A Data Quality Framework for the Secondary Use of Electronic Health Information

University of Minnesota Ph.D. dissertation. April 2016. Major: Health Informatics. Advisor: Bonnie Westra. 1 computer file (PDF); ix, 101 pages.Electronic health record (EHR) systems are designed to replace paper charts and facilitate the delivery of care. Since EHR data is now readily available in electronic form, it is increasingly used for other purposes. This is expected to improve health outcomes for patients; however, the benefits will only be realized if the data that is captured in the EHR is of sufficient quality to support these secondary uses. This research demonstrated that a healthcare data quality framework can be developed that produces metrics that characterize underlying EHR data quality and it can be used to quantify the impact of data quality issues on the correctness of the intended use of the data. The framework described in this research defined a Data Quality (DQ) Ontology and implemented an assessment method. The DQ Ontology was developed by mining the healthcare data quality literature for important terms used to discuss data quality concepts and these terms were harmonized into an ontology. Four high-level data quality dimensions (CorrectnessMeasure, ConsistencyMeasure, CompletenessMeasure and CurrencyMeasure) categorized 19 lower level Measures. The ontology serves as an unambiguous vocabulary and allows more precision when discussing healthcare data quality. The DQ Ontology is expressed with sufficient rigor that it can be used for logical inference and computation. The data quality framework was used to characterize data quality of an EHR for 10 data quality Measures. The results demonstrate that data quality can be quantified and Metrics can track data quality trends over time and for specific domain concepts. The DQ framework produces scalar quantities which can be computed on individual domain concepts and can be meaningfully aggregated at different levels of an information model. The data quality assessment process was also used to quantify the impact of data quality issues on a task. The EHR data was systematically degraded and a measure of the impact on the correctness of CMS178 eMeasure (Urinary Catheter Removal after Surgery) was computed. This information can help healthcare organizations prioritize data quality improvement efforts to focus on the areas that are most important and determine if the data can support its intended use

Computational model validation using a novel multiscale multidimensional spatio-temporal meta model checking approach

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonComputational models of complex biological systems can provide a better understanding of how living systems function but need to be validated before they are employed for real-life (e.g. clinical) applications. One of the most frequently employed in silico approaches for validating such models is model checking. Traditional model checking approaches are limited to uniscale non-spatial computational models because they do not explicitly distinguish between different scales, and do not take properties of (emergent) spatial structures (e.g. density of multicellular population) into account. This thesis defines a novel multiscale multidimensional spatio-temporal meta model checking methodology which enables validating multiscale (spatial) computational models of biological systems relative to how both numeric (e.g. concentrations) and spatial system properties are expected to change over time and across multiple scales. The methodology has two important advantages. First it supports computational models encoded using various high-level modelling formalisms because it is defined relative to time series data and not the models used to produce them. Secondly the methodology is generic because it can be automatically reconfigured according to case study specific types of spatial structures and properties using the meta model checking approach. In addition the methodology could be employed for multiple domains of science, but we illustrate its applicability here only against biological case studies. To automate the computational model validation process, the approach was implemented in software tools, which are made freely available online. Their efficacy is illustrated against two uniscale and four multiscale quantitative computational models encoding phase variation in bacterial colonies and the chemotactic aggregation of cells, respectively the rat cardiovascular system dynamics, the uterine contractions of labour, the Xenopus laevis cell cycle and the acute inflammation of the gut and lung. This novel model checking approach will enable the efficient construction of reliable multiscale computational models of complex systems.Brunel University Londo

Informatics for Health 2017 : advancing both science and practice

Conference report, The Informatics for Health congress, 24-26 April 2017, in Manchester, UK.Introduction : The Informatics for Health congress, 24-26 April 2017, in Manchester, UK, brought together the Medical Informatics Europe (MIE) conference and the Farr Institute International Conference. This special issue of the Journal of Innovation in Health Informatics contains 113 presentation abstracts and 149 poster abstracts from the congress. Discussion : The twin programmes of “Big Data” and “Digital Health” are not always joined up by coherent policy and investment priorities. Substantial global investment in health IT and data science has led to sound progress but highly variable outcomes. Society needs an approach that brings together the science and the practice of health informatics. The goal is multi-level Learning Health Systems that consume and intelligently act upon both patient data and organizational intervention outcomes. Conclusions : Informatics for Health demonstrated the art of the possible, seen in the breadth and depth of our contributions. We call upon policy makers, research funders and programme leaders to learn from this joined-up approach.Publisher PDFPeer reviewe

Federal Register

Daily publication of the U.S. Office of the Federal Register contains rules and regulations, proposed legislation and rule changes, and other notices, including "Presidential proclamations and Executive Orders, Federal agency documents having general applicability and legal effect, documents required to be published by act of Congress, and other Federal agency documents of public interest" (p. ii). Table of Contents starts on page iii