An ecosystem service perspective on urban nature, physical activity, and health.
Proceedings of the National Academy of Sciences of the United States of America
2021; 118 (22)
Rethinking PICO in the Machine Learning Era: ML-PICO.
Applied clinical informatics
2021; 12 (2): 407-416
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.
View details for DOI 10.1073/pnas.2018472118
View details for PubMedID 33990458
Community-Based Approaches to Reducing Health Inequities and Fostering Environmental Justice through Global Youth-Engaged Citizen Science.
International journal of environmental research and public health
2021; 18 (3)
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.
View details for DOI 10.1055/s-0041-1729752
View details for PubMedID 34010977
The Value of OpenNotes for Pediatric Patients, Their Families and Impact on the Patient-Physician Relationship.
Applied clinical informatics
2021; 12 (1): 76?81
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.
View details for DOI 10.3390/ijerph18030892
View details for PubMedID 33494135
Predicting Environmental Allergies from Real World Data Through a Mobile Study Platform.
Journal of asthma and allergy
2021; 14: 259?64
OpenNotes: Toward a Participatory Pediatric Health System.
2018; 142 (4)
OpenNotes: Toward a Participatory Pediatric Health System
2018; 142 (4)
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.
View details for DOI 10.1055/s-0040-1721781
View details for PubMedID 33567464