Using telephone call rates and nurse-to-patient ratios as measures of resilient performance under high patient flow conditions

Patient admissions, discharges, and transfers are high work demand activities that have been associated with 30-day readmissions and increased patient mortality. Most mitigation strategies target peak demand, but variable demand may be more significant. Self-organizing holarchic open systems (SOHOs) and resilience engineering frameworks may explain system behavior, but a few quantitative studies of resilient organizational performance have been published. We used three measures to explore SOHO and resilience engineering constructs. We collected hourly data over 2 years, from five inter-related units in a cardiovascular disease division of a metropolitan teaching hospital. Our results show that information flows (inbound, outbound, answered, and unanswered telephone calls) representing anticipatory management are related to patient flows (patient admissions discharges and transfers) and nurse-staffing levels (nurse-to-patient ratios). We also found overall system stability despite high patient flow effects in lower level units. Unexpectedly, the time to recovery from high patient flow events lasted up to 7 days. We conclude that constructs proposed by resilience engineering can be quantified using simple measures collated within routine operations. The application of nonlinear statistical analyses can uncover important insights about resilient performance that may assist managers in better preparing for managing and recovering from unexpected variation in patient flow.

     

Differences in day and night shift clinical performance in anesthesiology

OBJECTIVE: This study examined whether anesthesia residents (physicians in training) performed clinical duties in the operating room differently during the day versus at night. BACKGROUND: Fatigue from sleep deprivation and working through the night is common for physicians, particularly during residency training. METHODS: Using a repeated-measures design, we studied 13 pairs of day-night matched anesthesia cases. Dependent measures included task times, workload ratings, response to an alarm light latency task, and mood. RESULTS: Residents spent significantly less time on manual tasks and more time on monitoring tasks during the maintenance phase at night than during the day. Residents reported more negative mood at night than during the day, both pre- and postoperation. However, time of day had no effect on the mood change between pre- and postoperation. Workload ratings and the response time to an alarm light latency task were not significantly different between night and day cases. CONCLUSIONS: Because night shift residents had been awake and working for more than 16 hr, the observed differences in task performance and mood may be attributed to fatigue. The changes in task distribution during night shift work may represent compensatory strategies to maintain patient care quality while keeping perceived workload at a manageable level. APPLICATIONS: Fatigue effects during night shifts should be considered when designing work-rest schedules for clinicians. This matched-case control scheme can also be applied to study other phenomena associated with patient safety in the actual clinical environment.     

A real-time assessment of work stress in physicians and nurses.

Objective: This study adapted ecological momentary assessment methods to: (a) examine differences in work stress between nurses and physicians, and (b) to study relationships between work stress, work activity patterns, and sleep. Design: A total of 185 physicians and 119 nurses (206 women, 98 men) working in four teaching hospitals participated in an observational study of work stress. Main Outcome Measures: Participants carried handheld computers that randomly prompted them for work activity, patient load, and work stress information. Results: Participants completed more than 9,500 random interval surveys during the study (an average of 30.8 surveys per person-week). Approximately 85% of all surveys were completed in full (73.3%) or partially (11.6%). Emotional stress scores among physicians were nearly 50% higher (26.9[19.0]) than those of nurses (18.1[14.9], r[302] = .37, p rs[159] = .14-.26, ps ps r[302] = ?.12, .17, ps Conclusions: The findings identify patterns of work stress in relationship to work activities, sleep habits, and provider differences that may be used to assist ongoing hospital work reform efforts. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

The respiratory effects of the cytokine regulating agent HP 228 alone and in combination with morphine in human volunteers

In the course of the eukaryotic ribosomal biogenesis, both the nuclear import and export are involved. We have studied the nuclear and nucleolar localization of the human ribosomal protein S7. We examined the subcellular distribution of the S7:beta-galactosidase fusion protein in SAOS-2 cells. We have identified two evolutionarily conserved domains, both of which are necessary for S7 nuclear and nucleolar targeting: amino acids 98 to 109 and 115 to 118. Out of the S7 protein context, a fragment 98...118, containing these domains, is sufficient for nuclear transport and nucleolar accumulation. Interestingly, a tetrapeptide 115KRPR118, which can act as an independent nuclear localization signal (NLS), is not sufficient for exclusively nuclear accumulation of the S7 protein if the adjacent region 98...109 is deleted. In addition, site-directed mutagenesis revealed that critical residues for nuclear targeting in this tetrapeptide and in the full-length S7 protein are different. While mutation of a Pro117 significantly impaired nuclear import of S7, similar substitution within the tetrapeptide-NLS had no effect on nuclear targeting. This suggests that to function perfectly, proper secondary structure of the S7 nuclear targeting domain is required.     

Enhancing IoT anomaly detection performance for federated learning

Federated Learning (FL) with mobile computing and the Internet of Things (IoT) is an effective cooperative learning approach. However, several technical challenges still need to be addressed. For instance, dividing the training process among several devices may impact the performance of Machine Learning (ML) algorithms, often significantly degrading prediction accuracy compared to centralized learning. One of the primary reasons for such performance degradation is that each device can access only a small fraction of data (that it generates), which limits the efficacy of the local ML model constructed on that device. The performance degradation could be exacerbated when the participating devices produce different classes of events, which is known as the class balance problem. Moreover, if the participating devices are of different types, each device may never observe the same types of events, which leads to the device heterogeneity problem. In this study, we investigate how data augmentation can be applied to address these challenges and improving detection performance in an anomaly detection task using IoT datasets. Our extensive experimental results with three publicly accessible IoT datasets show the performance improvement of up to 22.9% with the approach of data augmentation, compared to the baseline (without relying on data augmentation). In particular, stratified random sampling and uniform random sampling show the best improvement in detection performance with only a modest increase in computation time, whereas the data augmentation scheme using Generative Adversarial Networks is the most time-consuming with limited performance benefits.