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/ ---

The clinical trial planning and design services market is experiencing robust growth, driven by increasing demand for efficient and cost-effective clinical trials. The rising prevalence of chronic diseases globally, coupled with the burgeoning pharmaceutical and biotechnology industries, fuels this expansion. Technological advancements, such as the adoption of AI and machine learning in trial design, are further streamlining processes and reducing timelines, leading to improved trial outcomes. While regulatory hurdles and complexities in international collaborations pose challenges, the market is expected to maintain a healthy Compound Annual Growth Rate (CAGR) of approximately 10% between 2025 and 2033. This growth is particularly pronounced in North America and Europe, which currently hold significant market shares due to established research infrastructure and higher research spending. The market is segmented by service type (e.g., protocol development, statistical planning, regulatory strategy), therapeutic area, and client type (pharmaceutical companies, CROs, academic institutions). Competition is intense, with both large global players and specialized niche providers vying for market share. The future outlook is promising, with continued investment in innovative technologies and a growing focus on accelerating drug development expected to drive substantial market growth in the coming years. By 2029, the market is projected to surpass $8 billion, reflecting the increasing importance of optimized clinical trial design in bringing life-saving therapies to patients more quickly and efficiently. The competitive landscape is characterized by a mix of large multinational companies offering comprehensive services and smaller specialized firms focusing on specific therapeutic areas or trial phases. Consolidation and strategic partnerships are likely to shape the market further. The growing adoption of cloud-based solutions and data analytics platforms is enhancing collaboration and data management throughout the clinical trial lifecycle. Furthermore, a focus on patient-centric trial designs is gaining traction, emphasizing improved patient recruitment, retention, and overall trial experience. Regulatory changes and evolving ethical considerations will continue to influence the market, necessitating adaptability and compliance from service providers. The increasing demand for real-world evidence (RWE) integration into clinical trial design represents a significant future growth driver, demanding expertise in data analysis and regulatory compliance.

To improve the utilization and convenience of clinical information for researchers and patients, the Ministry of Food and Drug Safety provides a list of approved information for clinical trials on coronavirus.

Clinical studies, especially randomized controlled trials, are essential for generating evidence for clinical practice. However, generalizability is a long-standing concern when applying trial results to real-world patients. Generalizability assessment is thus important, nevertheless, not consistently practiced. We performed a systematic scoping review to understand the practice of generalizability assessment. We identified 187 relevant papers and systematically organized these studies in a taxonomy with three dimensions: (1) data availability (i.e., before or after trial [a priori vs a posteriori generalizability]), (2) result outputs (i.e., score vs non-score), and (3) populations of interest. We further reported disease areas, underrepresented subgroups, and types of data used to profile target populations. We observed an increasing trend of generalizability assessments, but less than 30% of studies reported positive generalizability results. As a priori generalizability can be assessed using only study design information (primarily eligibility criteria), it gives investigators a golden opportunity to adjust the study design before the trial starts. Nevertheless, less than 40% of the studies in our review assessed a priori generalizability. With the wide adoption of electronic health records systems, rich real-world patient databases are increasingly available for generalizability assessment; however, informatics tools are lacking to support the adoption of generalizability assessment practice.

Methods We performed the literature search over the following 4 databases: MEDLINE, Cochrane, PychINFO, and CINAHL. Following the Institute of Medicine’s standards for systematic review and Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), we conducted the scoping review in the following six steps: 1) gaining an initial understanding about clinical trial generalizability assessment, population representativeness, internal validity, and external validity, 2) identifying relevant keywords, 3) formulating four search queries to identify relevant articles in the 4 databases, 4) screening the articles by reviewing titles and abstracts, 5) reviewing articles’ full-text to further filter out irrelevant ones based on inclusion and exclusion criteria, and 6) coding the articles for data extraction.

Study selection and screening process

We used an iterative process to identify and refine the search keywords and search strategies. We identified 5,352 articles as of February 2019 from MEDLINE, CINAHL, PychINFO, and Cochrane. After removing duplicates, 3,569 records were assessed for relevancy by two researchers (ZH and XT) through reviewing the titles and abstracts against the inclusion and exclusion criteria. Conflicts were resolved with a third reviewer (JB). During the screening process, we also iteratively refined the inclusion and exclusion criteria. Out of the 3,569 articles, 3,275 were excluded through the title and abstract screening process. Subsequently, we reviewed the full texts of 294 articles, among which 106 articles were further excluded based on the exclusion criteria. The inter-rater reliability of the full-text review between the two annotators is 0.901 (i.e., Cohen’s kappa, p < .001). 187 articles were included in the final scoping review.

Data extraction and reporting

We coded and extracted data from the 187 eligible articles according to the following aspects: (1) whether the study performed an a priori generalizability assessment or a posteriori generalizability assessment or both; (2) the compared populations and the conclusions of the assessment; (3) the outputs of the results (e.g., generalizability scores, descriptive comparison); (4) whether the study focused on a specific disease. If so, we extracted the disease and disease category; (5) whether the study focused on a particular population subgroup (e.g., elderly). If so, we extracted the specific population subgroup; (6) the type(s) of the real-world patient data used to profile the target population (i.e., trial data, hospital data, regional data, national data, and international data). Note that trial data can also be regional, national, or even international, depending on the scale of the trial. Regardless, we considered them in the category of “trial data” as the study population of a trial is typically small compared to observational cohorts or real-world data. For observational cohorts or real-world data (e.g., EHRs), we extracted the specific scale of the database (i.e., regional, national, and international). For the studies that compared the characteristics of different populations to indicate generalizability issues, we further coded the populations that were compared (e.g., enrolled patients, eligible patients, general population, ineligible patients), and the types of characteristics that were compared (i.e., demographic information, clinical attributes and comorbidities, treatment outcomes, and adverse events). We then used Fisher’s exact test to assess whether there is a difference in the types of characteristics compared between a priori and a posteriori generalizability assessment studies.

A collection of 1200 texts (292 173 tokens) about clinical trials studies and clinical trials announcements in Spanish:

• 500 abstracts from journals published under a Creative Commons license, e.g. available in PubMed or the Scientific Electronic Library Online (SciELO).
• 700 clinical trials announcements published in the European Clinical Trials Register and Repositorio Español de Estudios Clínicos.

Texts were annotated with entities from the Unified Medical Language System semantic groups: anatomy (ANAT), pharmacological and chemical substances (CHEM), pathologies (DISO), and lab tests, diagnostic or therapeutic procedures (PROC). 46 699 entities were annotated (13.98% are nested entities). 10% of the corpus was doubly annotated, and inter-annotator agreement (IAA) achieved a mean F-measure of 85.65% (±4.79, strict match) and a mean F-measure of 93.94% (±3.31, relaxed match).

The corpus is freely distributed for research and educational purposes under a Creative Commons Non-Commercial Attribution (CC-BY-NC-A) License.

National clinical trial registry by Ministry of Health of China to join World Health Organization International Clinical Trial Registration Platform (WHO ICTRP Primary Registry), and the approved Primary Registry of WHO ICTRP. It registers both Chinese and global clinical trials, receives data from Partner Registers certified by the WHO ICTRP, and submits data to the WHO ICTRP Central Repository for global search. Moreover, based upon the talent and technical platform, consisting of Chinese Evidence-based Medicine Centre of Ministry of Health of China, Virtual Research Centre of Evidence-Based Medicine of Ministry of Education of China, Chinese Cochrane Centre, UK Cochrane Centre and International Clinical Epidemiology Network Resource and Training Centre in West China Hospital, Sichuan University (INCLEN CERTC), ChiCTR is responsible for providing consultations on trial design, central randomization service, guidance on the writing of clinical trial reports and relevant training. WHO takes the lead in establishing the global clinical trial registration system, which is agreed upon by governments from all over the world. There are both ethical and scientific reasons for clinical trial registration. Trial participants expect that their contributions to biomedical knowledge will be used to improve health care for everyone. Open access to information about ongoing and completed trials meets the ethical duty to trial participants, and promotes greater trust and public confidence in clinical research. Furthermore, trial registration ensures that the results of all trials can be tracked down and should help to reduce unnecessary duplication of research through greater awareness of existing trials and results. The mission of ChiCTR is to Unite clinicians, clinical epidemiologists, biostatisticians, epidemiologists and health care managers both at home and abroad, to manage clinical trials in a strict and scientific manner, and to promote their quality in China, so as to provide reliable evidences from clinical trials for health care workers, consumers and medical policy decision makers, and also to use medical resources more effectively to provide better service for Chinese people and all human beings. Any trial performed in human beings is considered as a clinical trial, and should be registered before its implementation. All the registered clinical trials will be granted a unique registration number by WHO ICTRP.

The North American clinical trials market, encompassing the United States, Canada, and Mexico, is experiencing robust growth, projected to reach a substantial size by 2033. A compound annual growth rate (CAGR) of 8.10% from 2019 to 2024 indicates a significant upward trajectory driven by several key factors. The increasing prevalence of chronic diseases like cancer, diabetes, and cardiovascular conditions fuels the demand for new treatments and therapies, leading to a surge in clinical trials. Furthermore, advancements in medical technology, including personalized medicine and innovative trial designs (such as adaptive clinical trials), are streamlining the process and accelerating drug development. Government initiatives promoting research and development, along with substantial investments from pharmaceutical and biotechnology companies, contribute significantly to market expansion. The market is segmented by phase (I-IV), design (randomized controlled trials, observational studies, etc.), and geography. The United States, with its advanced healthcare infrastructure and substantial funding, commands the largest market share within North America. While Canada and Mexico represent smaller portions, their markets are also growing, reflecting increasing investment in healthcare research and infrastructure development in these regions. The diverse range of clinical trial designs within the North American market caters to various research needs. Randomized controlled trials, particularly double-blind studies, remain prevalent, providing strong evidence for drug efficacy and safety. However, the adoption of more efficient designs like adaptive clinical trials is rising, allowing for greater flexibility and cost-effectiveness in clinical research. Observational studies, such as cohort and case-control studies, supplement randomized trials by providing valuable real-world data on treatment effectiveness and safety. The competitive landscape comprises major pharmaceutical companies (Pfizer, Eli Lilly, Roche), Contract Research Organizations (CROs) (IQVIA, Parexel), and specialized clinical research laboratories. The intense competition fosters innovation and drives efficiency within the clinical trial industry. However, challenges such as high costs, stringent regulatory requirements, and patient recruitment difficulties continue to present hurdles for growth. Despite these obstacles, the long-term outlook remains positive, driven by the persistent need for new therapies and advancements in clinical trial methodologies. Recent developments include: In September 2022, IVERIC bio, Inc. started an Open-label Extension (OLE) phase 3 trial to assess the safety of intravitreal administration of avacincaptad pegol (complement C5 inhibitor) in patients with geographic atrophy who previously completed phase 3 study ISEE2008 (GATHER2)., In September 2022, the University of Illinois at Chicago conducted a clinical trial to investigate the blood flow and blood pressure in down syndrome or Trisomy 21: FBI21.. Key drivers for this market are: Demand for Clinical Trials, High R&D Expenditure of the Pharmaceutical Industry; Rising Prevalence of Diseases. Potential restraints include: Demand for Clinical Trials, High R&D Expenditure of the Pharmaceutical Industry; Rising Prevalence of Diseases. Notable trends are: Phase III is the Largest Segment Under Phases that is Expected to Grow During the Forecast Period.

We investigate whether private research investments are distorted away from long-term projects. Our theoretical model highlights two potential sources of this distortion: short-termism and the fixed patent term. Our empirical context is cancer research, where clinical trials -- and hence, project durations -- are shorter for late-stage cancer treatments relative to early-stage treatments or cancer prevention. Using newly constructed data, we document several sources of evidence that together show private research investments are distorted away from long-term projects. The value of life-years at stake appears large. We analyze three potential policy responses: surrogate (non-mortality) clinical-trial endpoints, targeted R&D subsidies, and patent design. (JEL D92, G31, I11, L65, O31, O34)

The pediatric clinical trial market is anticipated to expand at a CAGR of 5.9% during the projected period. The market value is projected to increase from US$ 17,918.1 million in 2024 to US$ 31,661.0 million by 2034. The market was valued at US$ 16,831.0 million in 2023 and grew at a CAGR of 6.5% from 2019 to 2023.

Category-wise Outlook

Country-wise Analysis

This statistic depicts the percentage of executives that selected current and projected areas of most impact to real-world evidence (RWE) in clinical trials, as of 2018. According to the source, 60 percent of executives said that a better understanding of subpopulations and heterogeneity of treatment effects would be most impactful to current real-world evidence.

Background: Extracting the sample size from randomized controlled trials (RCTs) remains a challenge to developing better search functionalities or automating systematic reviews. Most current approaches rely on the sample size being explicitly mentioned in the abstract. Data collection: A random sample of 996 randomized controlled trials (RCTs) from seven major journals (British Medical Journal, JAMA, JAMA Oncology, Journal of Clinical Oncology, Lancet, Lancet Oncology, New England Journal of Medicine) published between 2010 and 2022 were labeled. To do so, abstracts were retrieved as a txt file from PubMed and parsed using regular expressions (i.e., expressions that match certain patterns in text). For each trial, the number of people who were randomized was retrieved by looking at the abstract, followed by the full publication if the number could not be determined with certainty from the abstract. In addition, six different entities were tagged in each abstract, independent of whether ..., , , # Randomized Controlled Trials with Annotated Sample Sizes

https://doi.org/10.5061/dryad.g1jwstr0b

A random sample of 996 randomized controlled trials (RCTs) from seven major journals (British Medical Journal, JAMA, JAMA Oncology, Journal of Clinical Oncology, Lancet, Lancet Oncology, New England Journal of Medicine) published between 2010 and 2022 were labeled. To do so, abstracts were retrieved as a txt file from PubMed and parsed using regular expressions (i.e., expressions that match certain patterns in text).Â

For each trial, the number of people who were randomized was retrieved by looking at the abstract, followed by the full publication if the number could not be determined with certainty from the abstract.

In addition, six different entities were tagged in each abstract, independent of whether the information was presented using words or integers. If the number of people who were randomized was explicitly stated (e.g., using the wo...

The data are the clinical evidence for the existence of SARS-CoV-2 "vaccination" poisoning and PCR tests cannot test "vaccinated" SARS-CoV-2 viral infections. The hospital system in PRC changed the lower threshold of basophil indicators in blood tests to hide the clinical evidence indicators from the patients. The monitoring photos are from the clinical trial for medicine-induced hemodialysis. The one-week clinical trial has detached protein allergic reactions from the platelet with mild process, increased the patient's basophils health even though the eosinophil increased. It evidences the Spike 2 protein's immune attacks.

Clinical Research & Development Solution Market Outlook

The global Clinical Research & Development Solution market size was valued at approximately USD 15.4 billion in 2023 and is projected to reach USD 25.8 billion by 2032, with a compound annual growth rate (CAGR) of 5.8% over the forecast period. This market growth is driven by several factors, including technological advancements, increasing demand for personalized medicine, and the growing prevalence of chronic diseases worldwide.

One of the key growth factors of the Clinical Research & Development Solution market is the rapid technological advancements in healthcare and life sciences. The integration of artificial intelligence (AI), machine learning (ML), and big data analytics has revolutionized the way clinical trials are designed, monitored, and analyzed. These technologies enable quicker data processing, more accurate patient recruitment, and enhanced predictive analytics, thereby reducing the time and cost associated with clinical trials. Additionally, the adoption of electronic data capture (EDC) systems and electronic health records (EHR) has streamlined data collection, making clinical research more efficient and reliable.

Another significant driver for market growth is the increasing demand for personalized medicine. Personalized medicine tailors treatment to the individual characteristics of each patient, which requires extensive clinical research to understand the genetic, environmental, and lifestyle factors that influence health and disease. This has led to a surge in demand for advanced clinical research solutions that can handle large volumes of complex data. The ability to develop targeted therapies not only improves patient outcomes but also enhances the efficiency of drug development processes, thus driving market growth.

The rising prevalence of chronic diseases such as cancer, diabetes, and cardiovascular diseases is also contributing to the growth of the Clinical Research & Development Solution market. As the global population ages, the incidence of these diseases is expected to rise, necessitating extensive clinical research to develop new treatments and therapies. Governments and healthcare organizations are increasingly investing in clinical research to address these health challenges, further propelling market growth.

Regionally, North America dominates the Clinical Research & Development Solution market due to its well-established healthcare infrastructure, high R&D expenditure, and the presence of major pharmaceutical and biotechnology companies. Europe follows closely, driven by favorable government initiatives and a strong focus on research and innovation. The Asia Pacific region is expected to witness significant growth during the forecast period, attributed to the increasing number of clinical trials conducted in emerging economies such as China and India, along with the rising healthcare expenditure and improving regulatory framework in these countries.

In the evolving landscape of clinical research, Real World Evidence Solutions are becoming increasingly pivotal. These solutions leverage real-world data to provide insights into the effectiveness and safety of medical interventions outside the controlled environment of clinical trials. By integrating data from electronic health records, insurance claims, and patient registries, Real World Evidence Solutions offer a comprehensive view of patient outcomes and treatment efficacy. This approach not only complements traditional clinical trials but also accelerates the drug development process by providing valuable insights into how treatments perform in diverse populations. As healthcare systems worldwide shift towards value-based care, the demand for Real World Evidence Solutions is expected to grow, driving innovation and efficiency in clinical research.

Component Analysis

The Clinical Research & Development Solution market is segmented by component into software and services. The software segment encompasses various applications such as electronic data capture (EDC) systems, clinical trial management systems (CTMS), and electronic health records (EHR), among others. These software solutions are designed to streamline the clinical research process, improve data accuracy, and enhance regulatory compliance. The increasing adoption of these advanced software solutions is driven by their ability to reduce the time and cost associated with clinical trials while ensuring data inte

Big Data Analytics for Clinical Research Market Outlook

As per our latest research, the Big Data Analytics for Clinical Research market size reached USD 7.45 billion globally in 2024, reflecting a robust adoption pace driven by the increasing digitization of healthcare and clinical trial processes. The market is forecasted to grow at a CAGR of 17.2% from 2025 to 2033, reaching an estimated USD 25.54 billion by 2033. This significant growth is primarily attributed to the rising need for real-time data-driven decision-making, the proliferation of electronic health records (EHRs), and the growing emphasis on precision medicine and personalized healthcare solutions. The industry is experiencing rapid technological advancements, making big data analytics a cornerstone in transforming clinical research methodologies and outcomes.

Several key growth factors are propelling the expansion of the Big Data Analytics for Clinical Research market. One of the primary drivers is the exponential increase in clinical data volumes from diverse sources, including EHRs, wearable devices, genomics, and imaging. Healthcare providers and research organizations are leveraging big data analytics to extract actionable insights from these massive datasets, accelerating drug discovery, optimizing clinical trial design, and improving patient outcomes. The integration of artificial intelligence (AI) and machine learning (ML) algorithms with big data platforms has further enhanced the ability to identify patterns, predict patient responses, and streamline the entire research process. These technological advancements are reducing the time and cost associated with clinical research, making it more efficient and effective.

Another significant factor fueling market growth is the increasing collaboration between pharmaceutical & biotechnology companies and technology firms. These partnerships are fostering the development of advanced analytics solutions tailored specifically for clinical research applications. The demand for real-world evidence (RWE) and real-time patient monitoring is rising, particularly in the context of post-market surveillance and regulatory compliance. Big data analytics is enabling stakeholders to gain deeper insights into patient populations, treatment efficacy, and adverse event patterns, thereby supporting evidence-based decision-making. Furthermore, the shift towards decentralized and virtual clinical trials is creating new opportunities for leveraging big data to monitor patient engagement, adherence, and safety remotely.

The regulatory landscape is also evolving to accommodate the growing use of big data analytics in clinical research. Regulatory agencies such as the FDA and EMA are increasingly recognizing the value of data-driven approaches for enhancing the reliability and transparency of clinical trials. This has led to the establishment of guidelines and frameworks that encourage the adoption of big data technologies while ensuring data privacy and security. However, the implementation of stringent data protection regulations, such as GDPR and HIPAA, poses challenges related to data integration, interoperability, and compliance. Despite these challenges, the overall outlook for the Big Data Analytics for Clinical Research market remains highly positive, with sustained investments in digital health infrastructure and analytics capabilities.

From a regional perspective, North America currently dominates the Big Data Analytics for Clinical Research market, accounting for the largest share due to its advanced healthcare infrastructure, high adoption of digital technologies, and strong presence of leading pharmaceutical companies. Europe follows closely, driven by increasing government initiatives to promote health data interoperability and research collaborations. The Asia Pacific region is emerging as a high-growth market, supported by expanding healthcare IT investments, rising clinical trial activities, and growing awareness of data-driven healthcare solutions. Latin America and the Middle East & Africa are also witnessing gradual adoption, albeit at a slower pace, due to infrastructural and regulatory challenges. Overall, the global market is poised for substantial growth across all major regions over the forecast period.

The Real-World Evidence (RWE) Solutions market is experiencing robust growth, projected to reach $828.46 million in 2025 and expand at a compound annual growth rate (CAGR) of 13% from 2025 to 2033. This significant expansion is driven by several key factors. The increasing adoption of RWE in regulatory decision-making, fueled by the need for more efficient and cost-effective drug development, is a primary driver. Furthermore, the rising availability of large, diverse datasets from electronic health records (EHRs), claims databases, and wearable devices provides rich sources of real-world data for analysis. Pharmaceutical companies and healthcare providers are actively investing in RWE solutions to improve clinical trial design, enhance post-market surveillance, and optimize treatment strategies, further bolstering market growth. The market is segmented by type (e.g., software, services) and application (e.g., drug development, post-market surveillance), each exhibiting unique growth trajectories influenced by specific technological advancements and regulatory landscapes. Competitive strategies among leading companies, such as Clinigen Group Plc, ICON Plc, and IQVIA Inc., focus on strategic partnerships, technological innovation, and expansion into new geographical markets. These companies are engaged in developing advanced analytical tools and data integration platforms to cater to growing demands for comprehensive RWE solutions. The North American market currently holds a substantial share, driven by robust regulatory frameworks and advanced healthcare infrastructure. However, other regions, particularly Asia Pacific, are expected to witness significant growth in the coming years due to increasing healthcare expenditure and technological advancements. The restraints on market growth are primarily related to data privacy concerns, regulatory hurdles in accessing and utilizing real-world data, and the need for robust data standardization across different sources. However, proactive measures like developing better data security protocols, clarifying regulatory guidelines, and investing in data harmonization initiatives are mitigating these challenges. The future of the RWE Solutions market hinges on continuous technological innovation, particularly in areas like artificial intelligence (AI) and machine learning (ML), which can enhance data analysis and generate valuable insights from complex datasets. Further growth will depend on fostering collaboration among stakeholders, including regulatory bodies, healthcare providers, and technology companies, to create a more conducive environment for RWE adoption.

Clinical trials are the gold standard for generating reliable medical evidence, but patient search of relevant trials often suffers from information overload. With nearly 700 COVID-19 trials conducted in the United States as of August 2020, it is imperative that trial seekers can search for COVID-related clinical trials efficiently to enable rapid recruitment to these studies. We developed a web application called COVID-19 Trial Finder, which facilitates COVID-19 trial search in the United States, first by location and radius distance from trial sites, then by brief, dynamically-generated medical questions to allow users to pre-screen their eligibility for nearby COVID-19 trials with minimum human computer interaction. A simulation study using 20 patient case reports demonstrates the accuracy and effectiveness of the COVID-19 Trial Finder.

Real World Evidence Solutions Market size was valued at USD 1.30 Billion in 2024 and is projected to reach USD 3.71 Billion by 2031, growing at a CAGR of 13.92% during the forecast period 2024-2031.

Global Real World Evidence Solutions Market Drivers

The market drivers for the Real World Evidence Solutions Market can be influenced by various factors. These may include:

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

Objective: Evidence synthesis teams, physicians, policy makers, and patients and their families all have an interest in following the outcomes of clinical trials and would benefit from being able to evaluate both the results posted in trial registries and in the publications that arise from them. Manual searching for publications arising from a given trial is a laborious and uncertain process. We sought to create a statistical model to automatically identify PubMed articles likely to report clinical outcome results from each registered trial in ClinicalTrials.gov.

Materials and Methods: A machine learning-based model was trained on pairs (publications linked to specific registered trials). Multiple features were constructed based on the degree of matching between the PubMed article metadata and specific fields of the trial registry, as well as matching with the set of publications already known to be linked to that trial.

Results: Evaluation of the model using NCT-linked articles as gold standard showed that they tend to be top ranked (median best rank = 1.0), and 91% of them are ranked in the top ten.

Discussion: Based on this model, we have created a free, public web based tool at http://arrowsmith.psych.uic.edu/cgi-bin/arrowsmith_uic/TrialPubLinking/trial_pub_link_start.cgi that, given any registered trial in ClinicalTrials.gov, presents a ranked list of the PubMed articles in order of estimated probability that they report clinical outcome data from that trial. The tool should greatly facilitate studies of trial outcome results and their relation to the original trial designs.

According to Cognitive Market Research, the Global Clinical Trial Management Systems Market Size will be USD XX Billion in 2023 and is set to achieve a market size of USD XX Billion by the end of 2031 growing at a CAGR of XX% from 2024 to 2031.

Based on mode of Delivery, The biggest share was web and cloud-based systems held in 2022 because of advantages including low technical problems and remote data access. 
Based on application, Pharmaceutical companies and hospitals make notable investments in handling clinical trials.
According to type, enterprise had the biggest share of the Clinical Trials Management System (CTMS) market in 2022. Over the course of the forecast period, enterprise is also anticipated to grow at the quickest CAGR, or over 14%. 
In terms of components, the software segment held the largest proportion of the worldwide Clinical Trials Management System (CTMS) market in 2022.
The North America region accounted for the highest market share in the Global Clinical Trial Management Systems Market. 

CURRENT SCENARIO OF THE CLINICAL TRIAL MANAGEMENT SYSTEMS MARKET

Key Factors Driving the Grwoth of the Clinical Trial Management Systems Market

Increasing Number of Clinical Trials Propel Market Growth 

Clinical trials are a crucial part of evidence-based medicine because they help predict the effectiveness of novel medications, diagnostic tools, and vaccinations. To make sure the novel intervention is secure and efficient, they usually engage big crowds of individuals. Most of the trials are conducted when a medicine is being developed.

According to the data provided by the National Library of Medicine (NLM), ~52,000 new studies were registered with NLM (ClinicalTrials.gov) in 2020, which increased to ~58,000 in 2023. In January 2023, the NLM reported 38,837 active clinical trials in the US and 105,172 active trials worldwide.

According to the European Medicine Agency, in the European Union (EU), ~4,000 clinical trials are authorized annually, of which about 60% of clinical trials are associated with the pharmaceutical industry. (Source:https://www.efpia.eu/about-medicines/development-of-medicines/regulations-safety-supply/clinical-trials/)

The market for clinical trials is expanding as a result of an increase in studies for new, effective medicines as chronic diseases are becoming more commonplace worldwide. Clinical trials are become increasingly difficult due to larger sample numbers, a wider range of patient demographics, and various study sites. Trial operations can be made more efficient with the use of CTMS systems, which also enhance data management and stakeholder cooperation. Clinical investigations also generate enormous amounts of data that need to be meticulously gathered, handled, and examined. With the use of CTMS platforms' data integration, real-time reporting, and data visualization capabilities, sponsors and researchers may streamline trials and make more informed decisions.

Furthermore, in the highly regulated pharmaceutical industry, adherence to regulatory criteria is crucial. By offering capabilities for managing regulatory documents, ensuring procedure adherence, and preserving data integrity, CTMS solutions improve compliance processes.

Furthermore, the launch of advanced innovations by market players is boosting the growth of the market during 2022-2030.

For instance, in 2023, Oracle Corporation, a prominent technology company, introduced its newest CTMS solution, called Oracle Health Sciences Clinical One CTMS. This new solution boasts advanced features such as real-time data analytics, centralized data management, and improved compliance capabilities that aim to simplify clinical trial processes and enhance overall efficiency (Source:https://www.efpia.eu/about-medicines/development-of-medicines/regulations-safety-supply/clinical-trials/)

Importing of clinical trials is encouraged by rising disease prevalence which is propelling the growth of clinical trial management system market.

The number of drugs in development that need extensive clinical trials before approval has significantly increased due to the rising prevalence of chronic diseases like diabetes, cancer, and AIDS in various parts of the world. Due to the high prevalence of cancer, diabetes, and respiratory diseases, emerging nations like China and India performed about 31% of the ...

Introduction

Drug Discovery Statistics: Drug discovery continues to be a complicated procedure that encompasses everything concerning new medicines, initiating with research and eventually culminating with clinical trials. In the year 2024, Drug discovery statistics have grown remarkably due to technological advancements and rising investments.