Aug 2026· Proceedings of the International Symposium on Human Factors and Ergonomics in Health Care· Vol 15, pp. 107 - 111· 0 citations· 9 references
TL;DR
GenAI-assisted processes can provide rapid, actionable design mitigations that reduce error likelihood and enhance patient autonomy, establishing a replicable pipeline for producing heuristic-driven design libraries across diverse medical device contexts.
Abstract
Home healthcare technologies, particularly those supporting high-risk, self-administered treatments like at-home dialysis, present significant challenges for human factors professionals. Patients must navigate complex procedures while managing anxiety, fatigue, and comorbidities. Errors in process sequence or use can have severe consequences. This research leverages generative artificial intelligence to systematically generate, classify, and curate design patterns that support error-tolerant user interface solutions. Using a specialized Gemini Gem implementation, 70 design principles grounded in IEC 62366-1 and FDA guidance were codified, alongside a library of 50 UX design patterns addressing critical HCI concerns such as trust calibration, cognitive load, and alarm confusion. The GenAI-driven evaluation demonstrated a high degree of accuracy in assessing compliance across four existing at-home dialysis interfaces. The results indicate that GenAI-assisted processes can provide rapid, actionable design mitigations that reduce error likelihood and enhance patient autonomy. Long-term, this methodology establishes a replicable pipeline for producing heuristic-driven design libraries across diverse medical device contexts, supporting HF professionals in guiding safe, scalable, and responsible AI use in healthcare.
BackgroundBecause automated peritoneal dialysis (APD) systems are used in the home and operated by patients or their care partners, the human-device interface must be thoughtfully designed to facilitate intuitive operation to reduce the risk of errors that could occur during their use. User-friendly design should also help reduce anxiety to peritoneal dialysis (PD) adoption by patients incident to end-stage kidney disease (ESKD). Usability issues remain a significant barrier to PD adoption and are an important contributor to premature death, serious injuries, and PD technique failure.MethodsA summative human factors usability study was conducted on a novel, gravity-based APD device (Archimedes™) with 15 current or former PD patients and 15 dialysis nurses. Participants were trained for 2 h, followed by a training decay period, then evaluated with critical Use Scenario tasks consisting of Simulated Use tasks and Knowledge tasks reflecting tasks that could result in patient harm if performed incorrectly.ResultsOf the Simulated Use tasks evaluated, 97.3% were deemed successful across all users. This high success rate demonstrates the effectiveness of the Archimedes APD device in facilitating the tasks required for PD. For patients, success outcomes were achieved in 96.8% of Simulated Use tasks. Nurses achieved a success rate of 97.8% for Simulated Use tasks evaluated. For Knowledge tasks, success outcomes were achieved in 99.3% and 98.6% of tasks for patients and nurses.ConclusionIn this summative human factors usability study, the Archimedes APD system usability was found to be safe, accessible and easy for its intended users and use environments after the relatively short training period compared to the status quo.
Nupur Gupta, Vikram Aggarwal, James A. Sloand et al.· Peritoneal Dialysis Internat...· 0 citations
Healthcare software prototypes usually have conversational help, scheduling, role-specific dashboards, and real-time data. Their evaluations focus more on feature lists than on failure modes. This paper reports a reproducible evaluation of safety and access control for Medicare. React, Node.js/Express, MongoDB, and socket.io. AI.IO educational health care platform. We froze a 20-prompt safety benchmark covering emergencies, first aid, medication use, mental health and general questions. We defined eight source-level security checks, including JSON Web Token middleware, role guards, appointment ownership, patient-only creation, password hashing, secret handling and Socket.IO room access. The baseline deterministic chatbot passed 7 out of 20 prompts (35%) and 5 out of 8 security checks. The dynamic loopback test also showed that an unauthenticated client could join the doctor telemetry room. Remediation introduced specific intent-matching, explicit high-risk routes, authenticated socket.io middleware, room-level role restrictions, and appointment ownership enforcement. Our final benchmark passed all 20 prompts and all eight source-level checks. The study does not claim clinical efficacy: no patient records, clinician participants, diagnostic model, or clinical outcomes were assessed. The Atlas network allowlist blocked database-backed exploitation and post-remediation dynamic socket tests, which were reported as unexecuted. Thus, the contribution is a transparent case study of how an ambitious student prototype can be transformed into a more testable, safety-aware system.
Kaustubha Khandagale, S. Bhosle, Akhilesh Kurhadkar et al.· DMPedia Lecture Notes in Com...· 0 citations
This position paper presents a manifesto for a longitudinal, three-fold methodological pivot in health human-AI interaction, and proposes moving beyond static satisfaction metrics towards relational metrics —Longitudinal Trust Calibration, Automation Bias Drift, and Error Recovery Velocity—that track the maturity and resilience of the human-AI partnership.
Mariana de Oliveira, Célia F. Cruz, Nuno Matela· Information Hiding· 0 citations
A systematic appraisal of high-impact literature across databases was conducted, demonstrating that integrated closed-loop medication management infrastructures substantially reduce MAEs by automating patient identification, prescription label parsing via computer vision, and real-time physiological response tracking.
Shivanand H Honakeri, Hemanth C K, Latha Venkatesh· Journal of Nursing Future Ca...· 0 citations
This mixed-methods evaluation suggests that a deliberately constrained, language-focused AI system can improve the accessibility of medical notes while preserving clinical accuracy and safety while extending into clinical interpretation.
Nicholas Lamb· Frontiers in Digital Health· 0 citations
Overall, the paper synthesizes design principles for human-centered AI that emphasize localization, explainability, training, and accountability, arguing that effectiveness depends more on interaction design than technical sophistication.
Azmine Toushik Wasi, Mahdiya Rahman Sukanya· Information Hiding· 0 citations
A new method, called CW-Net, translates the reasoning process of an autonomous vehicle’s AI system into understandable concepts that explain its behavior.
