Abstract:BackgroundCohort studies and registries rely on massive amounts of personal medical data. Therefore, data protection and information security as well as ethical aspects gain in importance and need to be considered as early as possible during the establishment of a study. Resulting legal and ethical obligations require a precise implementation of appropriate technical and organisational measures for a Trusted Third Party.MethodsThis paper defines and organises a consistent workflow-management to realize a Trust… Show more
“…The CDM infrastructure and DZHK‐wide harmonization and standardization enable a high data security. High data quality is the basis for good clinical and epidemiological research …”
Section: Discussionmentioning
confidence: 99%
“…High data quality is the basis for good clinical and epidemiological research. 5,10 All in all, the approval for quality control for about 55% of the entered data is given by the recruiting study centres via the review right. For the other data, the documentation is not completed, and the quality control cannot be applied.…”
Section: Discussionmentioning
confidence: 99%
“…The TORCH data protection concept is based on the template of the MOSAIC project. 5,6 It regulates individual TORCH-specific aspects to be considered in the main study centre and the individual recruiting study centres regarding data protection and safety. The concept was positively evaluated by the TMF in March 2015.…”
AimsThe multicentric TranslatiOnal Registry for CardiomyopatHies (TORCH) of the German Centre for Cardiovascular Research aims to recruit 2300 patients with non‐ischemic cardiomyopthies.Methods and resultsThe investigations were performed after standard operating procedures. The data are collected in standardized electronic case report forms provided by the data holding of the central data management of the German Centre for Cardiovascular Research using secuTrial (interActive Systems GmbH, Berlin, Germany). The personal‐identifying data and informed consent are collected, stored, and quality‐checked by the independent Trusted Third Party in Greifswald. The quality management of the medical data is performed by the data and quality centre Greifswald. In December 2014, the recruitment for TORCH has started. Currently, data and biomaterial from about 1397 patients and more than 74 500 biomaterial aliquots were collected. Regular study centre‐specific quality reports address completeness and plausibility of data and provide detailed information about current missing or implausible data entries to improve the data quality by using a query management in addition.ConclusionsA regular quality control and reporting improve the data quality in TORCH and will support high‐quality data analysis and the translation of research results into routine care.
“…The CDM infrastructure and DZHK‐wide harmonization and standardization enable a high data security. High data quality is the basis for good clinical and epidemiological research …”
Section: Discussionmentioning
confidence: 99%
“…High data quality is the basis for good clinical and epidemiological research. 5,10 All in all, the approval for quality control for about 55% of the entered data is given by the recruiting study centres via the review right. For the other data, the documentation is not completed, and the quality control cannot be applied.…”
Section: Discussionmentioning
confidence: 99%
“…The TORCH data protection concept is based on the template of the MOSAIC project. 5,6 It regulates individual TORCH-specific aspects to be considered in the main study centre and the individual recruiting study centres regarding data protection and safety. The concept was positively evaluated by the TMF in March 2015.…”
AimsThe multicentric TranslatiOnal Registry for CardiomyopatHies (TORCH) of the German Centre for Cardiovascular Research aims to recruit 2300 patients with non‐ischemic cardiomyopthies.Methods and resultsThe investigations were performed after standard operating procedures. The data are collected in standardized electronic case report forms provided by the data holding of the central data management of the German Centre for Cardiovascular Research using secuTrial (interActive Systems GmbH, Berlin, Germany). The personal‐identifying data and informed consent are collected, stored, and quality‐checked by the independent Trusted Third Party in Greifswald. The quality management of the medical data is performed by the data and quality centre Greifswald. In December 2014, the recruitment for TORCH has started. Currently, data and biomaterial from about 1397 patients and more than 74 500 biomaterial aliquots were collected. Regular study centre‐specific quality reports address completeness and plausibility of data and provide detailed information about current missing or implausible data entries to improve the data quality by using a query management in addition.ConclusionsA regular quality control and reporting improve the data quality in TORCH and will support high‐quality data analysis and the translation of research results into routine care.
“…They received training in how to use the documentation system and have completed appropriate psychiatric/psychotherapeutic education programmes. The telemedical conversation was conducted on the basis of eCRFs in a computer-aided documentation system in accordance with the current standards for data security and data privacy [15,16]. The standardised conversation contained a structured standardised part and an individualised part.…”
Background: Schizophrenia and bipolar disorder are serious psychiatric disorders with a high disease burden, many years lived with disability and a high level of risk of relapses and re-hospitalisations. Besides, both diseases are often accompanied with a reduced quality of life. A low quality of life is one predictor for relapses. This study examines whether a telemedical care programme can improve quality of life. Methods: “Post stationary telemedical care of patients with severe psychiatric disorders” (Tecla) is a prospective controlled randomised intervention trial to implement and evaluate a telemedical care concept for patients with schizophrenia and bipolar disorder. Participants were randomised in an intervention or a control group. The intervention group received telemedical care including regular, individualised telephone calls and SMS messages. The quality of life was measured with the German version of WHOQOL-BREF. Effects of telemedicine on quality of life after 6 months were analysed using t-tests to compare the intervention with the control group. Participants also evaluated the telemedical care program based on a short standardised interview.Results: 118 participants were recruited, thereof 57.6% men (n = 68). Participants were 43 years old on average (SD) 13)). The IG showed higher QoL scores than the control group (CG) 6 months after the baseline for the WHOQOL total sum score (t-test (CI) 93.1 (92.4-93.8) vs 89.7 (88.8-90.6), p < 0.0001) and for 4 of 5 domains: global 62.0 (60.9-63.0) vs. 56.8 (55.6-58.1), p < 0.0001; physical health 63.8 (63.0-64.7) vs. 59.6 (58.5-60.6), p < 0.0001; psychological 60.9 (60.0-61.9) vs. 56.4 (55.1-57.6), p < 0.0001; environment 70.8 (70.1-71.6) vs. 67.5 (66.7-68.3), p < 0.0001).Conclusion: The Tecla telemedical care concept has led to improvements in quality of life for patients with severe psychiatric disorders. It provides a low-threshold and suitable component of psychiatric treatment. Trial registration: German Clinical Trials Register, DRKS00008548, registered 21 May 2015 – retrospectively registered, https://www.drks.de/drks_web/setLocale_EN.do
“…Location privacy protection method mainly refers to the fact that the user provides false user location privacy information or anonymous user's identity information and location information to the server in the process of location service. The model of location privacy protection is divided into 2 categories, which are trusted third party (TTP, shown in Figure 1) and free trusted third party (FTTP) [13]. This paper only discusses the previous class method.…”
With the development of Augmented Reality technology, the application of location based service (LBS) is more and more popular, which provides enormous convenience to people’s life. User location information could be obtained at anytime and anywhere. So user location privacy security suffers huge threats. Therefore, it is crucial to pay attention to location privacy protection in LBS. Based on the architecture of the trusted third party (TTP), we analyzed the advantages and shortages of existing location privacy protection methods in LBS on mobile terminal. Then we proposed the improvedK-value location privacy protection method according to privacy level, which combinesk-anonymity method with pseudonym method. Through the simulation experiment, the results show that this improved method can anonymize all service requests effectively. In addition to the experiment of execution time, it demonstrated that our proposed method can realize the location privacy protection more efficiently.
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