Empowering diversity: striving for inclusivity by leveraging the American Medical Informatics Association's "For Your Informatics" Podcast.

Importance: Starting in 2018, the 'Women in American Medical Informatics Association (AMIA) Podcast' was women-focused, in 2021 the podcast was rebranded and relaunched as the "For Your Informatics Podcast" (FYI) to expand the scope of the podcast to include other historically underrepresented groups. That expansion of the scope, together with a rebranding and marketing campaign, led to a larger audience and engagement of the AMIA community.

Objectives: The goals of this case report are to characterize our rebranding and expanding decisions, and to assess how they impacted our listenership and engagement to achieve the Podcast goals of increasing diversity among the Podcast team, guests, audience, and improve audience engagement.

Designing an Interprofessional Online Course to Foster Learning Health Systems.

The Learning Health Systems (LHS) framework demonstrates the potential for iterative interrogation of health data in real time and implementation of insights into practice. Yet, the lack of appropriately skilled workforce results in an inability to leverage existing data to design innovative solutions. We developed a tailored professional development program to foster a skilled workforce.

Automatic Extraction of Skin and Soft Tissue Infection Status from Clinical Notes.

The reliable identification of skin and soft tissue infections (SSTIs) from electronic health records is important for a number of applications, including quality improvement, clinical guideline construction, and epidemiological analysis. However, in the United States, types of SSTIs (e.g.

Analyzing the Spread of Informatics with PubMed.

We analyzed PubMed citations since 1988 to explore the dissemination of medical/health informatics concepts between countries and across medical domains. We extracted countries from the PubMed author affiliation field to identify and analyze the top 10 informatics publishing countries. We found that the informatics publications are becoming more similar over time and that the rate of exchange across countries has increased with the introduction of e-publishing.

Telehealth in Cystic Fibrosis. A systematic review incorporating a novel scoring system and expert weighting to identify a 'top 10 manuscripts' to inform future best practices implementation.

The ongoing development and integration of telehealth within CF care has been accelerated in response to the Covid-19 pandemic, with many centres publishing their experiences. Now, as the restrictions of the pandemic ease, the use of telehealth appears to be waning, with many centres returning to routine traditional face-to-face services. For most, telehealth is not integrated into clinical care models, and there is a lack of guidance on how to integrate such a service into clinical care.

Virtual monitoring in CF - the importance of continuous monitoring in a multi-organ chronic condition.

Cystic Fibrosis (CF) is a chronic life-limiting condition that affects multiple organs within the body. Patients must adhere to strict medication regimens, physiotherapy, diet, and attend regular clinic appointments to manage their condition effectively. This necessary but burdensome requirement has prompted investigations into how different digital health technologies can enhance current care by providing the opportunity to virtually monitor patients.

Developing a deep learning natural language processing algorithm for automated reporting of adverse drug reactions.

The detection of adverse drug reactions (ADRs) is critical to our understanding of the safety and risk-benefit profile of medications. With an incidence that has not changed over the last 30 years, ADRs are a significant source of patient morbidity, responsible for 5%-10% of acute care hospital admissions worldwide. Spontaneous reporting of ADRs has long been the standard method of reporting, however this approach is known to have high rates of under-reporting, a problem that limits pharmacovigilance efforts.

Artificial Intelligence and Deep Learning for Rheumatologists.

Deep learning has emerged as the leading method in machine learning, spawning a rapidly growing field of academic research and commercial applications across medicine. Deep learning could have particular relevance to rheumatology if correctly utilized. The greatest benefits of deep learning methods are seen with unstructured data frequently found in rheumatology, such as images and text, where traditional machine learning methods have struggled to unlock the trove of information held within these data formats.

Information Extraction From Electronic Health Records to Predict Readmission Following Acute Myocardial Infarction: Does Natural Language Processing Using Clinical Notes Improve Prediction of Readmission?

Background Social risk factors influence rehospitalization rates yet are challenging to incorporate into prediction models. Integration of social risk factors using natural language processing (NLP) and machine learning could improve risk prediction of 30-day readmission following an acute myocardial infarction. Methods and Results Patients were enrolled into derivation and validation cohorts.

WITHDRAWN: Designing a professional development online short course to foster Learning Healthcare Systems.

This article has been withdrawn: please see Elsevier Policy on Article Withdrawal (http://www.elsevier.com/locate/withdrawalpolicy).

Telehealth and virtual health monitoring in cystic fibrosis.

Purpose Of Review: At many institutions, the Covid-19 pandemic made it necessary to rapidly change the way services are provided to patients, including those with cystic fibrosis (CF). The purpose of this review is to explore the past, present and future of telehealth and virtual monitoring in CF and to highlight certain challenges/considerations in developing such services.

Recent Findings: The Covid-19 pandemic has proven that telehealth and virtual monitoring are a feasible means for safely providing services to CF patients when traditional care is not possible.

A Scoping Review and Content Analysis of Common Depressive Symptoms of Young People.

School nurses are the most accessible health care providers for many young people including adolescents and young adults. Early identification of depression results in improved outcomes, but little information is available comprehensively describing depressive symptoms specific to this population. The aim of this study was to develop a taxonomy of depressive symptoms that were manifested and described by young people based on a scoping review and content analysis.

Development of Electronic Health Record-Based Prediction Models for 30-Day Readmission Risk Among Patients Hospitalized for Acute Myocardial Infarction.

Importance: In the US, more than 600 000 adults will experience an acute myocardial infarction (AMI) each year, and up to 20% of the patients will be rehospitalized within 30 days. This study highlights the need for consideration of calibration in these risk models.

Objective: To compare multiple machine learning risk prediction models using an electronic health record (EHR)-derived data set standardized to a common data model.

Trends in Low-Value Carotid Imaging in the Veterans Health Administration From 2007 to 2016.

Importance: As part of the Choosing Wisely campaign, primary care, surgery, and neurology societies have identified carotid imaging ordered for screening, preoperative evaluation, and syncope as frequently low value.

Objective: To determine the changes in overall and indication-specific rates of carotid imaging following Choosing Wisely recommendations.

Design, Setting, And Participants: This serial cross-sectional study compared annual rates of carotid imaging before Choosing Wisely recommendations (ie, 2007 to 2012) and after (ie, 2013 to 2016) among adults receiving care in the Veterans Health Administration (VHA) national health system.

Portable Automated Surveillance of Surgical Site Infections Using Natural Language Processing: Development and Validation.

Objectives: We present the development and validation of a portable NLP approach for automated surveillance of SSIs.

Summary Of Background Data: The surveillance of SSIs is labor-intensive limiting the generalizability and scalability of surgical quality surveillance programs.

Methods: We abstracted patient clinical text notes after surgical procedures from 2 independent healthcare systems using different electronic healthcare records.

Comparative Effectiveness of Carotid Endarterectomy vs Initial Medical Therapy in Patients With Asymptomatic Carotid Stenosis.

Importance: Carotid endarterectomy (CEA) among asymptomatic patients involves a trade-off between a higher short-term perioperative risk in exchange for a lower long-term risk of stroke. The clinical benefit observed in randomized clinical trials (RCTs) may not extend to real-world practice.

Objective: To examine whether early intervention (CEA) was superior to initial medical therapy in real-world practice in preventing fatal and nonfatal strokes among patients with asymptomatic carotid stenosis.

Using Natural Language Processing to improve EHR Structured Data-based Surgical Site Infection Surveillance.

Surgical Site Infection surveillance in healthcare systems is labor intensive and plagued by underreporting as current methodology relies heavily on manual chart review. The rapid adoption of electronic health records (EHRs) has the potential to allow the secondary use of EHR data for quality surveillance programs. This study aims to investigate the effectiveness of integrating natural language processing (NLP) outputs with structured EHR data to build machine learning models for SSI identification using real-world clinical data.

Determination of Marital Status of Patients from Structured and Unstructured Electronic Healthcare Data.

Social Determinants of Health, including marital status, are becoming increasingly identified as key drivers of health care utilization. This paper describes a robust method to determine the marital status of patients using structured and unstructured electronic healthcare data from a single academic institution in the United States. We developed and validated a natural language processing pipeline (NLP) for the ascertainment of marital status from clinical notes and compared the performance against two baseline methods: a machine learning n-gram model, and structured data obtained from the electronic health record.

Impact of Different Electronic Cohort Definitions to Identify Patients With Atrial Fibrillation From the Electronic Medical Record.

Background Electronic medical records (EMRs) allow identification of disease-specific patient populations, but varying electronic cohort definitions could result in different populations. We compared the characteristics of an electronic medical record-derived atrial fibrillation (AF) patient population using 5 different electronic cohort definitions. Methods and Results Adult patients with at least 1 AF billing code from January 1, 2010, to December 31, 2017, were included.

Interactive NLP in Clinical Care: Identifying Incidental Findings in Radiology Reports.

Background: Despite advances in natural language processing (NLP), extracting information from clinical text is expensive. Interactive tools that are capable of easing the construction, review, and revision of NLP models can reduce this cost and improve the utility of clinical reports for clinical and secondary use.

Objectives: We present the design and implementation of an interactive NLP tool for identifying incidental findings in radiology reports, along with a user study evaluating the performance and usability of the tool.

Recent Advances in Using Natural Language Processing to Address Public Health Research Questions Using Social Media and ConsumerGenerated Data.

Objective: We present a narrative review of recent work on the utilisation of Natural Language Processing (NLP) for the analysis of social media (including online health communities) specifically for public health applications.

Methods: We conducted a literature review of NLP research that utilised social media or online consumer-generated text for public health applications, focussing on the years 2016 to 2018. Papers were identified in several ways, including PubMed searches and the inspection of recent conference proceedings from the Association of Computational Linguistics (ACL), the Conference on Human Factors in Computing Systems (CHI), and the International AAAI (Association for the Advancement of Artificial Intelligence) Conference on Web and Social Media (ICWSM).

Natural Language Processing Accurately Identifies Dysphagia Indications for Esophagogastroduodenoscopy Procedures in a Large US Integrated Healthcare System: Implications for Classifying Overuse and Quality Measurement.

Recent evidence suggests almost half of repeat esophagogastroduodenoscopy procedures (EGDs) are overused; this prior research relied on administrative data that are often inaccurate. Our primary objective was to determine and compare the accuracy of natural language processing and administrative data to manual chart review to identify dysphagia indications for EGD procedures within the national VA healthcare system. From 396,856 EGD notes identified from 2008-2014, we classified 119,920 as "index" procedures in 2010-2012.

Determining Onset for Familial Breast and Colorectal Cancer from Family History Comments in the Electronic Health Record.

. Family health history (FHH) can be used to identify individuals at elevated risk for familial cancers. Risk criteria for common cancers rely on age of onset, which is documented inconsistently as structured and unstructured data in electronic health records (EHRs).