The global academic medical center market is projected to expand at a CAGR of 11.38% over the forecast period (2023-2030), reaching a market value of $819.63 billion by 2030. This growth is driven by several factors, including the rising demand for personalized medicine, the increasing prevalence of chronic diseases, and the growing need for specialized healthcare services. The Asia-Pacific region is expected to witness the highest growth, with the market expanding at a CAGR of 13.78% during the forecast period. This growth is attributed to factors such as the increasing healthcare spending, the growing population, and the expanding healthcare infrastructure in the region. The key segments of the academic medical center market include academic level (undergraduate, graduate, postgraduate), hospital type (university teaching hospitals, community hospitals, private hospitals, public hospitals), focus area (clinical care, research, education), and funding source (government funding, university funding, private donations, patient revenue). The major players operating in the market include Mayo Clinic, University of Toronto, Faculty of Medicine, Johns Hopkins Hospital, Charité – Universitätsmedizin Berlin, University of California, San Francisco Medical Center, NYU Langone Health, University of Washington Medical Center, Cleveland Clinic, University of Oxford, Karolinska Institute, Stanford University Medical Center, University of Edinburgh, UCLA Medical Center, and Massachusetts General Hospital. These players have adopted strategies such as mergers and acquisitions, geographical expansion, and the development of new products and services to enhance their market presence. Recent developments include: , The Academic Medical Center (AMC) market is projected to expand significantly over the coming years, driven by factors such as rising demand for healthcare services, technological advancements, and increasing government initiatives to improve healthcare infrastructure., In 2023, the United States accounted for the largest share of the AMC market. The region's advanced healthcare system, strong research and development capabilities, and high healthcare expenditure are major factors contributing to its dominance., Other key markets include Europe and Asia-Pacific, which are also experiencing significant growth due to increasing demand for healthcare services and government initiatives to improve healthcare infrastructure., Recent news developments in the AMC market include the increasing adoption of telemedicine and digital health technologies, which enable remote patient monitoring and provide greater access to healthcare services., Additionally, there is a growing focus on precision medicine and personalized treatments, which are expected to drive demand for specialized medical centers and advanced diagnostic and treatment technologies., Academic Medical Center Market Segmentation Insights. Key drivers for this market are: Advanced research capabilities Personalized patient care Innovative treatment approaches Precision medicine Data-driven healthcare management . Potential restraints include: 1 Growing demand for specialized healthcare services 2 Technological advancements in medical diagnosis and treatment 3 Increasing collaborations between academia and industry 4 Government initiatives to support medical research and education 5 Rising healthcare costs and insurance coverage limitations .

Nodes, internal edges, and external edges used to construct network graph representations of 40 academic medical centre websites.

Healthcare Data Analytics Market size was valued at USD 32.87 Billion in 2024 and is projected to reach USD 173.57 Billion by 2032, growing at a CAGR of 23.12% during the forecasted period 2026 to 2032.

The healthcare data analytics market is driven by the increasing need to enhance patient care quality, reduce healthcare costs, and streamline operations within healthcare facilities. With the growing volume of patient data generated from electronic health records (EHRs), wearable devices, and telemedicine, healthcare providers seek advanced analytics to gain actionable insights, improve patient outcomes, and optimize resource allocation. Government regulations promoting data-driven healthcare and value-based care models further accelerate adoption. Additionally, advancements in artificial intelligence (AI) and machine learning (ML) enable predictive analytics, aiding in early diagnosis, personalized treatment plans, and efficient disease management, which are crucial in an aging population.

This survey charted Finnish citizens' as well as social and healthcare service professionals' attitudes and views concerning secondary use of health and social care data in research and development of services. The study contained two target groups: (1) persons who suffered or had a close relative or acquaintance who suffered from one or more chronic conditions, diseases or disorders, and (2) social and healthcare service professionals. First, the respondents' opinions on the reliability of a variety of authorities and organisations were examined (e.g. the police, Kela, register and statistics authorities, universities) as well as trust in appropriate handling of personal data. They were also asked which type of information they deemed personal or not (e.g. bank account number and balance, purchase history at a grocery store, web browsing history, patient records, genetic information, social security number, phone number). They were asked to evaluate which principles they considered important in handling personal health data (e.g. being able to access one's personal data and to have inaccurate data rectified, and being able to restrict data processing), and the study also surveyed how interested the respondents were in keeping track of the use of their health data, and how willing they would be to permit the use of anonymous health data and genetic information for a variety of purposes (e.g. medicine and treatment development, development of equipment and services, and operations of insurance companies). Next, it was examined whether the respondents kept track of their physical activity with a smartphone or a fitness tracker, for instance, and if they would be willing to permit the use of anonymous data concerning physical activity for a variety of purposes. In addition, the respondents' attitudes were charted with regard to developing medicine research by combining anonymous health data and patient records with other data on, for instance, physical activity, alcohol use, grocery store purchase history, web browsing history, and social media use. The study also examined the willingness to permit access to personal health data for social and healthcare service professionals in a service situation, as well as for social and healthcare authorities and other authorities outside of a service situation. Finally, it was charted how important the respondents deemed different factors relating to data collection (e.g. being able to decide for which purposes personal data, or even anonymous data, can be used, and increasing awareness on how health data can be utilised in scientific research). The reliability of a variety of authorities and organisations, such as social welfare/healthcare organisations, academic researchers and pharmaceutical companies, was also examined in terms of data security and purposes for using data. Background variables included, among others, mother tongue, marital status, household composition, housing tenure, socioeconomic class, political party preference, left-right political self-placement, gross income, economic activity and occupational status, and respondent group (citizen/healthcare service professional/social service professional).

The Health Services Training Report (HST) Database tracks the overall number of Personnel and Accounting Integrated Data Systems (PAID) and Without Compensation (WOC) Trainee positions by the cooperating academic institutions for all medical center approved health services programs. Information in the database comes from all Veterans Affairs Medical Centers (VAMCs) who have Office of Academic Affiliations (OAA) approved HST programs. Worksheets and memos are distributed to participating VAMCs by the OAA annually. VAMC personnel enter the information electronically into the database located at the OAA Support Center (OAASC) in St. Louis, Missouri. The main user of this database is the OAA.

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

Training programs must be designed to prepare physical and occupational therapy students to use electronic health records (EHR) and interprofessional collaboration. This report aims to describe physical and occupational therapy students’ perceptions of integrating an academic EHR (AEHR) in their problem-based learning (PBL) curricula in the College of Health Professions, Sacred Heart University, Fairfield, Connecticut, the United States. A paper-based case approach to PBL was adapted by creating patient cases in an AEHR. Students were asked to complete chart reviews and review provider notes to enhance their learning. An online survey was conducted to determine their perceptions of using AEHR from May 2014 to August 2015.

This dataset consists of an evaluation form, resulting data, and a slide deck from a case study describing the development, implementation, and evaluation of a 1.5 hour clinical research data management workshop for an academic medical center research community. This workshop was developed by the health sciences library.

This study aimed to characterize the learning strategies of medical students at Trinity School of Medicine and to explore potential correlations between deep learning approach and the students’ academic scores. The study was a questionnaire-based, cross-sectional, observational study. A total of 169 medical students in the basic science years of training were included in the study after giving informed consent. The Biggs’s Revised Two-Factor Study Process questionnaire in paper form was distributed to subjects from January to November 2017. A total of 169 questionnaires were distributed and 132 students (response rate of 78.1%) responded.

According to Cognitive Market Research, the global healthcare education market size is USD 108.7 billion in 2024 and will expand at a compound annual growth rate (CAGR) of 8.6% from 2024 to 2031. Market Dynamics of Healthcare Education Market

Key Drivers for Healthcare Education Market

The growing need for healthcare experts- One factor propelling the healthcare education industry forward is the growing need for qualified healthcare workers. There is a great need for trained healthcare staff to meet the rising demand for medical treatment caused by the increasing number of older adults around the world. The rapid advancements in medical research are exacerbating the need for healthcare providers to have current knowledge and skills. As a result, schools and programs are stepping up their game to satisfy this need, with a particular emphasis on developing qualified medical professionals. There is an ever-increasing demand for all-encompassing healthcare education programs because of the rising importance of patient safety and high-quality care, both of which demand continuous education and professional growth.
The demand for healthcare education is being propelled by the increasing focus on training healthcare personnel to maintain high standards of patient safety and quality care.

Key Restraints for Healthcare Education Market

The market growth is hindered due to the high cost of healthcare education.
A shortage of appropriately trained instructors to lead healthcare-specific classes for healthcare education restricts the market growth.
The unpopularity of healthcare education in rural areas hamper market

The lack of popularity of rural healthcare education is a significant impediment to the expansion of the healthcare education market. In rural and remote parts of the country, there is poor access to quality education facilities, digital learning resources, and trained personnel necessary to provide good healthcare training. This leads to poor awareness of health problems, disease prevention measures, hygiene norms, and the need for professional medical treatment. As a result, both demand for healthcare training and supply of people willing or capable of studying for healthcare professions are low. Socioeconomic conditions also worsen the situation. Rural populations in many places are poor, have no means of transportation, and cultural or language barriers that prevent them from accessing educational programs. In some places, cultural beliefs and suspicion of modern medicine also discourage people from seeking or appreciating healthcare knowledge. Without focused investments and outreach programs, healthcare education initiatives find it difficult to reach these communities, making the market inaccessible and less effective. The shortage of trained healthcare professionals in rural areas also creates a congested system, making it less effective for education and training programs. Overcoming this issue involves major government support, technology integration, and community-led awareness campaigns to make healthcare education more accessible and attractive in rural areas. Opportunities for Healthcare Education

Increasing adoption of adaptive learning opportunity for market 

The growing use of adaptive learning technologies poses a major opportunity for the healthcare education market. Adaptive learning applies artificial intelligence and data analytics to customize education content according to the pace, performance, and comprehension of individual learners. For instance, In 2023, high fidelity simulation and computerized mannequins. Additional examples include electronic learning modules, electronic portfolios, virtual patient interaction, massive open online courses and the flipped classroom phenomenon. TEL has become prevalent mainly because of the convenience of internet accessibility that allows the retrieval and exchange of information within a split second. (Source- https://ieeexplore.ieee.org/ In healthcare education, this method can significantly improve learning outcomes through the delivery of customized training modules for doctors, nurses, technologists, and other healthcare workers. With the increasing sophistication of medical information and quick developments in healthcare practice, adaptive learning systems will assist the learners to remain current with fresh information while keeping them on course with their particular areas of requ...

In order to understand student perceptions of the original advisory process, baseline information was gathered by administering a questionnaire developed by the authors to physical therapy and occupational therapy students at the University of Mississippi Medical Center who had just completed a first full academic year in their respective programs in 2015.

The global medical research software market is experiencing robust growth, driven by the increasing adoption of digital technologies within the pharmaceutical and academic research sectors. The market, valued at approximately $2.5 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 12% from 2025 to 2033. This expansion is fueled by several key factors, including the rising need for efficient data management and analysis in clinical trials, the growing complexity of research studies, and the increasing demand for regulatory compliance solutions. The integration of artificial intelligence (AI) and machine learning (ML) into medical research software is further accelerating market growth by enhancing data processing capabilities and accelerating research timelines. This trend is particularly evident in areas such as clinical trial management systems (CTMS), where software is streamlining processes and reducing operational costs. The market is segmented by software type (Clinical Research Software and Laboratory Research Software) and application (Pharmaceutical Companies, Academic and Research Institutions, and Others). Pharmaceutical companies currently dominate the market share due to their significant investment in research and development. However, academic and research institutions are also experiencing notable growth in software adoption as they seek to improve research efficiency and collaboration. Geographical segmentation reveals strong market presence in North America and Europe, driven by established research infrastructure and regulatory frameworks. However, emerging markets in Asia-Pacific are showcasing promising growth potential, driven by increasing healthcare spending and the expansion of research initiatives within the region. While the market faces challenges like the high cost of software implementation and the need for robust data security measures, the overall outlook for the medical research software market remains highly positive, promising substantial growth opportunities in the coming years.

US Healthcare NPI Data is a comprehensive resource offering detailed information on health providers registered in the United States.

Dataset Highlights:

• NPI Numbers: Unique identification numbers for health providers.
• Contact Details: Includes addresses and phone numbers.
• State License Numbers: State-specific licensing information.
• Additional Identifiers: Other identifiers related to the providers.
• Business Names: Names of the provider’s business entities.
• Taxonomies: Classification of provider types and specialties.

Taxonomy Data:

• Includes codes, groupings, and classifications.
• Facilitates detailed analysis and categorization of providers.

Data Updates:

• Weekly Delta Changes: Ensures the dataset is current with the latest changes.
• Monthly Full Refresh: Comprehensive update to maintain accuracy.

Use Cases:

• Market Analysis: Understand the distribution and types of healthcare providers across the US. Analyze market trends and identify potential gaps in healthcare services.
• Outreach: Create targeted marketing campaigns to reach specific types of healthcare providers. Use contact details for direct outreach and engagement with providers.
• Research: Conduct in-depth research on healthcare providers and their specialties. Analyze provider attributes to support academic or commercial research projects.
• Compliance and Verification: Verify provider credentials and compliance with state licensing requirements. Ensure accurate provider information for regulatory and compliance purposes.

Data Quality and Reliability:

• The dataset is meticulously curated to ensure high quality and reliability. Regular updates, both weekly and monthly, ensure that users have access to the most current information. The comprehensive nature of the data, combined with its regular updates, makes it a valuable tool for a wide range of applications in the healthcare sector.

Access and Integration: - CSV Format: The dataset is provided in CSV format, making it easy to integrate with various data analysis tools and platforms. - Ease of Use: The structured format of the data ensures that it can be easily imported, analyzed, and utilized for various applications without extensive preprocessing.

Ideal for:

• Healthcare Professionals: Physicians, nurses, and other healthcare providers who need to verify information about their peers.
• Analysts: Data analysts and business analysts who require detailed and accurate healthcare provider data for their projects.
• Businesses: Companies in the healthcare sector looking to understand market dynamics and reach out to providers.
• Researchers: Academic and commercial researchers conducting studies on healthcare providers and services.

Why Choose This Dataset?

• Comprehensive Coverage: Detailed information on millions of healthcare providers across the US.
• Regular Updates: Weekly and monthly updates ensure that the data remains current and reliable.
• Ease of Integration: Provided in a user-friendly CSV format for easy integration with your existing systems.
• Versatility: Suitable for a wide range of applications, from market analysis to compliance and research.

By leveraging the US Healthcare NPI & Taxonomy Data, users can gain valuable insights into the healthcare landscape, enhance their outreach efforts, and conduct detailed research with confidence in the accuracy and comprehensiveness of the data.

Summary:

• This dataset is an invaluable resource for anyone needing detailed and up-to-date information on US healthcare providers. Whether for market analysis, research, outreach, or compliance, the US Healthcare NPI & Taxonomy Data offers the detailed, reliable information needed to achieve your goals.

This paper studies the effect of improved early life health care on mortality and long-run academic achievement in school. We use the idea that medical treatments often follow rules of thumb for assigning care to patients, such as the classification of Very Low Birth Weight (VLBW), which assigns infants special care at a specific birth weight cutoff. Using detailed administrative data on schooling and birth records from Chile and Norway, we establish that children who receive extra medical care at birth have lower mortality rates and higher test scores and grades in school. These gains are in the order of 0.15-0.22 standard deviations.

To the Editor: Reporting of aggregate results helps mitigate disclosure biases affecting medical research. Although the reporting of summary results is currently mandated by the Food and Drug Administration Amendments Act of 2007 (FDAAA), published findings suggest underreporting. Two recent proposals are aimed at improving public reporting of aggregate results. These are a Notice of Proposed Rulemaking (NPRM) to expand FDAAA requirements to include the results of trials of unapproved products, and a draft policy requiring the results of all National Institutes of Health (NIH)-funded trials, including those not subject to the FDAAA.

Dataset

This dataset was created by Claudia Dodge

Contents

This project aimed to test and create new ways for researchers, disabled people and other health and social care service users to work in partnership so that research can (1) be shaped and informed by the experiences of service users (2) better meet service user needs (3) positively impact society. The data could not be archived due to ethical considerations. Interview schedules are made available for future reuse.

Global healthcare data storage market worth at $5.52 Billion in 2024, is expected to $20.90 Billion by 2034, with a CAGR of 14.24% from 2025 to 2034.

The data was collected during a research project investigating the conceptualization of research quality in the context of health and welfare research. Research quality is often discussed in terms of excellence, emphasizing replicability and trustworthiness. Practice-based research instead emphasis implementability and practical impact, and thus, may reflect other values and logics and challenge how high-quality practice-based research is defined. The aim of this study is to explore what characterizes excellent practice-based research.

The data was collected using the Group Concept Mapping methodology. Four data collection activities were used to collect the data: brainstorming, sorting, rating of importance, and rating of experience.

48 participants participated in the brainstorming session to generate the list of statements. 22 participants participated in the sorting activity which generated the similarity matrix. 13 participants rated the statements based on importance and 10 based on experience.

All participants were affiliated with or employed at a local or regional Research and development (R&D) organization and engaged in health and welfare research in Sweden in different ways.

The material consists of four data files:

1) List_of_statements.csv: List of statements from the brainstorming activity 2) Similarity_matrix1724400634.csv: Similarity matrix from the sorting activity. The similarity matrix show how many times each statement was sorted together with all other statements. This data-file was used as input for the multidimensional scaling. 3) Raw_rating_report_importance_1724400874.csv: Rating data for the importance rating. This data show how important each participant rated each statement on a 5-point scale. The scale ranged from 1=unimportant, to 5=very important. When data is missing, the corresponding cell has been left blank. 4) Raw_rating_report_experience_1724400881.csv: Rating data for the experience rating. This data show how much experience each participant rated that they had with each statement on a 5-point scale. The scale ranged from 1=The research characterizes the research I have experience of to a very low degree, to 5= The research characterizes the research I have experience of to a very high degree on the experience scale. When data is missing, the corresponding cell has been left blank.

All statement numbers correspond to the statement list in the file "List of statements".

The researcher tests the QA capability of ChatGPT in the medical field from the following aspects:1. Test their reserve capacity for medical knowledge2. Check their ability to read literature and understand medical literature3. Test their ability of auxiliary diagnosis after reading case data4. Test its error correction ability for case data5. Test its ability to standardize medical terms6. Test their evaluation ability to experts7. Check their ability to evaluate medical institutionsThe conclusion is:ChatGPT has great potential in the application of medical and health care, and may directly replace human beings or even professionals at a certain level in some fields;The researcher preliminarily believe that ChatGPT has basic medical knowledge and the ability of multiple rounds of dialogue, and its ability to understand Chinese is not weak;ChatGPT has the ability to read, understand and correct cases;ChatGPT has the ability of information extraction and terminology standardization, and is quite excellent;ChatGPT has the reasoning ability of medical knowledge;ChatGPT has the ability of continuous learning. After continuous training, its level has improved significantly;ChatGPT does not have the academic evaluation ability of Chinese medical talents, and the results are not ideal;ChatGPT does not have the academic evaluation ability of Chinese medical institutions, and the results are not ideal;ChatGPT is an epoch-making product, which can become a useful assistant for medical diagnosis and treatment, knowledge service, literature reading, review and paper writing.