Expertise and the interpretation of computerized physiological data: implications for the design of computerized monitoring in neonatal intensive care

This paper presents the outcomes from a cognitive engineering project addressing the design problems of computerized monitoring in neonatal intensive care. Cognitive engineering is viewed, in this project, as a symbiosis between cognitive science and design practice. A range of methodologies has been used: interviews with neonatal staff, ward observations and experimental techniques. The results of these investigations are reported, focusing specifically on the differences between junior and senior physicians in their interpretation of monitored physiological data. It was found that the senior doctors made better use of the different knowledge sources available than the junior doctors. The senior doctors were able to identify more relevant physiological patterns and generated more and better inferences than did their junior colleagues. Expertise differences are discussed in the context of previous psychological research in medical expertise. Finally, the paper discusses the potential utility of these outcomes to inform the design of computerized decision support in neonatal intensive care

Epidemiology of antibiotic resistance in culture-positive hospitalized patients in selected hospitals in Khartoum, Sudan

Objective: To study the prevelence of antibiotic resistance and the prevalent bacterial isolates in hospitalized patients in Khartoum hospitals. Materials & Methods: A cross-sectional prevalence study was carried out during the period of April–November 2015 in Khartoum; 226 bacterial cultures were included. Identification of isolates using standard biochemical tests and antibiotic susceptibilities were determined using disc diffusion method. Results were interpreted according to the standards of the British society of antimicrobial chemotherapy. Results: Eight bacterial species were isolated: Staphylococcus aureus, Enterococcus faecalis, Streptococcus spp., Klebsiella pneumoniae, Pseudomonas spp., Escherichia coli, Proteus spp., and Acinetobacter spp. S. aureus was the most prevalent, the majority of which were resistant to methicillin/oxacillin (MRSA). Cultures in our study were mainly from urine (36.7%), blood samples (37.2%), and wound cultures (19%). More than 90% of the tested isolates were resistant to cefuroxime; 54% and 73.8% of Gram-positive and Gram-negative isolates, respectively, were resistant to ceftazidime. Furthermore, there was a high meropenem resistance among Gram-negative isolates tested. Multi-resistant Acinetobacter spp. as well as vancomycin-resistant S. aureus was isolated. Gram-negative isolates showed good susceptibilities to aminoglycosides as well as ciprofloxacin. However, the high resistance rate to these antibiotics was observed in Gram-positive isolates in these hospitals. Conclusion: Methicillin-resistant S. aureus was the most prevalent organism. Gramnegative isolates showed good susceptibilities to aminoglycosides and ciprofloxacin. There were high resistance rates to cefuroxime, ceftazidime, and meropenem. Five vancomycin-resistant S. aureus were identified

The COVID-19 emergency response assessment study: a prospective longitudinal survey of frontline doctors in the UK and Ireland

OBJECTIVES: The psychological impact of the COVID-19 pandemic on doctors is a significant concern. Due to the emergence of multiple pandemic waves, longitudinal data on the impact of COVID-19 are vital to ensure an adequate psychological care response. The primary aim was to assess the prevalence and degree of psychological distress and trauma in frontline doctors during the acceleration, peak and deceleration of the COVID-19 first wave. Personal and professional factors associated with psychological distress are also reported. DESIGN: A prospective online three-part longitudinal survey. SETTING: Acute hospitals in the UK and Ireland. PARTICIPANTS: Frontline doctors working in emergency medicine, anaesthetics and intensive care medicine during the first wave of the COVID-19 pandemic in March 2020. PRIMARY OUTCOME MEASURES: Psychological distress and trauma measured using the General Health Questionnaire-12 and the Impact of Events-Revised. RESULTS: The initial acceleration survey distributed across networks generated a sample of 5440 doctors. Peak and deceleration response rates from the original sample were 71.6% (n=3896) and 56.6% (n=3079), respectively. Prevalence of psychological distress was 44.7% (n=1334) during the acceleration, 36.9% (n=1098) at peak and 31.5% (n=918) at the deceleration phase. The prevalence of trauma was 23.7% (n=647) at peak and 17.7% (n=484) at deceleration. The prevalence of probable post-traumatic stress disorder was 12.6% (n=343) at peak and 10.1% (n=276) at deceleration. Worry of family infection due to clinical work was the factor most strongly associated with both distress (R(2)=0.06) and trauma (R(2)=0.10). CONCLUSION: Findings reflect a pattern of elevated distress at acceleration and peak, with some natural recovery. It is essential that policymakers seek to prevent future adverse effects through (a) provision of vital equipment to mitigate physical and psychological harm, (b) increased awareness and recognition of signs of psychological distress and (c) the development of clear pathways to effective psychological care. TRIAL REGISTRATION NUMBER: ISRCTN10666798

Eye-CU: Sleep Pose Classification for Healthcare using Multimodal Multiview Data

Manual analysis of body poses of bed-ridden patients requires staff to continuously track and record patient poses. Two limitations in the dissemination of pose-related therapies are scarce human resources and unreliable automated systems. This work addresses these issues by introducing a new method and a new system for robust automated classification of sleep poses in an Intensive Care Unit (ICU) environment. The new method, coupled-constrained Least-Squares (cc-LS), uses multimodal and multiview (MM) data and finds the set of modality trust values that minimizes the difference between expected and estimated labels. The new system, Eye-CU, is an affordable multi-sensor modular system for unobtrusive data collection and analysis in healthcare. Experimental results indicate that the performance of cc-LS matches the performance of existing methods in ideal scenarios. This method outperforms the latest techniques in challenging scenarios by 13% for those with poor illumination and by 70% for those with both poor illumination and occlusions. Results also show that a reduced Eye-CU configuration can classify poses without pressure information with only a slight drop in its performance.Comment: Ten-page manuscript including references and ten figure