Utilization of IT for Clinical Study Master-Protocol generation : Umsetzung eines Masterprotokolls für Klinische Studien in einer IT-Software

Voraussetzung für eine valide klinische Studie sind umfassende, lückenlose, schrift-lich niedergelegte Rahmenbedingungen. Bedingt durch die Vielzahl experimen-teller, ethischer und juristischer Anforderungen genügen viele Studienprotokolle den GCP (Good Clinical Practice) Mindestanforderungen nicht. Im Rahmen dieser Arbeit konnten alle Erfordernisse zur Generierung eines Studienprotokolls entsprechend den Mindestanforderungen der GCP erstmals vollständig in Software übertragen werden. Die Software basiert auf dem SIOPE (European Society of Pediatric Oncology) Template. Es lässt sich sehr einfach an individuelle Benutzeranforderungen und verschiedene Gebiete klinischer For-schung anpassen. Die Software unterstützt sowohl Single- wie auch Multicenter-Studien und ist in der Lage, mehrsprachige Protokolle zu generieren. Durch eine fehlertolerante, grafische Benutzeroberfläche und umfangreiche Hilfe-Funktionen können auch unerfahrene Anwender perfekte Studienprotokolle erstellen. Das Programm ist Web-basiert, es ermöglicht simultanen Zugriff mehrerer Anwender und alle Daten stehen den autorisierten Benutzern sofort zur Verfügung. Das fertige Protokoll ist online verfügbar und kann als PDF ausgedruckt werden. Anwenderseitig ist lediglich ein Laptop/PC, ein handelsüblicher Web Browser und Internetzugang erforderlich. Ein besonderes Augenmerk wurde auf Datensicherheit, Zugriffskontrolle und Audit Trail gelegt, welche lückenlos gewährleistet sind. Die Software ist in ObTiMA (Ontology based Trial Management Application) integriert.Only a complete and very comprehensive protocol enables a valid clinical study. Due to the highly diverse experimental, ethical and legal requirements many proto-cols do not meet the GCP (Good Clinical Practice) standard. Through this work all requirements could completely be mapped in software for the first time. The program is based on the SIOPE (European Society of Pediatric Oncology) template and it is also easily extendible and adaptable to any other area of clinical research. The software supports single- and multicenter studies and generates multilingual protocols. Even inexperienced users can generate a perfect study protocol guided by a failure preventing graphical user interface and a highly elaborated help system. The program is web based and supports simultaneous use by multiple persons. All data entered are immediately available for all authorized users and the protocol can be printed out at any location. There is no installation or maintenance work on the part of user required, just a PC or laptop, internet access and a common web browser is necessary for protocol generation. Access and version control, personnel data protection and audit trail are seamlessly ensured. The software is integrated in ObTiMA (Ontology based Trial Management Application)

Health WARNer: An advance clinical decision support system, with knowledge discovery, integration and management features

Master'sMASTER OF SCIENC

Information Systems and Healthcare XXXIV: Clinical Knowledge Management Systems—Literature Review and Research Issues for Information Systems

Knowledge Management (KM) has emerged as a possible solution to many of the challenges facing U.S. and international healthcare systems. These challenges include concerns regarding the safety and quality of patient care, critical inefficiency, disparate technologies and information standards, rapidly rising costs and clinical information overload. In this paper, we focus on clinical knowledge management systems (CKMS) research. The objectives of the paper are to evaluate the current state of knowledge management systems diffusion in the clinical setting, assess the present status and focus of CKMS research efforts, and identify research gaps and opportunities for future work across the medical informatics and information systems disciplines. The study analyzes the literature along two dimensions: (1) the knowledge management processes of creation, capture, transfer, and application, and (2) the clinical processes of diagnosis, treatment, monitoring and prognosis. The study reveals that the vast majority of CKMS research has been conducted by the medical and health informatics communities. Information systems (IS) researchers have played a limited role in past CKMS research. Overall, the results indicate that there is considerable potential for IS researchers to contribute their expertise to the improvement of clinical process through technology-based KM approaches

Analysis of financial and technical feasibilty of a clinicians generated data platform of fybromyalgia syndrome patients

This master thesis analyzes the technical and economical feasibility for a medical database, based on clinically generated data of patients with the fibromyalgia syndrome. The main idea is to collect patient data on a regular basis during standard visiting hours at their doctor. Therefore it is essential to provide a data collection platform that can be simply used by the patient and doctor. The collected information (no personal data) shall be shared between researchers to enhance collaborative studies, make studies with rare diseases possible as well as to reduce the cost and effort to gather a big enough cohort group for the study. There are already several medical databases in place that collect and share patient information for research. Yet, despite the significant socioeconomic impact of fibromyalgia, no large database about this disease exists. An introduction to the fibromyalgia syndrome and its impact on society are given. Furthermore medical database technologies and medical database projects for other diseases are described. The presented technologies are further analyzed for their usefulness of creating a database to collect information about fibromyalgia syndrome patients and to use it to enhance its research. Additionally the legal requirements for maintaining such a platform as well as the potential cost are examined. Two possible business models to provide such a platform with funding are presented. Last but not least a possible use case for the collection of patient data via a survey created with REDCap and the integration process into i2b2 has been created and possible suggestions for improvements in the future have been made to bring the platform to a release ready state

The use of microarray data integration to improve cancer prognosis

Microarray is a high-throughput technology used to simultaneously measuring the expression of thousands of genes in each sample. Therefore, it has the potential to benefit the treatment of complicated diseases like cancer. This study made efforts to improve the application of microarray technologies to clinical medicine with two separate, but related phases. The first phase dealt with the generation of clinically valuable expression profiles from microarray data. By re-analyzing several published cancer datasets, we first confirmed that microarray data presented extra information about prognosis of cancer patients beyond currently used indexes such as tumor size. At the same time, it was noticed that those indexes generally confounded the correlation between gene expression and cancer outcome, so the contents of expression profiles were highly dependent on the clinical background of sample patients. Consequently, integrating multiple datasets was revealed by this study to obtain more general and reproducible cancer expression profiles. A novel data analysis procedure incorporating bootstrap re-sampling and training/testing validation was performed to impartially compare strategies of expression profiling. The results illustrated that after two independent datasets were integrated, the resultant expression profiles more correctly differentiated cancer patients in terms of disease outcome. The second phase of this study was to develop MAMA (Meta-Analysis of MicroArray), a data mining platform for conveniently collecting, managing, and analyzing multiple microarray datasets altogether. The complete MAMA system included three components: a relational database storing microarray cancer datasets; a web server providing the access to the database; and a client-side application implementing data manipulation and analysis methods. MAMA had an open-source framework allowing other developers to plug in their own data analysis methods. Moreover, it made cross-dataset analysis possible by standardizing annotation of samples and sequences in microarray datasets.Doctor of Philosoph

Efficient Decision Support Systems

This series is directed to diverse managerial professionals who are leading the transformation of individual domains by using expert information and domain knowledge to drive decision support systems (DSSs). The series offers a broad range of subjects addressed in specific areas such as health care, business management, banking, agriculture, environmental improvement, natural resource and spatial management, aviation administration, and hybrid applications of information technology aimed to interdisciplinary issues. This book series is composed of three volumes: Volume 1 consists of general concepts and methodology of DSSs; Volume 2 consists of applications of DSSs in the biomedical domain; Volume 3 consists of hybrid applications of DSSs in multidisciplinary domains. The book is shaped decision support strategies in the new infrastructure that assists the readers in full use of the creative technology to manipulate input data and to transform information into useful decisions for decision makers

A Model-Based Approach to Comprehensive Risk Management for Medical Devices

The European medical technology industry consists of around 27,000 companies, more than 95% of them small and medium-sized enterprises (SMEs), with over 675,000 employees [MEDT17]. In the European Union (EU) alone, medical devices constituted by far the biggest part of the medical technology (MedTech) sector with a market of 95 billion euros in annual sales in 2015 [EURO15].The European medical technology industry consists of around 27,000 companies, more than 95% of them small and medium-sized enterprises (SMEs), with over 675,000 employees [MEDT17]. In the European Union (EU) alone, medical devices constituted by far the biggest part of the medical technology (MedTech) sector with a market of 95 billion euros in annual sales in 2015 [EURO15]