Information Systems and Healthcare XXII: Characterizing and Visualizing the Quality of Health Information

We all need ways to assess the quality of the information we look for, but this task is critically important when we are seeking health information. Healthcare consumers increasingly seek and use health information to address their health concerns. However, many health consumers lack the time and expertise required to make solid judgments about the quality of health information they encounter. A full range of quality appraisal methods for health information offer help, yet health consumers use those methods infrequently. Health consumers need better support to overcome barriers to efficiency, scalability, and transparency often associated with this breadth of valuable methods. Furthermore, they need ways to assess the quality of health information they find in the context of their own, individually situated needs. Our goals were to investigate the concept of health information quality and to explore how we can provide health consumers with better support by highlighting, rather than hiding, important aspects of health information quality. First, by reviewing and synthesizing criteria used by a broad range of quality appraisal methods for health information, we identified four focal characteristics of health information quality: content, reference, authorship, and publisher. Together, these four characteristics of intrinsic quality provide an organizing framework for health consumers to assess the quality of health information along multiple dimensions according to their own needs. Next, we used a user-center approach to design a prototype tool that concretely illustrates our framework by allowing the user to highlight multiple dimensions of health information quality. We present a usage case example of this illustrative tool, which visualizes the quality of MEDLINE search results. Our work provides a new perspective on health information quality by acknowledging and supporting consumers\u27 needs for transparency and flexibility as they take a prominent role in health information quality assessment

Supporting Collaborative Health Tracking in the Hospital: Patients' Perspectives

The hospital setting creates a high-stakes environment where patients' lives depend on accurate tracking of health data. Despite recent work emphasizing the importance of patients' engagement in their own health care, less is known about how patients track their health and care in the hospital. Through interviews and design probes, we investigated hospitalized patients' tracking activity and analyzed our results using the stage-based personal informatics model. We used this model to understand how to support the tracking needs of hospitalized patients at each stage. In this paper, we discuss hospitalized patients' needs for collaboratively tracking their health with their care team. We suggest future extensions of the stage-based model to accommodate collaborative tracking situations, such as hospitals, where data is collected, analyzed, and acted on by multiple people. Our findings uncover new directions for HCI research and highlight ways to support patients in tracking their care and improving patient safety

MKEM: a Multi-level Knowledge Emergence Model for mining undiscovered public knowledge

<p>Abstract</p> <p>Background</p> <p>Since Swanson proposed the Undiscovered Public Knowledge (UPK) model, there have been many approaches to uncover UPK by mining the biomedical literature. These earlier works, however, required substantial manual intervention to reduce the number of possible connections and are mainly applied to disease-effect relation. With the advancement in biomedical science, it has become imperative to extract and combine information from multiple disjoint researches, studies and articles to infer new hypotheses and expand knowledge.</p> <p>Methods</p> <p>We propose MKEM, a Multi-level Knowledge Emergence Model, to discover implicit relationships using Natural Language Processing techniques such as Link Grammar and Ontologies such as Unified Medical Language System (UMLS) MetaMap. The contribution of MKEM is as follows: First, we propose a flexible knowledge emergence model to extract implicit relationships across different levels such as molecular level for gene and protein and Phenomic level for disease and treatment. Second, we employ MetaMap for tagging biological concepts. Third, we provide an empirical and systematic approach to discover novel relationships.</p> <p>Results</p> <p>We applied our system on 5000 abstracts downloaded from PubMed database. We performed the performance evaluation as a gold standard is not yet available. Our system performed with a good precision and recall and we generated 24 hypotheses.</p> <p>Conclusions</p> <p>Our experiments show that MKEM is a powerful tool to discover hidden relationships residing in extracted entities that were represented by our Substance-Effect-Process-Disease-Body Part (SEPDB) model. </p

Mucin Variable Number Tandem Repeat Polymorphisms and Severity of Cystic Fibrosis Lung Disease: Significant Association with MUC5AC

Variability in cystic fibrosis (CF) lung disease is partially due to non-CFTR genetic modifiers. Mucin genes are very polymorphic, and mucins play a key role in the pathogenesis of CF lung disease; therefore, mucin genes are strong candidates as genetic modifiers. DNA from CF patients recruited for extremes of lung phenotype was analyzed by Southern blot or PCR to define variable number tandem repeat (VNTR) length polymorphisms for MUC1, MUC2, MUC5AC, and MUC7. VNTR length polymorphisms were tested for association with lung disease severity and for linkage disequilibrium (LD) with flanking single nucleotide polymorphisms (SNPs). No strong associations were found for MUC1, MUC2, or MUC7. A significant association was found between the overall distribution of MUC5AC VNTR length and CF lung disease severity (p = 0.025; n = 468 patients); plus, there was robust association of the specific 6.4 kb HinfI VNTR fragment with severity of lung disease (p = 6.2 x 10(-4) after Bonferroni correction). There was strong LD between MUC5AC VNTR length modes and flanking SNPs. The severity-associated 6.4 kb VNTR allele of MUC5AC was confirmed to be genetically distinct from the 6.3 kb allele, as it showed significantly stronger association with nearby SNPs. These data provide detailed respiratory mucin gene VNTR allele distributions in CF patients. Our data also show a novel link between the MUC5AC 6.4 kb VNTR allele and severity of CF lung disease. The LD pattern with surrounding SNPs suggests that the 6.4 kb allele contains, or is linked to, important functional genetic variation

Establishing the value of genomics in medicine: the IGNITE Pragmatic Trials Network.

PURPOSE: A critical gap in the adoption of genomic medicine into medical practice is the need for the rigorous evaluation of the utility of genomic medicine interventions. METHODS: The Implementing Genomics in Practice Pragmatic Trials Network (IGNITE PTN) was formed in 2018 to measure the clinical utility and cost-effectiveness of genomic medicine interventions, to assess approaches for real-world application of genomic medicine in diverse clinical settings, and to produce generalizable knowledge on clinical trials using genomic interventions. Five clinical sites and a coordinating center evaluated trial proposals and developed working groups to enable their implementation. RESULTS: Two pragmatic clinical trials (PCTs) have been initiated, one evaluating genetic risk APOL1 variants in African Americans in the management of their hypertension, and the other to evaluate the use of pharmacogenetic testing for medications to manage acute and chronic pain as well as depression. CONCLUSION: IGNITE PTN is a network that carries out PCTs in genomic medicine; it is focused on diversity and inclusion of underrepresented minority trial participants; it uses electronic health records and clinical decision support to deliver the interventions. IGNITE PTN will develop the evidence to support (or oppose) the adoption of genomic medicine interventions by patients, providers, and payers