@article{koscelny_generative_2025,

title = {Generative AI responses are a dime a dozen; Making them count is the challenge – Evaluating information presentation styles in healthcare chatbots using hierarchical Bayesian regression models},

author = {Samuel N. Koscelny and Sara Sadralashrafi and David M. Neyens},

url = {https://linkinghub.elsevier.com/retrieve/pii/S0003687025000511},

doi = {10.1016/j.apergo.2025.104515},

issn = {36870},

year  = {2025},

date = {2025-10-01},

urldate = {2026-04-29},

journal = {Applied Ergonomics},

volume = {128},

pages = {104515},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{deforest_modeling_2025,

title = {Modeling anesthesia medication delivery using the SEIPS 101 tools},

author = {Elise DeForest and Ken Catchpole and Connor Lusk and James H. Abernathy and David M. Neyens},

url = {https://linkinghub.elsevier.com/retrieve/pii/S0003687025000912},

doi = {10.1016/j.apergo.2025.104555},

issn = {36870},

year  = {2025},

date = {2025-10-01},

urldate = {2026-04-29},

journal = {Applied Ergonomics},

volume = {128},

pages = {104555},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{catchpole_rethinking_2025,

title = {Rethinking Anesthesia Medication “Errors”: The OR-SMART Patient Safety Learning Laboratory},

author = {Ken R. Catchpole and David M. Neyens and James H. Abernathy and Joshua Biro},

url = {https://journals.lww.com/10.1097/PTS.0000000000001384},

doi = {10.1097/PTS.0000000000001384},

issn = {1549-8417, 1549-8425},

year  = {2025},

date = {2025-10-01},

urldate = {2026-04-29},

journal = {Journal of Patient Safety},

volume = {21},

number = {7},

pages = {503–509},

abstract = {Purpose: 

 We combine the results of multiple studies to describe a systems engineering approach to a well-recognized patient safety problem. The goal of the Operating Room Systems-based Medication Administration error Reduction Team (OR-SMART) patient safety learning laboratory was to study the anesthesia medication work system to identify the characteristics of technologies and interventions that might feasibly reduce anesthesia medication errors. 

 Scope: 

 The work was conducted at 2 large urban academic medical centers: Johns Hopkins and the Medical University of South Carolina. We sampled across many different types of anesthesia work, understanding the challenges of work-as-done, and applying systems safety principles and evaluation frameworks. 

 Methods: 

 This was a mixed-methods study. Sources of data varied, with formal and informal interviews, formal and informal observations, video-based observations, hospital and national databases, and information from local incidents. Clinically embedded human factors professionals at both hospital sites facilitated informal sources of data. We explored the variable definitions of error; individual and organizational variability in decision-making; how syringes are used, stored, and moved within an operating room (OR); and used the Systems Engineering Initiative for Patient Safety framework to model medication processes. We were able to identify more than 100 possible interventions, and then prioritized a few for development and testing. 

 Results: 

 We identified medication icon labels, a syringe holder hub, and workspace design guidelines as interventions for evaluation. Significant benefits of medication label icons were found in simulation and were highly utilized in practice. The syringe hub demonstrated high acceptability at one site but substantially less at another. A virtual reality-based evaluation of the OR design found that situational awareness, visual monitoring, and available workspace were subjectively improved.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{koscelny_multi-site_2025,

title = {A multi-site big data analysis of factors impacting the time to disposition in pediatric mental and behavioural health emergency department visits},

author = {Samuel N. Koscelny and David M. Neyens and Farzad Zeinali and Kevin Taaffe and Anjali Joseph and Ann Dietrich},

url = {https://www.tandfonline.com/doi/full/10.1080/20476965.2025.2577093},

doi = {10.1080/20476965.2025.2577093},

issn = {2047-6965, 2047-6973},

year  = {2025},

date = {2025-10-01},

urldate = {2026-04-29},

journal = {Health Systems},

pages = {1–14},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{conner_engineering_2025,

title = {Engineering Identity of Non-Traditional Students in an Undergraduate Transfer Program},

author = {Shannon Conner and Tyler Harvey and D. Matthew Boyer and Mary E. Kurz and David Neyens},

url = {https://www.tandfonline.com/doi/full/10.1080/07377363.2025.2468403},

doi = {10.1080/07377363.2025.2468403},

issn = {0737-7363, 1948-4801},

year  = {2025},

date = {2025-09-01},

urldate = {2026-04-29},

journal = {The Journal of Continuing Higher Education},

volume = {73},

number = {3},

pages = {271–285},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{koscelny_visual_2025,

title = {Visual Attention in Healthcare Chatbot Interactions: Quantifying Varying Communication Styles with the K-Coefficient},

author = {Samuel N. Koscelny and Robin Rucker and Michael Reed and Andrew T. Duchowski and David M. Neyens},

url = {https://journals.sagepub.com/doi/10.1177/10711813251358797},

doi = {10.1177/10711813251358797},

issn = {1071-1813, 2169-5067},

year  = {2025},

date = {2025-09-01},

urldate = {2026-04-29},

journal = {Proceedings of the Human Factors and Ergonomics Society Annual Meeting},

volume = {69},

number = {1},

pages = {153–159},

abstract = {Advancements in artificial intelligence (AI) are transforming healthcare chatbots, improving their potential to support patient education and engagement. The effectiveness of healthcare chatbots depends not only on technical ability but also on how their communication style impacts user engagement, particularly through visual attention. To investigate this, a between-subjects study evaluated the effect of chatbot communication style (conversational vs. informative) on eye-gaze behavior in a knowledge-seeking task. Eye metrics were analyzed using quantile and linear regression models. Quantile regression models revealed the distribution of fixation duration was always higher in the informative condition, while saccadic amplitude and the K-coefficient varied across quantiles. The linear regression model showed that both communication style and stimulus progression significantly increased the K-coefficient over time. These findings demonstrate that chatbot communication style influences user visual attention over time, underscoring the need for future work to align chatbot communication styles with user attention patterns.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{koscelny_mapping_2025,

title = {Mapping Utilization Trends: Identifying and Understanding High-Frequency ED Users in Pediatric MBH Care},

author = {Samuel N. Koscelny and David M. Neyens and Ann Dietrich and Anjali Joseph},

url = {https://journals.sagepub.com/doi/10.1177/10711813251358783},

doi = {10.1177/10711813251358783},

issn = {1071-1813, 2169-5067},

year  = {2025},

date = {2025-09-01},

urldate = {2026-04-29},

journal = {Proceedings of the Human Factors and Ergonomics Society Annual Meeting},

volume = {69},

number = {1},

pages = {504–509},

abstract = {Pediatric emergency departments are experiencing a surge in visits related to mental and behavioral health (MBH), straining already overburdened healthcare systems. Understanding which subgroups of pediatric patients are most likely to return for repeated MBH-related visits is critical for designing targeted interventions and improving continuity of care. EHR data from 203,925 pediatric patients was used to identify utilization patterns among pediatric patients with MBH visits. We applied a SMOTE-enhanced, two-stage clustering algorithm based on total and consecutive MBH/non-MBH visits. Descriptive statistics and temporal visualizations were used to characterize and describe the resulting patient subgroups. Five distinct ED utilization clusters were identified, ranging from low utilizers with isolated MBH visits to high-frequency users with complex clinical profiles and substantial non-MBH utilization. These findings highlight the need for future research to identify high-risk trajectories and examine whether non-MBH visits may signal emerging behavioral health crises in pediatric populations.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{sadralashrafi_users_2025,

title = {Users Willingness to Use Healthcare Chatbot and its Perceived Impact on Health Self-Management: A Survey Study},

author = {Sara Sadralashrafi and David M. Neyens and Yi-Ching Lee},

url = {https://journals.sagepub.com/doi/10.1177/10711813251359995},

doi = {10.1177/10711813251359995},

issn = {1071-1813, 2169-5067},

year  = {2025},

date = {2025-09-01},

urldate = {2026-04-29},

journal = {Proceedings of the Human Factors and Ergonomics Society Annual Meeting},

volume = {69},

number = {1},

pages = {2041–2048},

abstract = {Understanding the factors that influence users’ perceptions of healthcare chatbots is crucial for ensuring effective adoption and optimizing chatbots’ role in health service delivery. In this study, we conducted an online survey to identify specific beliefs and perspectives associated with users’ willingness to use healthcare chatbots and its perceived usefulness for health self-management. The results suggest that a willingness to use healthcare chatbots was positively associated with experiencing less tension during interactions with chatbots compared to interactions with clinician ( 

 p 

 < .001), using chatbots to prepare questions before medical appointments ( 

 p = .003), and a willingness to recommend them to others ( 

 p 

 < .001). Views on health self-management using healthcare chatbots were also positively related to prior AI experience ( 

 p 

 < .001), data accuracy ( 

 p = .025), time efficiency ( 

 p = .022), and post-appointment use ( 

 p 

 < .001). These insights provide a nuanced understanding of user needs, enabling future chatbot designs to better address expectations and enhance user experience, satisfaction, and sustainable adoption.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{soman_using_2025,

title = {Using patient journey mapping and provider workflows to understand process barriers to pediatric mental and behavioral health care in emergency departments},

author = {Devi Abhishek Soman and Samuel Nelson Koscelny and David Neyens and Ann Dietrich and Meera Narasimhan and Kevin Taaffe and David Allison and Anjali Joseph},

url = {https://linkinghub.elsevier.com/retrieve/pii/S0003687025000481},

doi = {10.1016/j.apergo.2025.104512},

issn = {36870},

year  = {2025},

date = {2025-07-01},

urldate = {2026-04-29},

journal = {Applied Ergonomics},

volume = {126},

pages = {104512},

keywords = {},

pubstate = {published},

tppubtype = {article}

}