Applying Data Warehousing to a Phase III Clinical Trial From the Fondazione Italiana Linfomi Ensures Superior Data Quality and Improved Assessment of Clinical Outcomes

Data collection in clinical trials is becoming complex, with a huge number of variables that need to be recorded, verified, and analyzed to effectively measure clinical outcomes. In this study, we used data warehouse (DW) concepts to achieve this goal. A DW was developed to accommodate data from a large clinical trial, including all the characteristics collected. We present the results related to baseline variables with the following objectives: developing a data quality (DQ) control strategy and improving outcome analysis according to the clinical trial primary end points

CLINICAL DATA WAREHOUSE: A REVIEW

Clinical decisions are crucial because they are related to human lives. Thus, managers and decision makers inthe clinical environment seek new solutions that can support their decisions. A clinical data warehouse (CDW) is animportant solution that is used to achieve clinical stakeholders’ goals by merging heterogeneous data sources in a centralrepository and using this repository to find answers related to the strategic clinical domain, thereby supporting clinicaldecisions. CDW implementation faces numerous obstacles, starting with the data sources and ending with the tools thatview the clinical information. This paper presents a systematic overview of purpose of CDWs as well as the characteristics;requirements; data sources; extract, transform and load (ETL) process; security and privacy concerns; design approach;architecture; and challenges and difficulties related to implementing a successful CDW. PubMed and Google Scholarare used to find papers related to CDW. Among the total of 784 papers, only 42 are included in the literature review. Thesepapers are classified based on five perspectives, namely methodology, data, system, ETL tool and purpose, to findinsights related to aspects of CDW. This review can contribute answers to questions related to CDW and providerecommendations for implementing a successful CDW

Framework for Data Mining In Healthcare Information System in Developing Countries: A Case of Tanzania

\ud Globally the healthcare sector is abundant with data and hence using data mining techniques in this area seems promising. Healthcare sector collects huge amounts of data on a daily basis. Transferring data into secure electronic system of medical health can save lives and reduce the cost of healthcare services as well as early discovery of contagious diseases with advanced collection of medical data. In this study we have proposed a best fit for data mining techniques in healthcare based on a case study. The proposed framework aims to provide self healthcare treatments where by several monitoring equipments using the cyberspace devices have been developed to help patients manage their medical conditions at home for example, diabetic patients can test their blood sugar level by using e-device, which, with the click of a computer mouse, downloads the results to a healthcare practitioner, minimizes time to wait for medical treatments, and minimizes the delay time in providing medical treatments. Data mining is a new technology used in different types of sectors to improve the effectiveness and efficiency of business model as well as solving problems in business world.\u

Framework for Interoperable and Distributed Extraction-Transformation-Loading (ETL) Based on Service Oriented Architecture

Extraction. Transformation and Loading (ETL) are the major functionalities in data warehouse (DW) solutions. Lack of component distribution and interoperability is a gap that leads to many problems in the ETL domain, which is due to tightly-coupled components in the current ETL framework. This research discusses how to distribute the Extraction, Transformation and Loading components so as to achieve distribution and interoperability of these ETL components. In addition, it shows how the ETL framework can be extended. To achieve that, Service Oriented Architecture (SOA) is adopted to address the mentioned missing features of distribution and interoperability by restructuring the current ETL framework. This research contributes towards the field of ETL by adding the distribution and inter- operability concepts to the ETL framework. This Ieads to contributions towards the area of data warehousing and business intelligence, because ETL is a core concept in this area. The Design Science Approach (DSA) and Scrum methodologies were adopted for achieving the research goals. The integration of DSA and Scrum provides the suitable methods for achieving the research objectives. The new ETL framework is realized by developing and testing a prototype that is based on the new ETL framework. This prototype is successfully evaluated using three case studies that are conducted using the data and tools of three different organizations. These organizations use data warehouse solutions for the purpose of generating statistical reports that help their top management to take decisions. Results of the case studies show that distribution and interoperability can be achieved by using the new ETL framework

Multidimensional framework for analysing next-generation sequencing data in a clinical diagnostic environment

Next-generation sequencing (NGS), also called massively parallel sequencing, is a high-throughput technology that allows the determination of the nucleotide sequences of entire or specific regions of the genome. The application of this technology in a clinical environment enables personalized diagnostics for patients, for instance, allowing the identification of variants that might cause a disease. In this sense, clinical diagnostic laboratories are responsible for providing a robust and appropriate workflow that enables the obtention of genomic information ready to be interpreted by a clinician. The Molecular Biology CORE Laboratory in the Hospital Clinic de Barcelona performs hundreds of analyses each year, providing service to several diagnostic laboratories. Be sides, with the increasing number of NGS applications in clinical diagnostics, the number of analyses is expected to keep growing in the following years. Quality data is generated from different sources in each of these NGS analyses, including laboratory procedures, DNA sequencing, and bioinformatics analyses. These quality data must be carefully evaluated and validated to ensure the results' reliability. Moreover, the accumulation of quality data from each analysis can be used to assess the performance of the laboratory and to identify potential sources of technical artefacts that might lower the quality of the experiments. Hence, a database is needed to store and manage quality data for easy accessibility and analysis over time. In this thesis, we aim to develop a data warehouse to analyze and monitor NGS quality data coming from different data sources. To do that, we will perform the following steps: 1) design a multidimensional data model to ensure that data will be efficiently stored; 2) data extraction from different sources; 3) database loading; 4) design a visualization tool to enable descriptive analyses of the quality data. The designed tool will allow the historical exploration of quality parameters, as well as the evaluation of an experiment's quality metrics compared to the rest. With this tool, we are enabling the identification of areas of improvement by discovering sources of variation that might affect the quality of clinical NGS data

I2ECR: Integrated and Intelligent Environment for Clinical Research

Clinical trials are designed to produce new knowledge about a certain disease, drug or treatment. During these studies, a huge amount of data is collected about participants, therapies, clinical procedures, outcomes, adverse events and so on. A multicenter, randomized, phase III clinical trial in Hematology enrolls up to hundreds of subjects and evaluates post-treatment outcomes on stratified sub- groups of subjects for a period of many years. Therefore, data collection in clinical trials is becoming complex, with huge amount of clinical and biological variables. Outside the medical field, data warehouses (DWs) are widely employed. A Data Ware-house is a “collection of integrated, subject-oriented databases designed to support the decision-making process”. To verify whether DWs might be useful for data quality and association analysis, a team of biomedical engineers, clinicians, biologists and statisticians developed the “I2ECR” project. I2ECR is an Integrated and Intelligent Environment for Clinical Research where clinical and omics data stand together for clinical use (reporting) and for generation of new clinical knowledge. I2ECR has been built from the “MCL0208” phase III, prospective, clinical trial, sponsored by the Fondazione Italiana Linfomi (FIL); this is actually a translational study, accounting for many clinical data, along with several clinical prognostic indexes (e.g. MIPI - Mantle International Prognostic Index), pathological information, treatment and outcome data, biological assessments of disease (MRD - Minimal Residue Disease), as well as many biological, ancillary studies, such as Mutational Analysis, Gene Expression Profiling (GEP) and Pharmacogenomics. In this trial forty-eight Italian medical centers were actively involved, for a total of 300 enrolled subjects. Therefore, I2ECR main objectives are: • to propose an integration project on clinical and molecular data quality concepts. The application of a clear row-data analysis as well as clinical trial monitoring strategies to implement a digital platform where clinical, biological and “omics” data are imported from different sources and well-integrated in a data- ware-house • to be a dynamic repository of data congruency quality rules. I2ECR allows to monitor, in a semi-automatic manner, the quality of data, in relation to the clinical data imported from eCRFs (electronic Case Report Forms) and from biologic and mutational datasets internally edited by local laboratories. Therefore, I2ECR will be able to detect missing data and mistakes derived from non-conventional data- entry activities by centers. • to provide to clinical stake-holders a platform from where they can easily design statistical and data mining analysis. The term Data Mining (DM) identifies a set of tools to searching for hidden patterns of interest in large and multivariate datasets. The applications of DM techniques in the medical field range from outcome prediction and patient classification to genomic medicine and molecular biology. I2ECR allows to clinical stake-holders to propose innovative methods of supervised and unsupervised feature extraction, data classification and statistical analysis on heterogeneous datasets associated to the MCL0208 clinical trial. Although MCL0208 study is the first example of data-population of I2ECR, the environment will be able to import data from clinical studies designed for other onco-hematologic diseases, too

A Multidimensional Data Warehouse for Community Health Centers

Community health centers (CHCs) play a pivotal role in healthcare delivery to vulnerable populations, but have not yet benefited from a data warehouse that can support improvements in clinical and financial outcomes across the practice. We have developed a multidimensional clinic data warehouse (CDW) by working with 7 CHCs across the state of Indiana and integrating their operational, financial and electronic patient records to support ongoing delivery of care. We describe in detail the rationale for the project, the data architecture employed, the content of the data warehouse, along with a description of the challenges experienced and strategies used in the development of this repository that may help other researchers, managers and leaders in health informatics. The resulting multidimensional data warehouse is highly practical and is designed to provide a foundation for wide-ranging healthcare data analytics over time and across the community health research enterprise