Articles of Interest

Uncovering Latent Failures Using Human Factors Approach as a Diagnostic Tool for Quality Improvement in Orthopedic Surgery

Khan, A, Cohen, T, Shappell, S & Boquet, A. (2025). Uncovering Latent Failures Using Human Factors Approach as a Diagnostic Tool for Quality Improvement in Orthopedic Surgery. American Journal of Medical Quality, 40, 255-260. https://doi.org/10.1097/JMQ.0000000000000265

Abstract

Human factors significantly influence medical quality, especially in complex environments like orthopedic surgery, where latent failures can compromise patient safety. A total of 3168 intraoperative events were observed across 40 orthopedic procedures and classified using the Human Factors Analysis and Classification System (HFACS). Three trained coders independently applied HFACS across 4 tiers and 19 causal categories. Interrater reliability was measured through percent agreement and Fleiss' Kappa using unanimous, majority, and reconciled coding conditions. Nearly all observed disruptions (98.97%) were classified as preconditions to unsafe acts, most (68.75%) stemmed from crew resource management failures, distractions from personal electronic devices, poor communication, and sales representative presence. A total of 19.47% of disruptions were due to personal readiness, due to the sales representation supporting role in ensuring technologies. An additional 5.87% were due to physical environment issues like equipment noise.

Conclusions: The HFACS framework demonstrated strong reliability in identifying systemic weaknesses within orthopedic surgical workflows. These findings emphasize the urgent need for structured interventions that reduce distractions, improve team communication, and regulate vendor interactions in the operating room, all essential steps toward advancing safety and enhancing overall patient care quality.

AI Can Detect Facial Signs of Consciousness in Brain-Injured Patients Before Doctors

(2025). AI Can Detect Facial Signs of Consciousness in Brain-Injured Patients Before Doctors. Biomedical Safety & Standards, 55, 119-120. https://doi.org/10.1097/01.BMSAS.0001172188.69833.83AI Can Detect Facial Signs of Consciousness in Brain-Injured Patients Before Doctors. (2025). Biomedical Safety & Standards, 55, 119-120. https://doi.org/10.1097/01.BMSAS.0001172188.69833.83

Excerpt:

A study 1 published in Nature Communications Medicine looked at a central challenge in the acute brain injury (ABI) setting: reliably discerning residual consciousness in patients who appear unresponsive at the bedside.
Traditional examinations of comatose patients depend on overt, clinician-observable command following. According to the authors, however, some patients with ABI exhibit cognitive-motor dissociation, or consciousness without clearly visible motor output. Investigators hypothesized that low-amplitude, purposeful facial movements can precede overt responses detectable during routine examinations and therefore could serve as early indicators of recovery. To test this, the authors developed "SeeMe," a computer vision technique that quantifies subtle facial movements in response to standardized auditory commands.

Migration of toxic elements from recycled paper food contact materials to food simulants: compatibility and influence of sample preparation methods 

Athanasios Kourkopoulos, Dick Theodorus Hubertus Maria Sijm, Misha Vrolijk, Migration of toxic elements from recycled paper food contact materials to food simulants: compatibility and influence of sample preparation methods, Food Quality and Safety, Volume 9, 2025, fyaf002, https://doi.org/10.1093/fqsafe/fyaf002  

Abstract

Impact of a Multi-faceted Quality Improvement Initiative Aimed at Reducing Primary Cesarean Delivery Rates

Melissa Peskin MD, Brittany H Sanford MD, Celia A Muoser MD MPH, Emma Chew Murphy MD, Amoli Kulkarni MD, Beatrice Lynch MD, Tess Nienaltow MS IV, Marlena Gehret Plagianos, Peter Bernstein MD MPH and Pe’er Dar MD.
Joint Commission Journal on Quality and Patient Safety, The, 2025-12-22, Article in Press: Accepted Manuscript, Copyright © 2025 The Joint Commission 

Abstract

Introduction
The ideal approach to safely reduce the rising rates of cesarean deliveries (CD) is unknown. We evaluated the impact of a multi-faceted quality improvement initiative aimed at curbing the rising trend in the institutional nulliparous term singleton vertex (NTSV) CD rate at an urban, academic, safety-net hospital.

Methods
The primary intervention was a bi-weekly interdisciplinary educational meeting, wherein NTSV CD cases were reviewed and audited. In addition, an educational bulletin, NTSV score cards, labor position training, and efforts to promote guideline adherence were introduced. We examined the institutional NTSV CD rates by quarters and evaluated the trend, modeling the probability of a CD for the pre and post intervention periods using logistic regression with time as an ordinal variable. We also examined cesarean indications as well as maternal and neonatal complications before and after the QI implementation.

Results
There was a trend towards a decrease in CDs during the post-intervention period, falling from 32.9% to 25% in the twelve months following implementation. Using an interrupted time series analysis to assess differences in trends, the trend before intervention was +2.63% per quarter (p < 0.001, 95% CI 2.30-3.03), while the change in trend post-intervention was -3.78% per quarter (p < 0.001, 95% CI -4.74, -2.81, R 2 =0.973). This suggests an increasing trend prior to intervention that was effectively reversed. Both maternal and neonatal complications rate remained comparable between pre- and post-intervention periods for those who had NTSV CDs.

Conclusion
Multifaceted strategies incorporating interactive case review meetings can lead to practice change and may be a feasible strategy to reduce an institutional NTSV CD rate.

Understanding Clinical Decision Support Failures in Pediatric Intensive Care Units via Applied Systems Safety Engineering and Human Factors Problem Analysis: Insights From the DISCOVER Learning Lab

Zackoff, M, Graciela, A, Collins, K, Loeb, D, Meisman, A, James, K, Dewan, M. (2025). Understanding Clinical Decision Support Failures in Pediatric Intensive Care Units via Applied Systems Safety Engineering and Human Factors Problem Analysis: Insights From the DISCOVER Learning Lab. Journal of Patient Safety, 21, S21-S28. https://doi.org/10.1097/PTS.0000000000001358

Abstract

Objectives: Children receiving care in pediatric intensive care units (PICUs) are vulnerable to decompensation and diagnostic error due to the complex and dynamic nature of pediatric critical illness. In the PICU, the few clinical decision support (CDS) tools that have been implemented to support diagnostic accuracy (i.e., the ability to detect the presence of a condition) have not led to an increase in clinician adoption of desired practices nor demonstrated clear clinical benefit.

Methods: The DISCOVER Learning Lab analyzed workflow and failure modes in diagnosing and managing clinical decompensation in the PICU, using systems safety engineering and human factors to examine intersections with established CDS. Methods employed included qualitative interviews, workflow mapping, immersive virtual reality (VR) systems testing via a digital twin environment, and a failure modes effect analysis.

Results: Workflow mapping and qualitative interviews revealed barriers to communication, workflow inefficiencies, and limited access to up-to-date clinical information during critical events in the PICU. The immersive VR systems testing elucidated how PICU staff members currently interact with CDS tools and how various tools could better integrate into or influence clinical workflows. Critical failure modes were identified with corresponding opportunity areas for intervention.

Conclusions: The application of a systems safety engineering and human factors approach to problem analysis, partnered with novel use of immersive VR and digital twin technology, led to valuable insights into common failure modes and potential opportunity areas to improve diagnostic accuracy and care delivery in a quaternary referral center PICU.