of efforts, and ensures seamless data flow across different trial stages. As the trend towards integrated solutions continues to grow, the software segment is expected to witness substantial growth during the forecast period.

In addition to software, the services segment plays a crucial role in the clinical trial management tool market. Services encompass a range of

The core objective of the project is to offer alternative models of clinical education. A contemporary model was developed, based on reducing clinical placements, which are a burden to public hospitals. Approximately 190 Monash University undergraduate students from paramedics, occupational therapy, physiotherapy and nursing were asked to complete a 7-point Likert Scale paper-based questionnaire that aimed to assess the clinical relevance and students’ general perceptions and attitudes of viewing clinical simulations via DVD. Thematic analysis was also undertaken using a focus group that consisted of participants from paramedics, occupational therapy, physiotherapy and nursing. The data collection includes qualitative data from the focus groups, paper questions and paper questionnaires; quantitative data includes surveys, an excel spreadsheet, SPSS files; audio files of focus groups and paper transcriptions; demographics data was recorded in Excel and SPSS.

Healthcare Survey Tools Market Outlook

The global healthcare survey tools market size was valued at approximately USD 1.2 billion in 2023 and is projected to reach around USD 3.5 billion by 2032, growing at a compound annual growth rate (CAGR) of 12.5% over the forecast period. This substantial growth is fueled by the increasing demand for real-time patient feedback, the necessity for healthcare organizations to stay compliant with regulatory standards, and the rising adoption of digital health solutions.

One of the most critical growth factors influencing the healthcare survey tools market is the heightened focus on patient-centric care. Healthcare providers are increasingly emphasizing patient feedback to ensure better care outcomes and enhance patient satisfaction. The shift towards value-based care models, which prioritize patient experiences and outcomes over service volume, necessitates the use of efficient survey tools. Additionally, regulatory bodies like the Centers for Medicare & Medicaid Services (CMS) have mandated patient experience surveys, further propelling market growth.

Another significant factor driving the market is the technological advancements in survey tools. The integration of Artificial Intelligence (AI), Machine Learning (ML), and Natural Language Processing (NLP) has revolutionized healthcare survey tools, making them more intuitive, scalable, and capable of providing in-depth analysis. These technologies enable real-time feedback collection and analysis, allowing healthcare organizations to promptly address issues and improve their services. Furthermore, the increasing penetration of smartphones and the internet facilitates easier access to survey tools, thereby boosting their adoption.

The COVID-19 pandemic has also significantly accelerated the growth of this market. The pandemic highlighted the need for robust healthcare feedback mechanisms to quickly adapt to evolving challenges. Organizations have had to rapidly gather and analyze patient and employee feedback to manage crisis situations effectively. This urgency has led to an increased reliance on digital survey tools, which provide quick and accurate insights, thereby contributing to market growth.

From a regional perspective, North America is anticipated to hold the largest market share, driven by the regionÂ’s advanced healthcare infrastructure, high adoption of digital health technologies, and stringent regulatory requirements. The Asia Pacific region is expected to witness the highest growth rate during the forecast period, fueled by increasing healthcare investments, a growing focus on patient care quality, and the rising prevalence of chronic diseases.

The rise of Online Survey Software and Tools has been pivotal in transforming how healthcare organizations collect and analyze feedback. These tools offer a versatile platform for designing, distributing, and analyzing surveys, making it easier for healthcare providers to gather insights from patients and staff. With the ability to customize surveys and integrate them with existing healthcare systems, these tools enhance the efficiency of feedback collection processes. Moreover, the real-time analytics capabilities of these tools enable healthcare organizations to swiftly address issues and improve service quality, aligning with the industry's shift towards patient-centered care.

Product Type Analysis

The healthcare survey tools market by product type is segmented into Software and Services. Software solutions dominate this segment, offering various functionalities, including design, distribution, and analysis of surveys. The ease of customization and the ability to integrate with existing healthcare systems make software solutions particularly appealing. Software tools often come equipped with advanced analytics features, enabling healthcare providers to convert raw data into actionable insights swiftly. This capability is crucial for organizations aiming to improve patient satisfaction and care quality continuously.

Services, though a smaller segment compared to software, play a vital role in the market. These services typically include consulting, customization, and support, helping organizations maximize the utility of their survey tools. Vendors offer specialized services, such as training healthcare staff on effectively using the tools and interpreting the data. This ensures that organizations can fully leverage the technology to

The All CMS Data Feeds dataset is an expansive resource offering access to 118 unique report feeds, providing in-depth insights into various aspects of the U.S. healthcare system. With over 25.8 billion rows of data meticulously collected since 2007, this dataset is invaluable for healthcare professionals, analysts, researchers, and businesses seeking to understand and analyze healthcare trends, performance metrics, and demographic shifts over time. The dataset is updated monthly, ensuring that users always have access to the most current and relevant data available.

Dataset Overview:

118 Report Feeds: - The dataset includes a wide array of report feeds, each providing unique insights into different dimensions of healthcare. These topics range from Medicare and Medicaid service metrics, patient demographics, provider information, financial data, and much more. The breadth of information ensures that users can find relevant data for nearly any healthcare-related analysis. - As CMS releases new report feeds, they are automatically added to this dataset, keeping it current and expanding its utility for users.

25.8 Billion Rows of Data:

• With over 25.8 billion rows of data, this dataset provides a comprehensive view of the U.S. healthcare system. This extensive volume of data allows for granular analysis, enabling users to uncover insights that might be missed in smaller datasets. The data is also meticulously cleaned and aligned, ensuring accuracy and ease of use.

Historical Data Since 2007: - The dataset spans from 2007 to the present, offering a rich historical perspective that is essential for tracking long-term trends and changes in healthcare delivery, policy impacts, and patient outcomes. This historical data is particularly valuable for conducting longitudinal studies and evaluating the effects of various healthcare interventions over time.

Monthly Updates:

• To ensure that users have access to the most current information, the dataset is updated monthly. These updates include new reports as well as revisions to existing data, making the dataset a continuously evolving resource that stays relevant and accurate.

Data Sourced from CMS:

• The data in this dataset is sourced directly from the Centers for Medicare & Medicaid Services (CMS). After collection, the data is meticulously cleaned and its attributes are aligned, ensuring consistency, accuracy, and ease of use for any application. Furthermore, any new updates or releases from CMS are automatically integrated into the dataset, keeping it comprehensive and current.

Use Cases:

Market Analysis:

• The dataset is ideal for market analysts who need to understand the dynamics of the healthcare industry. The extensive historical data allows for detailed segmentation and analysis, helping users identify trends, market shifts, and growth opportunities. The comprehensive nature of the data enables users to perform in-depth analyses of specific market segments, making it a valuable tool for strategic decision-making.

Healthcare Research:

• Researchers will find the All CMS Data Feeds dataset to be a robust foundation for academic and commercial research. The historical data, combined with the breadth of coverage across various healthcare metrics, supports rigorous, in-depth analysis. Researchers can explore the effects of healthcare policies, study patient outcomes, analyze provider performance, and more, all within a single, comprehensive dataset.

Performance Tracking:

• Healthcare providers and organizations can use the dataset to track performance metrics over time. By comparing data across different periods, organizations can identify areas for improvement, monitor the effectiveness of initiatives, and ensure compliance with regulatory standards. The dataset provides the detailed, reliable data needed to track and analyze key performance indicators.

Compliance and Regulatory Reporting:

• The dataset is also an essential tool for compliance officers and those involved in regulatory reporting. With detailed data on provider performance, patient outcomes, and healthcare utilization, the dataset helps organizations meet regulatory requirements, prepare for audits, and ensure adherence to best practices. The accuracy and comprehensiveness of the data make it a trusted resource for regulatory compliance.

Data Quality and Reliability:

The All CMS Data Feeds dataset is designed with a strong emphasis on data quality and reliability. Each row of data is meticulously cleaned and aligned, ensuring that it is both accurate and consistent. This attention to detail makes the dataset a trusted resource for high-stakes applications, where data quality is critical.

Integration and Usability:

Ease of Integration:

• The dataset is provided in a CSV format, which is widely compatible with most data analysis tools and platforms. This ensures that users can easily integrate the data into their existing wo...

Additional file 2. Data Collection Tool and Database.

The Electronic Data Capture (EDC) Tools market is experiencing robust growth, driven by the increasing adoption of electronic health records (EHRs), the rising prevalence of chronic diseases necessitating large-scale clinical trials, and the growing demand for efficient and streamlined clinical research processes. The market's size in 2025 is estimated at $2.5 billion, exhibiting a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033. This growth trajectory is projected to propel the market value to approximately $7.2 billion by 2033. Key market drivers include the need for enhanced data management, improved data quality and reduced costs associated with manual data entry and analysis in clinical trials. Furthermore, technological advancements such as cloud-based EDC solutions, integration with other clinical trial management systems, and the implementation of AI-powered analytics are significantly contributing to market expansion. The market is segmented by deployment (cloud-based and on-premise), by end-user (pharmaceutical companies, CROs, academic institutions), and by region (North America, Europe, Asia Pacific, Rest of World). Competitive pressures among established players like Dacima Software, OpenClinica, and ArisGlobal, alongside emerging innovative startups, are fostering a dynamic and evolving market landscape. The restraining factors include the high initial investment cost associated with implementing EDC systems, the need for comprehensive training and support for users, and concerns about data security and privacy. However, the long-term benefits of improved data management, reduced operational costs, and accelerated clinical trial timelines are expected to outweigh these challenges. The increasing adoption of EDC tools across various geographical regions, particularly in emerging economies with growing healthcare infrastructure and research activities, further fuels the market's expansion. This growth is further supported by regulatory initiatives promoting the use of electronic systems in clinical research, leading to greater standardization and interoperability within the industry. The market is expected to continue its upward trajectory, driven by an increasing need for data-driven insights in healthcare research and development.

Healthcare Survey Software Market Outlook

The global healthcare survey software market size was valued at USD 1.5 billion in 2023 and is projected to reach USD 5.8 billion by 2032, growing at a compound annual growth rate (CAGR) of 16.2% during the forecast period. The key growth factor propelling the healthcare survey software market includes the increasing importance of patient feedback and data analytics in improving healthcare services and outcomes.

Healthcare organizations are increasingly recognizing the value of patient feedback in enhancing the quality of care. As healthcare becomes more patient-centric, the demand for efficient tools to gather, analyze, and act on patient feedback has surged. Survey software provides a streamlined method for collecting patient opinions on various aspects of their care, including satisfaction with treatment, interaction with healthcare staff, and overall experience. This feedback is invaluable for healthcare providers aiming to improve service delivery, patient satisfaction, and clinical outcomes.

Another significant growth factor is the advancement in data analytics and integration capabilities. Modern healthcare survey software solutions are equipped with powerful analytics tools that allow organizations to derive actionable insights from collected data. These insights can be used to identify trends, pinpoint areas for improvement, and make informed decisions. The ability to integrate survey data with electronic health records (EHRs) and other healthcare systems further enhances the utility of these platforms, providing a comprehensive view of patient health and experiences.

The shift towards digitalization in healthcare is also driving market growth. The adoption of digital tools for various administrative and clinical tasks has increased efficiency and accuracy, and survey software is no exception. The convenience of deploying surveys through digital platforms, including mobile and web applications, ensures higher response rates and real-time data collection. Additionally, the cloud-based deployment of survey software solutions offers scalability, remote accessibility, and reduced IT overheads, which are particularly beneficial for large healthcare organizations.

Survey Software plays a pivotal role in the healthcare industry by providing comprehensive tools for collecting and analyzing patient feedback. These platforms enable healthcare providers to design customized surveys that capture detailed patient experiences, leading to more personalized care. The integration of survey software with existing healthcare systems allows for seamless data collection and analysis, offering insights that drive improvements in patient care and operational efficiency. As the demand for precise and actionable patient feedback grows, survey software becomes an indispensable asset in the healthcare sector, facilitating better decision-making and enhancing patient satisfaction.

Regionally, North America holds the largest market share due to the advanced healthcare infrastructure, high adoption rate of digital technologies, and the presence of major market players. However, the Asia Pacific region is expected to witness the highest growth rate during the forecast period. Factors contributing to this growth include increasing investments in healthcare infrastructure, rising awareness about the importance of patient feedback, and the burgeoning use of digital healthcare solutions. Europe also shows significant potential, driven by regulatory support for patient-centric care and the adoption of advanced healthcare technologies.

Component Analysis

The healthcare survey software market is segmented by component into software and services. The software segment includes various types of survey software solutions that facilitate the creation, distribution, and analysis of surveys. These solutions come with features like customizable templates, automated survey distribution, and advanced analytics. The growing need for real-time feedback and data-driven decision-making is driving the adoption of sophisticated software solutions in the healthcare sector.

Within the software segment, the demand for advanced analytics tools is particularly high. These tools enable healthcare providers to analyze survey data comprehensively and derive actionable insights. The integration capabilities of software solutions with other healthcare systems, such as EHRs and patient management systems, further enh

Healthcare Data Annotation Tools Market Size And Forecast

Healthcare Data Annotation Tools Market size was valued at USD 167.40 Million in 2023 and is projected to reach USD 719.15 Million by 2030, growing at a CAGR of 27.5% during the forecast period 2024-2030.

Global Healthcare Data Annotation Tools Market Drivers

The market drivers for the Healthcare Data Annotation Tools Market can be influenced by various factors. These may include:

Increased Use of AI in Healthcare: There is an increasing need for high-quality annotated data in healthcare due to the use of AI and machine learning for activities like diagnostics, medical imaging analysis, and predictive analytics. Labelled Medical Datasets Are Necessary: Labelled datasets are necessary for machine learning model training and validation. Tools for annotating healthcare data are essential for accurately labelling patient records, medical imaging, and other types of healthcare data. Technological Developments in Medical Imaging: New developments in medical imaging technologies, such CT and MRI scans, provide a lot of complex data. These photos can be labelled and annotated with the help of data annotation tools for AI model training. Drug Development and Discovery: Artificial Intelligence is being utilised in pharmaceutical research to find and develop new drugs. Training AI models in this domain requires annotated data on biological processes, molecular structures, and clinical trial details. Accurate Diagnosis Improvement: AI models that can help medical practitioners diagnose patients more accurately, detect diseases early, and improve patient outcomes can be developed thanks to annotated datasets. Personalised Health Care: AI models that are capable of analysing patient-specific data are necessary given the trend towards personalised treatment. Training algorithms to generate individualised treatment suggestions requires access to annotated healthcare data. Standards of Quality and Regulatory Compliance: Accurate and well-annotated datasets are necessary for model training and validation in order to comply with regulatory regulations and quality standards in the healthcare industry, guaranteeing the dependability and security of AI applications. Healthcare Record Digitization is Growing: Large volumes of data are produced by the digital transformation of healthcare records, particularly electronic health records (EHRs), which can be used for artificial intelligence (AI) applications. Tools for annotating data help get this data ready for analysis. Partnership Between Tech and Healthcare Companies: AI solutions are developed through partnerships between technology businesses and healthcare organisations. For these cooperative efforts to be successful, accurate data annotation is essential. Demand for Empirical Data: For AI applications in healthcare, real-world evidence—obtained from real clinical procedures and patient data—is invaluable. Annotated real-world data aids in the creation of reliable and broadly applicable models. Expanding Recognition of Telemedicine: Large datasets that can be annotated to train AI models for telehealth applications are produced by the growing use of telemedicine and remote healthcare services. Emphasis on Early Intervention and Disease Prevention: In line with the healthcare industry's emphasis on proactive healthcare, AI models trained on annotated data can support early intervention and illness prevention measures. Innovation and Market Competitiveness: Innovation in healthcare technology is stimulated by the competitive environment. Aiming to create state-of-the-art AI solutions, organisations are driving the need for superior annotated healthcare data.

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Cancer Registry Software Market Size 2025-2029

The cancer registry software market size is forecast to increase by USD 121.9 million, at a CAGR of 14% between 2024 and 2029.

The market is witnessing significant growth due to the escalating prevalence of cancer cases worldwide. The increasing incidence of various types of cancer necessitates the implementation of advanced registry software solutions to manage and analyze patient data more efficiently. Moreover, the burgeoning clinical research in oncology further drives the demand for these systems, as they facilitate data collection, management, and analysis for research purposes. However, the market faces challenges in the form of stringent data privacy and security concerns. With the growing amount of sensitive patient information being stored and shared digitally, ensuring robust data security becomes crucial. The potential risks of data breaches and unauthorized access can significantly impact both patients and healthcare providers, necessitating the adoption of advanced security measures. Companies in the market must prioritize data security and privacy to gain the trust of healthcare organizations and patients alike.

What will be the Size of the Cancer Registry Software Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
Request Free SampleThe market is a dynamic and evolving landscape, continually adapting to advancements in healthcare technology and the growing demand for comprehensive cancer data management. This market encompasses various applications, including disease registry management, cancer staging system, data warehousing, cancer incidence tracking, registry software architecture, data integration platform, clinical data capture, case reporting system, statistical reporting, cancer screening programs, and more. These tools play a crucial role in cancer surveillance systems, enabling the collection, analysis, and reporting of epidemiological data for public health surveillance. They facilitate data encryption for patient data privacy, ensuring HIPAA compliance. Data interoperability and data quality metrics are essential components, allowing for seamless integration of various health informatics tools. Real-time data updates and database management systems are integral to maintaining accurate and up-to-date information. Predictive modeling tools and data mining techniques contribute to risk factor identification and mortality data analysis. Data visualization tools offer valuable insights into the complexities of cancer data. Cancer registry software architecture supports population-based registry initiatives, ensuring secure data storage and registry reporting features. Oncology data management tools enable clinical data capture, case reporting, and statistical reporting, enhancing overall patient care. The ongoing development and refinement of these tools reflect the continuous unfolding of market activities and evolving patterns in cancer data management.

How is this Cancer Registry Software Industry segmented?

The cancer registry software industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments. End-userGovernment and third partyPharma biotech and medical device companiesHospitals and medical practicePrivate payersResearch institutesTypeStand-alone softwareIntegrated softwareDeploymentOn-premisesCloud-basedGeographyNorth AmericaUSCanadaMexicoEuropeFranceGermanyItalySpainUKAPACChinaJapanRest of World (ROW)

By End-user Insights

The government and third party segment is estimated to witness significant growth during the forecast period.Cancer registry software solutions play a vital role in assisting government and third-party agencies in managing and analyzing data related to cancer cases. These systems enable the tracking of cancer incidence, prevalence, and mortality rates, providing essential information for public health planning, resource allocation, and policy development. Analyzing trends and patterns in registry data helps identify high-risk populations, geographic disparities, and emerging cancer types. Governments utilize cancer registry software to monitor and improve the quality of cancer care. By evaluating variations in treatment practices and adherence to clinical guidelines, they can benchmark outcomes against national or international standards. Additionally, these software solutions facilitate data interoperability, ensuring data quality metrics and HIPAA compliance. Data encryption, data visualization tools, and predictive modeling capabilities enhance the functionality of cancer registry software. Epidemiological data analysis and risk factor identificatio

Background: Public engagement in health and biomedical research is being influenced by the paradigm of citizen science. However, conventional health and biomedical research relies on sophisticated research data management tools and methods. Considering these, what contribution can citizen science make in this field of research? How can it follow research protocols and produce reliable results?

Objective: The aim of this paper is to analyse research data management practices in existing biomedical citizen science studies, so as to provide insights for members of the public and of the research community considering this approach to research.

Methods: A scoping review was conducted on this topic to determine data management characteristics of health and bio medical citizen science research. From this review and related web searching, we chose five online platforms and a specific research project associated with each, to understand their research data management approaches and enablers.

Results: Health and biomedical citizen science platforms and projects are diverse in terms of types of work with data and data management activities that in themselves may have scientific merit. However, consistent approaches in the use of research data management models or practices seem lacking, or at least are not evident.

Conclusions: There is potential for important data collection and analysis activities to be opaque or irreproducible in health and biomedical citizen science initiatives without the implementation of a research data management model that is transparent and accessible to team members and to external audiences. This situation might be improved with participatory development of standards that can be applied to diverse projects and platforms, across the research data life cycle.

Abstract

The 2017 BHFS is an assessment of health care facilities in the formal sector of Bangladesh. The survey provides information on the availability of basic and essential health care services and the readiness of health facilities to provide quality services to clients.

The main objectives of the 2017 BHFS were to: - Assess the availability of health services, including maternal and child health, family planning, diabetes, cardiovascular disease, tuberculosis, and nutrition services. - Ascertain general preparedness of the health facilities and availability of basic amenities, equipment, laboratory services, essential medicines, standard precautions for infection control, and human resources at the facilities. - Assess service-specific readiness of health facilities to provide maternal, newborn, and child health care; FP services; and treatment of diabetes, cardiovascular disease, and tuberculosis, measured in terms of the WHO-recommended minimum conditions required to provide quality services. - Compare findings among facility types and managing authorities.

Geographic coverage

National coverage

Analysis unit

Health establishments, hospitals, health centers, health workers

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample for the 2017 Bangladesh Health Facility Survey (BHFS) was a stratified random sample of 1,600 health facilities designed to provide representative results for Bangladesh, for the different facility types and different management authorities, and for each of the eight divisions of the country. Stratification was achieved by separating the health facilities by facility type within each division. Implicit stratification by management authorities was achieved by sorting the frame based on the management authorities within each explicit sampling stratum before sample selection.

The sample for the 2017 BHFS covered all types of registered health facilities in all eight divisions of the country: Barisal, Chittagong, Dhaka, Khulna, Mymensingh, Rajshahi, Rangpur, and Sylhet. The survey was designed to report results separately for the eight divisions and the six types of public health facilities included: community clinics (CCs), union subcenters/rural dispensaries (USC/RDs), union health and family welfare centers (UHFWCs), upazila health complexes (UHCs), mother and child welfare centers (MCWCs), and district hospitals (DHs). Results are also reported separately for NGO clinics and hospitals and private hospitals. UHFWCs include regular FWCs and upgraded FWCs (UpFWCs).

For further details on sample selection, see Section 2.5 of the final report.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The 2017 BHFS used two types of data collection tools: - Facility Inventory Questionnaire - Health Care Provider Interview Questionnaire

Both the Facility Inventory and Health Care Provider Interview questionnaires were loaded onto tablet computers and administered as computer-assisted personal interviews (CAPIs).

The Facility Inventory Questionnaire was organized into three modules: - Module 1 collected information on service availability and included two sections. - Module 2 collected information on general facility readiness. This module included seven sections that covered topics such as facility infrastructure (i.e., sources of water and electricity), staffing, health management information systems, health statistics, processing of instruments for re-use, health care waste management, availability of basic supplies and equipment, laboratory diagnostic capacity, and medicines and commodities. - Module 3 collected information on service-specific readiness. The 12 sections in this module included specific service areas such as child health (child vaccination, growth monitoring, and curative care), FP, adolescent health, nutrition, antenatal care (ANC), delivery and newborn care, tuberculosis, NCDs, caesarean delivery, blood typing and compatibility, blood transfusion services, and general facility cleanliness.

The Health Care Provider Interview Questionnaire collected information from a sample of health service providers. The data included qualifications, training, experience, continuing education, supervision received, and perceptions of the service delivery environment.