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. Questions were guided by 3 frameworks (health information technology evaluation, access to care, and health information technology life cycle) and involved practice leaders' perspectives on the process of telemedicine implementation in their practice, with a specific focus on the process of maturation and facilitators/barriers. Two researchers conducted inductive coding of qualitative data open-ended questions to identify common themes. Transcripts were electronically generated by virtual platform software. RESULTS: Twenty-five interviews were administered for practice leaders representing 87 primary care practices in 2 states. We identified the following 4 major themes: (1) the ease of telemedicine adoption depended on both patients' and clinicians' prior experience using virtual health platforms, (2) regulation of telemedicine varied across states and differentially affected the rollout processes, (3) visit triage rules were unclear, and (4) there were positive and negative effects of telemedicine on clinicians and patients. CONCLUSIONS: Practice leaders identified several challenges to telemedicine implementation and highlighted 2 areas, including telemedicine visit triage guidelines and telemedicine-specific staffing and scheduling protocols, for improvement.

Development of a novel instrument to characterize telemedicine programs in primary care.

BACKGROUND: Given the rapid deployment of telemedicine at the onset of the COVID - 19 pandemic, updated assessment methods are needed to study and characterize telemedicine programs. We developed a novel semi - structured survey instrument to systematically describe the characteristics and implementation processes of telemedicine programs in primary care. METHODS: In the context of a larger study aiming to describe telemedicine programs in primary care, a survey was developed in 3 iterative steps: 1) literature review to obtain a list of telemedicine features, facilitators, and barriers; 2) application of three evaluation frameworks; and 3) stakeholder engagement through a 2-stage feedback process. During survey refinement, items were tested against the evaluation frameworks while ensuring it could be completed within 20-25 min. Data reduction techniques were applied to explore opportunity for condensed variables/items. RESULTS: Sixty initially identified telemedicine features were reduced to 32 items / questions after stakeholder feedback. Per the life cycle framework, respondents are asked to report a month in which their telemedicine program reached a steady state, i.e., "maturation". Subsequent questions on telemedicine features are then stratified by telemedicine services offered at the pandemic onset and the reported point of maturation. Several open - ended questions allow for additional telemedicine experiences to be captured. Data reduction techniques revealed no indication for data reduction. CONCLUSION: This 32-item semi-structured survey standardizes the description of primary care telemedicine programs in terms of features as well as maturation process. This tool will facilitate evaluation of and comparisons between telemedicine programs across the United States, particularly those that were deployed at the pandemic onset.

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. In this article, we introduce three taxonomies intended to systematize terminology in the literature, derived from an ongoing systematic literature review. The first taxonomy provides a systematic nomenclature for the outcome measures assessed in the studies, including perceptions, decisions, and actions. The second taxonomy is a nomenclature for the data formats assessed, including numbers (and different formats for numbers) and graphics. The third taxonomy describes the quantitative concepts being conveyed, from the simplest (a single value at a single point in time) to more complex ones (including a risk-benefit trade-off and a trend over time). Finally, we demonstrate how these three taxonomies can be used to resolve ambiguities and apparent contradictions in the literature.

Imprecision and Preferences in Interpretation of Verbal Probabilities in Health: a Systematic Review.

INTRODUCTION: Many health providers and communicators who are concerned that patients will not understand numbers instead use verbal probabilities (e.g., terms such as "rare" or "common") to convey the gist of a health message. OBJECTIVE: To assess patient interpretation of and preferences for verbal probability information in health contexts. METHODS: We conducted a systematic review of literature published through September 2020. Original studies conducted in English with samples representative of lay populations were included if they assessed health-related information and elicited either (a) numerical estimates of verbal probability terms or (b) preferences for verbal vs. quantitative risk information. RESULTS: We identified 33 original studies that referenced 145 verbal probability terms, 45 of which were included in at least two studies and 19 in three or more. Numerical interpretations of each verbal term were extremely variable. For example, average interpretations of the term "rare" ranged from 7 to 21%, and for "common," the range was 34 to 71%. In a subset of 9 studies, lay estimates of verbal probability terms were far higher than the standard interpretations established by the European Commission for drug labels. In 10 of 12 samples where preferences were elicited, most participants preferred numerical information, alone or in combination with verbal labels. CONCLUSION: Numerical interpretation of verbal probabilities is extremely variable and does not correspond well to the numerical probabilities established by expert panels. Most patients appear to prefer quantitative risk information, alone or in combination with verbal labels. Health professionals should be aware that avoiding numeric information to describe risks may not match patient preferences, and that patients interpret verbal risk terms in a highly variable way.

Health Literacy 2030: Is It Time to Redefine the Term?

For decades, health literacy has been used to describe the ability of individuals to locate, interpret, and apply health information to their decisions. The US Department of Health and Human Services has now proposed redefining the term to emphasize the role of society in providing accessible, comprehensible information. This redefinition would reflect a welcome shift to encompass the roles of those who communicate information, not simply those who seek it. However, redefining an accepted term would have serious negative effects on the indexing of the research literature and create difficulties interpreting studies conducted under the previous definition. Therefore, we strongly caution against redefining the accepted term. Instead, we propose introducing a new term-health information fluency-defined as universal effective use of health information. The old term can continue to be used to describe the set of concerns about individual skills, but by promoting the new term, the Department of Health and Human Services can encourage research into creating accurate, accessible health information that people can easily find, understand, and use to inform their decisions.

Observational study to understand interpreter service use in emergency medicine: why the key may lie outside of the initial provider assessment.

OBJECTIVE: To characterise the use of interpreter services and other strategies used to communicate with limited English proficient (LEP) patients throughout their emergency department visit. METHODS: We performed a process tracing study observing LEP patients throughout their stay in the emergency department. A single observer completed 47 hours of observation of 103 communication episodes between staff and nine patients with LEP documenting the strategy used to communicate (eg, professional interpreter, family member, own language skills) and duration of conversations for each communicative encounter with hospital staff members. Data collection occurred in a single emergency department in the eastern USA between July 2017 and February 2018. RESULTS: The most common strategy (per communicative encounter) was for the emergency department staff to communicate with the patient in English (observed in 29.1% of encounters). Total time spent in communicating was highest using telephone-based interpreters (32.9% of total time spent communicating) and in-person interpreters (29.2% of total time spent communicating). Communicative mechanism also varied by care task/phase of care with the most use of interpreter services or Spanish proficient staff (as primary communicator) occurring during triage (100%) and the initial provider assessment (100%) and the lowest interpreter service use during ongoing evaluation and treatment tasks (24.3%). CONCLUSIONS: Emergency department staff use various mechanisms to communicate with LEP patients throughout their length of stay. Utilisation of interpreter services was poorest during evaluation and treatment tasks, indicating that this area should be a focus for improving communication with LEP patients.

Emergency Physician Task Switching Increases With the Introduction of a Commercial Electronic Health Record.

STUDY OBJECTIVE: We evaluate how the transition from a homegrown electronic health record to a commercial one affects emergency physician work activities from initial introduction to long-term use. METHODS: We completed a quasi-experimental study across 3 periods during the transition from a homegrown system to a commercially available electronic health record with computerized provider order entry. Observation periods consisted of pre-implementation, 1 month before the implementation of the commercial electronic health record; "go-live" 1 week after implementation; and post-implementation, 3 to 4 months after use began. Fourteen physicians were observed in each period (N=42) with a minute-by-minute observation template to record emergency physician time allocation across 5 task-based categories (computer, verbal communication, patient room, paper [chart/laboratory results], and other). The average number of tasks physicians engaged in per minute was also analyzed as an indicator of task switching. RESULTS: From pre- to post-implementation, there were no significant differences in the amount of time spent on the various task categories. There were changes in time allocation from pre-implementation to go-live and go-live to pre-implementation, characterized by a significant increase in time spent on computer tasks during go-live relative to the other periods. Critically, the number of tasks physicians engaged in per minute increased from 1.7 during pre-implementation to 1.9 during post-implementation (difference 0.19 tasks per minute; 95% confidence interval 0.039 to 0.35). CONCLUSION: The increase in the number of tasks physicians engaged in per minute post-implementation indicates that physicians switched tasks more frequently. Frequent task switching behavior raises patient safety concerns.

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. We present newly developed taxonomies of outcome measures and tasks that are guiding a large-scale systematic review of the health numbers communication literature. Using these taxonomies allows a designer to determine whether a specific data presentation format or feature supports or inhibits the desired audience cognitions, feelings, or behaviors. We argue that taking a granular, outcomes-based approach to designing and evaluating information visualization research is essential to deriving practical, actionable knowledge from it.

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. Overall, these 4 types of visualizations do not show strong evidence of improving comprehension, risk perception, or health behaviors over static graphics. DISCUSSION: Evidence is not yet strong that interactivity or animation is more effective than static graphics for communicating probabilities in health. We discuss 2 possibilities: that the most effective visualizations haven't been studied, and that the visualizations aren't effective. CONCLUSION: Future studies should rigorously compare participant performance with novel interactive or animated visualizations against their performance with static visualizations. Such evidence would help determine whether health communicators should emphasize novel interactive visualizations or rely on older forms of visual communication, which may be accessible to broader audiences, including those with limited digital access.

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. Setting & Participants: Patients receiving in-center hemodialysis, peritoneal dialysis, or home hemodialysis in a nonprofit dialysis organization in New York City. Outcomes: (1) Reach of the SMS survey, (2) Acceptability using the 4-item Acceptability of Intervention Measure, and (3) Patient preferences for modes of survey administration. Analytical Approach: We used Fisher exact tests and multivariable logistic regression to assess sociodemographic and clinical predictors of SMS survey response. Qualitative methods were used to analyze open-ended responses capturing patient preferences. Results: Among 1,008 patients, 310 responded to the SMS survey (response rate 31%). In multivariable adjusted analyses, participants who were age 80 years and above (aOR, 0.49; 95% CI, 0.25-0.96) were less likely to respond to the SMS survey compared with those aged 18 to 44 years. Non-Hispanic Black (aOR, 0.58; 95% CI, 0.39-0.86), Hispanic (aOR, 0.31; 95% CI, 0.19-0.51), and Asian or Pacific Islander (aOR, 0.46; 95% CI, 0.28-0.74) individuals were less likely to respond compared with non-Hispanic White participants. Participants residing in census tracts with higher Social Vulnerability Index, indicating greater neighborhood-level social vulnerability, were less likely to respond to the SMS survey (fifth vs first quintile aOR, 0.61; 95% CI, 0.37-0.99). Over 80% of a sample of survey respondents and nonrespondents completely agreed or agreed with the Acceptability of Intervention Measure. Qualitative analysis identified 4 drivers of patient preferences for survey administration: (1) convenience (subtopics: efficiency, multitasking, comfort, and synchronicity); (2) privacy; (3) interpersonal interaction; and (4) accessibility (subtopics: vision, language, and fatigue). Limitations: Generalizability, length of survey. Conclusions: An SMS text message-based survey had moderate reach among patients receiving dialysis and was highly acceptable, but response rates were lower in older (age ≥ 80), non-White individuals and those with greater neighborhood-level social vulnerability. Future research should examine barriers and facilitators to mHealth among patients receiving dialysis to ensure equitable implementation of mHealth-based technologies.

We conducted a short message service (SMS) text message-based survey that assessed coronavirus disease 2019 (COVID-19) vaccine hesitancy among patients receiving dialysis in New York City. Overall response rate was 31%, and those with age ≥ 80, non-White individuals, and participants with greater neighborhood-level social vulnerability were less likely to respond to the survey. Over 80% of participants found SMS-based surveys to be highly acceptable. Qualitative analysis showed that participants cared about the convenience, privacy, interpersonal interaction, and accessibility of surveys. Our results suggest that SMS text message surveys are a promising strategy to collect patient-reported data among patients receiving dialysis.

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. Presentation formats consisted of text only, numeric only, gradient number line, and segmented number line. For each format viewed, participants answered questions regarding each outcome. RESULTS: Five hundred four participants (mean age 31 years) completed the survey. For the risk classification question, performance was high (93%) with no significant differences between presentation formats. There were main effects of risk level (all P < .001) such that participants perceived higher risk, were more likely to agree to treatment, and more trusting in their obstetrics team as the risk level increased, but we found inconsistencies in which presentation format corresponded to the highest perceived risk, trust, or behavioral intention. The gradient number line was the most preferred format (43%). DISCUSSION AND CONCLUSION: All formats resulted high accuracy related to the classification outcome (primary), but there were nuanced differences in risk perceptions, behavioral intentions, and trust. Investigators should choose health data visualizations based on the primary goal they want lay audiences to accomplish with the ML risk score.

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). Current discussions of RAI, however, are limited in their consideration of the patient as an active participant with AI. Therefore, we propose a patient-centered, rather than a patient-adjacent, approach to RAI and XAI, that identifies autonomy, beneficence, justice, trust, privacy, and transparency as core concepts to uphold for health professionals and patients. We present empirical evidence that these principles are strongly valued by patients. We further suggest possible design solutions that uphold these principles and acknowledge the pressing need for further research about practical applications to uphold these principles.

Community Health Recommendations Driven by mHealth Population Surveillance Data Amongst Burmese Displaced People in Eastern India: A Pilot Usability Assessment of a Mobile Health Application for Data Collection.

With recent increases in armed conflict and forced migration, refugee health has become a growing priority amongst those who work in global health. Refugees and forced migrants, also known as displaced persons, face barriers to accessing health services and are often at an increased risk for adverse health outcomes, such as sexual violence, infectious diseases, poor maternal outcomes, and mental health concerns. Mobile health (mHealth) applications have been shown to increase access and improve health outcomes among refugee populations. Our study aims to evaluate the feasibility of using a novel mHealth application to conduct population health surveillance data collection amongst a population of Myanmar citizens who have been forced to relocate to eastern India. The data collected in a low-resource setting through the mHealth application will be used to identify priority areas for intervention which will assist in the development of a tailored intervention plan that best suits our population.

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.g., improving recall, decreasing worry, taking action). We used this literature to create goal-driven, evidence-based guidance to support health communicators in conveying probabilities. We then conducted semi-structured interviews with 39 health communicators to understand: communicators' goals for expressing probabilities, formats they choose to convey probabilities, and perceptions of prototypes of our "communicating numbers clearly" guidance. We found that communicators struggled to articulate granular goals for their communication, impeding their ability to select appropriate guidance. Future work should consider how best to support health communicators in selecting granular, differentiable goals to support broadly comprehensible information design.

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. adults reporting female sex at birth focused on bioethical considerations for AI-based technologies in mental healthcare, stratifying by previous pregnancy. Survey respondents (n = 258) were open to AI-based technologies in mental healthcare but concerned about medical harm and inappropriate data sharing. They held clinicians, developers, healthcare systems, and the government responsible for harm. Most reported it was "very important" for them to understand AI output. More previously pregnant respondents reported being told AI played a small role in mental healthcare was "very important" versus those not previously pregnant (P = .03). We conclude that protections against harm, transparency around data use, preservation of the patient-clinician relationship, and patient comprehension of AI predictions may facilitate trust in AI-based technologies for mental healthcare among women.

Limited English Proficiency in Older Adults Referred to the Cardiovascular Team.

Limited English proficiency (LEP) is defined as individuals in whom English is not the primary language and who have limited ability to read, speak, write, or understand the English language. Cardiovascular (CV) team members routinely encounter language barriers in their practice. These barriers have a significant impact on the quality of CV care that patients with LEP receive. Despite evidence demonstrating the negative association between language barriers and health disparities, the impact on CV care is insufficiently known. In addition, older adults with CV disease and LEP are facing increasing risk of adverse events when complex medical information is not optimally delivered. Overcoming language barriers in CV care will need a thoughtful approach. Although well recognized, the initial step will be to continue to highlight the importance of language needs identification and appropriate use of professional interpreter services. In parallel, a health system-level approach is essential that describes initiatives and key policies to ensure a high-level quality of care for a growing LEP population. This review aims to present the topic of LEP during the CV care of older adults, for continued awareness along with practical considerations for clinical use and directions for future research.

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. We observed significant variations in baseline characteristics and longitudinal EF change behavior of the HF cohorts from a previous study that is based on HF registry data. Data gathered from this longitudinal study were used to develop multiple machine learning models to predict changes in ejection fraction measurements in HF patients. Across all three sites, we observed higher performance in predicting EF increase over a 1-year duration, with similarly higher performance predicting an EF increase of 30% from baseline compared to lower percentage increases. In predicting EF decrease we found moderate to high performance with low confidence for various models. Among various machine learning models, XGBoost was the best performing model for predicting EF changes. Across the three sites, the XGBoost model had an F1-score of 87.2, 89.9, and 88.6 and AUC of 0.83, 0.87, and 0.90 in predicting a 30% increase in EF, and had an F1-score of 95.0, 90.6, 90.1 and AUC of 0.54, 0.56, 0.68 in predicting a 30% decrease in EF. Among features that contribute to predicting EF changes, baseline ejection fraction measurement, age, gender, and heart diseases were found to be statistically significant.