scholarly journals Portal of Medical Data Models: Stakeholder Feedback and Requirements

2021 ◽  
Author(s):  
Alexandra Meidt ◽  
Sarah Riepenhausen ◽  
Philipp Neuhaus ◽  
Stefan Hegselmann ◽  
Ahmed Rafee ◽  
...  
Keyword(s):  
Health Research ◽  
Medical Data ◽  
Data Models ◽  
Online Surveys ◽  
Secondary Use ◽  
German Health ◽  
The University ◽  
Medical Forms

The Portal of Medical Data Models has been developed since 2011 by the University of Münster. Its main goals are transparency, standardization and secondary use of medical metadata. Via two online surveys feedback from stakeholders of German health research was collected regarding the portal’s contents. The surveys confirmed great interest in secondary use of medical forms.

scholarly journals Structured lifestyle education for people with schizophrenia, schizoaffective disorder and first-episode psychosis (STEPWISE): randomised controlled trial

2018 ◽  
Vol 214 (2) ◽  
pp. 63-73 ◽  
Author(s):  
Richard I. G. Holt ◽  
Rebecca Gossage-Worrall ◽  
Daniel Hind ◽  
Michael J. Bradburn ◽  
Paul McCrone ◽  
...  
Keyword(s):  
Mental Health ◽  
Physical Activity ◽  
Health Research ◽  
Controlled Trial ◽  
First Episode Psychosis ◽  
First Episode ◽  
Episode Psychosis ◽  
Randomised Controlled ◽  
The University ◽  
Lifestyle Education

BackgroundObesity is a major challenge for people with schizophrenia.AimsWe assessed whether STEPWISE, a theory-based, group structured lifestyle education programme could support weight reduction in people with schizophrenia.MethodIn this randomised controlled trial (study registration: ISRCTN19447796), we recruited adults with schizophrenia, schizoaffective disorder or first-episode psychosis from ten mental health organisations in England. Participants were randomly allocated to the STEPWISE intervention or treatment as usual. The 12-month intervention comprised four 2.5 h weekly group sessions, followed by 2-weekly maintenance contact and group sessions at 4, 7 and 10 months. The primary outcome was weight change after 12 months. Key secondary outcomes included diet, physical activity, biomedical measures and patient-related outcome measures. Cost-effectiveness was assessed and a mixed-methods process evaluation was included.ResultsBetween 10 March 2015 and 31 March 2016, we recruited 414 people (intervention 208, usual care 206) with 341 (84.4%) participants completing the trial. At 12 months, weight reduction did not differ between groups (mean difference 0.0 kg, 95% CI −1.6 to 1.7, P = 0.963); physical activity, dietary intake and biochemical measures were unchanged. STEPWISE was well-received by participants and facilitators. The healthcare perspective incremental cost-effectiveness ratio was £246 921 per quality-adjusted life-year gained.ConclusionsParticipants were successfully recruited and retained, indicating a strong interest in weight interventions; however, the STEPWISE intervention was neither clinically nor cost-effective. Further research is needed to determine how to manage overweight and obesity in people with schizophrenia.Declaration of interestR.I.G.H. received fees for lecturing, consultancy work and attendance at conferences from the following: Boehringer Ingelheim, Eli Lilly, Janssen, Lundbeck, Novo Nordisk, Novartis, Otsuka, Sanofi, Sunovion, Takeda, MSD. M.J.D. reports personal fees from Novo Nordisk, Sanofi-Aventis, Lilly, Merck Sharp & Dohme, Boehringer Ingelheim, AstraZeneca, Janssen, Servier, Mitsubishi Tanabe Pharma Corporation, Takeda Pharmaceuticals International Inc.; and, grants from Novo Nordisk, Sanofi-Aventis, Lilly, Boehringer Ingelheim, Janssen. K.K. has received fees for consultancy and speaker for Novartis, Novo Nordisk, Sanofi-Aventis, Lilly, Servier and Merck Sharp & Dohme. He has received grants in support of investigator and investigator-initiated trials from Novartis, Novo Nordisk, Sanofi-Aventis, Lilly, Pfizer, Boehringer Ingelheim and Merck Sharp & Dohme. K.K. has received funds for research, honoraria for speaking at meetings and has served on advisory boards for Lilly, Sanofi-Aventis, Merck Sharp & Dohme and Novo Nordisk. D.Sh. is expert advisor to the NICE Centre for guidelines; board member of the National Collaborating Centre for Mental Health (NCCMH); clinical advisor (paid consultancy basis) to National Clinical Audit of Psychosis (NCAP); views are personal and not those of NICE, NCCMH or NCAP. J.P. received personal fees for involvement in the study from a National Institute for Health Research (NIHR) grant. M.E.C. and Y.D. report grants from NIHR Health Technology Assessment, during the conduct of the study; and The Leicester Diabetes Centre, an organisation (employer) jointly hosted by an NHS Hospital Trust and the University of Leicester and who is holder (through the University of Leicester) of the copyright of the STEPWISE programme and of the DESMOND suite of programmes, training and intervention fidelity framework that were used in this study. S.R. has received honorarium from Lundbeck for lecturing. F.G. reports personal fees from Otsuka and Lundbeck, personal fees and non-financial support from Sunovion, outside the submitted work; and has a family member with professional links to Lilly and GSK, including shares. F.G. is in part funded by the National Institute for Health Research Collaboration for Leadership in Applied Health Research & Care Funding scheme, by the Maudsley Charity and by the Stanley Medical Research Institute and is supported by the by the Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King's College London.

scholarly journals 2520

2017 ◽  
Vol 1 (S1) ◽  
pp. 82-82
Author(s):  
Meghan Spiroff ◽  
Lisa Connally ◽  
Anita Johnson ◽  
Aalap Doshi ◽  
Patricia Piechowski
Keyword(s):  
Clinical Trials ◽  
Best Practices ◽  
Health Research ◽  
Participant Recruitment ◽  
Gender And Age ◽  
Study Population ◽  
Gender And Age Differences ◽  
The University ◽  
Racial Ethnic ◽  
Research Studies

OBJECTIVES/SPECIFIC AIMS: Across the Clinical and Translational Science Award (CTSA) Consortium, participant recruitment into clinical trials is essential to advance science. Without proper participant recruitment, clinical trials do not result in gains in scientific knowledge, wastes time, funds, and other resources (Mahon et al., 2015). METHODS/STUDY POPULATION: Participant recruitment programs across the consortium are inconsistent in staffing, program services, and program goals. The participant recruitment program at the University of Michigan’s (U-M) Michigan Institute for Clinical & Health Research (MICHR) provides expertise, tools, and resources to facilitate participant recruitment in clinical and health research studies. RESULTS/ANTICIPATED RESULTS: We will explain our program infrastructure, staffing, services, and discuss how we maintain an engaged registry with over 27,000 participants interested in research studies at U-M. DISCUSSION/SIGNIFICANCE OF IMPACT: Proper recruitment into clinical trials results in findings that are relevant for genetic, cultural, linguistic, racial/ethnic, gender, and age differences (Cottler et al., 2013). We hope to share our best practices that aid in the development and success of participant recruitment across the CTSA Consortium.

The Use of Small Area Estimates in Place-Based Health Research

2020 ◽  
Vol 110 (6) ◽  
pp. 829-832
Author(s):  
Amanda Y. Kong ◽  
Xingyou Zhang
Keyword(s):  
Health Research ◽  
Small Area ◽  
Small Samples ◽  
Robert Wood Johnson Foundation ◽  
Secondary Use ◽  
Modeling Techniques ◽  
Control And Prevention ◽  
Survey Estimates ◽  
The Impact ◽  
Area Health

Interest in the impact of the built environment on health behaviors, outcomes, and disparities is increasing, and the growing development of statistical modeling techniques has allowed researchers to better investigate these relationships. However, without enough data that are identifiable at smaller geographic levels (e.g., census tract), place-based health researchers are unable to reliably estimate the prevalence of a health outcome at these more granular and potentially more salient neighborhood levels. When reliable direct survey estimates cannot be produced because of small samples or a lack of samples, estimates based on small area estimation techniques are often used. As place-based health research and the production and secondary use of small area estimates increase, it is critical that researchers understand both the underlying methods used to create these estimates and their limitations. Without this foundation, researchers may fit inappropriate models, or interpret findings inaccurately. As a demonstrative example, we focus this discussion on the small area health indicator estimates recently produced through the 500 Cities Project by the Robert Wood Johnson Foundation, the Centers for Disease Control and Prevention (CDC), and the CDC Foundation.

Mediation and graph data models for medical data integration

2013 ◽  
Author(s):  
Maria Constanza Pabon ◽  
Guillermo Andres Montoya ◽  
Martha Millan
Keyword(s):  
Data Integration ◽  
Medical Data ◽  
Data Models ◽  
Graph Data

scholarly journals ODM Clinical Data Generator: Syntactically Correct Clinical Data Based on Metadata Definition

2021 ◽  
Author(s):  
Tobias J. Brix ◽  
Ludger Becker ◽  
Timm Harbich ◽  
Johannes Oehm ◽  
Maximilian Fechner ◽  
...  
Keyword(s):  
Clinical Data ◽  
Web Application ◽  
Clinical Trial Data ◽  
Medical Data ◽  
Data Models ◽  
Clinical Test ◽  
Data Types ◽  
Data Generator ◽  
Definition Of ◽  
Privacy Issues

The Operational Data Model (ODM) is a data standard for interchanging clinical trial data. ODM contains the metadata definition of a study, i.e., case report forms, as well as the clinical data, i.e., the answers of the participants. The portal of medical data models is an infrastructure for creation, exchange, and analysis of medical metadata models. There, over 23000 metadata definitions can be downloaded in ODM format. Due to data protection law and privacy issues, clinical data is not contained in these files. Access to exemplary clinical test data in the desired metadata definition is necessary in order to evaluate systems claiming to support ODM or to evaluate if a planned statistical analysis can be performed with the defined data types. In this work, we present a web application, which generates syntactically correct clinical data in ODM format based on an uploaded ODM metadata definition. Data types and range constraints are taken into account. Data for up to one million participants can be generated in a reasonable amount of time. Thus, in combination with the portal of medical data models, a large number of ODM files including metadata definition and clinical data can be provided for testing of any ODM supporting system. The current version of the application can be tested at https://cdgen.uni-muenster.de and source code is available, under MIT license, at https://imigitlab.uni-muenster.de/published/odm-clinical-data-generator.

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