Impact of COVID-19 lockdown on patient-provider electronic communications.

Background: SARS CoV-2 virus (COVID-19) impacted the practice of healthcare in the United States, with technology being used to facilitate access to care and reduce iatrogenic spread. Since then, patient message volume to primary care providers has increased. However, the volume and trend of electronic communications after lockdown remain poorly described in the literature.

Chasm Between Cancer Quality Measures and Electronic Health Record Data Quality.

Purpose: The Medicare Access and CHIP Reauthorization Act of 2015 (MACRA) requires eligible clinicians to report clinical quality measures (CQMs) in the Merit-Based Incentive Payment System (MIPS) to maximize reimbursement. To determine whether structured data in electronic health records (EHRs) were adequate to report MIPS CQMs, EHR data aggregated by ASCO's CancerLinQ platform were analyzed.

Materials And Methods: Using the CancerLinQ health technology platform, 19 Oncology MIPS (oMIPS) CQMs were evaluated to determine the presence of data elements (DEs) necessary to satisfy each CQM and the DE percent population with patient data (fill rates).

Revealing Intention In Health-related Peer Interactions: Implications For Optimizing Patient Engagement In Self-health Management.

Risky health behaviors such as poor diet, physical inactivity are the main contributors to the development of diabetes, one of the major causes of death and disability in the United States. Online health communities provide new avenues for individuals to efficiently manage their health conditions and adopt a positive lifestyle. So far, analysis of health-related online social exchanges has focused solely on communication content and structure of social ties, ignoring implicit user intentions underlying communication exchanges.

PI Prob: A risk prediction and clinical guidance system for evaluating patients with recurrent infections.

Background: Primary immunodeficiency diseases represent an expanding set of heterogeneous conditions which are difficult to recognize clinically. Diagnostic rates outside of the newborn period have not changed appreciably. This concern underscores a need for novel methods of disease detection.

Rate of change in investigational treatment options: An analysis of reports from a large precision oncology decision support effort.

Purpose: Genomic analysis of individual patients is now affordable, and therapies targeting specific molecular aberrations are being tested in clinical trials. Genomically-informed therapy is relevant to many clinical domains, but is particularly applicable to cancer treatment. However, even specialized clinicians need help to interpret genomic data, to navigate the complicated space of clinical trials, and to keep up with the rapidly expanding biomedical literature.

Diabetes Self-Management in the Age of Social Media: Large-Scale Analysis of Peer Interactions Using Semiautomated Methods.

Background: Online communities have been gaining popularity as support venues for chronic disease management. User engagement, information exposure, and social influence mechanisms can play a significant role in the utility of these platforms.

Objective: In this paper, we characterize peer interactions in an online community for chronic disease management.

Identification and Ranking of Biomedical Informatics Researcher Citation Statistics through a Google Scholar Scraper.

To overcome limitations of previously developed scientific productivity ranking services, we created the Biomedical Informatics Researchers ranking website (rank.informatics-review.com).

A novel approach for propensity score matching and stratification for multiple treatments: Application to an electronic health record-derived study.

Currently, methods for conducting multiple treatment propensity scoring in the presence of high-dimensional covariate spaces that result from "big data" are lacking-the most prominent method relies on inverse probability treatment weighting (IPTW). However, IPTW only utilizes one element of the generalized propensity score (GPS) vector, which can lead to a loss of information and inadequate covariate balance in the presence of multiple treatments. This limitation motivates the development of a novel propensity score method that uses the entire GPS vector to establish a scalar balancing score that, when adjusted for, achieves covariate balance in the presence of potentially high-dimensional covariates.

Applying a deep learning-based sequence labeling approach to detect attributes of medical concepts in clinical text.

Background: To detect attributes of medical concepts in clinical text, a traditional method often consists of two steps: named entity recognition of attributes and then relation classification between medical concepts and attributes. Here we present a novel solution, in which attribute detection of given concepts is converted into a sequence labeling problem, thus attribute entity recognition and relation classification are done simultaneously within one step.

Methods: A neural architecture combining bidirectional Long Short-Term Memory networks and Conditional Random fields (Bi-LSTMs-CRF) was adopted to detect various medical concept-attribute pairs in an efficient way.

Oncogenic enhancer of zeste homolog 2 is an actionable target in patients with non-small cell lung cancer.

Background: The aims of this study were to investigate the link between enhancer of zeste homolog 2 (EZH2) and histone deacetylase (HDAC) in preclinical studies and in human lung cancer tissue microarrays.

Methods: Enhancer of zeste homolog 2 and HDAC1 mRNA expression in two lung adenocarcinoma (LUAD) datasets (MDACC and TCGA) were correlated with patient outcomes. We evaluated the association of EZH2 and HDAC1 expression with response to the HDAC1 inhibitor, suberoylanilide hydroxamic acid (SAHA).

Multi-Institutional, Large-Scale, International Applied Clinical Informatics Research Through the Clinical Informatics Research Collaborative (CIRCLE).

There is a critical need need for multi-institutional, large-scale, international applied clinical informatics research, given the global, widespread use of commercially-available electronic health records with different designs, capabilities, configurations, and implementation strategies. The Clinical Informatics Research Collaborative (CIRCLE) aims to identify and develop best practices for safe and effective health information technology design, development, implementation, use, and evaluation.

Characterization of Behavioral Transitions Through Social Media Analysis: A Mixed-Methods Approach.

Unhealthy behaviors are a socioeconomic burden and lead to the development of chronic diseases. Relapse is a common issue that most individuals deal with as they adopt and sustain a positive healthy lifestyle. Proper identification of behavioral transitions can help design agile, adaptive, and just-in-time interventions.

Training Leaders in Health Informatics.

Despite the widespread adoption of electronic health records (EHRs) in the U.S. over the past decade, significant improvements, especially in patient safety, have yet to be realized.

NewCope: A Theory-Linked Mobile Application for Stress Education and Management.

The negative effects of long-term stress on health outcomes are well-documented. Emerging technologies that harness mobile technologies have been linked to positive effects on stress management. However, the ways in which existing inter- and intrapersonal theories of behavior change are integrated into development processes of these mHealth technologies for stress coping are limited.

OCTANE: Oncology Clinical Trial Annotation Engine.

Purpose: Many targeted therapies are currently available only via clinical trials. Therefore, routine precision oncology using biomarker-based assignment to drug depends on matching patients to clinical trials. A comprehensive and up-to-date trial database is necessary for optimal patient-trial matching.

ML-Net: multi-label classification of biomedical texts with deep neural networks.

Objective: In multi-label text classification, each textual document is assigned 1 or more labels. As an important task that has broad applications in biomedicine, a number of different computational methods have been proposed. Many of these methods, however, have only modest accuracy or efficiency and limited success in practical use.

Gene2vec: distributed representation of genes based on co-expression.

Background: Existing functional description of genes are categorical, discrete, and mostly through manual process. In this work, we explore the idea of gene embedding, distributed representation of genes, in the spirit of word embedding.

Results: From a pure data-driven fashion, we trained a 200-dimension vector representation of all human genes, using gene co-expression patterns in 984 data sets from the GEO databases.

Cancer-Related Internet Use and Online Social Networking Among Patients in an Early-Phase Clinical Trials Clinic at a Comprehensive Cancer Center.

Purpose: We examined patterns, correlates, and the impact of cancer-related Internet use among patients with advanced cancer in a phase I clinical trials clinic for molecularly targeted oncologic agents.

Methods: An anonymous questionnaire on Internet use for cancer-related purposes that incorporated input from phase I clinical trial oncologists and patients was self-administered by patients age ≥ 18 years in a phase I clinic. Multivariable modeling was used.

Phylogeny analysis from gene-order data with massive duplications.

Background: Gene order changes, under rearrangements, insertions, deletions and duplications, have been used as a new type of data source for phylogenetic reconstruction. Because these changes are rare compared to sequence mutations, they allow the inference of phylogeny further back in evolutionary time. There exist many computational methods for the reconstruction of gene-order phylogenies, including widely used maximum parsimonious methods and maximum likelihood methods.

Retrofitting Concept Vector Representations of Medical Concepts to Improve Estimates of Semantic Similarity and Relatedness.

Estimation of semantic similarity and relatedness between biomedical concepts has utility for many informatics applications. Automated methods fall into two categories: methods based on distributional statistics drawn from text corpora, and methods using the structure of existing knowledge resources. Methods in the former category disregard taxonomic structure, while those in the latter fail to consider semantically relevant empirical information.

Enabling Health Reform through Regional Health Information Exchange: A Model Study from China.

Objective: To investigate and share the major challenges and experiences of building a regional health information exchange system in China in the context of health reform.

Methods: This study used interviews, focus groups, a field study, and a literature review to collect insights and analyze data. The study examined Xinjin's approach to developing and implementing a health information exchange project, using exchange usage data for analysis.

Enabling Health Reform through Regional Health Information Exchange: A Model Study from China.

. To investigate and share the major challenges and experiences of building a regional health information exchange system in China in the context of health reform. .

Implementing electronic health records (EHRs): health care provider perceptions before and after transition from a local basic EHR to a commercial comprehensive EHR.

Objective: We assessed changes in the percentage of providers with positive perceptions of electronic health record (EHR) benefit before and after transition from a local basic to a commercial comprehensive EHR.

Methods: Changes in the percentage of providers with positive perceptions of EHR benefit were captured via a survey of academic health care providers before (baseline) and at 6-12 months (short term) and 12-24 months (long term) after the transition. We analyzed 32 items for the overall group and by practice setting, provider age, and specialty using separate multivariable-adjusted random effects logistic regression models.