scholarly journals The Current Status of European and National Financial Sources for Clinical Research and Their Impact on Paediatric Non-commercial Clinical Trials: A Case Study of the Czech Republic

2020 ◽  
Vol 54 (6) ◽  
pp. 1461-1472
Author(s):  
L. Horavova ◽  
K. Nebeska ◽  
L. Souckova ◽  
R. Demlova ◽  
P. Babula
Keyword(s):  
Clinical Trials ◽  
Clinical Research ◽  
Healthcare Providers ◽  
Paediatric Population ◽  
Current Status ◽  
Therapeutic Area ◽  
Healthcare Provision ◽  
Non Profit ◽  
Pharmaceutical Industries ◽  
Design Characteristics

Abstract Introduction Paediatric non-commercial interventional clinical trials (NICTs) are crucial for healthcare provision. In spite of the fact that current regulations and initiatives try to enhance the quantity and quality of paediatric NICTs, there are still shortcomings that need to be addressed in order to accelerate the conduct of relevant clinical trials in children. To improve the current landscape of paediatric clinical research, it is necessary to identify and analyse the main trends and shortcomings, along with their impact on national performance in paediatric NICTs and this is the aim of this work. Method A retrospective systematic search of paediatric NICTs was performed on four international clinical trials registries. Entries were filtered by date from 01/01/2004 to 31/12/2017. Each identified paediatric NICT was screened and analysed for sponsors, funders, type of intervention, therapeutic area, design characteristics and associated publications. Results The search identified 439 unique NICTs. When stratifying the trials by enrolment ages, 86 trials were found involving the paediatric population. Most trials investigated the use of medicinal products and were focused on cancer or cardiovascular diseases. The most common sources of the funding were non-profit organizations. Furthermore, from the total number of completed trials, only half of them already published their results. Conclusion The main shortcomings—specifically, ethical, methodological and, in particular, economic obstacles were identified. There is a continual need for greater support and collaboration between all major stakeholders including health policymakers, grant agencies, research institutions, pharmaceutical industries and healthcare providers at the national and international level.

scholarly journals Automated data extraction software for medical summary using text mining (T-Library)

2019 ◽  
Author(s):  
Tomohide Yamada ◽  
Yoshinobu Kondo ◽  
Ryo Momosaki
Keyword(s):  
Clinical Trials ◽  
Deep Learning ◽  
Clinical Research ◽  
Data Extraction ◽  
Healthcare Providers ◽  
Clinical Information ◽  
Patient Records ◽  
Unstructured Text ◽  
Risk Engine ◽  
Easy Operation

The electronic medical record (EMR) is a source of clinical information and is used for clinical research. Clinical researchers leverage this information by employing staffs to manually extracting data from the unstructured text. This process can be both error-prone and labor-intensive. This software (T-Library) is a software which automatically extracts key clinical data from patient records and can potentially help healthcare providers and researchers save money, make treatment decisions and manage clinical trials. This software saves labor for data transcription in clinical research. This is a vital step toward getting researchers rapid access to the information they need. This is also the attempt to cluster patients’ morbid states and establish accurate and constantly updated risk engine of complications’ crises, using deep learning. Strengths: 1) Quick and Easy operation URL: http://www.picoron.com/tlibrary/

Cooperative group and pharmaceutical sponsored clinical trials: Perceptions of U.S. community oncologists.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. e13571-e13571
Author(s):  
Richard Scott Swain ◽  
Marjorie E. Zettler ◽  
Yolaine Jeune-Smith ◽  
Bruce A. Feinberg ◽  
Ajeet Gajra
Keyword(s):  
Clinical Trials ◽  
Clinical Research ◽  
Healthcare Providers ◽  
Loved One ◽  
Academic Affiliation ◽  
Development Teams ◽  
Hypothetical Scenario ◽  
Academic Settings ◽  
Survey Responses ◽  
Patient Advocacy Groups

e13571 Background: Many community-based oncologists in the US participate in clinical trials. These trials largely fall into two categories: trials run by cooperative (co-op) groups, funded and supported by the National Cancer Institute and trials developed, and supported by the pharmaceutical (pharma) industry. This study aimed to assess participation in, and perceptions regarding, co-op versus pharma trials among US community oncologists. Methods: We invited healthcare providers (HCP) across the continental US to attend 4 virtual meetings held between September and November 2020. Participants submitted their demographic information and responses to targeted questions regarding their opinions about co-op- and pharma-sponsored trials via a web-based pre-meeting survey. We evaluated participant HCP practice demographics and survey responses using descriptive statistics. Results: Of 259 surveyed participants, HCPs specialized in hematology-oncology (57%) and medical oncology (40%) with mean (median) 19 (18) years’ clinical experience. Most HCPs (178; 69%) reported participating in clinical research, and of these, 137 (77%) participated in co-op-led and 156 (88%) participated in pharma-led clinical research. HCPs preferred participating in both pharma and co-op (49%), pharma only (22%), and co-op only (11%) trials, while 18% preferred not to participate. Co-op trials were considered more prestigious to lead (86%), less likely to imply a conflict of interest (59%), and to address more pertinent questions (58%), while pharma trials had perceived advantages of better compensation (61%) and superior efficiency (48%). Co-op trials were perceived as not being financially sustainable (69%) and slower to accrue patients (85%) than pharma-led trials. Relatedly, in a hypothetical scenario of competing trials with identical design, the majority (60%) of HCPs preferred enrolling a loved one in a co-op trial. HCPs practicing in facilities with academic affiliation (34%) and in non-academic (66%) settings reported similar perceptions about co-op- and pharma-led trials; though, HCPs in academic settings were more likely to participate in clinical research compared to those in non-academic settings (82% vs. 62% [ P=0.001], respectively). Conclusions: In our survey of experienced HCPs, co-op-led clinical trials were perceived generally more favorably than pharma-led trials, even with concerns regarding cost, feasibility, and slower recruitment. However, despite their preference for co-op trials, HCPs were more likely to participate in pharma-led trials. Almost a third of surveyed HCPs are not participating in clinical trials and a fifth do not wish to. These findings can inform stakeholders (co-op trial leadership, pharma drug development teams, and patient advocacy groups) regarding appropriate education, design, and messaging regarding future clinical trials in oncology.

scholarly journals Automated data extraction software for medical summary using text mining (T-Library)

2019 ◽  
Author(s):  
Tomohide Yamada ◽  
Yoshinobu Kondo ◽  
Ryo Momosaki
Keyword(s):  
Clinical Trials ◽  
Deep Learning ◽  
Clinical Research ◽  
Data Extraction ◽  
Healthcare Providers ◽  
Clinical Information ◽  
Patient Records ◽  
Unstructured Text ◽  
Risk Engine ◽  
Easy Operation

The electronic medical record (EMR) is a source of clinical information and is used for clinical research. Clinical researchers leverage this information by employing staffs to manually extracting data from the unstructured text. This process can be both error-prone and labor-intensive. This software (T-Library) is a software which automatically extracts key clinical data from patient records and can potentially help healthcare providers and researchers save money, make treatment decisions and manage clinical trials. This software saves labor for data transcription in clinical research. This is a vital step toward getting researchers rapid access to the information they need. This is also the attempt to cluster patients’ morbid states and establish accurate and constantly updated risk engine of complications’ crises, using deep learning. Strengths: 1) Quick and Easy operation URL: http://www.picoron.com/tlibrary/

scholarly journals The Current Status and Future Direction of Clinical Research in Japan From a Regulatory Perspective

2022 ◽  
Vol 8 ◽  
Author(s):  
Hideki Maeda
Keyword(s):  
Clinical Trials ◽  
Clinical Study ◽  
Clinical Research ◽  
Personal Information ◽  
Human Subjects ◽  
Legal Framework ◽  
Current Status ◽  
Biological Research ◽  
Marketing Approval ◽  
Ethical Guidelines

In Japan, a law called the Clinical Trials Act went into being effective on April 1, 2018, and clinical research on human subjects conducted in Japan has been undergone major changes. Those other than clinical trials for marketing approval of drugs or medical devices are broadly classified into “specific clinical trials” and others, and regulations have been tightened for each. As a result, clinical interventional study was drastically reduced, and observational clinical study increased. For the observational clinical study, the two previous ethical guidelines were merged into the “Ethical Guidelines for Medical and Biological Research Involving Human Subjects,” which was enacted in March 2021. The observational clinical study is now subjected to these ethical guidelines. In addition, changes are planned for the Act on the Protection of Personal Information, which greatly affects data collection in clinical research. Clinical research in Japan must be conducted appropriately while adapting to these various changes in the external environment and legal framework. Adapting to these changes is not an easy task, as it requires increased financial and human resources for all stakeholders.

scholarly journals Automated data extraction software for medical summary using text mining (T-Library)

2019 ◽  
Author(s):  
Tomohide Yamada ◽  
Yoshinobu Kondo ◽  
Ryo Momosaki
Keyword(s):  
Clinical Trials ◽  
Deep Learning ◽  
Clinical Research ◽  
Data Extraction ◽  
Healthcare Providers ◽  
Clinical Information ◽  
Patient Records ◽  
Unstructured Text ◽  
Risk Engine ◽  
Easy Operation

The electronic medical record (EMR) is a source of clinical information and is used for clinical research. Clinical researchers leverage this information by employing staffs to manually extracting data from the unstructured text. This process can be both error-prone and labor-intensive. This software (T-Library) is a software which automatically extracts key clinical data from patient records and can potentially help healthcare providers and researchers save money, make treatment decisions and manage clinical trials. This software saves labor for data transcription in clinical research. This is a vital step toward getting researchers rapid access to the information they need. This is also the attempt to cluster patients’ morbid states and establish accurate and constantly updated risk engine of complications’ crises, using deep learning. Strengths: 1) Quick and Easy operation URL: http://www.picoron.com/tlibrary/

scholarly journals Automated data extraction software for medical summary using text mining (T-Library)

2019 ◽  
Author(s):  
Tomohide Yamada ◽  
Yoshinobu Kondo ◽  
Ryo Momosaki
Keyword(s):  
Clinical Trials ◽  
Deep Learning ◽  
Clinical Research ◽  
Data Extraction ◽  
Healthcare Providers ◽  
Clinical Information ◽  
Patient Records ◽  
Unstructured Text ◽  
Risk Engine ◽  
Easy Operation

The electronic medical record (EMR) is a source of clinical information and is used for clinical research. Clinical researchers leverage this information by employing staffs to manually extracting data from the unstructured text. This process can be both error-prone and labor-intensive. This software (T-Library) is a software which automatically extracts key clinical data from patient records and can potentially help healthcare providers and researchers save money, make treatment decisions and manage clinical trials. This software saves labor for data transcription in clinical research. This is a vital step toward getting researchers rapid access to the information they need. This is also the attempt to cluster patients’ morbid states and establish accurate and constantly updated risk engine of complications’ crises, using deep learning. Strengths: 1) Quick and Easy operation URL: http://www.picoron.com/tlibrary/

Clinical trials — the personal perspective of the research physician?

2020 ◽  
Vol 75 (3) ◽  
pp. 256-263
Author(s):  
Maria Y. Egorova ◽  
Irina A. Shuvalova ◽  
Olga I. Zvonareva ◽  
Igor D. Pimenov ◽  
Olga S. Kobyakova ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Medical Care ◽  
New Technologies ◽  
Healthcare Providers ◽  
Study Drug ◽  
Well Being ◽  
Personal Perspective ◽  
Public And Private ◽  
Factors Affecting ◽  
To Receive

Background. The organization of clinical trials (CTs) requires the participation and coordination of healthcare providers, patients, public and private parties. Obstacles to the participation of any of these groups pose a risk of lowering the potential for the implementation of CTs. Researchers are a key human resource in conducting of CT. Their motivation for participation can have a significant impact on the recruitment and retention of patients, on the quality of the data collected, which determines the overall outcome of the study. Aims to assess the factors affecting the inclusion of Russian physicians-researchers in CT, and to determine their role in relations with patients-participants. Materials and methods. The study was organized as a part of the Russian multicenter face-to-face study. A survey was conducted of researchers from 10 cities of Russia (20172018). The participation in the survey for doctors was anonymous and voluntary. Results. The study involved 78 respondents. Most research doctors highly value the importance of research for science (4,84 0,39), society (4,67 0,46) and slightly lower for participating patients (4,44 0,61). The expectations of medical researchers are related to improving their financial situation and attaining new experience (n = 14; 18,18%). However, the opportunity to work with new technologies of treatment and diagnosis (n = 41; 52,56%) acted as a motivating factor. According to the questionnaire, the vast majority of research doctors (n = 29; 37,18%) believe that the main reason for patients to participate in CT is to receive quality and free medical care. The most significant obstacle to the inclusion of participants in CT was the side effects of the study drug (n = 38; 48,71%). Conclusions. The potential of clinical researchers in Russia is very high. The patient-participant acts for the research doctor as the subject of the study, and not the object, so the well-being of the patient is not indifferent to the doctor. However, the features of the functioning of our health care system form the motivation of doctors-researchers (additional earnings, professional self-development) and the way they perceive the motivation of patients (CT as an opportunity to receive quality medical care).

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