Health Informatics Journal最新文献

Background: Patients with sarcopenia often go undetected in busy clinical practices since the muscle measurements are not easily incorporated into routine clinical practice. The current research fills the gap by utilizing unstructured clinical notes combined with structured data from electronic health records (EHR), to increase sarcopenia detection. Methods: We developed and evaluated four approaches to first extract clinical note features, then integrate with structured data for sarcopenia detection models. Case studies were used to demonstrate the interpretation of the results and show the important association between predictors and outcomes. Results: Out of 1304 participants, 1055 were controls, 249 met at least one criterion for Sarcopenia. The best performing model which incorporated both data-driven and knowledge-driven approaches to integrate clinical note features demonstrated a higher mean area under the curve (AUC = 73.93%, (95% CI, 73.83-74.02)) compared to the baseline model (AUC 71.59%, (95%CI, 71.56-71.61)). The case study shows that the important clinical note predictors may contribute to detection of sarcopenia such as "cane", "walker", "unsteady", etc. Conclusions: Incorporating clinical note features in sarcopenia detection models can identify a greater number of patients at risk for sarcopenia, potentially leading to targeted muscle testing assessments and corresponding treatments to address sarcopenia.

Objective: To audit and compare search autocomplete results in Spanish and English during the early COVID-19 pandemic in the New York metropolitan area. The pandemic led to significant online search activity about the disease, its spread, and remedies. As gatekeepers, search engines like Google can influence public opinion. Autocomplete predictions help users complete searches faster but may also shape their views. Understanding these differences is crucial to identify biases and ensure equitable information dissemination. Methods: The study tracked autocomplete results daily for five COVID-19 related search terms in English and Spanish over 100+ days in 2020, yielding a total of 9164 autocomplete predictions. Results: Queries in Spanish yielded fewer autocomplete options and often included more negative content than English autocompletes. The topical coverage differed, with Spanish autocompletes including themes related to religion and spirituality that were absent in the English search autocompletes. Conclusion: The contrast in search autocomplete results could lead to divergent impressions about the pandemic and remedial actions among different sections of society. Continuous auditing of autocompletes by public health stakeholders and search engine organizations is recommended to reduce potential bias and misinformation.

Background: Despite the ongoing efforts to digitalize the healthcare sector in developing countries, the full adoption of big data analytics in healthcare settings is yet to be attained Exploring opportunities and challenges encountered is essential for designing and implementing effective interventional strategies. Objective: Exploring opportunities and challenges towards integrating big data analytics technologies in the healthcare industry in developing countries. Methodology: This was a narrative review study design. A literature search on different databases was conducted including PubMed, ScienceDirect, MEDLINE, Scopus, and Google Scholar. Articles with predetermined keywords and written in English were included. Results: Big data analytics finds its application in population health management and clinical decision-support systems even in developing countries. The major challenges towards the integration of big data analytics in the healthcare sector in developing countries include fragmentation of healthcare data and lack of interoperability, data security, privacy and confidentiality concerns, limited resources and inadequate regulatory and policy frameworks for governing big data analytics technologies and limited reliable power and internet infrastructures. Conclusion: Digitalization of healthcare delivery in developing countries faces several significant challenges. However, the integration of big data analytics can potentially open new avenues for enhancing healthcare delivery with cost-effective benefits.

Objectives: This study proposes a novel architecture for designing digital twins in healthcare units. Methods: A systematic research methodology was employed to develop architecture design patterns. In particular, a systematic literature review was conducted using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) framework to answer specific research questions and provide guidelines for designing the architecture. Subsequently, a case study was designed and analyzed at a chemotherapy treatment center for outpatients. Results: System architecture knowledge was distilled from this real-world case study, supplemented by existing software and systems design patterns. A novel five-layer architecture for digital twins in healthcare units was proposed with a focus on the security and privacy of patients' information. Conclusion: The proposed digital twin architecture for healthcare units offers a comprehensive solution that provides modularity, scalability, security, and interoperability. The architecture provides a robust framework for effectively and efficiently managing healthcare environments.

Poorly controlled chronic obstructive pulmonary disease (COPD) can negatively impact quality of life but mobile applications (apps) are popular digital tools that may mitigate these support needs. However, it is unclear if public mobile COPD apps are acceptable to healthcare professionals and patients, people living with COPD. Objectives: The primary objective is to determine people with COPD and healthcare professionals' perspectives on the appropriateness of public mobile COPD apps for supporting individuals' needs. The secondary objectives were to identify the ideal features and styles of mobile COPD apps for COPD self-management; and to identify the facilitators, barriers and needs for future COPD app research and development. Methods: Public mobile COPD apps were rated by questionnaires administered before and after focus group meetings. Ratings were reported as medians with interquartile ranges and median scores were categorized into three levels of appropriateness: 1-3 for inappropriate; 4-6 for uncertain; and 7-9 for appropriate. Results: A total of 6 people with COPD (mean age 68.2 ± 4.8years) and 22 healthcare professionals (mean age 45 ± 8.3years) completed this study. People with COPD identified one and healthcare professionals identified three public mobile COPD apps to be appropriate. They had different preferences for features and engagement styles but similar preferences for facilitators and barriers to use. Stakeholders mutually rated one public mobile COPD app as appropriate for self-management and emphasized the need for apps to be supplementary and customizable, rather than replacements for clinical management.

Objective: This study examined the potential of simple animations with a low level of detail and their impact on patient's ability to recall information. Also, we examined how the patients' digital health literacy influenced the association.Methods: Over 900 Danish adults were continuously included in this experimental study, and they were allocated to either an animation with a low or high level of detail. Participants answered questionnaires about demographics, digital health literacy, and the ability to recall information. The association between level of detail and information recall was examined by OR (95% CI).Results: The results showed no association between the level of detail and information recall.Conclusion: This novel study supports the potential of simple animations, and future research could advantageously investigate animations with more significant differences in level of detail. The results should be cautiously interpreted, as selection and information problems may have caused bias.

Objectives: Machine learning-based analytics over uni-modal medical data has shown considerable promise and is now routinely deployed in diagnostic procedures. However, patient data consists of diverse types of data. By exploiting such data, multimodal approaches promise to revolutionize our ability to provide personalized care. Attempts to combine two modalities in a single diagnostic task have utilized the evolving field of multimodal representation learning (MRL), which learns a shared latent space between related modality samples. This new space can be used to improve the performance of machine-learning-based analytics. So far, however, our understanding of how modalities have been applied in MRL-based medical applications and which modalities are best suited for specific medical tasks is still unclear, as previous reviews have not addressed the medical analytics domain and its unique challenges and opportunities. Instead, this work aims to review the landscape of MRL for medical tasks to highlight opportunities for advancing medical applications. Methods: This paper presents a framework for positioning MRL techniques and medical modalities. More than 1000 papers related to medical analytics were reviewed, positioned, and classified using the proposed framework in the most extensive review to date. The paper further provides an online tool for researchers and developers of medical analytics to dive into the rapidly changing landscape of MRL for medical applications. Results: The main finding is that work in the domain has been sparse: only a few medical informatics tasks have been the target of much MRL-based work, with the overwhelming majority of tasks being diagnostic rather than prognostic. Similarly, numerous potentially compatible information modality combinations are unexplored or under-explored for most medical tasks. Conclusions: There is much to gain from using MRL in many unexplored combinations of medical tasks and modalities. This work can guide researchers working on a specific medical application to identify under-explored modality combinations and identify novel and emerging MRL techniques that can be adapted to the task at hand.

A vast number of neurodegenerative disorders arise from neurotoxicity. In neurotoxicity, more than 250 RNA molecules are up and downregulated. The manuscript investigates the exposure of chlorpyrifos organophosphate pesticide (COP) effect on total RNA in murine brain tissue in 4 genotypes for in silico neurodegeneration development. The GSE58103 dataset from the Gene Expression Omnibus (GEO) database applies for data preprocessing, normalization, and quality control. Differential expression analysis (DEG) uses the limma package in R. Study compared expression profiles from murine fetal brain tissues across four genotypes: PON-1 knockout (KO), tgHuPON1Q192 (Q-tg), tgHuPON1R192 (R-tg), and wild-type (WT). We analyze 60 samples, 15 samples per genotype, to identify DEGs. The significance criteria are adjusted p-value <.05 and a |log2 fold change| > 1. The study identifies microRNA485 as the potential biomarker of COP toxicity using the GSE58103 dataset. Significant differences exist for microRNA485 between KO and WT groups by differential expression analysis. Moreover, graphical analysis shows sample relationships among genotype groups. MicroRNA485 represents a promising biomarker for developmental COP neurotoxicity by utilizing in silico analysis in scientific practice.

Objectives: Pilot 5 utilizes AI and robotics to develop a robotic nurse assisting hospital staff in response to workforce shortages and rising care demands due to an aging population. This project aims to optimize resources, reduce errors, and improve patient satisfaction through personalized care. Methods: The Living Lab approach was implemented to split the study into sprints. The first split involves working with project partners and stakeholders to define the problem, brainstorm functionalities, and identify limitations (24 participants). The second split focuses on further requirement gathering, exploring real-world use cases, and considering ethical and privacy concerns (51 participants). Results: The project used iterative development cycles (5-8 months) to continuously improve the solution. Surveys revealed high satisfaction rates, with average scores of 4.0 and 3.6 for Sprints 1 and 2, respectively. Similarly, a team morale survey indicated a positive trend, with average scores of 7.6 and 8.18 for Sprints 1 and 2, respectively. Conclusion: Pilot 5 offers a promising solution to the evolving needs of modern hospitals. This study explores the integration of a social robotic system into nursing care to enhance quality and emphasizes stakeholder engagement, participatory design, and user-centered approaches in AI healthcare solutions.

Objectives: New prevention approaches that use engaging and innovative technologies are needed to reduce high rates of substance use and violence among university students. The present study developed and pilot-tested virtual reality (VR) technology that presented university students with immersive environments where they practiced skills with virtual peers. Methods: After viewing e-learning modules with prevention content, students engaged with immersive VR module prototypes to practice cognitive-behavioral skills for preventing risk behaviors, including assertive communication, negotiation, compromise, conflict resolution, and bystander intervention strategies. Results: Paired t-tests showed increases in life skills knowledge and anti-violence attitudes among students from the pretest to posttest assessments. Students and educators were enthusiastic about the VR prototypes, rating them as feasible, relevant, appealing, engaging, and innovative for prevention. Participants provided feedback on ways to improve the VR experience by including a greater variety of conflict situations, more nuanced branched scenarios and response options, and a more complete representation of all scenario outcomes. Conclusions: Findings suggest that VR scenarios are a promising strategy for enhancing life skills to help prevent health risk behaviors among university students.