Reach, Acceptability, and Patient Preferences of a Mobile Health-Based Survey to Assess COVID-19 Vaccine Hesitancy Among Patients Receiving Dialysis.

Rationale & Objective: The majority of patients with kidney failure receiving dialysis own mobile devices, but the use of mobile health (mHealth) technologies to conduct surveys in this population is limited. We assessed the reach and acceptability of a short message service (SMS) text message-based survey that assessed coronavirus disease 2019 (COVID-19) vaccine hesitancy among patients receiving dialysis.

Study Design & Exposure: A cross-sectional SMS-based survey conducted in January 2021.

Insufficient evidence for interactive or animated graphics for communicating probability.

Objectives: We sought to analyze interactive visualizations and animations of health probability data (such as chances of disease or side effects) that have been studied in head-to-head comparisons with either static graphics or numerical communications.

Materials And Methods: Secondary analysis of a large systematic review on ways to communicate numbers in health.

Results: We group the research to show that 4 types of animated or interactive visualizations have been studied by multiple researchers: those that simulate experience of probabilistic events; those that demonstrate the randomness of those events; those that reduce information overload by directing attention sequentially to different items of information; and those that promote elaborative thinking.

Recentering responsible and explainable artificial intelligence research on patients: implications in perinatal psychiatry.

In the setting of underdiagnosed and undertreated perinatal depression (PD), Artificial intelligence (AI) solutions are poised to help predict and treat PD. In the near future, perinatal patients may interact with AI during clinical decision-making, in their patient portals, or through AI-powered chatbots delivering psychotherapy. The increase in potential AI applications has led to discussions regarding responsible AI and explainable AI (XAI).

Designing Support to help Health Communication Professionals Convey Numbers Clearly to the Public - A Needs Assessment and Formative Usability Evaluation.

Communicating health-related probabilities to patients and the public presents challenges, although multiple studies have demonstrated that we can promote comprehension and appropriate application of numbers by matching presentation formats (e.g., percentage, bar charts, icon arrays) to communication goal (e.

Visualizing machine learning-based predictions of postpartum depression risk for lay audiences.

Objectives: To determine if different formats for conveying machine learning (ML)-derived postpartum depression risks impact patient classification of recommended actions (primary outcome) and intention to seek care, perceived risk, trust, and preferences (secondary outcomes).

Materials And Methods: We recruited English-speaking females of childbearing age (18-45 years) using an online survey platform. We created 2 exposure variables (presentation format and risk severity), each with 4 levels, manipulated within-subject.

Do you want to promote recall, perceptions, or behavior? The best data visualization depends on the communication goal.

Data visualizations can be effective and inclusive means for helping people understand health-related data. Yet numerous high-quality studies comparing data visualizations have yielded relatively little practical design guidance because of a lack of clarity about what communicators want their audience to accomplish. When conducting rigorous evaluations of communication (eg, applying the ISO 9186 method), describing the process simply as evaluating "comprehension" or "interpretation" of visualizations fails to do justice to the true range of outcomes being studied.

Women's perspectives on the use of artificial intelligence (AI)-based technologies in mental healthcare.

This study aimed to evaluate women's attitudes towards artificial intelligence (AI)-based technologies used in mental health care. We conducted a cross-sectional, online survey of U.S.

The Telemedicine Experience in Primary Care Practices in the United States: Insights From Practice Leaders.

Purpose: The need to rapidly implement telemedicine in primary care during the coronavirus disease 2019 (COVID-19) pandemic was addressed differently by various practices. Using qualitative data from semistructured interviews with primary care practice leaders, we aimed to report commonly shared experiences and unique perspectives regarding telemedicine implementation and evolution/maturation since March 2020.

Methods: We administered a semistructured, 25-minute, virtual interview with 25 primary care practice leaders from 2 health systems in 2 states (New York and Florida) included in PCORnet, the Patient-Centered Outcomes Research Institute clinical research network.

Prediction of left ventricular ejection fraction changes in heart failure patients using machine learning and electronic health records: a multi-site study.

Left ventricular ejection fraction (EF) is a key measure in the diagnosis and treatment of heart failure (HF) and many patients experience changes in EF overtime. Large-scale analysis of longitudinal changes in EF using electronic health records (EHRs) is limited. In a multi-site retrospective study using EHR data from three academic medical centers, we investigated longitudinal changes in EF measurements in patients diagnosed with HF.

Identifying Nonpatient Authors of Patient Portal Secure Messages in Oncology: A Proof-of-Concept Demonstration of Natural Language Processing Methods.

Purpose: Patient portal secure messages are not always authored by the patient account holder. Understanding who authored the message is particularly important in an oncology setting where symptom reporting is crucial to patient treatment. Natural language processing has the potential to detect messages not authored by the patient automatically.

Developing Population Health Surveillance Using mHealth in Low-Resource Settings: Qualitative Assessment and Pilot Evaluation.

Background: Population surveillance data are essential for understanding population needs and evaluating health programs. Governmental and nongovernmental organizations in western Myanmar did not previous have means for conducting robust, electronic population health surveillance.

Objective: This study involved developing mobile health (mHealth)-based population health surveillance in a rural, low-resource setting with minimal cellular infrastructure in western Myanmar.

Design and validation of a FHIR-based EHR-driven phenotyping toolbox.

Objectives: To develop and validate a standards-based phenotyping tool to author electronic health record (EHR)-based phenotype definitions and demonstrate execution of the definitions against heterogeneous clinical research data platforms.

Materials And Methods: We developed an open-source, standards-compliant phenotyping tool known as the PhEMA Workbench that enables a phenotype representation using the Fast Healthcare Interoperability Resources (FHIR) and Clinical Quality Language (CQL) standards. We then demonstrated how this tool can be used to conduct EHR-based phenotyping, including phenotype authoring, execution, and validation.

Strategies to optimize comprehension of numerical medication instructions: A systematic review and concept map.

Objective: To develop evidence-based recommendations for improving comprehension of quantitative medication instructions.

Methods: This review included a literature search from inception to November 2021. Studies were included for the following: 1) original research; 2) compared multiple formats for presenting quantitative medication information on dose, frequency, and/or time; 3) included patients/lay-people; 4) assessed comprehension-related outcomes quantitatively.

Taxonomies for synthesizing the evidence on communicating numbers in health: Goals, format, and structure.

Many people, especially those with low numeracy, are known to have difficulty interpreting and applying quantitative information to health decisions. These difficulties have resulted in a rich body of research about better ways to communicate numbers. Synthesizing this body of research into evidence-based guidance, however, is complicated by inconsistencies in research terminology and researcher goals.

Trust in AI: why we should be designing for APPROPRIATE reliance.

Use of artificial intelligence in healthcare, such as machine learning-based predictive algorithms, holds promise for advancing outcomes, but few systems are used in routine clinical practice. Trust has been cited as an important challenge to meaningful use of artificial intelligence in clinical practice. Artificial intelligence systems often involve automating cognitively challenging tasks.

Guidance for publishing qualitative research in informatics.

Qualitative research, the analysis of nonquantitative and nonquantifiable data through methods such as interviews and observation, is integral to the field of biomedical and health informatics. To demonstrate the integrity and quality of their qualitative research, authors should report important elements of their work. This perspective article offers guidance about reporting components of the research, including theory, the research question, sampling, data collection methods, data analysis, results, and discussion.

Lower objectively and subjectively assessed numeracy are both associated with poorer self-rated health.

To compare an objective with a subjective numeracy assessment for association with self-reported health status, where numeracy refers to "the degree to which individuals have the capacity to access, process, interpret, communicate, and act on numerical, quantitative, graphical, biostatistical, and probabilistic health information needed to make effective health decisions" RESULTS: We completed a secondary analysis of two population-based surveys, the Empire State Poll (n = 763) and the Program for the International Assessment of Adult Competencies (PIAAC; n = 2609). The first survey assessed numeracy with a 3-item subjective instrument. The second assessed numeracy with more than 20 math problems.