Clinical Data Models and Data Quality Assessments

Start Date: 08/16/2020

Course Type: Common Course

Course Link: https://www.coursera.org/learn/clinical-data-models-and-data-quality-assessments

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About Course

This course aims to teach the concepts of clinical data models and common data models. Upon completion of this course, learners will be able to interpret and evaluate data model designs using Entity-Relationship Diagrams (ERDs), differentiate between data models and articulate how each are used to support clinical care and data science, and create SQL statements in Google BigQuery to query the MIMIC3 clinical data model and the OMOP common data model.

Course Syllabus

Introduction: Clinical Data Models and Common Data Models
Tools: Querying Clinical Data Models
Techniques: Extract-Transform-Load and Terminology Mapping
Techniques: Data Quality Assessments

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Course Introduction

Clinical Data Models and Data Quality Assessments An overview of data quality assessment and clinical data model analysis, including the definitions of quality assurance, quality improvement, and quality risk. This course is an introduction to different data model and quality improvement approaches in the practice of data analysis and management of clinical data. This includes an overview of different data model tools and how to perform an analysis. An overview of good practices and guidelines is also provided. This course also covers the quality assurance process, including definitions of quality assurance, quality improvement, and quality assessment.Module 1: Introduction to Data Modeling and Quality Assurance Module 2: Quality Assurance Process Module 3: Quality Assessment Process Module 4: Reporting and Reporting Criteria Contemporary Contagions in the United States, Part I In this course, we will explore the forces of social change in the United States today, how they intersect with established institutions, and turn to the challenges and opportunities for change that we collectively face. We will focus on the causes of social change, examine the social structures that create social norms and expectations, and consider the issues of diversity, inclusion, and equity that are of paramount importance in the ongoing "reform" process. We will explore a number of critical issues, using a variety of data sources, including social movements, government data, newspapers and magazines, and the personal stories of many of the participants. The course also features the contributions of

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Related Wiki Topic

Article Example
Clinical data management Clinical data management (CDM) is a critical phase in clinical research, which leads to generation of high-quality, reliable, and statistically sound data from clinical trials. Clinical data management assures collection, integration and availability of data at appropriate quality and cost. It also supports the conduct, management and analysis of studies across the spectrum of clinical research as defined by the National Institutes of Health (NIH). The ultimate goal of CDM is to assure that data support conclusions drawn from research. Achieving this goal protects public health and confidence in marketed therapeutics.
Data quality Data quality assurance is the process of data profiling to discover inconsistencies and other anomalies in the data, as well as performing data cleansing activities (e.g. removing outliers, missing data interpolation) to improve the data quality.
Clinical data management Quality Control is applied at various stages in the Clinical data management process and is normally mandated by SOP.
Data quality The market is going some way to providing data quality assurance. A number of vendors make tools for analyzing and repairing poor quality data "in situ," service providers can clean the data on a contract basis and consultants can advise on fixing processes or systems to avoid data quality problems in the first place. Most data quality tools offer a series of tools for improving data, which may include some or all of the following:
Clinical data management The data management plan describes the activities to be conducted in the course of processing data. Key topics to cover include the SOPs to be followed, clinical data management system to be used, description of data sources, data handling processes, data transfer formats and process, and quality control procedures to be applied.
Data quality Problems with data quality don't only arise from "incorrect" data; "inconsistent" data is a problem as well. Eliminating data shadow systems and centralizing data in a warehouse is one of the initiatives a company can take to ensure data consistency.
Data quality In practice, data quality is a concern for professionals involved with a wide range of information systems, ranging from data warehousing and business intelligence to customer relationship management and supply chain management. One industry study estimated the total cost to the U.S. economy of data quality problems at over U.S. $600 billion per annum (Eckerson, 2002). Incorrect data – which includes invalid and outdated information – can originate from different data sources – through data entry, or data migration and conversion projects.
Data quality Enterprises, scientists, and researchers are starting to participate within data curation communities to improve the quality of their common data.
Data quality Data quality control is the process of controlling the usage of data with known quality measurements for an application or a process. This process is usually done after a Data Quality Assurance (QA) process, which consists of discovery of data inconsistency and correction.
Data quality Companies with an emphasis on marketing often focused their quality efforts on name and address information, but data quality is recognized as an important property of all types of data. Principles of data quality can be applied to supply chain data, transactional data, and nearly every other category of data found. For example, making supply chain data conform to a certain standard has value to an organization by: 1) avoiding overstocking of similar but slightly different stock; 2) avoiding false stock-out; 3) improving the understanding of vendor purchases to negotiate volume discounts; and 4) avoiding logistics costs in stocking and shipping parts across a large organization.
Clinical data acquisition There is arguably no more important document than the instrument that is used to acquire the data from the clinical trial with the exception of the protocol, which specifies the conduct of that clinical trial. The quality of the data collected relies first and foremost on the quality of that instrument. No matter how much time and effort go into conducting the clinical trial, if the correct data points were not collected, a meaningful analysis may not be possible. It follows, therefore, that the design, development and quality assurance of such an instrument must be given the utmost attention.
Data quality Data quality refers to the condition of a set of values of qualitative or quantitative variables. There are many definitions of data quality but data is generally considered high quality if it is "fit for [its] intended uses in operations, decision making and planning.". Alternatively, data is deemed of high quality if it correctly represents the real-world construct to which it refers. Furthermore, apart from these definitions, as data volume increases, the question of internal data consistency becomes significant, regardless of fitness for use for any particular external purpose. People's views on data quality can often be in disagreement, even when discussing the same set of data used for the same purpose. Data cleansing may be required in order to ensure data quality.
Data quality Data Quality (DQ) is a niche area required for the integrity of the data management by covering gaps of data issues. This is one of the key functions that aid data governance by monitoring data to find exceptions undiscovered by current data management operations. Data Quality checks may be defined at attribute level to have full control on its remediation steps.
Clinical data management The clinical data manager plays a key role in the setup and conduct of a clinical trial. The data collected during a clinical trial forms the basis of subsequent safety and efficacy analysis which in turn drive decision making on product development in the pharmaceutical industry. The clinical data manager is involved in early discussions about data collection options and then oversees development of data collection tools based on the clinical trial protocol. Once subject enrollment begins, the data manager ensures that data is collected, validated, complete, and consistent. The clinical data manager liaises with other data providers (e.g. a central laboratory processing blood samples collected) and ensures that such data is transmitted securely and is consistent with other data collected in the clinical trial. At the completion of the clinical trial, the clinical data manager ensures that all data expected to be captured has been accounted for and that all data management activities are complete. At this stage, the data is declared final (terminology varies, but common descriptions are "Database Lock" and "Database Freeze"), and the clinical data manager transfers data for statistical analysis.
Data quality Regretfully, from a software development perspective, Data Quality is often seen as a non functional requirement. And as such, key data quality checks/processes are not factored into the final software solution. Within Healthcare, wearable technologies or Body Area Networks, generate large volumes of data. The level of detail required to ensure data quality is extremely high and is often under estimated. This is also true for the vast majority of mHealth apps, EHRs and other health related software solutions. However, some open source tools exist that examine data quality. The primary reason for this, stems from the extra cost involved is added a higher degree of rigor within the software architecture.
Data quality Data QA processes provides following information to Data Quality Control (QC):
Portal of Medical Data Models Primary goals for medical data models are releasing reliable medical forms and data models, establishing transparent and interoperable standards for medical research and raising efficiency in the design of case report files. Besides improving the quality of documentation forms by reusing reliable forms and data-models (Secondary Use, Best Practise), the comparability of research outcomes shall be enhanced.
Data quality There are a number of theoretical frameworks for understanding data quality. A systems-theoretical approach influenced by American pragmatism expands the definition of data quality to include information quality, and emphasizes the inclusiveness of the fundamental dimensions of accuracy and precision on the basis of the theory of science (Ivanov, 1972). One framework, dubbed "Zero Defect Data" (Hansen, 1991) adapts the principles of statistical process control to data quality. Another framework seeks to integrate the product perspective (conformance to specifications) and the service perspective (meeting consumers' expectations) (Kahn et al. 2002). Another framework is based in semiotics to evaluate the quality of the form, meaning and use of the data (Price and Shanks, 2004). One highly theoretical approach analyzes the ontological nature of information systems to define data quality rigorously (Wand and Wang, 1996).
Data quality This list is taken from the online book "Data Quality: High-impact Strategies". See also the glossary of data quality terms.
Clinical data management system A clinical data management system or CDMS is a tool used in clinical research to manage the data of a clinical trial. The clinical trial data gathered at the investigator site in the case report form are stored in the CDMS. To reduce the possibility of errors due to human entry, the systems employ various means to verify the data. Systems for clinical data management can be self-contained or part of the functionality of a CTMS. A CTMS with clinical data management functionality can help with the validation of clinical data as well as helps the site employ for other important activities like building patient registries and assist in patient recruitment efforts.