Veterans engineering resource center: the DREAM project

Due to technological advances, data collected from direct healthcare delivery is growing by the day. The constantly growing data that was collected from various resources including patient visits, images, laboratory results and physician notes, though important, has no significance beyond its satisfying reporting and/or documentation requirements and potential application to specific clinical situations, mainly due to the voluminous and heterogeneous nature of the data. With this tremendous amount of data, manual extraction of information is expensive, time consuming, and subject to human error. Fortunately, information technologies have enabled the generation and collection of this data and also the efficient extraction of useful information. Currently, there is a broad spectrum of secondary uses of this clinical data including clinical and translational research, public health and policy analysis, and quality measurement and improvement. The following case study examines a pilot project undertaken by the Veterans Engineering Resource Center(VERC) to design a data mining software utility called Data Resource Engine & Analytical Model (DREAM).This software should be operable within the VA IT infrastructure and will allow providers to view aggregate patient data rapidly and accurately using electronic health records

MS

thesisThe early detection of infectious disease outbreaks is key to their management and initiation of mitigation strategies. This is true whether the disease is naturally occurring or due to intentional release as an act of terrorism. In recent times, this has become evident with the anthrax bioterrorism attacks of October 2001, the occurrence of emerging infections such as West Nile Virus and Severe Acute Respiratory Syndrome of the concern for a new pandemic of influenza based on H5N1 avian influenza. Public health surveillance efforts at the University of Utah have been place for several years and came to the forefront during the 2002 Winter Olympic Games. At that time, an electronic medical record-based system was developed and deployed to perform daily surveillance of patients visiting the clinics and emergency department of the University of Utah Health Care System. This effort was then followed by a detailed validation of the computer rules used in the surveillance system, with special emphasis on the early detection of central nervous system (CNS) syndromes such as meningitis and encephalitis. These syndromes are of importance to both emerging infections such as West Nile Virus and for NIH/CDC Category B threat agents such as Eastern and Western Equine Encephalitis. True CNS syndromes caused by infectious agents represent a small proportion of patients seen at the emergency department of a large tertiary hospital. "Reason for visit" chief complaint data were poor predictors for the early detection of CNS syndromes. Orders and early results from the laboratory testing of cerebro-spinal fluid were useful for the early detection of meningitis and encephalitis. Overall, computer-based surveillance methods have a role to play in the early detection of infectious diseases. In particular, this project has contributed to public health surveillance by moving the field beyond complaint data and has shown the validity of suing computer-based rules for the detection of meningitis and encephalitis

Doctor of Philosophy

dissertationPublic health surveillance systems are crucial for the timely detection and response to public health threats. Since the terrorist attacks of September 11, 2001, and the release of anthrax in the following month, there has been a heightened interest in public health surveillance. The years immediately following these attacks were met with increased awareness and funding from the federal government which has significantly strengthened the United States surveillance capabilities; however, despite these improvements, there are substantial challenges faced by today's public health surveillance systems. Problems with the current surveillance systems include: a) lack of leveraging unstructured public health data for surveillance purposes; and b) lack of information integration and the ability to leverage resources, applications or other surveillance efforts due to systems being built on a centralized model. This research addresses these problems by focusing on the development and evaluation of new informatics methods to improve the public health surveillance. To address the problems above, we first identified a current public surveillance workflow which is affected by the problems described and has the opportunity for enhancement through current informatics techniques. The 122 Mortality Surveillance for Pneumonia and Influenza was chosen as the primary use case for this dissertation work. The second step involved demonstrating the feasibility of using unstructured public health data, in this case death certificates. For this we created and evaluated a pipeline iv composed of a detection rule and natural language processor, for the coding of death certificates and the identification of pneumonia and influenza cases. The second problem was addressed by presenting the rationale of creating a federated model by leveraging grid technology concepts and tools for the sharing and epidemiological analyses of public health data. As a case study of this approach, a secured virtual organization was created where users are able to access two grid data services, using death certificates from the Utah Department of Health, and two analytical grid services, MetaMap and R. A scientific workflow was created using the published services to replicate the mortality surveillance workflow. To validate these approaches, and provide proofs-of-concepts, a series of real-world scenarios were conducted

The BioRef Infrastructure, a Framework for Real-Time, Federated, Privacy-Preserving, and Personalized Reference Intervals: Design, Development, and Application.

BACKGROUND Reference intervals (RIs) for patient test results are in standard use across many medical disciplines, allowing physicians to identify measurements indicating potentially pathological states with relative ease. The process of inferring cohort-specific RIs is, however, often ignored because of the high costs and cumbersome efforts associated with it. Sophisticated analysis tools are required to automatically infer relevant and locally specific RIs directly from routine laboratory data. These tools would effectively connect clinical laboratory databases to physicians and provide personalized target ranges for the respective cohort population. OBJECTIVE This study aims to describe the BioRef infrastructure, a multicentric governance and IT framework for the estimation and assessment of patient group-specific RIs from routine clinical laboratory data using an innovative decentralized data-sharing approach and a sophisticated, clinically oriented graphical user interface for data analysis. METHODS A common governance agreement and interoperability standards have been established, allowing the harmonization of multidimensional laboratory measurements from multiple clinical databases into a unified "big data" resource. International coding systems, such as the International Classification of Diseases, Tenth Revision (ICD-10); unique identifiers for medical devices from the Global Unique Device Identification Database; type identifiers from the Global Medical Device Nomenclature; and a universal transfer logic, such as the Resource Description Framework (RDF), are used to align the routine laboratory data of each data provider for use within the BioRef framework. With a decentralized data-sharing approach, the BioRef data can be evaluated by end users from each cohort site following a strict "no copy, no move" principle, that is, only data aggregates for the intercohort analysis of target ranges are exchanged. RESULTS The TI4Health distributed and secure analytics system was used to implement the proposed federated and privacy-preserving approach and comply with the limitations applied to sensitive patient data. Under the BioRef interoperability consensus, clinical partners enable the computation of RIs via the TI4Health graphical user interface for query without exposing the underlying raw data. The interface was developed for use by physicians and clinical laboratory specialists and allows intuitive and interactive data stratification by patient factors (age, sex, and personal medical history) as well as laboratory analysis determinants (device, analyzer, and test kit identifier). This consolidated effort enables the creation of extremely detailed and patient group-specific queries, allowing the generation of individualized, covariate-adjusted RIs on the fly. CONCLUSIONS With the BioRef-TI4Health infrastructure, a framework for clinical physicians and researchers to define precise RIs immediately in a convenient, privacy-preserving, and reproducible manner has been implemented, promoting a vital part of practicing precision medicine while streamlining compliance and avoiding transfers of raw patient data. This new approach can provide a crucial update on RIs and improve patient care for personalized medicine

Medical Informatics and Data Analysis

During recent years, the use of advanced data analysis methods has increased in clinical and epidemiological research. This book emphasizes the practical aspects of new data analysis methods, and provides insight into new challenges in biostatistics, epidemiology, health sciences, dentistry, and clinical medicine. This book provides a readable text, giving advice on the reporting of new data analytical methods and data presentation. The book consists of 13 articles. Each article is self-contained and may be read independently according to the needs of the reader. The book is essential reading for postgraduate students as well as researchers from medicine and other sciences where statistical data analysis plays a central role

Health ManagementInformation Systems for Resource Allocation and Purchasing in Developing Countries

World Bank, Health Nutrition and Population, Discussion Paper: The paper begins with the premise that it is not possible to implement an efficient, modern RAP strategy today without the effective use of information technology. The paper then leads the architect through the functionality of the systems components and environment needed to support RAP, pausing to justify them at each step. The paper can be used as a long-term guide through the systems development process as it is not necessary (and likely not possible) to implement all functions at once. The paper’s intended audience is those members of a planning and strategy body, working in conjunction with technical experts, who are charged with designing and implementing a RAP strategy in a developing country

Using machine learning for automated de-identification and clinical coding of free text data in electronic medical records

The widespread adoption of Electronic Medical Records (EMRs) in hospitals continues to increase the amount of patient data that are digitally stored. Although the primary use of the EMR is to support patient care by making all relevant information accessible, governments and health organisations are looking for ways to unleash the potential of these data for secondary purposes, including clinical research, disease surveillance and automation of healthcare processes and workflows. EMRs include large quantities of free text documents that contain valuable information. The greatest challenges in using the free text data in EMRs include the removal of personally identifiable information and the extraction of relevant information for specific tasks such as clinical coding. Machine learning-based automated approaches can potentially address these challenges. This thesis aims to explore and improve the performance of machine learning models for automated de-identification and clinical coding of free text data in EMRs, as captured in hospital discharge summaries, and facilitate the applications of these approaches in real-world use cases. It does so by 1) implementing an end-to-end de-identification framework using an ensemble of deep learning models; 2) developing a web-based system for de-identification of free text (DEFT) with an interactive learning loop; 3) proposing and implementing a hierarchical label-wise attention transformer model (HiLAT) for explainable International Classification of Diseases (ICD) coding; and 4) investigating the use of extreme multi-label long text transformer-based models for automated ICD coding. The key findings include: 1) An end-to-end framework using an ensemble of deep learning base-models achieved excellent performance on the de-identification task. 2) A new web-based de-identification software system (DEFT) can be readily and easily adopted by data custodians and researchers to perform de-identification of free text in EMRs. 3) A novel domain-specific transformer-based model (HiLAT) achieved state-of-the-art (SOTA) results for predicting ICD codes on a Medical Information Mart for Intensive Care (MIMIC-III) dataset comprising the discharge summaries (n=12,808) that are coded with at least one of the most 50 frequent diagnosis and procedure codes. In addition, the label-wise attention scores for the tokens in the discharge summary presented a potential explainability tool for checking the face validity of ICD code predictions. 4) An optimised transformer-based model, PLM-ICD, achieved the latest SOTA results for ICD coding on all the discharge summaries of the MIMIC-III dataset (n=59,652). The segmentation method, which split the long text consecutively into multiple small chunks, addressed the problem of applying transformer-based models to long text datasets. However, using transformer-based models on extremely large label sets needs further research. These findings demonstrate that the de-identification and clinical coding tasks can benefit from the application of machine learning approaches, present practical tools for implementing these approaches, and highlight priorities for further research

Sharing electronic patient records among providers via the World Wide Web

Thesis (M.S.)--Massachusetts Institute of Technology, Whitaker College of Health Sciences and Technology, 1998.Includes bibliographical references (leaves 71-72).by John D. Halamka.M.S