Faculty Bibliography

Objective/UNASSIGNED:The widespread deployment of electronic health records (EHRs) has introduced new sources of error and inefficiencies to the process of ordering medications in the hospital setting. Existing work identifies orders that require pharmacy intervention by comparing them to a patient's medical records. In this work, we develop a machine learning model for identifying medication orders requiring intervention using only provider behavior and other contextual features that may reflect these new sources of inefficiencies. Materials and Methods/UNASSIGNED:Data on providers' actions in the EHR system and pharmacy orders were collected over a 2-week period in a major metropolitan hospital system. A classification model was then built to identify orders requiring pharmacist intervention. We tune the model to the context in which it would be deployed and evaluate global and local feature importance. Results/UNASSIGNED:The resultant model had an area under the receiver-operator characteristic curve of 0.91 and an area under the precision-recall curve of 0.44. Conclusions/UNASSIGNED:Providers' actions can serve as useful predictors in identifying medication orders that require pharmacy intervention. Careful model tuning for the clinical context in which the model is deployed can help to create an effective tool for improving health outcomes without using sensitive patient data.

BACKGROUND:Self-focused augmented reality (AR) technologies are growing in popularity and present an opportunity to address health communication and behavior change challenges. OBJECTIVE:We aimed to examine the impact of self-focus AR and vicarious reinforcement on psychological predictors of behavior change during the COVID-19 pandemic. In addition, our study included measures of fear and message minimization to assess potential adverse reactions to the design interventions. METHODS:A web-based between-subjects experiment (n = 335) was conducted to compare the health perceptions of participants in self-focus AR & vicarious reinforcement design conditions to those in a control condition. RESULTS:We found that participants who experienced self-focus AR & vicarious reinforcement scored higher in perceived threat severity (P = 0.03) and susceptibility (P = 0.01) when compared to the control. A significant indirect effect of self-focus AR & vicarious reinforcement on intention was found with perceived threat severity as a mediator (b = .06, 95% CI= [.02, .12], SE = .02). Self-focus AR & vicarious reinforcement did not result in higher levels of fear (P = 0.32) or message minimization (P = 0.42) when compared to the control. CONCLUSIONS:Augmenting one's reflection with vicarious reinforcement may be an effective strategy for health communication designers. While our study's results did not show adverse effects in regards to fear and message minimization, utilization of self-focus AR should be done with care due to possible adverse effects of heightened levels of fear as a health communication strategy. CLINICALTRIAL/UNASSIGNED/:

Psychological ownership defines how we behave in and interact with the social world and the objects around us. Shared Augmented Reality (shared AR) may challenge conventional understanding of psychological ownership because virtual objects created by one user in a social place are available for other participants to see, interact with, and edit. Moreover, confusion may arise when one user attaches a virtual object in a shared AR environment onto the physical object that is owned by a different user. The goal of this study is to investigate tensions around psychological ownership in shared AR. Drawing on prior work, we developed a conceptualization of psychological ownership in shared AR in terms of five underlying dimensions: possession, control, identity, responsibility, and territoriality. We studied several shared AR scenarios through a laboratory experiment that was intended to highlight normative tensions. We divided participants into pairs, whereby one participant in each pair created the virtual object (object-creator) and placed it over the other person's (space proprietor) physical object or space. We recorded participants"™ perceptions of psychological ownership along the 5 dimensions through surveys and interviews. Our results reveal that the paired participants failed to form a mutual understanding of ownership over the virtual objects. In addition, the introduction of virtual objects called into question participants"™ sense of psychological ownership over the physical articles to which the virtual objects were attached. Building on our results, we offer a set of design principles for shared AR environments, intended specifically to alleviate psychological ownership-related concerns. Herein, we also discuss the implications of our findings for research and practice in this field.

Specialisation and plasticity are important for many forms of collective behaviour, but the interplay between these factors is little understood. In insect societies, workers are often developmentally primed to specialise in different tasks, sometimes with morphological or physiological adaptations, facilitating a division of labour. Workers may also plastically switch between tasks or vary their effort. The degree to which developmentally primed specialisation limits plasticity is not clear and has not been systematically tested in ecologically relevant contexts. We addressed this question in 20 free-foraging bumble bee (Bombus terrestris) colonies by continually manipulating colonies to contain either a typically diverse, or a reduced ("homogeneous"), worker body size distribution while keeping the same mean body size, over two trials. Pooling both trials, diverse colonies produced a larger comb mass, an index of colony performance. The link between body size and task was further corroborated by the finding that foragers were larger than nurses even in homogeneous colonies with a very narrow body size range. However, the overall effect of size diversity stemmed mostly from one trial. In the other trial, homogeneous and diverse colonies showed comparable performance. By comparing behavioural profiles based on several thousand observations of individuals, we found evidence that workers in homogeneous colonies in this trial rescued colony performance by plastically increasing behavioural specialisation and/or individual effort, compared to same-sized individuals in diverse colonies. Our results are consistent with a benefit to colonies of large and small specialists under certain conditions, but also suggest that plasticity or effort can compensate for reduced (size-related) specialisation. Thus, we suggest that an intricate interplay between specialisation and plasticity is functionally adaptive in bumble bee colonies.

The transformation of patient-clinician relationships with AI-based medical advice is discussed. many new tools are based on entirely new "˜black-box"™ AI-based technologies, whose inner workings are likely not fully understood by patients or clinicians. Most patients with Type 1 diabetes now use continuous glucose monitors and insulin pumps to tightly manage their disease. Their clinicians carefully review the data streams from both devices to recommend dosage adjustments. Recently new automated recommender systems to monitor and analyze food intake, insulin doses, physical activity, and other factors influencing glucose levels, and provide data-intensive, AI-based recommendations on how to titrate the regimen, are in different stages of FDA approval using "˜black box"™ technology, which is an alluring proposition for a clinical scenario that requires identification of meaningful patterns in complex and voluminous data.

OBJECTIVE:Through the coronavirus disease 2019 (COVID-19) pandemic, telemedicine became a necessary entry point into the process of diagnosis, triage and treatment. Racial and ethnic disparities in health care have been well documented in COVID-19 with respect to risk of infection and in-hospital outcomes once admitted, and here we assess disparities in those who access healthcare via telemedicine for COVID-19 . MATERIALS AND METHODS/METHODS:Electronic health record data of patients at New York University Langone Health between March 19th and April 30, 2020 were used to conduct descriptive and multilevel regression analyses with respect to visit type (telemedicine or in-person), suspected COVID diagnosis and COVID test results. RESULTS:Controlling for individual and community-level attributes, Black patients had 0.6 times the adjusted odds (95%CI:0.58-0.63) of accessing care through telemedicine compared to white patients, though they are increasingly accessing telemedicine for urgent care, driven by a younger and female population. COVID diagnoses were significantly more likely for Black versus white telemedicine patients. DISCUSSION/CONCLUSIONS:There are disparities for Black patients accessing telemedicine, however increased uptake by young, female Black patients. Mean income and decreased mean household size of Zip code were also significantly related to telemedicine use. CONCLUSION/CONCLUSIONS:Telemedicine access disparities reflect those in in-person healthcare access. Roots of disparate use are complex and reflect individual, community, and structural factors, including their intersection; many of which are due to systemic racism. Evidence regarding disparities that manifest through telemedicine can be used to inform tool design and systemic efforts to promote digital health equity.

The COVID-19 pandemic has transformed daily life, as individuals engage in social distancing to prevent the spread of the disease. Consequently, patients' access to outpatient rehabilitation care was curtailed and their prospect for recovery has been compromised. Telerehabilitation has the potential to provide these patients with equally-efficacious therapy in their homes. Using commercial gaming devices with embedded motion sensors, data on movement can be collected toward objective assessment of motor performance, followed by training and documentation of progress. Herein, we present a low-cost telerehabilitation system dedicated to bimanual exercise, wherein the healthy arm drives movements of the affected arm. In the proposed setting, a patient manipulates a dowel embedded with a sensor in front of a Microsoft Kinect sensor. In order to provide an engaging environment for the exercise, the dowel is interfaced with a personal computer, to serve as a controller. The patient's gestures are translated into interactive actions in a custom-made citizen-science project. Along with the system, we introduce an algorithm for classification of the bimanual movements, whose inner workings are detailed in terms of the procedures performed for dimensionality reduction, feature extraction, and movement classification. We demonstrate the feasibility of our system on eight healthy subjects, offering support to the validity of the algorithm. These preliminary findings set forth the development of precise motion analysis algorithms in affordable home-based rehabilitation.

Applications in a range of domains, including route planning and well-being, offer advice based on the social information available in prior users' aggregated activity. When designing these applications, is it better to offer: a) advice that if strictly adhered to is more likely to result in an individual successfully achieving their goal, even if fewer users will choose to adopt it? or b) advice that is likely to be adopted by a larger number of users, but which is sub-optimal with regard to any particular individual achieving their goal? We identify this dilemma, characterized as Goal-Directed vs. Adoption-Directed advice, and investigate the design questions it raises through an online experiment undertaken in four advice domains (financial investment, making healthier lifestyle choices, route planning, training for a 5k run), with three user types, and across two levels of uncertainty. We report findings that suggest a preference for advice favoring individual goal attainment over higher user adoption rates, albeit with significant variation across advice domains; and discuss their design implications.

When searching for new information, do people focus their search on places not-yet discovered by others, or on places that others also focus on? Through a controlled experiment, we investigated heuristic rules that people adopt in social information search, a growing characteristic of how people find information in this hyperconnected world. Three people were connected online to simultaneously search for specific objects in multiple images, under either a cooperative or a competitive setting. They were provided with information about the current number of objects collected and the cumulative time spent on each image. People used such information to decide when to stop the current search and which image to explore next. Further, people paid more attention to others and distribute search efforts when cooperating, compared to when competing against others. Our findings highlight the heuristic rules that people adopt when searching in groups for new information.

This study provides data on the feasibility and impact of video-enabled telemedicine use among patients and providers and its impact on urgent and non-urgent health care delivery from one large health system (NYU Langone Health) at the epicenter of the COVID-19 outbreak in the United States. Between March 2nd and April 14th 2020, telemedicine visits increased from 369.1 daily to 866.8 daily (135% increase) in urgent care after the system-wide expansion of virtual health visits in response to COVID-19, and from 94.7 daily to 4209.3 (4345% increase) in non-urgent care post expansion. Of all virtual visits post expansion, 56.2% and 17.6% urgent and non-urgent visits, respectively, were COVID-19-related. Telemedicine usage was highest by patients aged 20-44, particularly for urgent care. The COVID-19 pandemic has driven rapid expansion of telemedicine use for urgent care and non-urgent care visits beyond baseline periods. This reflects an important change in telemedicine that other institutions facing the COVID-19 pandemic should anticipate.