Family Perception of OpenNotes in the Neonatal Intensive Care Unit.

BACKGROUND: OpenNotes, or sharing of medical notes via a patient portal, has been studied extensively in the adult population, but less in pediatric populations, and even more rarely in inpatient pediatric or intensive care settings. OBJECTIVES: This study aimed to understand families' interaction with and perception of inpatient hospital notes shared via patient portal in a community Neonatal Intensive Care Unit (NICU). METHODS: At the end of the NICU discharge education, completed in the patient portal before discharge, families were offered an anonymous survey on OpenNotes. RESULTS: Out of 446 NICU patients from March 16, 2022 to March 16, 2023, there were 59 respondents (13%). Race was primarily Asian (48%), and English was the predominant language (93%). Most families indicated that the notes were "very or somewhat easy to understand" (93%). Seventy-three percent of respondents felt much better about the doctor(s) after reading the notes, and 53% contacted the physicians about something in the notes. Six (16%) felt that OpenNotes were more confusing than helpful. CONCLUSION: To the authors' knowledge, this is the first study on NICU families' perceptions of OpenNotes, which indicated positive interactions with the doctors' daily progress notes and gave important suggestions for improvement.

A decentralized, prospective, observational study to collect real-world data from patients with myasthenia gravis using smartphones.

Introduction: We conducted a 3-month, prospective study in a population of patients with Myasthenia Gravis (MG), utilizing a fully decentralized approach for recruitment and monitoring (ClinicalTrials.gov Identifier: NCT04590716). The study objectives were to assess the feasibility of collecting real-world data through a smartphone-based research platform, in order to characterize symptom involvement during MG exacerbations. Methods: Primary data collection included daily electronically recorded patient-reported outcomes (ePROs) on the presence of MG symptoms, the level of symptom severity (using the MG-Activities of Daily Living assessment, MG-ADL), and exacerbation status. Participants were also given the option to contribute data on their physical activity levels from their own wearable devices. Results: The study enrolled and onboarded 113 participants across 37 US states, and 73% (N= 82) completed the study. The mean age of participants was 53.6 years, 60% were female. Participants were representative of a moderate to severe MG phenotype, with frequent exacerbations, high symptom burden and multiple comorbidities. 55% of participants (N=45) reported MG exacerbations during the study, with an average of 6.3 exacerbation days per participant. Median average MG-ADL scores for participants during self-reported exacerbation and non-exacerbation periods were 7 (interquartile range 4-9, range 1-19) and 0.3 (interquartile range 0-0.8, range 0-9), respectively. Analyses examining relationships between patient-reported and patient-generated health data streams and exacerbation status demonstrated concordance between self-reported MG-ADL scores and exacerbation status, and identified features that may be used to understand and predict the onset of MG symptom exacerbations, including: 1.) dynamic changes in day-to-day symptom reporting and severity 2.) daily step counts as a measure of physical activity and 3.) clinical characteristics of the patient, including the amount of time since their initial diagnosis and their active medications related to MG treatment. Finally, application of unsupervised machine learning methods identified unique clusters of exacerbation subtypes, each with their own specific representation of symptoms and symptom severity. Conclusion: While these symptom signatures require further study and validation, our results suggest that digital phenotyping, characterized by increased multidimensionality and frequency of the data collection, holds promise for furthering our understanding of clinically significant exacerbations and reimagining the approach to treating MG as a heterogeneous condition.

Development and Assessment of an Artificial Intelligence-Based Tool for Ptosis Measurement in Adult Myasthenia Gravis Patients Using Selfie Video Clips Recorded on Smartphones.

Introduction: Myasthenia gravis (MG) is a rare autoimmune disease characterized by muscle weakness and fatigue. Ptosis (eyelid drooping) occurs due to fatigue of the muscles for eyelid elevation and is one symptom widely used by patients and healthcare providers to track progression of the disease. Margin reflex distance 1 (MRD1) is an accepted clinical measure of ptosis and is typically assessed using a hand-held ruler. In this work, we develop an AI model that enables automated measurement of MRD1 in self-recorded video clips collected using patient smartphones. Methods: A 3-month prospective observational study collected a dataset of video clips from patients with MG. Study participants were asked to perform an eyelid fatigability exercise to elicit ptosis while filming "selfie" videos on their smartphones. These images were collected in nonclinical settings, with no in-person training. The dataset was annotated by non-clinicians for (1) eye landmarks to establish ground truth MRD1 and (2) the quality of the video frames. The ground truth MRD1 (in millimeters, mm) was calculated from eye landmark annotations in the video frames using a standard conversion factor, the horizontal visible iris diameter of the human eye. To develop the model, we trained a neural network for eye landmark detection consisting of a ResNet50 backbone plus two dense layers of 78 dimensions on publicly available datasets. Only the ResNet50 backbone was used, discarding the last two layers. The embeddings from the ResNet50 were used as features for a support vector regressor (SVR) using a linear kernel, for regression to MRD1, in mm. The SVR was trained on data collected remotely from MG patients in the prospective study, split into training and development folds. The model's performance for MRD1 estimation was evaluated on a separate test fold from the study dataset. Results: On the full test fold (N = 664 images), the correlation between the ground truth and predicted MRD1 values was strong (r = 0.732). The mean absolute error was 0.822 mm; the mean of differences was -0.256 mm; and 95% limits of agreement (LOA) were -0.214-1.768 mm. Model performance showed no improvement when test data were gated to exclude "poor" quality images. Conclusions: On data generated under highly challenging real-world conditions from a variety of different smartphone devices, the model predicts MRD1 with a strong correlation (r = 0.732) between ground truth and predicted MRD1.

Shifting into Action: from Data Segmentation to Equitable Interoperability for Adolescents (and Everyone Else).

BACKGROUND: Technological improvements and, subsequently, the federal 21st Century Cures Act have resulted in increased access to and interoperability of electronic protected health information (ePHI). These not only have many benefits, but also have created unique challenges for privacy and confidentiality for adolescent patients. The inability to granularly protect sensitive data and a lack of standards have resulted in limited confidentiality protection and inequitable access to health information. OBJECTIVES: This study aimed to understand the challenges to safe, equitable access, and interoperability of ePHI for adolescents and to identify strategies that have been developed, ongoing needs, and work in progress. METHODS: Shift, a national task force formalized in 2020, is a group of more than 200 expert stakeholder members working to improve functionality to standardize efforts to granularly identify and protect sensitive ePHI to promote equitable interoperability. RESULTS: Shift has created high-priority clinical use cases and organized challenges into the areas of Standards and Terminology; Usability and Implementation; and Ethics, Legal, and Policy. CONCLUSION: Current technical standards and value sets of terminology for sensitive data have been immature and inconsistent. Shift, a national diverse working group of stakeholders, is addressing challenges inherent in the protection of privacy and confidentiality for adolescent patients. The diversity of expertise and perspectives has been essential to identify and address these challenges.

Climate change and health informatics: pilot survey of perspectives across the field.

OBJECTIVE: Establish a baseline of informatics professionals' perspectives on climate change and health. MATERIALS AND METHODS: Anonymized survey sent to 9 informatics listservs March 31, 2022 to April 15, 2022. RESULTS: N = 85 participants completed part or all of survey. Majority of participants worked at hospitals with 1000+ employees (73%) in urban areas (60%) in the United States. Respondents broadly reported general understanding of climate change and health (51%), but 71% reported unfamiliarity with technologies that could help clinicians and informaticians address the impacts of climate change. Seventy-one percent of surveyed wanted climate-driven environmental health information included in EHRs. Seventy-six percent of respondents reported that informaticians should be involved in institutional decarbonization. Seventy-eight percent of respondents felt that it was extremely, very, or moderately important to receive education on climate change. DISCUSSION: General consensus on need to engage informaticians in climate change response, but gaps identified in knowledge dissemination and tools for adaptation and mitigation. CONCLUSION: Informaticians broadly concerned about climate change and want to be engaged in efforts to combat it, but further education and tool development needed.

An ecosystem service perspective on urban nature, physical activity, and health.

Nature underpins human well-being in critical ways, especially in health. Nature provides pollination of nutritious crops, purification of drinking water, protection from floods, and climate security, among other well-studied health benefits. A crucial, yet challenging, research frontier is clarifying how nature promotes physical activity for its many mental and physical health benefits, particularly in densely populated cities with scarce and dwindling access to nature. Here we frame this frontier by conceptually developing a spatial decision-support tool that shows where, how, and for whom urban nature promotes physical activity, to inform urban greening efforts and broader health assessments. We synthesize what is known, present a model framework, and detail the model steps and data needs that can yield generalizable spatial models and an effective tool for assessing the urban nature-physical activity relationship. Current knowledge supports an initial model that can distinguish broad trends and enrich urban planning, spatial policy, and public health decisions. New, iterative research and application will reveal the importance of different types of urban nature, the different subpopulations who will benefit from it, and nature's potential contribution to creating more equitable, green, livable cities with active inhabitants.

Rethinking PICO in the Machine Learning Era: ML-PICO.

BACKGROUND: Machine learning (ML) has captured the attention of many clinicians who may not have formal training in this area but are otherwise increasingly exposed to ML literature that may be relevant to their clinical specialties. ML papers that follow an outcomes-based research format can be assessed using clinical research appraisal frameworks such as PICO (Population, Intervention, Comparison, Outcome). However, the PICO frameworks strain when applied to ML papers that create new ML models, which are akin to diagnostic tests. There is a need for a new framework to help assess such papers. OBJECTIVE: We propose a new framework to help clinicians systematically read and evaluate medical ML papers whose aim is to create a new ML model: ML-PICO (Machine Learning, Population, Identification, Crosscheck, Outcomes). We describe how the ML-PICO framework can be applied toward appraising literature describing ML models for health care. CONCLUSION: The relevance of ML to practitioners of clinical medicine is steadily increasing with a growing body of literature. Therefore, it is increasingly important for clinicians to be familiar with how to assess and best utilize these tools. In this paper we have described a practical framework on how to read ML papers that create a new ML model (or diagnostic test): ML-PICO. We hope that this can be used by clinicians to better evaluate the quality and utility of ML papers.

The Value of OpenNotes for Pediatric Patients, Their Families and Impact on the Patient-Physician Relationship.

BACKGROUND: OpenNotes, the sharing of medical notes via a patient portal, has been extensively studied in adults but not in pediatric populations. This has been a contributing factor in the slower adoption of OpenNotes by children's hospitals. The 21st Century Cures Act Final Rule has mandated the sharing of clinical notes electronically to all patients and as health systems prepare to comply, some concerns remain particularly with OpenNotes for pediatric populations. OBJECTIVES: After a gradual implementation of OpenNotes at an academic pediatric center, we sought to better understand how pediatric patients and families perceived OpenNotes. This article presents the detailed steps of this informatics-led rollout and patient survey results with a focus on pediatric-specific concerns. METHODS: We adapted a previous OpenNotes survey used for adult populations to a pediatric outpatient setting (with parents of children <12 years old). The survey was sent to patients and families via a notification email sent as a standard practice after a clinic visit, in English or Spanish. RESULTS: Approximately 7% of patients/families with access to OpenNotes read the note during the study period, and 159 (20%) of those patients responded to the survey. Of the survey respondents, 141 (89%) of patients and families understood their notes; 126 (80%) found the notes always or usually accurate; 24 (15%) contacted their clinicians after reading a note; and 153 (97%) patients/families felt the same or better about their doctor after reading the note. CONCLUSION: Although limited by relatively low survey response rate, OpenNotes was well-received by parents of pediatric patients without untoward consequences. The main concerns pediatricians raise about OpenNotes proved to not be issues in the pediatric population. Our results demonstrate clear benefits to adoption of OpenNotes. This provides reassurance that the transition to sharing notes with pediatric patients can be successful and value additive.

Community-Based Approaches to Reducing Health Inequities and Fostering Environmental Justice through Global Youth-Engaged Citizen Science.

Growing socioeconomic and structural disparities within and between nations have created unprecedented health inequities that have been felt most keenly among the world's youth. While policy approaches can help to mitigate such inequities, they are often challenging to enact in under-resourced and marginalized communities. Community-engaged participatory action research provides an alternative or complementary means for addressing the physical and social environmental contexts that can impact health inequities. The purpose of this article is to describe the application of a particular form of technology-enabled participatory action research, called the Our Voice citizen science research model, with youth. An overview of 20 Our Voice studies occurring across five continents indicates that youth and young adults from varied backgrounds and with interests in diverse issues affecting their communities can participate successfully in multiple contributory research processes, including those representing the full scientific endeavor. These activities can, in turn, lead to changes in physical and social environments of relevance to health, wellbeing, and, at times, climate stabilization. The article ends with future directions for the advancement of this type of community-engaged citizen science among young people across the socioeconomic spectrum.