scholarly journals Do Clinical Trials Meet Current Care Needs? Views of Digestive Oncology Specialists in Galicia (Spain) Using the Delphi Method

Healthcare ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 665
Author(s):  
Ana Fernández Montes ◽  
Nieves Martinez-Lago ◽  
Juan de la Cámara Gomez ◽  
Elena María Brozos Vázquez ◽  
Sonia Candamio Folgar ◽  
...  
Keyword(s):  
Decision Making ◽  
Clinical Trials ◽  
Pharmaceutical Industry ◽  
Delphi Method ◽  
Scientific Evidence ◽  
Care Needs ◽  
Real World Data ◽  
Oncological Treatment ◽  
The People ◽  
Digestive Oncology

Background: In recent years, abundant scientific evidence has been generated based on clinical trials (CT) in the field of oncology. The general objective of this paper is to find out the extent to which decision making is based on knowledge of the most recent CT. Its specific objectives are to pinpoint difficulties with decision making based on the CT performed and find out the motivations patients and clinicians have when taking part in a CT. Methodology: Combined, prospective study, based on the Delphi method. A lack of correspondence between the people who take part in CT and patients who come for consultation has been identified. A need for training in analysing and interpreting CT has also been identified and a lack of trust in the results of CT financed by the pharmaceutical industry itself has been perceived. Conclusions: There is a difficulty in selecting oncological treatment due to the lack of correspondence between the patients included in the CT and patients seen in consultation. In this process, real world data studies may be highly useful, as they may provide this group with greater training in interpreting CT and their results.

scholarly journals Do clinical trials meet current care needs? Views of digestive oncology specialists in Galicia (Spain) using the Delphi method

2021 ◽  
Author(s):  
Ana Fernández Montes ◽  
Nieves Martínez Lago ◽  
Juan de la Cámara Gomez ◽  
Elena María Brozos Vázquez ◽  
Sonia Candamio Folgar ◽  
...  
Keyword(s):  
Decision Making ◽  
Clinical Trials ◽  
Delphi Method ◽  
Healthcare Professionals ◽  
Scientific Evidence ◽  
Research Career ◽  
Care Needs ◽  
Real World Data ◽  
Oncological Treatment ◽  
The People

Abstract Background: In recent years, abundant scientific evidence has been generated based on clinical trials (CT) in the field of oncology. In spite of this, there are still major disagreements among the group when it comes to establishing a treatment and therapies for these kinds of patients. The general objective of this paper is to find out the extent to which decision-making is based on knowledge of the most recent CT. Its specific objectives are to pinpoint difficulties with decision-making based on the CT performed and find out the motivations patients and clinicians have when taking part in a CT.Methodology: Combined, prospective study, based on the Delphi method.Results: A lack of correspondence between the people who take part in CT and patients who come for consultation has been identified. Healthcare professionals' motivations for taking part in a CT include finding better treatment for the patient and boosting their research career. A need for training in analysing and interpreting CT has also been identified and a lack of trust in the results of CT financed by the pharmaceutical industry itself has been perceived. Conclusion: There is a difficulty in selecting oncological treatment due to the lack of correspondence between the patients included in the CT and patients seen in consultation. In this process, real world data studies may be highly useful, as may be providing this group with greater training in interpreting CT and their results. Aiding the professional progression of their research career is an incentive for clinicians to participate in a CT.

scholarly journals Health Canada & Canadian Society for Pharmaceutical Sciences - Use of Real World Data/Evidence to Inform Regulatory Decision Making

2020 ◽  
Vol 23 ◽  
pp. 1s-47s
Author(s):  
Real World Data Workshop Group CSPS/Health Canada
Keyword(s):  
Health Care ◽  
Decision Making ◽  
Clinical Trials ◽  
Real World ◽  
Randomized Clinical Trials ◽  
Regulatory Agencies ◽  
Real World Data ◽  
World Data ◽  
Regulatory Decision ◽  
Health Canada

Real world data (RWD) and real world evidence (RWE) are playing increasing roles in health-care decisions. Real world data are routinely employed to support reimbursement and coverage decisions for drugs and devices. More recently, clinical trials incorporating pragmatic designs and observational studies are considered to supplement traditional clinical trials (e.g., randomized clinical trials). Regulatory agencies and large co-operative groups including academia and industry are exploring whether leveraging big databases such as electronic medical records and claims databases can be used to garner clinical insights extending beyond those gained from randomized controlled studies. Whether RWE can ultimately replace or improve traditional clinical trials is the big question. The workshop held on December 3, 2019 at Health Canada included presenters from regulatory agencies, industry and academia. Health Canada, US FDA and European Medicine Agency presented current thinking, draft frameworks and guidance available in the public domain. While the three agencies might be at different stages of utilizing RWE for regulatory decision making, the consensus is not whether RWE would be used but when and how it can be incorporated into regulatory decision making while maintaining a high evidentiary bar. The complexity of data sourcing, curating databases, aligning on common data models, illustrated by high-profile work conducted as part of Sentinel, DSEN, OHDSI and Duke-Margolis initiatives, was presented and discussed during the workshop, creating great learning opportunities for the attendees. The design and analysis of RWE studies were compared and contrasted to those of RCTs. While there are gaps, they are closing quickly as novel analytical methods are employed and innovative ways of curating data, including natural language processing and artificial intelligence, are explored.   This proceeding contains summaries of information presented by the speakers, including current highlights about the use of RWE in regulatory decision making. In the world where the uptake of “big data” in everyday life is happening at unprecedented speed, we can expect RWE to be a fast-moving area and with the potential for big impact in health-care decision making in the years to come.

scholarly journals Discharge Planning and Home Care Needs Assessment for Older Patients in a Nursing Hospital

Medicina ◽  
2020 ◽  
Vol 56 (2) ◽  
pp. 60
Author(s):  
Lina Urbietė ◽  
Vita Lesauskaitė ◽  
Jūratė Macijauskienė
Keyword(s):  
Decision Making ◽  
Home Care ◽  
Cognitive Skills ◽  
Daily Living ◽  
Discharge Planning ◽  
Scientific Evidence ◽  
Care Needs ◽  
Face To Face ◽  
Nursing Services ◽  
At Home

Background and objectives: Following the accumulation of a sufficient amount of scientific evidence, it is now possible to appeal for changes in the organization of nursing services. Our aims are to assess the health status of patients discharged from nursing hospitals and to identify their home care needs by applying the international InterRAI Home Care (HC) assessment form. Material and methods: 152 geriatric patients (older than 65 years of age) discharged after a 90–120-day stay at a nursing hospital were examined using face-to-face interviews. The data from the medical records were also assessed. The capacities of patients were discussed with the patients themselves, nursing personnel, and relatives of the patients. Results: The analysis revealed that 45.4% of the respondents had severely impaired cognitive skills, while 27.6% had moderately impaired cognitive skills for decision making in daily living. People with greater cognitive difficulties were more dependent during daily instrumental activities and ordinary daily activities. The strongest relationship was established among the cognitive skills and management of medications, management of finances, and ordinary housework. For the greater part of respondents, a special need for permanent nursing (57.9%) or assistance (25.7%) was determined, i.e., official, state-funded nursing at home was appointed. The remaining respondents (16.4%) were not appointed further state-funded nursing or assistance at home, but an assessment of the independence of these patients based on the InterRai Activities of Daily Living Hierarchy Scale indicated that these skills varied from moderate independence (decision making was difficult only in new situations) to severely impaired skills (made no independent decisions or they were scarce). Despite the low independence of respondents, the majority of them would prefer nursing services at home to institutional nursing. Conclusions: The low independence observed in all participants, as well as their limited capacities, prove the need for nursing services at home and the necessity of their continuity. Despite the low independence of respondents, the majority of them would prefer nursing services at home to institutional nursing.

scholarly journals Health Canada & Canadian Society for Pharmaceutical Sciences - Use of Real World Data/Evidence to Inform Regulatory Decision Making

2020 ◽  
Vol 23 ◽  
pp. 1s-47s
Author(s):  
Real World Data Workshop Group CSPS/Health Canada
Keyword(s):  
Health Care ◽  
Decision Making ◽  
Clinical Trials ◽  
Real World ◽  
Randomized Clinical Trials ◽  
Regulatory Agencies ◽  
Real World Data ◽  
World Data ◽  
Regulatory Decision ◽  
Health Canada

Real world data (RWD) and real world evidence (RWE) are playing increasing roles in health-care decisions. Real world data are routinely employed to support reimbursement and coverage decisions for drugs and devices. More recently, clinical trials incorporating pragmatic designs and observational studies are considered to supplement traditional clinical trials (e.g., randomized clinical trials). Regulatory agencies and large co-operative groups including academia and industry are exploring whether leveraging big databases such as electronic medical records and claims databases can be used to garner clinical insights extending beyond those gained from randomized controlled studies. Whether RWE can ultimately replace or improve traditional clinical trials is the big question. The workshop held on December 3, 2019 at Health Canada included presenters from regulatory agencies, industry and academia. Health Canada, US FDA and European Medicine Agency presented current thinking, draft frameworks and guidance available in the public domain. While the three agencies might be at different stages of utilizing RWE for regulatory decision making, the consensus is not whether RWE would be used but when and how it can be incorporated into regulatory decision making while maintaining a high evidentiary bar. The complexity of data sourcing, curating databases, aligning on common data models, illustrated by high-profile work conducted as part of Sentinel, DSEN, OHDSI and Duke-Margolis initiatives, was presented and discussed during the workshop, creating great learning opportunities for the attendees. The design and analysis of RWE studies were compared and contrasted to those of RCTs. While there are gaps, they are closing quickly as novel analytical methods are employed and innovative ways of curating data, including natural language processing and artificial intelligence, are explored.   This proceeding contains summaries of information presented by the speakers, including current highlights about the use of RWE in regulatory decision making. In the world where the uptake of “big data” in everyday life is happening at unprecedented speed, we can expect RWE to be a fast-moving area and with the potential for big impact in health-care decision making in the years to come.

To Investigate the Critical Influencing Factors for Decision Making Process of Students Who Want to Study Abroad

2019 ◽  
Vol 118 (11) ◽  
pp. 619-624
Author(s):  
JueJueMyint Toe ◽  
Ali Abdulbaqi Ameen ◽  
Sui Reng Liana ◽  
Amiya Bhaumik
Keyword(s):  
Higher Education ◽  
Decision Making ◽  
Study Abroad ◽  
Education Level ◽  
Decision Making Process ◽  
International Level ◽  
Continuing Higher Education ◽  
Clear Trend ◽  
The People ◽  
Study Overseas

Myanmar is the developing country and its education system is not yet to international level. Hence, most of the young adults, who like to upgrade their knowledge global wide and to gain international recognized higher educational certificates, choose to study overseas rather than continuing higher education after their high education nowadays, that becomes the trend of young people to study overseas since the competency among the people is getting intense based on the education level in every industry. The purpose of this research is to understand that students’ decision making process of selecting university. The study will be conducted to see clear trend of Myanmar students’ decision making of studying in abroad. This research will cover the context of what is Myanmar students’ perception of abroad, how they consider among other countries and explaining those factors which determine Myanmar students’ choice and how they decide to study abroad.

Disclosing the results of clinical trials: how is the pharmaceutical industry doing?

2018 ◽  
Author(s):  
Antonia Panayi ◽  
◽  
Slavka Baronikova ◽  
Jim Purvis ◽  
Eric Southam ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Pharmaceutical Industry

scholarly journals Improving pragmatic clinical trial design using real-world data

2019 ◽  
Vol 16 (3) ◽  
pp. 273-282 ◽  
Author(s):  
Susan M Shortreed ◽  
Carolyn M Rutter ◽  
Andrea J Cook ◽  
Gregory E Simon
Keyword(s):  
Clinical Trials ◽  
Trial Design ◽  
Suicide Attempts ◽  
Real World Data ◽  
Electronic Health Record Data ◽  
Pragmatic Clinical Trial ◽  
Pragmatic Clinical Trials ◽  
Sample Size Calculations ◽  
Record Data ◽  
Electronic Health

Background Pragmatic clinical trials often use automated data sources such as electronic health records, claims, or registries to identify eligible individuals and collect outcome information. A specific advantage that this automated data collection often yields is having data on potential participants when design decisions are being made. We outline how this data can be used to inform trial design. Methods Our work is motivated by a pragmatic clinical trial evaluating the impact of suicide-prevention outreach interventions on fatal and non-fatal suicide attempts in the 18 months after randomization. We illustrate our recommended approaches for designing pragmatic clinical trials using historical data from the health systems participating in this study. Specifically, we illustrate how electronic health record data can be used to inform the selection of trial eligibility requirements, to estimate the distribution of participant characteristics over the course of the trial, and to conduct power and sample size calculations. Results Data from 122,873 people with patient health questionnaire (PHQ) responses, recorded in their electronic health records between 1 July 2010 and 31 March 2012, were used to show that the suicide attempt rate in the 18 months following completion of the questionnaire varies by response to item nine of the PHQ. We estimated that the proportion of individuals with a prior recorded elevated PHQ (i.e. history of suicidal ideation) would decrease from approximately 50% at the beginning of a trial to about 5%, 50 weeks later. Using electronic health record data, we conducted simulations to estimate the power to detect a 25% reduction in suicide attempts. Simulation-based power calculations estimated that randomizing 8000 participants per randomization arm would allow 90% power to detect a 25% reduction in the suicide attempt rate in the intervention arm compared to usual care at an alpha rate of 0.05. Conclusions Historical data can be used to inform the design of pragmatic clinical trials, a strength of trials that use automated data collection for randomizing participants and assessing outcomes. In particular, realistic sample size calculations can be conducted using real-world data from the health systems in which the trial will be conducted. Data-informed trial design should yield more realistic estimates of statistical power and maximize efficiency of trial recruitment.

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