Clinical Data Reuse or Secondary Use: Current Status and Potential Future Progress

Objective: To perform a review of recent research in clinical data reuse or secondary use, and envision future advances in this field. Methods: The review is based on a large literature search in MEDLINE (through PubMed), conference proceedings, and the ACM Digital Library, focusing only on research published between 2005 and early 2016. Each selected publication was reviewed by the authors, and a structured analysis and summarization of its content was developed. Results: The initial search produced 359 publications, reduced after a manual examination of abstracts and full publications. The following aspects of clinical data reuse are discussed: motivations and challenges, privacy and ethical concerns, data integration and interoperability, data models and terminologies, unstructured data reuse, structured data mining, clinical practice and research integration, and examples of clinical data reuse (quality measurement and learning healthcare systems). Conclusion: Reuse of clinical data is a fast-growing field recognized as essential to realize the potentials for high quality healthcare, improved healthcare management, reduced healthcare costs, population health management, and effective clinical research

Cohort discovery and risk stratification for Alzheimer’s disease: an electronic health record‐based approach

BackgroundWe sought to leverage data routinely collected in electronic health records (EHRs), with the goal of developing patient risk stratification tools for predicting risk of developing Alzheimer’s disease (AD).MethodUsing EHR data from the University of Michigan (UM) hospitals and consensus‐based diagnoses from the Michigan Alzheimer’s Disease Research Center, we developed and validated a cohort discovery tool for identifying patients with AD. Applied to all UM patients, these labels were used to train an EHR‐based machine learning model for predicting AD onset within 10 years.ResultsApplied to a test cohort of 1697 UM patients, the model achieved an area under the receiver operating characteristics curve of 0.70 (95% confidence interval = 0.63‐0.77). Important predictive factors included cardiovascular factors and laboratory blood testing.ConclusionRoutinely collected EHR data can be used to predict AD onset with modest accuracy. Mining routinely collected data could shed light on early indicators of AD appearance and progression.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/155901/1/trc212035-sup-0001-SuppMat.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/155901/2/trc212035_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/155901/3/trc212035.pd

Decision Support Based on Bio-PEPA Modeling and Decision Tree Induction: A New Approach, Applied to a Tuberculosis Case Study

The problem of selecting determinant features generating appropriate model structure is a challenge in epidemiological modelling. Disease spread is highly complex, and experts develop their understanding of its dynamic over years. There is an increasing variety and volume of epidemiological data which adds to the potential confusion. We propose here to make use of that data to better understand disease systems. Decision tree techniques have been extensively used to extract pertinent information and improve decision making. In this paper, we propose an innovative structured approach combining decision tree induction with Bio-PEPA computational modelling, and illustrate the approach through application to tuberculosis. By using decision tree induction, the enhanced Bio-PEPA model shows considerable improvement over the initial model with regard to the simulated results matching observed data. The key finding is that the developer expresses a realistic predictive model using relevant features, thus considering this approach as decision support, empowers the epidemiologist in his policy decision making

The Second International Conference on Health Information Technology Advancement

TABLE OF CONTENTS I. Message from the Conference Co-Chairs B. Han and S. Falan …………………………....….……………. 5 II. Message from the Transactions Editor H. Lee …...………..………….......………….……….………….... 7 III. Referred Papers A. Emerging Health Information Technology and Applications The Role of Mobile Technology in Enhancing the Use of Personal Health Records Mohamed Abouzahra and Joseph Tan………………….……………. 9 Mobile Health Information Technology and Patient Care: Methods, Themes, and Research Gaps Bahae Samhan, Majid Dadgar, and K. D. Joshi…………..…. 18 A Balanced Perspective to Perioperative Process Management Jim Ryan, Barbara Doster, Sandra Daily, and Carmen Lewis…..….…………… 30 The Impact of Big Data on the Healthcare Information Systems Kuo Lane Chen and Huei Lee………….…………… 43 B. Health Care Communication, Literacy, and Patient Care Quality Digital Illness Narratives: A New Form of Health Communication Jofen Han and Jo Wiley…..….……..…. 47 Relationships, Caring, and Near Misses: Michael’s Story Sharie Falan and Bernard Han……………….…..…. 53 What is Your Informatics Skills Level? -- The Reliability of an Informatics Competency Measurement Tool Xiaomeng Sun and Sharie Falan.….….….….….….…. 61 C. Health Information Standardization and Interoperability Standardization Needs for Effective Interoperability Marilyn Skrocki…………………….…….………….… 76 Data Interoperability and Information Security in Healthcare Reid Berryman, Nathan Yost, Nicholas Dunn, and Christopher Edwards.…. 84 Michigan Health Information Network (MiHIN) Shared Services vs. the HIE Shared Services in Other States Devon O’Toole, Sean O’Toole, and Logan Steely…..……….…… 94 D. Health information Security and Regulation A Threat Table Based Approach to Telemedicine Security John C. Pendergrass, Karen Heart, C. Ranganathan, and V.N. Venkatakrishnan …. 104 Managing Government Regulatory Requirements for Security and Privacy Using Existing Standard Models Gregory Schymik and Dan Shoemaker…….…….….….… 112 Challenges of Mobile Healthcare Application Security Alan Rea………………………….……………. 118 E. Healthcare Management and Administration Analytical Methods for Planning and Scheduling Daily Work in Inpatient Care Settings: Opportunities for Research and Practice Laila Cure….….……………..….….….….… 121 Predictive Modeling in Post-reform Marketplace Wu-Chyuan Gau, Andrew France, Maria E. Moutinho, Carl D. Smith, and Morgan C. Wang…………...…. 131 A Study on Generic Prescription Substitution Policy as a Cost Containment Approach for Michigan’s Medicaid System Khandaker Nayeemul Islam…….…...……...………………….… 140 F. Health Information Technology Quality Assessment and Medical Service Delivery Theoretical, Methodological and Practical Challenges in Designing Formative Evaluations of Personal eHealth Tools Michael S. Dohan and Joseph Tan……………….……. 150 The Principles of Good Health Care in the U.S. in the 2010s Andrew Targowski…………………….……. 161 Health Information Technology in American Medicine: A Historical Perspective Kenneth A. Fisher………………….……. 171 G. Health Information Technology and Medical Practice Monitoring and Assisting Maternity-Infant Care in Rural Areas (MAMICare) Juan C. Lavariega, Gustavo Córdova, Lorena G Gómez, Alfonso Avila….… 175 An Empirical Study of Home Healthcare Robots Adoption Using the UTUAT Model Ahmad Alaiad, Lina Zhou, and Gunes Koru.…………………….….………. 185 HDQM2: Healthcare Data Quality Maturity Model Javier Mauricio Pinto-Valverde, Miguel Ángel Pérez-Guardado, Lorena Gomez-Martinez, Martha Corrales-Estrada, and Juan Carlos Lavariega-Jarquín.… 199 IV. A List of Reviewers …………………………..…….………………………208 V. WMU – IT Forum 2014 Call for Papers …..…….…………………20

Self-supervised learning-based general laboratory progress pretrained model for cardiovascular event detection

The inherent nature of patient data poses several challenges. Prevalent cases amass substantial longitudinal data owing to their patient volume and consistent follow-ups, however, longitudinal laboratory data are renowned for their irregularity, temporality, absenteeism, and sparsity; In contrast, recruitment for rare or specific cases is often constrained due to their limited patient size and episodic observations. This study employed self-supervised learning (SSL) to pretrain a generalized laboratory progress (GLP) model that captures the overall progression of six common laboratory markers in prevalent cardiovascular cases, with the intention of transferring this knowledge to aid in the detection of specific cardiovascular event. GLP implemented a two-stage training approach, leveraging the information embedded within interpolated data and amplify the performance of SSL. After GLP pretraining, it is transferred for TVR detection. The proposed two-stage training improved the performance of pure SSL, and the transferability of GLP exhibited distinctiveness. After GLP processing, the classification exhibited a notable enhancement, with averaged accuracy rising from 0.63 to 0.90. All evaluated metrics demonstrated substantial superiority (p < 0.01) compared to prior GLP processing. Our study effectively engages in translational engineering by transferring patient progression of cardiovascular laboratory parameters from one patient group to another, transcending the limitations of data availability. The transferability of disease progression optimized the strategies of examinations and treatments, and improves patient prognosis while using commonly available laboratory parameters. The potential for expanding this approach to encompass other diseases holds great promise.Comment: published in IEEE Journal of Translational Engineering in Health & Medicin