Defending Against Medical Error: Personal Reflections on the Legacy of John Senders.

OBJECTIVE: To honor the legacy of John Senders, a distinguished member of the Human Factors and Ergonomics Society, by a short, personal history of him, but then to honor his legacy by extending it through our own professional opinions, with an emphasis on the study of human error and its implications for healthcare systems-two topics in which he excelled. BACKGROUND: The authors are familiar with the topic and subject matter. One was a friend of Senders for over 50 years. Another was a collaborator and joint author with Senders (as well as his stepdaughter). All three authors have extensive publications in the topic areas. METHOD, RESULTS, AND CONCLUSION: The authors used personal accounts of interactions with Senders at conferences, experiences living and working with him, and a brief review of his most personal, notable publications in healthcare. The reflections indicate a strong resonance on Senders' contributions to system design that are relevant today in healthcare's most challenging period in its history.

A Distracted Scientist: The Life and Contributions of John Senders.

OBJECTIVE: To provide an evaluative and personal overview of the life and contributions of Professor John Senders and to introduce this Special Issue dedicated to his memory. BACKGROUND: John Senders made many profound contributions to HF/E. These various topics are exemplified by the range of papers which compose the Special Issue. Collectively, these works document and demonstrate the impact of his many valuable research works. METHOD: The Special Issue serves to summarize Senders' collective body of work as can be extracted from archival sources. This introductory paper recounts a series of remembrances derived from personal relationships, as well as the products of cooperative investigative research. RESULTS: This collective evaluative process documents Senders' evident and deserved status in the highest pantheon of HF/E pioneers. It records his extraordinary life, replete with accounts of his insights and joie de vivre in exploring and explaining the world which surrounded him. APPLICATIONS: Senders' record of critical contributions provides the example, par excellence, of the successful and fulfilling life in science. It encourages all, both researchers and practitioners alike, in their own individual search for excellence.

Video-Based Pairwise Comparison: Enabling the Development of Automated Rating of Motor Dysfunction in Multiple Sclerosis.

OBJECTIVES: To examine the feasibility, reliability, granularity, and convergent validity of a video-based pairwise comparison technique that uses algorithmic support to enable automated rating of motor dysfunction in patients with multiple sclerosis (MS). DESIGN: Feasibility and larger cross-sectional cohort study. SETTING: The outpatient clinic of 2 specialist university medical centers. PARTICIPANTS: Selected sample from a cohort of patients with MS participating in the Assess MS study (N=42). Videos were randomly drawn from each strata of the ataxia severity-degrees as defined in the Expanded Disability Status Scale (EDSS). In Basel: 19 videos of 17 patients (mean age, 43.4±11.6y; 10 women). In Amsterdam: 50 videos of 25 patients (mean age, 50.0±10.0y; 15 women). INTERVENTIONS: Not applicable. MAIN OUTCOME MEASURES: In each center, neurologists (n=13; n=10) viewed pairs of videos of patients performing standardized movements (eg, finger-to-nose test) to assess relative performance. A comparative assessment score was calculated for each video using the TrueSkill algorithm and analyzed for intrarater (test-retest; ratio of agreement) and interrater reliability (intraclass correlation coefficient [ICC] for absolute agreement) and convergent validity (Spearman ρ). Granularity was estimated from the average difference in comparative assessment scores at which 80% of neurologists considered performance to be different. RESULTS: Intrarater reliability was excellent (median ratio of agreement≥0.87). The comparative assessment scores calculated from individual neurologists demonstrated good-excellent ICCs for interrater reliability (0.89; 0.71). The comparative assessment scores correlated (very) highly with their Neurostatus-EDSS equivalent (ρ=0.78, P<.001; ρ=0.91, P<.05), suggesting a more fine-grained rating. CONCLUSIONS: Video-based pairwise comparison of motor dysfunction allows for reliable and fine-grained capturing of clinical judgment about neurologic performance, which can contribute to the development of a consistent quantified metric of motor ability in MS.

Meeting (the) Pandemic: Videoconferencing Fatigue and Evolving Tensions of Sociality in Enterprise Video Meetings During COVID-19.

When COVID-19 led to mandatory working from home, significant blind spots in supporting the sociality of working life-in the moment and over time-were revealed in enterprise video meetings, and these were a key factor in reports about videoconferencing fatigue. Drawing on a large study (N = 849) of one global technology company's employees' experiences of all-remote video meetings during the COVID-19 pandemic, we use a dialectic method to explore the tensions expressed by employees around effectiveness and sociality, as well as their strategies to cope with these tensions. We argue that videoconferencing fatigue arose partly due to work practices and technologies designed with assumptions of steady states and taken-for-granted balances between task and social dimensions of work relationships. Our analysis offers a social lens on videoconferencing fatigue and suggests the need to reconceptualize ideas around designing technologies and practices to enable both effectiveness and sociality in the context of video meetings.

Setwise comparison: efficient fine-grained rating of movement videos using algorithmic support - a proof of concept study.

Purpose: Clinical ordinal rating scales of movements, e.g., the Expanded Disability Status Scale, have poor intra- and interrater reliability, are insensitive to subtle differences and result in coarse-grained ratings compared to relative comparative rating methods. We therefore established video-based setwise comparison as a fine-grained, reliable and efficient rating method of motor dysfunction using algorithmic support.Materials and methods: Eight neurologists rated a set of 40 multiple sclerosis patient videos of the Finger-to-Nose-Test using both the newly developed setwise comparison and the established pairwise comparison techniques, which result in a continuous rating scale. Reliability was assessed by the intra-class correlation coefficient. Construct validity was estimated as Pearson's correlation between the continuous scale and severity ratings according to the Neurostatus scale for upper-extremity tremor/dysmetria and the Nine-hole-peg-test. Comparing the time needed for ratings assessed efficiency.Results: Intra-class correlation coefficient was 0.83 for setwise and 0.7 for pairwise comparison. Correlation to the tremor/dysmetria score of the Neurostatus was 0.86 for both rating procedures and correlation to the Nine-hole-peg-test was 0.64 (setwise) and 0.66 (pairwise). The time needed to rate 40 videos was 22.9 ± 6.9 minutes (setwise) and 77.8 ± 14.5 minutes (pairwise).Conclusions: Setwise comparison is an efficient, valid and reliable method for fine-grained rating of motor dysfunction that can be applied to larger datasets. It is substantially more efficient than pairwise comparison.Implications for rehabilitationDisability rating is crucial in clinical neurorehabilitation and in clinical trials.Humans are naturally inconsistent in rating items on ordinal scales leading to poor intra- and interrater reliability, insensitivity to subtle differences and coarse-grained ratings.Video-based setwise comparison is a new rating method enabling fine-grained, reliable and efficient ratings of motor dysfunction using algorithmic support.

Reference videos reduce variability of motor dysfunction assessments in multiple sclerosis.

Motor dysfunction, particularly ataxia, is one of the predominant clinical manifestations in patients with multiple sclerosis (MS). Assessment of motor dysfunction suffers from a high variability. We investigated whether the clinical rating of ataxia can be improved through the use of reference videos, covering the spectrum of severity degrees as defined in the Neurostatus-Expanded Disability Status Scale. Twenty-five neurologists participated. The variability of their assessments was significantly lower when reference videos were used (SD = 0.12; range = 0.40 vs SD = 0.26; range = 0.88 without reference videos; p = 0.013). Reference videos reduced the variability of clinical assessments and may be useful tools to improve the precision and consistency in the clinical assessment of motor functions in MS.

Usability and Acceptability of ASSESS MS: Assessment of Motor Dysfunction in Multiple Sclerosis Using Depth-Sensing Computer Vision.

BACKGROUND: Sensor-based recordings of human movements are becoming increasingly important for the assessment of motor symptoms in neurological disorders beyond rehabilitative purposes. ASSESS MS is a movement recording and analysis system being developed to automate the classification of motor dysfunction in patients with multiple sclerosis (MS) using depth-sensing computer vision. It aims to provide a more consistent and finer-grained measurement of motor dysfunction than currently possible. OBJECTIVE: To test the usability and acceptability of ASSESS MS with health professionals and patients with MS. METHODS: A prospective, mixed-methods study was carried out at 3 centers. After a 1-hour training session, a convenience sample of 12 health professionals (6 neurologists and 6 nurses) used ASSESS MS to capture recordings of standardized movements performed by 51 volunteer patients. Metrics for effectiveness, efficiency, and acceptability were defined and used to analyze data captured by ASSESS MS, video recordings of each examination, feedback questionnaires, and follow-up interviews. RESULTS: All health professionals were able to complete recordings using ASSESS MS, achieving high levels of standardization on 3 of 4 metrics (movement performance, lateral positioning, and clear camera view but not distance positioning). Results were unaffected by patients' level of physical or cognitive disability. ASSESS MS was perceived as easy to use by both patients and health professionals with high scores on the Likert-scale questions and positive interview commentary. ASSESS MS was highly acceptable to patients on all dimensions considered, including attitudes to future use, interaction (with health professionals), and overall perceptions of ASSESS MS. Health professionals also accepted ASSESS MS, but with greater ambivalence arising from the need to alter patient interaction styles. There was little variation in results across participating centers, and no differences between neurologists and nurses. CONCLUSIONS: In typical clinical settings, ASSESS MS is usable and acceptable to both patients and health professionals, generating data of a quality suitable for clinical analysis. An iterative design process appears to have been successful in accounting for factors that permit ASSESS MS to be used by a range of health professionals in new settings with minimal training. The study shows the potential of shifting ubiquitous sensing technologies from research into the clinic through a design approach that gives appropriate attention to the clinic environment.

Quantifying progression of multiple sclerosis via classification of depth videos.

This paper presents new learning-based techniques for measuring disease progression in Multiple Sclerosis (MS) patients. Our system aims to augment conventional neurological examinations by adding quantitative evidence of disease progression. An off-the-shelf depth camera is used to image the patient at the examination, during which he/she is asked to perform carefully selected movements. Our algorithms then automatically analyze the videos, assessing the quality of each movement and classifying them as healthy or non-healthy. Our contribution is three-fold: We i) introduce ensembles of randomized SVM classifiers and compare them with decision forests on the task of depth video classification; ii) demonstrate automatic selection of discriminative landmarks in the depth videos, showing their clinical relevance; iii) validate our classification algorithms quantitatively on a new dataset of 1041 videos of both MS patients and healthy volunteers. We achieve average Dice scores well in excess of the 80% mark, confirming the validity of our approach in practical applications. Our results suggest that this technique could be fruitful for depth-camera supported clinical assessments for a range of conditions.