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Background: Healthcare crowdsourcing events (e.g. hackathons) facilitate interdisciplinary collaboration and encourage innovation. Peer-reviewed research has not yet considered a healthcare crowdsourcing event focusing on generative artificial intelligence (GenAI), which generates text in response to detailed prompts and has vast potential for improving the efficiency of healthcare organizations. Our event, the New York University Langone Health (NYULH) Prompt-a-thon, primarily sought to inspire and build AI fluency within our diverse NYULH community, and foster collaboration and innovation. Secondarily, we sought to analyze how participants' experience was influenced by their prior GenAI exposure and whether they received sample prompts during the workshop., Methods: Executing the event required the assembly of an expert planning committee, who recruited diverse participants, anticipated technological challenges, and prepared the event. The event was composed of didactics and workshop sessions, which educated and allowed participants to experiment with using GenAI on real healthcare data. Participants were given novel "project cards" associated with each dataset that illuminated the tasks GenAI could perform and, for a random set of teams, sample prompts to help them achieve each task (the public repository of project cards can be found at https://github.com/smallw03/NYULH-Generative-AI-Prompt-a-thon-Project-Cards). Afterwards, participants were asked to fill out a survey with 7-point Likert-style questions., Results: Our event was successful in educating and inspiring hundreds of enthusiastic in-person and virtual participants across our organization on the responsible use of GenAI in a low-cost and technologically feasible manner. All participants responded positively, on average, to each of the survey questions (e.g., confidence in their ability to use and trust GenAI). Critically, participants reported a self-perceived increase in their likelihood of using and promoting colleagues' use of GenAI for their daily work. No significant differences were seen in the surveys of those who received sample prompts with their project task descriptions., Conclusion: The first healthcare Prompt-a-thon was an overwhelming success, with minimal technological failures, positive responses from diverse participants and staff, and evidence of post-event engagement. These findings will be integral to planning future events at our institution, and to others looking to engage their workforce in utilizing GenAI., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Small et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Importance: Virtual patient-physician communications have increased since 2020 and negatively impacted primary care physician (PCP) well-being. Generative artificial intelligence (GenAI) drafts of patient messages could potentially reduce health care professional (HCP) workload and improve communication quality, but only if the drafts are considered useful., Objectives: To assess PCPs' perceptions of GenAI drafts and to examine linguistic characteristics associated with equity and perceived empathy., Design, Setting, and Participants: This cross-sectional quality improvement study tested the hypothesis that PCPs' ratings of GenAI drafts (created using the electronic health record [EHR] standard prompts) would be equivalent to HCP-generated responses on 3 dimensions. The study was conducted at NYU Langone Health using private patient-HCP communications at 3 internal medicine practices piloting GenAI., Exposures: Randomly assigned patient messages coupled with either an HCP message or the draft GenAI response., Main Outcomes and Measures: PCPs rated responses' information content quality (eg, relevance), using a Likert scale, communication quality (eg, verbosity), using a Likert scale, and whether they would use the draft or start anew (usable vs unusable). Branching logic further probed for empathy, personalization, and professionalism of responses. Computational linguistics methods assessed content differences in HCP vs GenAI responses, focusing on equity and empathy., Results: A total of 16 PCPs (8 [50.0%] female) reviewed 344 messages (175 GenAI drafted; 169 HCP drafted). Both GenAI and HCP responses were rated favorably. GenAI responses were rated higher for communication style than HCP responses (mean [SD], 3.70 [1.15] vs 3.38 [1.20]; P = .01, U = 12 568.5) but were similar to HCPs on information content (mean [SD], 3.53 [1.26] vs 3.41 [1.27]; P = .37; U = 13 981.0) and usable draft proportion (mean [SD], 0.69 [0.48] vs 0.65 [0.47], P = .49, t = -0.6842). Usable GenAI responses were considered more empathetic than usable HCP responses (32 of 86 [37.2%] vs 13 of 79 [16.5%]; difference, 125.5%), possibly attributable to more subjective (mean [SD], 0.54 [0.16] vs 0.31 [0.23]; P < .001; difference, 74.2%) and positive (mean [SD] polarity, 0.21 [0.14] vs 0.13 [0.25]; P = .02; difference, 61.5%) language; they were also numerically longer (mean [SD] word count, 90.5 [32.0] vs 65.4 [62.6]; difference, 38.4%), but the difference was not statistically significant (P = .07) and more linguistically complex (mean [SD] score, 125.2 [47.8] vs 95.4 [58.8]; P = .002; difference, 31.2%)., Conclusions: In this cross-sectional study of PCP perceptions of an EHR-integrated GenAI chatbot, GenAI was found to communicate information better and with more empathy than HCPs, highlighting its potential to enhance patient-HCP communication. However, GenAI drafts were less readable than HCPs', a significant concern for patients with low health or English literacy.

Purpose: The era of precision education is increasingly leveraging electronic health record (EHR) data to assess residents' clinical performance. But precision in what the EHR-based resident performance metrics are truly assessing is not fully understood. For instance, there is limited understanding of how EHR-based measures account for the influence of the team on an individual's performance-or conversely how an individual contributes to team performances. This study aims to elaborate on how the theoretical understandings of supportive and collaborative interdependence are captured in residents' EHR-based metrics., Method: Using a mixed methods study design, the authors conducted a secondary analysis of 5 existing quantitative and qualitative datasets used in previous EHR studies to investigate how aspects of interdependence shape the ways that team-based care is provided to patients., Results: Quantitative analyses of 16 EHR-based metrics found variability in faculty and resident performance (both between and within resident). Qualitative analyses revealed that faculty lack awareness of their own EHR-based performance metrics, which limits their ability to act interdependently with residents in an evidence-informed fashion. The lens of interdependence elucidates how resident practice patterns develop across residency training, shifting from supportive to collaborative interdependence over time. Joint displays merging the quantitative and qualitative analyses showed that residents are aware of variability in faculty's practice patterns and that viewing resident EHR-based measures without accounting for the interdependence of residents with faculty is problematic, particularly within the framework of precision education., Conclusions: To prepare for this new paradigm of precision education, educators need to develop and evaluate theoretically robust models that measure interdependence in EHR-based metrics, affording more nuanced interpretation of such metrics when assessing residents throughout training., (Copyright © 2024 the Association of American Medical Colleges.)

We have developed a device based on a microwire network formed from bio-functionalised silver nanowires (AgNWs) through dielectrophoresis (DEP) and hydrogel entrapment. This was achieved by carrying out the DEP assembly of AgNWs in an agarose aqueous solution above its gelling temperature and then cooling to encapsulate the assembled structure within the hydrogel which turns it into an electrically anisotropic material that contains up to 99% water. We have studied in detail the formation of microwires assembled from silver nanowires (AgNWs) in agarose gel, at fixed temperature and AC field voltage, which allowed us to build a "phase diagram" of the microwire assembly as a function of the agarose and AgNW concentration at three different lengths of the AgNWs. In this proof-of-concept study, the AgNWs were functionalised with thiolated biotin, then assembled into microwires by DEP and entrapped into a hydrogel. When exposed to a solution of streptavidin the biosensor device showed a marked increase of the cell conductivity due to compacting of the AgNWs which allows streptavidin detection. This generic type of biosensing device could find applications for detection of other biomolecules, the response to which can be directly converted to an electric signal. Conjugating specific antibodies on the AgNW surface would allow the detection of a matching antigen (e.g. protein) in the solution exposed to the anisotropic hydrogel.

We have designed a scaffold-free cell assembly method which can produce linear structures of individual living cells without using templates. The method involves dielectrophoretic assembly of cells suspended in a solution of a gelling agent above the gelling temperature. After the cell assembly in string-like structures was achieved with the AC electric field still applied, we gelled the solution by cooling it below its gelling point. The hydrogel entraps the assembled cell structure, preventing its disassembly and allowing further analysis without the presence of the external electric field. We pre-functionalised the cells with polyelectrolytes before the dielectrophoretic assembly which allowed us to line them up in multicellular strings and bind them together with oppositely charged polyelectrolyte after the gel formation. Finally, by dissolving the hydrogel, we released the linear chains of living cells which were collected and studied by optical and fluorescence microscopy. Cell viability tests with fluorescein diacetate confirmed that the cells in the formed worm-like structures remain viable after the cell assembly procedure. The linear cell aggregates are stable without the electric field and can be further cultured or treated with additional polyelectrolytes which make the method attractive for tissue engineering. We envisage that this technique could find possible applications for assembly of neuron cells in linear structures and more complex cell networks.

We have fabricated novel water sensitive transparent films on flexible substrates by inkjet printing of aqueous based inks of single- and multi-walled carbon nanotubes dispersed with ultra low concentrations of the biopolymers gellan and xanthan gum.

Fabrication of single-walled carbon nanotube (SWNT) networks using evaporation of SDS-SWNT sessile drops on a hydrophobized silicon substrate is reported. It is suggested that the organization of nanotubes during evaporation is controlled by aggregates (in the SDS-SWNT dispersion) and hydrophobicity of the substrate. On hydrophobic substrates, the evaporation of SDS-SWNT sessile drops proceeds through constant contact area. On hydrophilic substrates, nanotube aggregates in SDS-SWNT dispersion stop the contact line from moving, resulting in the formation of "coffee-stains". The (partial) removal of aggregates by centrifugation is essential for a freely moving contact line leading to the organization of nanotubes into a network of homogeneously distributed nanotubes on the most hydrophobic substrate. The evaporation of sessile drops was characterized by microscopic, spectroscopic, and topographical techniques.