RESUMEN

Health literacy is the central focus of Healthy People 2030, the fifth iteration of the U.S. national goals and objectives. People with low health literacy usually have trouble understanding health information, following post-visit instructions, and using prescriptions, which results in worse health outcomes and serious health disparities. In this study, we propose to leverage natural language processing techniques to improve health literacy in patient education materials by automatically translating illiterate languages in a given sentence. We scraped patient education materials from four online health information websites: MedlinePlus.gov, Drugs.com, Mayoclinic.org and Reddit.com. We trained and tested the state-of-the-art neural machine translation (NMT) models on a silver standard training dataset and a gold standard testing dataset, respectively. The experimental results showed that the Bidirectional Long Short-Term Memory (BiLSTM) NMT model outperformed Bidirectional Encoder Representations from Transformers (BERT)-based NMT models. We also verified the effectiveness of NMT models in translating health illiterate languages by comparing the ratio of health illiterate language in the sentence. The proposed NMT models were able to identify the correct complicated words and simplify into layman language while at the same time, the models suffer from sentence completeness, fluency, readability, and have difficulty in translating certain medical terms.

RESUMEN

Research shows that exposure to community and domestic violence leads to psychological trauma from childhood through adulthood, which can lead to poor health and early death. A team of health information management (HIM) professionals reviewed existing surveys to determine their suitability for assessing the quality of life (QoL) of people in trauma-affected communities (TACs). Keywords were used to search for papers describing validated QoL surveys. The obtained papers were screened, reviewed, and summarized to determine if they include the aspects needed for assessing QoL in TACs. Survey items from 20 surveys were identified as relevant to this study. Most of these 20 surveys cover one or two domains of QoL, and none of them were specifically designed for people in TACs. Therefore, it is necessary to develop a psychometrically sound assessment tool to quantify the levels of trauma, resilience, and well-being in TACs. HIM professionals have the required skills for this task.

Asunto(s)

RESUMEN

To improve the health and well-being of the medically underserved in a free clinic in Pittsburgh, Pennsylvania, a multidisciplinary team representing several health information management and information technology (IT) professionals, including faculty, students, researchers, and clinicians, created a novel IT system called imHealthy. The imHealthy system includes four critical components: a multidomain well-being questionnaire, a mobile app for data collection and tracking, a customization of an open-source electronic health record (EHR), and a data integration and well-being evaluation program leading to recommendations for personalized interventions to caregivers serving the medically underserved. This multidisciplinary team has worked closely on this project and finished critical components of the imHealthy system. Evaluations of these components will be conducted, and factors facilitating the design and adoption of the imHealthy system will be presented. The results from this research can serve as a model for free clinics with similar needs that identified by the research team in Cleveland, Indianapolis, Minnesota, Motor City, Orange County, San Diego, and St. Louis.

Asunto(s)

RESUMEN

In recent years, the term personalized medicine has received more and more attention in the field of healthcare. The increasing use of this term is closely related to the astonishing advancement in DNA sequencing technologies and other high-throughput biotechnologies. A large amount of personal genomic data can be generated by these technologies in a short time. Consequently, the needs for managing, analyzing, and interpreting these personal genomic data to facilitate personalized care are escalated. In this article, we discuss the challenges for implementing genomics-based personalized medicine in healthcare, current solutions to these challenges, and the roles of health information management (HIM) professionals in genomics-based personalized medicine.

Asunto(s)