Summary Objectives The current volume and complexity of genetic tests, and the molecular genetics knowledge and health knowledge related to interpretation of the results of those tests, are rapidly outstripping the ability of individual clinicians to recall, understand and convey to their patients information relevant to their care. The tailoring of molecular genetics knowledge and health knowledge in clinical settings is important both for the provision of personalized medicine and to reduce clinician information overload. In this paper we describe the incorporation, customization and demonstration of molecular genetic data (mainly sequence variants), molecular genetics knowledge and health knowledge into a standards-based electronic health record (EHR) prototype developed specifically for this study. Methods We extended the CCR (Continuity of Care Record), an existing EHR standard for representing clinical data, to include molecular genetic data. An EHR prototype was built based on the extended CCR and designed to display relevant molecular genetics knowledge and health knowledge from an existing knowledge base for cystic fibrosis (OntoKBCF). We reconstructed test records from published case reports and represented them in the CCR schema. We then used the EHR to dynamically filter molecular genetics knowledge and health knowledge from OntoKBCF using molecular genetic data and clinical data from the test cases. Results The molecular genetic data were successfully incorporated in the CCR by creating a category of laboratory results called “Molecular Genetics ” and specifying a particular class of test (“Gene Mutation Test”) in this category. Unlike other laboratory tests reported in the CCR, results of tests in this class required additional attributes (“Molecular Structure” and “Molecular Position”) to support interpretation by clinicians. These results, along with clinical data (age, sex, ethnicity, diagnostic procedures, and therapies) were used by the EHR to filter and present molecular genetics knowledge and health knowledge from OntoKBCF. Conclusions This research shows a feasible model for delivering patient sequence variants and presenting tailored molecular genetics knowledge and health knowledge via a standards-based EHR system prototype. EHR standards can be extended to include the necessary patient data (as we have demonstrated in the case of the CCR), while knowledge can be obtained from external knowledge bases that are created and maintained independently from the EHR. This approach can form the basis for a personalized medicine framework, a more comprehensive standards-based EHR system and a potential platform for advancing translational research by both disseminating results and providing opportunities for new insights into phenotype-genotype relationships.

Context: Several advantages of telemedicine have been reported, such as improved patient satisfaction, reduced costs, and broader access to specialists. However, most studies have focused on patient satisfaction with telemedicine services. A literature gap exists in evaluating health care professionals' (both referring and performing professionals) satisfaction in delivering telemedicine services. A clear picture of professionals' telemedicine satisfaction has the potential to improve care quality, care accessibility, and telemedicine adoption and to reduce overall health care costs. Objectives: To develop a framework to evaluate health care professionals' satisfaction in delivering telemedicine services and to articulate the factors that can contribute to their satisfaction. Methods: We developed an initial conceptual framework based on existing literature and the research team's expertise. The initial framework was validated by a convenience group of 12 osteopathic and allopathic physicians, and the framework was further revised and refined according to their input. Results: The refined framework includes 5 main components (professionals' demographics, care settings, motivations, experiences, and overall satisfaction) that can contribute to health care professionals' satisfaction or dissatisfaction in delivering telemedicine services. Detailed itemization is provided under each component, including hierarchical organizations of the items, definitions, and potential value sets. Conclusions: The refined conceptual framework may provide a comprehensive evaluation reference for measuring professionals' satisfaction in delivering telemedicine. Further customization according to the specific setting of telemedicine services is needed. This refined conceptual framework can also be used to identify areas that can be improved and to ultimately improve telemedicine services and care quality.

Background Vast volumes of data, coded through hierarchical terminologies (e.g., International Classification of Diseases, Tenth Revision–Clinical Modification [ICD10-CM], Medical Subject Headings [MeSH]), are generated routinely in electronic health record systems and medical literature databases. Although graphic representations can help to augment human understanding of such data sets, a graph with hundreds or thousands of nodes challenges human comprehension. To improve comprehension, new tools are needed to extract the overviews of such data sets. We aim to develop a visual interactive analytic tool for filtering and summarizing large health data sets coded with hierarchical terminologies (VIADS) as an online, and publicly accessible tool. The ultimate goals are to filter, summarize the health data sets, extract insights, compare and highlight the differences between various health data sets by using VIADS. The results generated from VIADS can be utilized as data-driven evidence to facilitate clinicians, clinical researchers, and health care administrators to make more informed clinical, research, and administrative decisions. We utilized the following tools and the development environments to develop VIADS: Django, Python, JavaScript, Vis.js, Graph.js, JQuery, Plotly, Chart.js, Unittest, R, and MySQL. Results VIADS was developed successfully and the beta version is accessible publicly. In this paper, we introduce the architecture design, development, and functionalities of VIADS. VIADS includes six modules: user account management module, data sets validation module, data analytic module, data visualization module, terminology module, dashboard. Currently, VIADS supports health data sets coded by ICD-9, ICD-10, and MeSH. We also present the visualization improvement provided by VIADS in regard to interactive features (e.g., zoom in and out, customization of graph layout, expanded information of nodes, 3D plots) and efficient screen space usage. Conclusions VIADS meets the design objectives and can be used to filter, summarize, compare, highlight and visualize large health data sets that coded by hierarchical terminologies, such as ICD-9, ICD-10 and MeSH. Our further usability and utility studies will provide more details about how the end users are using VIADS to facilitate their clinical, research or health administrative decision making.

Recent research has indicated that people draw spontaneous trait inferences (STIs) flexibly from others' behaviors. In two experiments, we examined how perceiver's mood states and actor's gender-behavior stereotypic consistency affected the likelihood of STIs. In Experiment 1, positive mood participants made STIs from trait-implying behavior sentences, while those in negative moods did not draw such trait inferences. In Experiment 2, we further examined the role of gender stereotypes in the effects of mood on STIs and found that positive mood participants made STIs only from gender stereotype-consistent behaviors, not from gender stereotype-inconsistent behaviors, while negative mood participants still did not make STIs regardless of gender stereotype consistency. Extending prior work, we provided initial evidence for the flexible formation of STIs in response to perceiver's mood states and actor's gender-behavior stereotypic consistency.

Background Scientific hypothesis generation is a critical step in scientific research that determines the direction and impact of any investigation. Despite its vital role, we have limited knowledge of the process itself, thus hindering our ability to address some critical questions. Objective This study aims to answer the following questions: To what extent can secondary data analytics tools facilitate the generation of scientific hypotheses during clinical research? Are the processes similar in developing clinical diagnoses during clinical practice and developing scientific hypotheses for clinical research projects? Furthermore, this study explores the process of scientific hypothesis generation in the context of clinical research. It was designed to compare the role of VIADS, a visual interactive analysis tool for filtering and summarizing large data sets coded with hierarchical terminologies, and the experience levels of study participants during the scientific hypothesis generation process. Methods This manuscript introduces a study design. Experienced and inexperienced clinical researchers are being recruited since July 2021 to take part in this 2×2 factorial study, in which all participants use the same data sets during scientific hypothesis–generation sessions and follow predetermined scripts. The clinical researchers are separated into experienced or inexperienced groups based on predetermined criteria and are then randomly assigned into groups that use and do not use VIADS via block randomization. The study sessions, screen activities, and audio recordings of participants are captured. Participants use the think-aloud protocol during the study sessions. After each study session, every participant is given a follow-up survey, with participants using VIADS completing an additional modified System Usability Scale survey. A panel of clinical research experts will assess the scientific hypotheses generated by participants based on predeveloped metrics. All data will be anonymized, transcribed, aggregated, and analyzed. Results Data collection for this study began in July 2021. Recruitment uses a brief online survey. The preliminary results showed that study participants can generate a few to over a dozen scientific hypotheses during a 2-hour study session, regardless of whether they used VIADS or other analytics tools. A metric to more accurately, comprehensively, and consistently assess scientific hypotheses within a clinical research context has been developed. Conclusions The scientific hypothesis–generation process is an advanced cognitive activity and a complex process. Our results so far show that clinical researchers can quickly generate initial scientific hypotheses based on data sets and prior experience. However, refining these scientific hypotheses is a much more time-consuming activity. To uncover the fundamental mechanisms underlying the generation of scientific hypotheses, we need breakthroughs that can capture thinking processes more precisely. International Registered Report Identifier (IRRID) DERR1-10.2196/39414