Understanding Symptom Self-Monitoring Needs Among Postpartum Black Patients: Qualitative Interview Study.

BACKGROUND: Pregnancy-related death is on the rise in the United States, and there are significant disparities in outcomes for Black patients. Most solutions that address pregnancy-related death are hospital based, which rely on patients recognizing symptoms and seeking care from a health system, an area where many Black patients have reported experiencing bias. There is a need for patient-centered solutions that support and encourage postpartum people to seek care for severe symptoms. OBJECTIVE: We aimed to determine the design needs for a mobile health (mHealth) patient-reported outcomes and decision-support system to assist Black patients in assessing when to seek medical care for severe postpartum symptoms. These findings may also support different perinatal populations and minoritized groups in other clinical settings. METHODS: We conducted semistructured interviews with 36 participants-15 (42%) obstetric health professionals, 10 (28%) mental health professionals, and 11 (31%) postpartum Black patients. The interview questions included the following: current practices for symptom monitoring, barriers to and facilitators of effective monitoring, and design requirements for an mHealth system that supports monitoring for severe symptoms. Interviews were audio recorded and transcribed. We analyzed transcripts using directed content analysis and the constant comparative process. We adopted a thematic analysis approach, eliciting themes deductively using conceptual frameworks from health behavior and human information processing, while also allowing new themes to inductively arise from the data. Our team involved multiple coders to promote reliability through a consensus process. RESULTS: Our findings revealed considerations related to relevant symptom inputs for postpartum support, the drivers that may affect symptom processing, and the design needs for symptom self-monitoring and patient decision-support interventions. First, participants viewed both somatic and psychological symptom inputs as important to capture. Second, self-perception; previous experience; sociocultural, financial, environmental, and health systems-level factors were all perceived to impact how patients processed, made decisions about, and acted upon their symptoms. Third, participants provided recommendations for system design that involved allowing for user control and freedom. They also stressed the importance of careful wording of decision-support messages, such that messages that recommend them to seek care convey urgency but do not provoke anxiety. Alternatively, messages that recommend they may not need care should make the patient feel heard and reassured. CONCLUSIONS: Future solutions for postpartum symptom monitoring should include both somatic and psychological symptoms, which may require combining existing measures to elicit symptoms in a nuanced manner. Solutions should allow for varied, safe interactions to suit individual needs. While mHealth or other apps may not be able to address all the social or financial needs of a person, they may at least provide information, so that patients can easily access other supportive resources.

Preparing for the bedside-optimizing a postpartum depression risk prediction model for clinical implementation in a health system.

OBJECTIVE: We developed and externally validated a machine-learning model to predict postpartum depression (PPD) using data from electronic health records (EHRs). Effort is under way to implement the PPD prediction model within the EHR system for clinical decision support. We describe the pre-implementation evaluation process that considered model performance, fairness, and clinical appropriateness. MATERIALS AND METHODS: We used EHR data from an academic medical center (AMC) and a clinical research network database from 2014 to 2020 to evaluate the predictive performance and net benefit of the PPD risk model. We used area under the curve and sensitivity as predictive performance and conducted a decision curve analysis. In assessing model fairness, we employed metrics such as disparate impact, equal opportunity, and predictive parity with the White race being the privileged value. The model was also reviewed by multidisciplinary experts for clinical appropriateness. Lastly, we debiased the model by comparing 5 different debiasing approaches of fairness through blindness and reweighing. RESULTS: We determined the classification threshold through a performance evaluation that prioritized sensitivity and decision curve analysis. The baseline PPD model exhibited some unfairness in the AMC data but had a fair performance in the clinical research network data. We revised the model by fairness through blindness, a debiasing approach that yielded the best overall performance and fairness, while considering clinical appropriateness suggested by the expert reviewers. DISCUSSION AND CONCLUSION: The findings emphasize the need for a thorough evaluation of intervention-specific models, considering predictive performance, fairness, and appropriateness before clinical implementation.

Designing Electronic Data Capture Systems for Sustainability in Low-Resource Settings: Viewpoint With Lessons Learned From Ethiopia and Myanmar.

Electronic data capture (EDC) is a crucial component in the design, evaluation, and sustainment of population health interventions. Low-resource settings, however, present unique challenges for developing a robust EDC system due to limited financial capital, differences in technological infrastructure, and insufficient involvement of those who understand the local context. Current literature focuses on the evaluation of health interventions using EDC but does not provide an in-depth description of the systems used or how they are developed. In this viewpoint, we present case descriptions from 2 low- and middle-income countries: Ethiopia and Myanmar. We address a gap in evidence by describing each EDC system in detail and discussing the pros and cons of different approaches. We then present common lessons learned from the 2 case descriptions as recommendations for considerations in developing and implementing EDC in low-resource settings, using a sociotechnical framework for studying health information technology in complex adaptive health care systems. Our recommendations highlight the importance of selecting hardware compatible with local infrastructure, using flexible software systems that facilitate communication across different languages and levels of literacy, and conducting iterative, participatory design with individuals with deep knowledge of local clinical and cultural norms.

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.

Perceptions of patient-reported outcome data access and sharing among patients with heart failure: ethical implications for research.

AIMS: In the face of growing expectations for data transparency and patient engagement in care, we evaluated preferences for patient-reported outcome (PRO) data access and sharing among patients with heart failure (HF) using an ethical framework. METHODS AND RESULTS: We conducted qualitative interviews with a purposive sample of patients with HF who participated in a larger 8-week study that involved the collection and return of PROs using a web-based interface. Guided by an ethical framework, patients were asked questions about their preferences for having PRO data returned to them and shared with other groups. Interview transcripts were coded by three study team members using directed content analysis. A total of 22 participants participated in semi-structured interviews. Participants were mostly male (73%), White (68%) with a mean age of 72. Themes were grouped into priorities, benefits, and barriers to data access and sharing. Priorities included ensuring anonymity when data are shared, transparency with intentions of data use, and having access to all collected data. Benefits included: using data as a communication prompt to discuss health with clinicians and using data to support self-management. Barriers included: challenges with interpreting returned results, and potential loss of benefits and anonymity when sharing data. CONCLUSION: Our interviews with HF patients highlight opportunities for researchers to return and share data through an ethical lens, by ensuring privacy and transparency with intentions of data use, returning collected data in comprehensible formats, and meeting individual expectations for data sharing.

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.

Using Patient Decision Aids for Cardiology Care in Diverse Populations.

PURPOSE OF REVIEW: Patient decision aids (PDAs) are tools that help guide treatment decisions and support shared decision-making when there is equipoise between treatment options. This review focuses on decision aids that are available to support cardiac treatment options for underrepresented groups. RECENT FINDINGS: PDAs have been developed to support multiple treatment decisions in cardiology related to coronary artery disease, valvular heart disease, cardiac arrhythmias, heart failure, and cholesterol management. By considering the unique needs and preferences of diverse populations, PDAs can enhance patient engagement and promote equitable healthcare delivery in cardiology. In this review, we examine the benefits, challenges, and current trends in implementing PDAs, with a focus on improving decision-making processes and outcomes for patients from underrepresented racial and ethnic groups. In addition, the article highlights key considerations when implementing PDAs and potential future directions in the field.

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.

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.

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.

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.

Returning study results to research participants: Data access, format, and sharing preferences.

INTRODUCTION: Research participants have a growing expectation for transparency with their collected information; however, there is little guidance on participant preferences for receiving health information and how researchers should return this information to participants. METHODS: We conducted a cross-sectional online survey with a representative sample of 502 participants in the United States. Participants were asked about their preferences for receiving, sharing, and the formatting of health information collected for research purposes. RESULTS: Most participants wanted their health information returned (84 %) to use it for their own knowledge and to manage their own health. Email was the most preferred format for receiving health data (67 %), followed by online website (44 %), and/or paper copy (32 %). Data format preferences varied by age, education, financial resources, subjective numeracy, and health literacy. Around one third of Generation Z (25 %), Millennials (30 %), and Generation X (29 %) participants preferred to receive their health information with a mobile app. In contrast, very few Baby Boomers (12 %) and none from the Silent Generation preferred the mobile app format. Having a paper copy of the data was preferred by 38 % of participants without a college degree compared to those with a college degree. Preferences were highest for sharing all health information with doctors and nurses (77 %), and some information with friends and family (66 %). CONCLUSION: Study findings support returning research information to participants in multiple formats, including email, online websites, and paper copy. Preferences for whom to share information with varied by stakeholders and by sociodemographic characteristics. Researchers should offer multiple formats to participants and tailor data sharing options to participants' preferences. Future research should further explore combinations of individual characteristics that may further influence data sharing and format preferences.

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.

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. METHODS: Patient portal secure messages from the Memorial Sloan Kettering Cancer Center were retrieved and manually annotated as a predicted unregistered proxy (ie, not written by the patient) or a presumed patient. After randomly splitting the annotated messages into training and test sets in a 70:30 ratio, a bag-of-words approach was used to extract features and then a Least Absolute Shrinkage and Selection Operator (LASSO) model was trained and used for classification. RESULTS: Portal secure messages (n = 2,000) were randomly selected from unique patient accounts and manually annotated. We excluded 335 messages from the data set as the annotators could not determine if they were written by a patient or proxy. Using the remaining 1,665 messages, a LASSO model was developed that achieved an area under the curve of 0.932 and an area under the precision recall curve of 0.748. The sensitivity and specificity related to classifying true-positive cases (predicted unregistered proxy-authored messages) and true negatives (presumed patient-authored messages) were 0.681 and 0.960, respectively. CONCLUSION: Our work demonstrates the feasibility of using unstructured, heterogenous patient portal secure messages to determine portal secure message authorship. Identifying patient authorship in real time can improve patient portal account security and can be used to improve the quality of the information extracted from the patient portal, such as patient-reported outcomes.