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/

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/

Tools in a clinical information system supporting clinical trials at a Swiss University Hospital

2014 ◽  
Vol 11 (6) ◽  
pp. 673-680 ◽  
Author(s):  
Michael Weisskopf ◽  
Guido Bucklar ◽  
Jürg Blaser
Keyword(s):  
Information System ◽  
Clinical Trials ◽  
Clinical Research ◽  
Hospital Admissions ◽  
Clinical Information System ◽  
Clinical Information ◽  
University Hospital ◽  
Trial Registry ◽  
Trial Participants ◽  
Year 2013

Background: Issues concerning inadequate source data of clinical trials rank second in the most common findings by regulatory authorities. The increasing use of electronic clinical information systems by healthcare providers offers an opportunity to facilitate and improve the conduct of clinical trials and the source documentation. We report on a number of tools implemented into the clinical information system of a university hospital to support clinical research. Methods: In 2011/2012, a set of tools was developed in the clinical information system of the University Hospital Zurich to support clinical research, including (1) a trial registry for documenting metadata on the clinical trials conducted at the hospital, (2) a patient–trial–assignment–tool to tag patients in the electronic medical charts as participants of specific trials, (3) medical record templates for the documentation of study visits and trial-related procedures, (4) online queries on trials and trial participants, (5) access to the electronic medical records for clinical monitors, (6) an alerting tool to notify of hospital admissions of trial participants, (7) queries to identify potentially eligible patients in the planning phase as trial feasibility checks and during the trial as recruitment support, and (8) order sets to facilitate the complete and accurate performance of study visit procedures. Results: The number of approximately 100 new registrations per year in the voluntary trial registry in the clinical information system now matches the numbers of the existing mandatory trial registry of the hospital. Likewise, the yearly numbers of patients tagged as trial participants as well as the use of the standardized trial record templates increased to 2408 documented trial enrolments and 190 reports generated/month in the year 2013. Accounts for 32 clinical monitors have been established in the first 2 years monitoring a total of 49 trials in 16 clinical departments. A total of 15 months after adding the optional feature of hospital admission alerts of trial participants, 107 running trials have activated this option, including 48 out of 97 studies (49.5%) registered in the year 2013, generating approximately 85 alerts per month. Conclusions: The popularity of the presented tools in the clinical information system illustrates their potential to facilitate the conduct of clinical trials. The tools also allow for enhanced transparency on trials conducted at the hospital. Future studies on monitoring and inspection findings will have to evaluate their impact on quality and safety.

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 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.

Flavopiridol, a Novel Cyclin-Dependent Kinase Inhibitor, in Clinical Development

2002 ◽  
Vol 36 (5) ◽  
pp. 905-911 ◽  
Author(s):  
Suoping Zhai ◽  
Adrian M Senderowicz ◽  
Edward A Sausville ◽  
William D Figg
Keyword(s):  
Clinical Trials ◽  
Antitumor Activity ◽  
Phase I ◽  
Kinase Inhibitor ◽  
Data Extraction ◽  
Single Agent ◽  
Clinical Information ◽  
Antitumor Agent ◽  
Medline Search ◽  
Phase I And Ii

OBJECTIVE: To review preclinical and clinical information on flavopiridol, an inhibitor of cyclin-dependent kinases (CDKs), tested as an antitumor agent. DATA SOURCES: Primary and review articles were identified by MEDLINE search (1990–June 2001). Abstracts from recent meetings were also used as source materials. DATA EXTRACTION: Flavopiridol was reviewed with regard to its mechanisms, preclinical and clinical results, pharmacokinetics, and metabolism. DATA SYNTHESIS: Flavopiridol is an inhibitor of several CDKs and displays unique anticancer properties. In addition to direct CDK inhibition, flavopiridol also exhibited other features such as inducing apoptosis in many cancer cell lines, decreasing cyclin D1 concentration, and inhibiting angiogenesis. Preclinical xenograft models showed significant antitumor activity for flavopiridol. The regimen using 72-hour continuous infusion every 2 weeks has been most extensively applied in clinical trials, with a 1-hour infusion currently being explored to achieve higher peak concentrations. Several Phase I and II trials have been reported, with some evidence of antitumor activity noted. Further Phase I and II trials using flavopiridol as a single agent and in combination with standard chemotherapeutic regimens and various tumor types are ongoing. CONCLUSIONS: Flavopiridol is the first CDK inhibitor to enter clinical trials. Several Phase I and Phase II clinical trials with different regimens (72-h or 1-h infusion) have been completed. Initial clinical trials have been intriguing, but many questions remain: What is the best regimen (≤72-h infusion)? Does optimal future development of this drug depend on the combination with other chemotherapy? What is the best combination of flavopiridol with other chemotherapy?

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).

The Use of Digital Clinical Trial Methods in the Evaluation of Digital Therapeutics: A Scoping Review (Preprint)

2019 ◽  
Author(s):  
Allison Hirsch ◽  
Mahip Grewal ◽  
Anthony James Martorell ◽  
Brian Michael Iacoviello
Keyword(s):  
Clinical Trial ◽  
Clinical Trials ◽  
Clinical Research ◽  
Scoping Review ◽  
Digital Technologies ◽  
Good Clinical Practice ◽  
The United States ◽  
Clinical Trial Research ◽  
Digital Methods ◽  
Clinical Trial Methods

BACKGROUND Digital Therapeutics (DTx) provide evidence based therapeutic health interventions that have been clinically validated to deliver therapeutic outcomes, such that the software is the treatment. Digital methodologies are increasingly adopted to conduct clinical trials due to advantages they provide including increases in efficiency and decreases in trial costs. Digital therapeutics are digital by design and can leverage the potential of digital and remote clinical trial methods. OBJECTIVE The principal purpose of this scoping review is to review the literature to determine whether digital technologies are being used in DTx clinical research, which type are being used and whether publications are noting any advantages to their use. As DTx development is an emerging field there are likely gaps in the knowledge base regarding DTx and clinical trials, and the purpose of this review is to illuminate those gaps. A secondary purpose is to consider questions which emerged during the review process including whether fully remote digital clinical research is appropriate for all health conditions and whether digital clinical trial methods are inline with the principles of Good Clinical Practice. METHODS 1,326 records were identified by searching research databases and 1,227 reviewed at the full-article level in order to determine if they were appropriate for inclusion. Confirmation of clinical trial status, use of digital clinical research methods and digital therapeutic status as well as inclusion and exclusion criteria were applied in order to determine relevant articles. Digital methods employed in DTx research were extracted from each article and these data were synthesized in order to determine which digital methods are currently used in clinical trial research. RESULTS After applying our criteria for scoping review inclusion, 11 articles were identified. All articles used at least one form of digital clinical research methodology enabling an element of remote research. The most commonly used digital methods are those related to recruitment, enrollment and the assessment of outcomes. A small number of articles reported using other methods such as online compensation (n = 3), or digital reminders for participants (n = 5). The majority of digital therapeutics clinical research using digital methods is conducted in the United States and increasing number of articles using digital methods are published each year. CONCLUSIONS Digital methods are used in clinical trial research evaluating DTx, though not frequently as evidenced by the low proportion of articles included in this review. Fully remote clinical trial research is not yet the standard, more frequently authors are using partially remote methods. Additionally, there is tremendous variability in the level of detail describing digital methods within the literature. As digital technologies continue to advance and the clinical research DTx literature matures, digital methods which facilitate remote research may be used more frequently.

Intelligent Drug Development

2014 ◽  
Author(s):  
Michael Tansey
Keyword(s):  
Clinical Trials ◽  
Cost Effectiveness ◽  
Drug Development ◽  
Clinical Research ◽  
Development Process ◽  
Drug Development Process ◽  
Individual Trial ◽  
Specific Technology

Clinical research is heavily regulated and involves coordination of numerous pharmaceutical-related disciplines. Each individual trial involves contractual, regulatory, and ethics approval at each site and in each country. Clinical trials have become so complex and government requirements so stringent that researchers often approach trials too cautiously, convinced that the process is bound to be insurmountably complicated and riddled with roadblocks. A step back is needed, an objective examination of the drug development process as a whole, and recommendations made for streamlining the process at all stages. With Intelligent Drug Development, Michael Tansey systematically addresses the key elements that affect the quality, timeliness, and cost-effectiveness of the drug-development process, and identifies steps that can be adjusted and made more efficient. Tansey uses his own experiences conducting clinical trials to create a guide that provides flexible, adaptable ways of implementing the necessary processes of development. Moreover, the processes described in the book are not dependent either on a particular company structure or on any specific technology; thus, Tansey's approach can be implemented at any company, regardless of size. The book includes specific examples that illustrate some of the ways in which the principles can be applied, as well as suggestions for providing a better context in which the changes can be implemented. The protocols for drug development and clinical research have grown increasingly complex in recent years, making Intelligent Drug Development a needed examination of the pharmaceutical process.

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