Integrating Wikipedia Articles and Images into an Information Resource for Radiology Patients

Patient Understanding of Oncologic Radiology Reports: Is Access to Electronic Medical Records Helpful?

Access to electronic medical record (EMR) patient portals made it easier for patients to quickly acquire the results of their radiology studies. However, there is little research on how well oncology patients understand the findings of radiology reports presented in the online portal without patient-physician discussion. This study assessed oncology patients' confidence and accuracy in interpreting radiology reports either with or without layman translations. A survey based on a radiology report was administered to oncology patients and caregivers. Two versions of the radiological report were randomly distributed, either a standard report or one with layman translations to evaluate participant understanding and accuracy of interpreting radiological results. Among 85 participants, a majority (67.8%) reported wanting patient portal access to radiological reports, yet less than a quarter (21.2%) felt confident in reading and interpreting radiological reports. Univariate binary logistic regression models showed that participants who read the lay report were 8 times more likely to find the radiology report easy to read. This research demonstrated that the inclusion of layman translation of standard radiology reports improves oncology patients' and caregivers' understanding of such reports with statistically significant and clinically meaningful increases in readability.

COVID-19–affected medical image analysis using DenserNet

The COrona VIrus Disease (COVID-19) outbreak has been announced as a pandemic by the World Health Organization (WHO) in mid-February 2020. With the current pandemic situation, the testing and detection of this disease are becoming a challenge in many regions across the globe because of the insufficiency of the suitable testing infrastructure. The shortage of kits to test COVID-19 has led to another crisis owing to worldwide supply-demand mismatch, and thereby, widen up a new research area that deals with the detection of COVID-19 without the test kit. In this paper, we investigate medical images, mostly chest X-ray images and thorax computed tomography (CT) scans to identify the attack of COVID-19. In countries, where the number of medical experts is lesser than the expected as recommended by WHO, this computer-aided system can be useful as it requires minimal human intervention. Consequently, this technology reduces the chances of contagious infection. This study may further help in the early detection of people with some similar symptoms of coronavirus. Early detection and intervention can play a pivotal role in coronavirus treatment. The primary goal of our work is to detect COVID-19–affected cases. However, this work can be extended to detect pneumonia because of Severe Acute Respiratory Syndrome, Acute Respiratory Distress Syndrome, Middle East Respiratory Syndrome, and bacteria-like Streptococcus. In this paper, we employ publicly available medical images obtained from various demographics, and propose a rapid cost-effective test leveraging a deep learning-based framework. Here, we propose a new architecture based on a densely connected convolutional neural network to analyze the COVID-19–affected medical images. We name our proposed architecture as DenserNet, which is an improvisation of DenseNet. Our proposed Denser Net architecture achieved 96.18% and 87.19% accuracies on two publicly available databases containing chest X-ray images and thorax CT scans, respectively, for the task of separating COVID-19 and non-COVID-19 images, which is quite encouraging.

Situating Wikipedia as a health information resource in various contexts: A scoping review

BACKGROUND

Wikipedia's health content is the most frequently visited resource for health information on the internet. While the literature provides strong evidence for its high usage, a comprehensive literature review of Wikipedia's role within the health context has not yet been reported.

OBJECTIVE

To conduct a comprehensive review of peer-reviewed, published literature to learn what the existing body of literature says about Wikipedia as a health information resource and what publication trends exist, if any.

METHODS

A comprehensive literature search in OVID Medline, OVID Embase, CINAHL, LISTA, Wilson's Web, AMED, and Web of Science was performed. Through a two-stage screening process, records were excluded if: Wikipedia was not a major or exclusive focus of the article; Wikipedia was not discussed within the context of a health or medical topic; the article was not available in English, the article was irretrievable, or; the article was a letter, commentary, editorial, or popular media article.

RESULTS

89 articles and conference proceedings were selected for inclusion in the review. Four categories of literature emerged: 1) studies that situate Wikipedia as a health information resource; 2) investigations into the quality of Wikipedia, 3) explorations of the utility of Wikipedia in education, and 4) studies that demonstrate the utility of Wikipedia in research.

CONCLUSION

The literature positions Wikipedia as a prominent health information resource in various contexts for the public, patients, students, and practitioners seeking health information online. Wikipedia's health content is accessed frequently, and its pages regularly rank highly in Google search results. While Wikipedia itself is well into its second decade, the academic discourse around Wikipedia within the context of health is still young and the academic literature is limited when attempts are made to understand Wikipedia as a health information resource. Possibilities for future research will be discussed.