In 2019, 83 percent of the physicians and 79 percent of students and residents surveyed in the U.S. said that patient data would be valuable to them clinically if it was sourced from a wearable device. Furthermore, 80 percent of physicians and 78 percent of students and residents said they would give clinical importance to patients self reported data if it was from a health app.

This statistic presents the opinions of U.S. respondents, by gender, concerning the importance of the issue of healthcare, as of October 2020. Results indicate that 74 percent of all respondents felt that health care was a very important issue at that time. A larger percentage of female respondents indicated that health care was "very important" than did male respondents.

Update — December 7, 2014. – Evidence-based medicine (EBM) is not working for many reasons, for example: 1. Incorrect in their foundations (paradox): hierarchical levels of evidence are supported by opinions (i.e., lowest strength of evidence according to EBM) instead of real data collected from different types of study designs (i.e., evidence). http://dx.doi.org/10.6084/m9.figshare.1122534 2. The effect of criminal practices by pharmaceutical companies is only possible because of the complicity of others: healthcare systems, professional associations, governmental and academic institutions. Pharmaceutical companies also corrupt at the personal level, politicians and political parties are on their payroll, medical professionals seduced by different types of gifts in exchange of prescriptions (i.e., bribery) which very likely results in patients not receiving the proper treatment for their disease, many times there is no such thing: healthy persons not needing pharmacological treatments of any kind are constantly misdiagnosed and treated with unnecessary drugs. Some medical professionals are converted in K.O.L. which is only a puppet appearing on stage to spread lies to their peers, a person supposedly trained to improve the well-being of others, now deceits on behalf of pharmaceutical companies. Probably the saddest thing is that many honest doctors are being misled by these lies created by the rules of pharmaceutical marketing instead of scientific, medical, and ethical principles. Interpretation of EBM in this context was not anticipated by their creators. “The main reason we take so many drugs is that drug companies don’t sell drugs, they sell lies about drugs.” ―Peter C. Gøtzsche “doctors and their organisations should recognise that it is unethical to receive money that has been earned in part through crimes that have harmed those people whose interests doctors are expected to take care of. Many crimes would be impossible to carry out if doctors weren’t willing to participate in them.” —Peter C Gøtzsche, The BMJ, 2012, Big pharma often commits corporate crime, and this must be stopped. Pending (Colombia): Health Promoter Entities (In Spanish: EPS ―Empresas Promotoras de Salud).

1. Misinterpretations New technologies or concepts are difficult to understand in the beginning, it doesn’t matter their simplicity, we need to get used to new tools aimed to improve our professional practice. Probably the best explanation is here in these videos (credits to Antonio Villafaina for sharing these videos with me). English https://www.youtube.com/watch?v=pQHX-SjgQvQ&w=420&h=315 Spanish https://www.youtube.com/watch?v=DApozQBrlhU&w=420&h=315 ----------------------- Hypothesis: hierarchical levels of evidence based medicine are wrong Dear Editor, I have data to support the hypothesis described in the title of this letter. Before rejecting the null hypothesis I would like to ask the following open question:Could you support with data that hierarchical levels of evidence based medicine are correct? (1,2) Additional explanation to this question: – Only respond to this question attaching publicly available raw data.– Be aware that more than a question this is a challenge: I have data (i.e., evidence) which is contrary to classic (i.e., McMaster) or current (i.e., Oxford) hierarchical levels of evidence based medicine. An important part of this data (but not all) is publicly available. References
2. Ramirez, Jorge H (2014): The EBM challenge. figshare. http://dx.doi.org/10.6084/m9.figshare.1135873
3. The EBM Challenge Day 1: No Answers. Competing interests: I endorse the principles of open data in human biomedical research Read this letter on The BMJ – August 13, 2014.http://www.bmj.com/content/348/bmj.g3725/rr/762595Re: Greenhalgh T, et al. Evidence based medicine: a movement in crisis? BMJ 2014; 348: g3725. _ Fileset contents Raw data: Excel archive: Raw data, interactive figures, and PubMed search terms. Google Spreadsheet is also available (URL below the article description). Figure 1. Unadjusted (Fig 1A) and adjusted (Fig 1B) PubMed publication trends (01/01/1992 to 30/06/2014). Figure 2. Adjusted PubMed publication trends (07/01/2008 to 29/06/2014) Figure 3. Google search trends: Jan 2004 to Jun 2014 / 1-week periods. Figure 4. PubMed publication trends (1962-2013) systematic reviews and meta-analysis, clinical trials, and observational studies.
   Figure 5. Ramirez, Jorge H (2014): Infographics: Unpublished US phase 3 clinical trials (2002-2014) completed before Jan 2011 = 50.8%. figshare.http://dx.doi.org/10.6084/m9.figshare.1121675 Raw data: "13377 studies found for: Completed | Interventional Studies | Phase 3 | received from 01/01/2002 to 01/01/2014 | Worldwide". This database complies with the terms and conditions of ClinicalTrials.gov: http://clinicaltrials.gov/ct2/about-site/terms-conditions Supplementary Figures (S1-S6). PubMed publication delay in the indexation processes does not explain the descending trends in the scientific output of evidence-based medicine. Acknowledgments I would like to acknowledge the following persons for providing valuable concepts in data visualization and infographics:
4. Maria Fernanda Ramírez. Professor of graphic design. Universidad del Valle. Cali, Colombia.
5. Lorena Franco. Graphic design student. Universidad del Valle. Cali, Colombia. Related articles by this author (Jorge H. Ramírez)
6. Ramirez JH. Lack of transparency in clinical trials: a call for action. Colomb Med (Cali) 2013;44(4):243-6. URL: http://www.ncbi.nlm.nih.gov/pubmed/24892242
7. Ramirez JH. Re: Evidence based medicine is broken (17 June 2014). http://www.bmj.com/node/759181
8. Ramirez JH. Re: Global rules for global health: why we need an independent, impartial WHO (19 June 2014). http://www.bmj.com/node/759151
9. Ramirez JH. PubMed publication trends (1992 to 2014): evidence based medicine and clinical practice guidelines (04 July 2014). http://www.bmj.com/content/348/bmj.g3725/rr/759895 Recommended articles
10. Greenhalgh Trisha, Howick Jeremy,Maskrey Neal. Evidence based medicine: a movement in crisis? BMJ 2014;348:g3725
11. Spence Des. Evidence based medicine is broken BMJ 2014; 348:g22
12. Schünemann Holger J, Oxman Andrew D,Brozek Jan, Glasziou Paul, JaeschkeRoman, Vist Gunn E et al. Grading quality of evidence and strength of recommendations for diagnostic tests and strategies BMJ 2008; 336:1106
13. Lau Joseph, Ioannidis John P A, TerrinNorma, Schmid Christopher H, OlkinIngram. The case of the misleading funnel plot BMJ 2006; 333:597
14. Moynihan R, Henry D, Moons KGM (2014) Using Evidence to Combat Overdiagnosis and Overtreatment: Evaluating Treatments, Tests, and Disease Definitions in the Time of Too Much. PLoS Med 11(7): e1001655. doi:10.1371/journal.pmed.1001655
15. Katz D. A-holistic view of evidence based medicinehttp://thehealthcareblog.com/blog/2014/05/02/a-holistic-view-of-evidence-based-medicine/ ---

Note: This dataset is historical only and there are not corresponding datasets for more recent time periods. For that more-recent information, please visit the Chicago Health Atlas at https://chicagohealthatlas.org. This dataset contains a selection of 27 indicators of public health significance by Chicago community area, with the most updated information available. The indicators are rates, percents, or other measures related to natality, mortality, infectious disease, lead poisoning, and economic status. See the full description at https://data.cityofchicago.org/api/assets/2107948F-357D-4ED7-ACC2-2E9266BBFFA2.

In 2020, around 46 percent of individuals worldwide aged 15 years and older stated they thought mental health was more important than physical health, while another 46 percent felt mental health was just as important as physical health. This statistic illustrates the perceived importance of mental health compared to physical health among individuals worldwide in 2020.

This paper is a collection of thoughts from multiple discussions about the importance of appreciating and embracing statistical thinking in public health research and education. We think that statistical simulations can play an important role in fostering statistical reasoning in public health and that they can be a great didactic tool for students to generate and learn from data. Two main points are of relevance here. First, simulations can foster critical thinking and improve our reasoning about public health problems by going from theoretical thoughts to practical implementation of designing a computer experiment. Second, simulations can support researchers and their students to better understand statistical concepts used when describing and analysing population health in terms of distributions. Overall, we advocate for the use of more simulations in public health research and education to strengthen statistical reasoning when studying the health of populations.

Objectives: Demonstrate the application of decision trees—classification and regression trees (CARTs), and their cousins, boosted regression trees (BRTs)—to understand structure in missing data. Setting: Data taken from employees at 3 different industrial sites in Australia. Participants: 7915 observations were included. Materials and methods: The approach was evaluated using an occupational health data set comprising results of questionnaires, medical tests and environmental monitoring. Statistical methods included standard statistical tests and the ‘rpart’ and ‘gbm’ packages for CART and BRT analyses, respectively, from the statistical software ‘R’. A simulation study was conducted to explore the capability of decision tree models in describing data with missingness artificially introduced. Results: CART and BRT models were effective in highlighting a missingness structure in the data, related to the type of data (medical or environmental), the site in which it was collected, the number of visits, and the presence of extreme values. The simulation study revealed that CART models were able to identify variables and values responsible for inducing missingness. There was greater variation in variable importance for unstructured as compared to structured missingness. Discussion: Both CART and BRT models were effective in describing structural missingness in data. CART models may be preferred over BRT models for exploratory analysis of missing data, and selecting variables important for predicting missingness. BRT models can show how values of other variables influence missingness, which may prove useful for researchers. Conclusions: Researchers are encouraged to use CART and BRT models to explore and understand missing data.

The National Health Interview Survey (NHIS) is the principal source of information on the health of the civilian noninstitutionalized population of the United States and is one of the major data collection programs of the National Center for Health Statistics (NCHS) which is part of the Centers for Disease Control and Prevention (CDC). The National Health Survey Act of 1956 provided for a continuing survey and special studies to secure accurate and current statistical information on the amount, distribution, and effects of illness and disability in the United States and the services rendered for or because of such conditions. The survey referred to in the Act, now called the National Health Interview Survey, was initiated in July 1957. Since 1960, the survey has been conducted by NCHS, which was formed when the National Health Survey and the National Vital Statistics Division were combined. NHIS data are used widely throughout the Department of Health and Human Services (DHHS) to monitor trends in illness and disability and to track progress toward achieving national health objectives. The data are also used by the public health research community for epidemiologic and policy analysis of such timely issues as characterizing those with various health problems, determining barriers to accessing and using appropriate health care, and evaluating Federal health programs. The NHIS also has a central role in the ongoing integration of household surveys in DHHS. The designs of two major DHHS national household surveys have been or are linked to the NHIS. The National Survey of Family Growth used the NHIS sampling frame in its first five cycles and the Medical Expenditure Panel Survey currently uses half of the NHIS sampling frame. Other linkage includes linking NHIS data to death certificates in the National Death Index (NDI). While the NHIS has been conducted continuously since 1957, the content of the survey has been updated about every 10-15 years. In 1996, a substantially revised NHIS questionnaire began field testing. This revised questionnaire, described in detail below, was implemented in 1997 and has improved the ability of the NHIS to provide important health information.

This statistic is based on a survey conducted in January 2023. It displays the agreement on whether the federal government should support legislation that encourages private investments in medical research in the U.S. The survey shows that 41 percent of respondents of the survey said that it is very important that the federal government should support incentives that encourage private investments in medical research.

BackgroundThere is increasing recognition of the importance of sharing research data within the international scientific community, but also of the ethical and social challenges this presents, particularly in the context of structural inequities and varied capacity in international research. Public involvement is essential to building locally responsive research policies, including on data sharing, but little research has involved stakeholders from low-to-middle income countries.MethodsBetween January and June 2014, a qualitative study was conducted in Kenya involving sixty stakeholders with varying experiences of research in a deliberative process to explore views on benefits and challenges in research data sharing. In-depth interviews and extended small group discussions based on information sharing and facilitated debate were used to collect data. Data were analysed using Framework Analysis, and charting flow and dynamics in debates.FindingsThe findings highlight both the opportunities and challenges of communicating about this complex and relatively novel topic for many stakeholders. For more and less research-experienced stakeholders, ethical research data sharing is likely to rest on the development and implementation of appropriate trust-building processes, linked to local perceptions of benefits and challenges. The central nature of trust is underpinned by uncertainties around who might request what data, for what purpose and when. Key benefits perceived in this consultation were concerned with the promotion of public health through science, with legitimate beneficiaries defined differently by different groups. Important challenges were risks to the interests of study participants, communities and originating researchers through stigmatisation, loss of privacy, impacting autonomy and unfair competition, including through forms of intentional and unintentional 'misuse' of data. Risks were also seen for science.DiscussionGiven background structural inequities in much international research, building trust in this low-to-middle income setting includes ensuring that the interests of study participants, primary communities and originating researchers will be promoted as far as possible, as well as protected. Important ways of building trust in data sharing include involving the public in policy development and implementation, promoting scientific collaborations around data sharing and building close partnerships between researchers and government health authorities to provide checks and balances on data sharing, and promote near and long-term translational benefits.

Vitamin D insufficiency appears to be prevalent in SLE patients. Multiple factors potentially contribute to lower vitamin D levels, including limited sun exposure, the use of sunscreen, darker skin complexion, aging, obesity, specific medical conditions, and certain medications. The study aims to assess the risk factors associated with low vitamin D levels in SLE patients in the southern part of Bangladesh, a region noted for a high prevalence of SLE. The research additionally investigates the possible correlation between vitamin D and the SLEDAI score, seeking to understand the potential benefits of vitamin D in enhancing disease outcomes for SLE patients. The study incorporates a dataset consisting of 50 patients from the southern part of Bangladesh and evaluates their clinical and demographic data. An initial exploratory data analysis is conducted to gain insights into the data, which includes calculating means and standard deviations, performing correlation analysis, and generating heat maps. Relevant inferential statistical tests, such as the Student’s t-test, are also employed. In the machine learning part of the analysis, this study utilizes supervised learning algorithms, specifically Linear Regression (LR) and Random Forest (RF). To optimize the hyperparameters of the RF model and mitigate the risk of overfitting given the small dataset, a 3-Fold cross-validation strategy is implemented. The study also calculates bootstrapped confidence intervals to provide robust uncertainty estimates and further validate the approach. A comprehensive feature importance analysis is carried out using RF feature importance, permutation-based feature importance, and SHAP values. The LR model yields an RMSE of 4.83 (CI: 2.70, 6.76) and MAE of 3.86 (CI: 2.06, 5.86), whereas the RF model achieves better results, with an RMSE of 2.98 (CI: 2.16, 3.76) and MAE of 2.68 (CI: 1.83,3.52). Both models identify Hb, CRP, ESR, and age as significant contributors to vitamin D level predictions. Despite the lack of a significant association between SLEDAI and vitamin D in the statistical analysis, the machine learning models suggest a potential nonlinear dependency of vitamin D on SLEDAI. These findings highlight the importance of these factors in managing vitamin D levels in SLE patients. The study concludes that there is a high prevalence of vitamin D insufficiency in SLE patients. Although a direct linear correlation between the SLEDAI score and vitamin D levels is not observed, machine learning models suggest the possibility of a nonlinear relationship. Furthermore, factors such as Hb, CRP, ESR, and age are identified as more significant in predicting vitamin D levels. Thus, the study suggests that monitoring these factors may be advantageous in managing vitamin D levels in SLE patients. Given the immunological nature of SLE, the potential role of vitamin D in SLE disease activity could be substantial. Therefore, it underscores the need for further large-scale studies to corroborate this hypothesis.

Healthcare Clinical Analytics Market Outlook

The global healthcare clinical analytics market size was valued at approximately $12.5 billion in 2023 and is projected to reach a staggering $35.4 billion by 2032, growing at a robust CAGR of 12.3% during the forecast period. This remarkable growth is primarily driven by the increasing adoption of advanced analytics in clinical settings, which is enhancing the quality of care, reducing costs, and improving patient outcomes. Moreover, the surge in healthcare data, advancements in machine learning, and government initiatives to promote electronic health records (EHRs) are significant growth factors propelling the market.

One of the primary growth factors for the healthcare clinical analytics market is the exponential rise in healthcare data. The proliferation of wearable devices, electronic health records, medical imaging, and other digital health technologies has generated vast amounts of data. This data, when analyzed effectively, can provide critical insights into patient care, disease trends, and treatment outcomes, thereby driving the demand for clinical analytics solutions. Additionally, the need for cost reduction in healthcare is pushing organizations to adopt analytics to streamline operations and optimize resource allocation.

Another significant driver is the increasing emphasis on personalized medicine and precision health. As healthcare moves towards a more individualized approach, clinical analytics plays a crucial role in tailoring treatments and interventions based on the genetic makeup, lifestyle, and environmental factors of patients. By leveraging data from various sources, healthcare providers can predict disease risks, customize treatment plans, and enhance preventive care measures. This shift towards precision health is expected to fuel the demand for advanced analytics tools in the coming years.

Moreover, regulatory requirements and government initiatives are playing a pivotal role in the market's growth. Governments worldwide are implementing policies to encourage the adoption of electronic health records and other health IT systems, which in turn drives the need for advanced analytics to interpret and utilize the data effectively. For instance, the Health Information Technology for Economic and Clinical Health (HITECH) Act in the United States has significantly accelerated the adoption of EHRs, thereby creating a fertile ground for the growth of clinical analytics.

Healthcare Descriptive Analytics plays a pivotal role in the realm of clinical analytics by providing detailed insights into past healthcare data. This type of analytics focuses on summarizing historical data to understand trends and patterns in patient care, resource utilization, and treatment outcomes. By leveraging descriptive analytics, healthcare organizations can gain a comprehensive view of their operational performance and patient demographics. This, in turn, aids in identifying areas for improvement and making informed decisions to enhance the quality of care. The integration of descriptive analytics with other advanced analytics techniques further enriches the data analysis process, enabling healthcare providers to uncover deeper insights and drive better health outcomes.

The regional outlook for the healthcare clinical analytics market reveals a diversified landscape. North America, particularly the United States, dominates the market due to the early adoption of advanced technologies, substantial healthcare spending, and favorable government policies. Europe follows closely, with countries like the UK, Germany, and France investing heavily in healthcare IT infrastructure. The Asia Pacific region is expected to witness the highest growth rate, driven by increasing healthcare investments, growing awareness of digital health, and supportive government initiatives in countries like China and India. Latin America and the Middle East & Africa are also showing promising growth, albeit at a slower pace, due to ongoing healthcare reforms and improving IT infrastructure.

Component Analysis

The healthcare clinical analytics market is segmented into three primary components: software, hardware, and services. The software segment is expected to hold the largest market share, driven by the growing need for advanced analytics platforms that can handle vast amounts of healthcare data. Software solutions encompass a range of tools, including predictive analytics, data visualization, and machine l

BackgroundThe scientific community increasingly is recognizing the need to bolster standards of data analysis given the widespread concern that basic mistakes in data analysis are contributing to the irreproducibility of many published research findings. The aim of this study was to investigate students’ attitudes towards statistics within a multi-site medical educational context, monitor their changes and impact on student achievement. In addition, we performed a systematic review to better support our future pedagogical decisions in teaching applied statistics to medical students.MethodsA validated Serbian Survey of Attitudes Towards Statistics (SATS-36) questionnaire was administered to medical students attending obligatory introductory courses in biostatistics from three medical universities in the Western Balkans. A systematic review of peer-reviewed publications was performed through searches of Scopus, Web of Science, Science Direct, Medline, and APA databases through 1994. A meta-analysis was performed for the correlation coefficients between SATS component scores and statistics achievement. Pooled estimates were calculated using random effects models.ResultsSATS-36 was completed by 461 medical students. Most of the students held positive attitudes towards statistics. Ability in mathematics and grade point average were associated in a multivariate regression model with the Cognitive Competence score, after adjusting for age, gender and computer ability. The results of 90 paired data showed that Affect, Cognitive Competence, and Effort scores demonstrated significant positive changes. The Cognitive Competence score showed the largest increase (M = 0.48, SD = 0.95). The positive correlation found between the Cognitive Competence score and students’ achievement (r = 0.41; p

This statistic shows the results of a 2014 Popsugar survey among American women asking them how important it is that health and fitness brands offer products that make them happy. During the survey, 5.9 percent of female respondents said it is very important.

How big is the Medical Tourism Market? The Medical Tourism Market size is expected to reach USD 84.92 billion in 2024 and grow at a CAGR of 23.03% to reach USD 239.37 billion by 2029.

   What is the current Medical Tourism Market size? 
   In 2024, the Medical Tourism Market size is expected to reach USD 84.92 billion.

   Who are the key players in Medical Tourism Market?
   Healthbase, Apollo Hospitals, KPJ Healthcare, Klinikum Medical Link and Medretreat are the major companies operating in the Medical Tourism Market. 

   Which is the fastest growing region in Medical Tourism Market? 
   Asia Pacific is estimated to grow at the highest CAGR over the forecast period (2024-2029). 

   Which region has the biggest share in Medical Tourism Market? 
   In 2024, the North America accounts for the largest market share in Medical Tourism Market. 

   What years does this Medical Tourism Market cover, and what was the market size in 2023? 
   In 2023, the Medical Tourism Market size was estimated at USD 65.36 billion. The report covers the Medical Tourism Market historical market size for years: 2021, 2022 and 2023. The report also forecasts the Medical Tourism Market size for years: 2024, 2025, 2026, 2027, 2028 and 2029.

   What is the dominant segment contributing to the largest market share in Medical Tourism? 
   Cosmetic Treatment is the dominant segment that holds the major share of the Medical Tourism Market.

   The Global Medical Tourism Market Report provides a comprehensive industry analysis of the medical tourism market, segmented by treatment type and geography. The market overview highlights the various treatment types including cosmetic, dental, cardiovascular, orthopedics, bariatric, fertility, ophthalmic, and other treatments. The industry statistics indicate significant market growth driven by the increasing demand for affordable and high-quality medical care.<br><br>In terms of market segmentation, the report covers North America, Europe, Asia-Pacific, the Middle East and Africa, and South America, providing a detailed market forecast for each region. The industry size and market value are presented in terms of USD, reflecting the market's economic impact. The market trends and growth rate are analyzed to provide insights into future market predictions.<br><br>The report also includes an industry outlook, focusing on key market leaders and their strategies. The market review highlights the competitive landscape and the role of both private and public healthcare service providers. Additionally, the report examines alternative treatment options and their market share.<br><br>For those seeking more detailed information, the report example and report pdf are available for further industry research. The market data and industry reports offer valuable insights for companies looking to understand the market dynamics and make informed decisions. The industry trends and market outlook provide a clear picture of the market's future direction.<br><br>Overall, the Global Medical Tourism Market Report is an essential resource for understanding the market's growth forecast and industry worth. It provides a thorough market analysis and industry information, making it a valuable tool for research companies and stakeholders in the medical tourism industry.

   Medical Tourism Also Known As: Patient Mobility, Transnational Healthcare, Therapeutic Tourism, Medical Vacation, Health Travel

   Medical Tourism Report Covers the Following Regions: NA, North America, North American, Northern America, Northern American, EU, Europe, European, APAC, Asia Pacific, Asian, MEA, Middle East and Africa, Middle Eastern and African, MENA, Middle East, Middle Eastern, SA, South America, South American

   Medical Tourism Report Covers the Following Countries: USA, United States, US, Canada, Mexican, Mexico, DE, Germany, German, UK, United Kingdom, FR, France, French, IT, Italy, Italian, ES, Spain, Spanish, China, Chinese, JP, Japan, Japanese, IN, India, Indian, AU, Australia, Australian, KR, South Korea, South Korean, GCC, Gulf Cooperation Council, ZA, South Africa, South African, BR, Brazil, Brazilian, AR, Argentina, Argentine

Introduction

Healthcare Staffing Statistics: Healthcare staffing is a crucial facet of the healthcare industry. Involves the recruitment, hiring, and management of qualified professionals to meet the ever-changing demands of patients and medical institutions.

This intricate process plays a pivotal role in ensuring high-quality patient care by matching individuals' skills and qualifications to specific roles, considering factors like patient load and location.

Effective healthcare staffing requires anticipating staffing needs, managing schedules, addressing turnover, and adhering to regulatory standards.

Inadequate staffing can jeopardize patient safety and care quality. Effective staffing enhances patient outcomes and experiences, making it a cornerstone of healthcare delivery.

In essence, healthcare staffing is a complex, indispensable process that directly impacts patient well-being and the overall success of healthcare organizations. Demanding meticulous planning and unwavering commitment to excellent patient care.

MEASURE Evaluation is the USAID Global Health Bureau's primary vehicle for supporting improvements in monitoring and evaluation in population, health and nutrition worldwide. They help to identify data needs, collect and analyze technically sound data, and use that data for health decision making. Some MEASURE Evaluation activities involve the collection of innovative evaluation data sets in order to increase the evidence-base on program impact and evaluate the strengths and weaknesses of recent evaluation methodological developments. Many of these data sets may be available to other researchers to answer questions of particular importance to global health and evaluation research. Some of these data sets are being added to the Dataverse on a rolling basis, as they become available. This collection on the Dataverse platform contains a growing variety and number of global health evaluation datasets.

There is increasing recognition of the importance of medical student wellbeing. The purpose of this study was to determine the characteristics that are associated with lower wellness in medical students. An anonymous, IRB-approved survey was distributed to medical students across the United States. Students were asked to rate their response to different statements regarding wellness on a scale from 1 to 10 with 1 indicating "Strongly Disagree" and 10 indicating "Strongly Agree". For statistical analyses, the Wilcoxon rank-sum test or Kruskal-Wallis test was applied. A statistical significance level of 0.05 was used for analysis. From November 2022 to February 2024, 370 medical students across the United States responded to this survey (response rate of 0.03%). Most respondents were women (n=259, 70%), White (n=164, 44%), first- and second-year medical students (n=107, 29%, n=98, 24%, respectively), and interested in internal medicine, a surgical subspecialty, or pediatrics (n=62, 17%, n=58, 16%, n=42, 11%, respectively). There are statistically significant different experiences of burnout based on the year of training in medical school (p<0.001) with third-year students having increased feelings of burnout, followed by their peers in a specified research year. Medical students living in the Midwest identified most strongly with being a workaholic compared to their peers living in other regions of the country (p=0.01). A medical student's specialty of interest influenced their confidence in matching their chosen specialty (p=0.003). There are no statistically significant differences in wellness between men and women or among different races. There are statistically significant differences in medical student wellness based on year of training and specialty interest. Our study suggests that additional resources and support may be helpful for students in their clinical or research years, or those interested in certain specialties.

Time series state level datasets showing important indicators regarding mental health. Includes data on mental health readmissions within 28 days, Mental health Community Care with within seven days of discharge and mental health average length of stay (days).

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US Population Health Management Market Size 2025-2029

The US population health management (PHM) market size is forecast to increase by USD 6.04 billion, at a CAGR of 7.4% between 2024 and 2029.

Population Health Management (PHM) is a critical aspect of healthcare delivery In the modern era, focusing on improving the health outcomes of large populations. The market is experiencing significant growth, driven by several key trends. One of the primary factors fueling this growth is the increasing adoption of healthcare IT solutions. These technologies enable healthcare providers to collect, manage, and analyze large amounts of patient data, facilitating personalized care and population health improvement. Another trend is the growing adoption of analytics in PHM. Analytics tools help identify patterns and insights from data, enabling early intervention and prevention of diseases. However, the high perceived costs associated with PHM solutions remain a challenge for market growth. Despite this, the benefits of PHM, including improved patient outcomes and reduced healthcare costs, make it a worthwhile investment for healthcare organizations.

What will be the Size of the market During the Forecast Period?

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Population Health Management (PHM) is a proactive healthcare approach focusing on improving the wider determinants of health and addressing health inequalities in various physical, economic, and social contexts. The market reflects the growing recognition of the importance of system-wide outcome focus, local intelligence, and data-driven decision-making in addressing ill health and managing chronic conditions such as cardiovascular disease. PHM integrates qualitative and quantitative data to identify and address the unique needs of populations, enabling personalized interventions and care models. Infrastructure, leadership, and information governance are crucial elements in implementing effective PHM strategies. 
Payment reform and incentives are driving the transformation of healthcare systems towards a more integrated care model, reducing hospitalization and improving overall population health. The market is experiencing significant growth due to the increasing awareness of the importance of addressing the root causes of ill health and the need for a more holistic approach to healthcare. This shift towards PHM is influenced by the economic, social, and demographic changes In the global population, emphasizing the need for a more resource-efficient and sustainable healthcare system.

How is this market segmented and which is the largest segment?

The market research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Product

  Software
  Services


Deployment

  Cloud
  On-premises


End-user

  Healthcare providers
  Healthcare payers
  Employers and government bodies


Geography

  US

By Product Insights

The software segment is estimated to witness significant growth during the forecast period.

Population Health Management (PHM) software is a crucial tool In the US healthcare sector, collecting and analyzing patient data from various healthcare systems to predict health conditions and improve overall patient care. Advanced data analytics, including data visualizations and business intelligence, enable PHM software to identify health risks within communities and promote value-based care. The adoption of PHM software is on the rise due to the increasing prevalence of chronic conditions and the demand for efficient, cost-effective healthcare. PHM software also facilitates system-wide outcome focus, integrating qualitative and quantitative data, local intelligence, and decision-making to redesign care services for at-risk groups.

The US healthcare transformation prioritizes PHM, with NHS England, NHS trusts, Public health, VCSE organizations, and Integrated Care Systems (ICSs) utilizing PHM software to address health inequalities and improve health outcomes. PHM software's infrastructure, leadership, information governance, and digital infrastructure support the integration of interventions, care models, hospitalization incentives, payment reforms, and integrated care systems. PHM software plays a vital role in addressing health issues such as cardiovascular disease (CVD) and improving overall population health.

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Market Dynamics

Our US Population Health Management (PHM) Market researchers analyzed the data with 2024 as the base year, along with the key drivers, trends, and challenges. A holistic analysis of drivers will help companies refine their marketing strategies to gain a competitive advantage.

What are the key market drivers leading to the rise in adopti