TASK DEFINITION: In this task, you're given a paragraph from the research paper and your task is to generate a suitable title for the research paper based on the given paper. Under 100 words is a good title length.
PROBLEM: Objective: Health literacy has become a global issue, and it is important that patients and individuals are able to use information technology to access health information and educational services. The research objective is to develop a Saudi e-health literacy scale (SeHL) for measuring e-health literacy among Saudis suffering from non-communicable diseases (NCD). Methods: Overall, 14 relevant papers in related interdisciplinary fields were reviewed to select the most useful literacy dimensions. From these articles, we extracted the most common dimensions used to measure e-health literacy across the disciplines. Multiple workshops with multidisciplinary team members reviewed and evaluated items for SeHL. Results: Four key aspects of e-health literacy-use of technology/media, information-seeking, usefulness and confidence-were identified and integrated as e-health literacy dimensions. These will be used to measure e-health literacy among Saudi patients with NCDs. A translation from Arabic to English was performed in order to ensure that translation process was accurate. A SeHL scale was developed to measure e-health literacy among Saudi patients. By understanding e-health literacy levels, we will be able to create a patient-education system to be used by patients in Saudi Arabia. Conclusions: As information technology is increasingly used by people of all ages all over the world, e-health literacy has been identified as a key factor in determining health outcomes. To date, no comprehensive scale exists to assess e-health literacy levels among speakers of Arabic, particularly among people with NCD such as diabetes, cardiovascular diseases and hypertension.

SOLUTION: Saudi Arabia, 5 Prince Sultan Military Medical City, AlSulimaniah, Riyadh 11159, Saudi Arabia, 6 King Faisal Specialist and Research Center

PROBLEM: Background: Adverse events are associated with significant morbidity, mortality and cost in hospitalized patients. Measuring adverse events is necessary for quality improvement, but current detection methods are inaccurate, untimely and expensive. The advent of electronic health records and the development of automated methods for encoding and classifying electronic narrative data, such as natural language processing, offer an opportunity to identify potentially better methods. The objective of this study is to determine the accuracy of using automated methods for detecting three highly prevalent adverse events: a) hospital-acquired pneumonia, b) catheter-associated bloodstream infections, and c) in-hospital falls. Methods/design: This validation study will be conducted at two large Canadian academic health centres: the McGill University Health Centre (MUHC) and The Ottawa Hospital (TOH). The study population consists of all medical, surgical and intensive care unit patients admitted to these centres between 2008 and 2014. An automated detection algorithm will be developed and validated for each of the three adverse events using electronic data extracted from multiple clinical databases. A random sample of MUHC patients will be used to develop the automated detection algorithms (cohort 1, development set). The accuracy of these algorithms will be assessed using chart review as the reference standard. Then, receiver operating characteristic curves will be used to identify optimal cut points for each of the data sources. Multivariate logistic regression and the areas under curve (AUC) will be used to identify the optimal combination of data sources that maximize the accuracy of adverse event detection. The most accurate algorithms will then be validated on a second random sample of MUHC patients (cohort 1, validation set), and accuracy will be measured using chart review as the reference standard. The most accurate algorithms validated at the MUHC will then be applied to TOH data (cohort 2), and their accuracy will be assessed using a reference standard assessment of the medical chart. Discussion: There is a need for more accurate, timely and efficient measures of adverse events in acute care hospitals. This is a critical requirement for evaluating the effectiveness of preventive interventions and for tracking progress in patient safety through time.

SOLUTION: Accuracy of using automated methods for detecting adverse events from electronic health record data: a research protocol

PROBLEM: Porcine epidemic diarrhea virus (PEDV) belongs to the genus Alphacoronavirus of the family Coronaviridae . PEDV was identifi ed as an emerging pathogen in US pig populations in 2013. Since then, this virus has been detected in at least 31 states in the USA and has caused signifi cant economic loss to the swine industry. Active surveillance and characterization of PEDV are essential for monitoring the virus. Obtaining comprehensive information about the PEDV genome can improve our understanding of the evolution of PEDV viruses, and the emergence of new strains, and can enhance vaccine designs. In this chapter, both a targeted amplifi cation method and a random-priming method are described to amplify the complete genome of PEDV for sequencing using the MiSeq platform. Overall, this protocol provides a useful twopronged approach to complete whole-genome sequences of PEDV depending on the amount of virus in the clinical samples.

SOLUTION:
Chapter 18 Whole-Genome Sequencing of Porcine Epidemic Diarrhea Virus by Illumina MiSeq Platform