Lester (Poland)

Disease investigation and contact tracing are long-standing public health strategies used to control the spread of infectious disease. Throughout the COVID-19 pandemic, health departments across the country have lacked the internal workforce capacity and technology needed to efficiently isolate positive cases and quarantine close contacts to slow the spread of SARS-CoV-2. This article describes an innovative disease investigation and contact tracing program developed through a formalized community partnership between a local county health department and local university. This innovative new program added 108 contact tracers to the county's public health workforce, as well as enabled these contact tracers to work remotely using a call center app and secure cloud-based platform to manage the county's caseload of cases and contacts. An overview of the requirements needed to develop this program (eg, hiring, health data security protocols, data source management), as well as lessons learned is discussed.

Until recently, the main effect of technology on professional or knowledge-based work has been to augment and expand it, partly as described in Autor, Levy and Murnane’s 2003 analysis. There are now increasingly instances of knowledge-based work being automated and substituted, developments that are more familiar from factory and basic administrative settings. Two widely-quoted studies, by Frey and Osborne (2013) and Susskind and Susskind (2015), point towards significant technology-driven job losses including in professional fields. Subsequent analyses indicate that while some occupations will disappear or be deskilled, others will be created. The argument made here is that the most significant effect will be occupational transformation, necessitating different types of skills in a net movement towards work that is more digitally-oriented but also complex, creative and value-based. These changes have implications that are already beginning to affect the way that professions are organised and how practitioners are educated and trained.

Background: Real world studies of silent brain infarction (SBI) and white matter disease (WMD) are impeded by challenges in cohort identification. Natural language processing (NLP) from imaging reports may facilitate future studies. However, electronic health records can be heterogeneous and the process of interpreting neuroimages and generating reports can vary. Understanding knowledge representation and relationships between neuroimages and imaging reports is crucial for using NLP to facilitate disease management and cohort identification. Methods: A balanced sample of head neuroimages (CT, MRI) of patients >50 years without clinical histories of symptomatic stroke, TIA, or dementia were obtained at Mayo Clinic and Tufts Medical Center. A team of 4 radiology residents performed report interpretation (RI) on 1000 reports according to a standardized protocol for the presence of SBIs, the presence of WMD, and WMD grade. A random subsample of 400 was doubly read for interrater reliability. For benchmarking, a team of 4 neuroradiologists directly reviewed and described findings on a subsample of 182 images, each doubly read. We assessed interrater reliability for direct review (DR) and RI, and agreement between these 2 information sources. An NLP algorithm was developed to review and extract findings from 1000 imaging reports. Results: For DR, interrater reliability was moderate for SBIs and WMD (k = 0.53, 95% CI 0.43-0.64 and k = 0.47, 95% CI 0.33-0.61) and good for WMD grade (Spearman 0.71, p<0.001). For RI, interrater reliability for SBIs, WMD and WMD grade was good (k = 0.88, 95% CI 0.80-0.97; k = 0.98, 95% CI 0.97-1.00; and Spearman = 0.985, p<0.001, respectively). Agreement between DR and RI was good for SBIs (k = 0.77, 95% CI 0.67-0.86) and WMD (k = 0.65, 95% CI 0.54-0.77). Spearman rank correlations comparing WMD grade interpretation DR to RI was 0.60 (p<0.001). In identifying the presence of SBIs and WMDs, the accuracy of the NLP algorithm was 0.991 and 0.994, respectively. Conclusion: For the presence of SBI and WMD, and WMD grade, agreement between RI and DR was similar to agreement between 2 neuroradiologists directly reviewing neuroimages. It is highly feasible to use NLP to identify patients with SBIs and WMDs for clinical studies.

Dysphagia screening protocols have been recommended to identify patients at risk for aspiration. The American Heart Association convened an evidence review committee to systematically review evidence for the effectiveness of dysphagia screening protocols to reduce the risk of pneumonia, death, or dependency after stroke. The Medline, Embase, and Cochrane databases were searched on November 1, 2016, to identify randomized controlled trials (RCTs) comparing dysphagia screening protocols or quality interventions with increased dysphagia screening rates and reporting outcomes of pneumonia, death, or dependency. Three RCTs were identified. One RCT found that a combined nursing quality improvement intervention targeting fever and glucose management and dysphagia screening reduced death and dependency but without reducing the pneumonia rate. Another RCT failed to find evidence that pneumonia rates were reduced by adding the cough reflex to routine dysphagia screening. A smaller RCT randomly assigned 2 hospital wards to a stroke care pathway including dysphagia screening or regular care and found that patients on the stroke care pathway were less likely to require intubation and mechanical ventilation; however, the study was small and at risk for bias. There were insufficient RCT data to determine the effect of dysphagia screening protocols on reducing the rates of pneumonia, death, or dependency after stroke. Additional trials are needed to compare the validity, feasibility, and clinical effectiveness of different screening methods for dysphagia.

Introduction: Prediction instruments for large vessel occlusion (LVO) have been proposed to identify patients for rapid transport to endovascular thrombectomy (EVT) capable hospitals. This Evidence Review Committee was commissioned by the AHA/ASA to systematically review evidence for the accuracy of LVO prediction instruments. Methods: Medline, Embase, and Cochrane databases were searched on October 27, 2016. Study quality was assessed using the Quality Assessment of Diagnostic Accuracy (QUADAS)-2 tool. Results: Thirty-six relevant studies were identified. Most (21/36) recruited patients with confirmed ischemic stroke, with few studies in the pre-hospital setting (4/36) and in populations that included hemorrhagic stroke or stroke mimics (12/36). Most studies had either some risk of bias or unclear risk of bias. Discrimination of LVO, as measured by the c-statistic, mostly ranged from 0.70-0.85. In meta-analysis, no threshold on any instrument predicted LVO with both high sensitivity and specificity (Table). With a positive LVO prediction test, the probability of LVO could be 50% or greater (depending on the LVO prevalence in the population), but the probability of LVO with a negative test could still be 10% or more. Conclusions: No scale predicted LVO with both high sensitivity and specificity. Systems that use LVO prediction instruments for triage will miss some patients with LVO and milder stroke. More prospective studies are needed in the pre-hospital setting in all patients with suspected stroke, including hemorrhagic stroke and stroke mimics.

The aim of the study was to investigate the possibility of Transmissible Viral Proventriculitis (TVP) occurrence in broiler chickens in Poland. In march 2016 proventriculi samples were collected from broiler chickens showing poor uniformity and decreased body weight, accompanied by enlarged proventriculi. Histopathological examination of affected proventriculi revealed typical lesions associated with TVP (vacuolar degeneration and necrosis of glandular epithelium, replacement of glandular epithelium by hyperplastic ductal epithelium and moderate to severe lymphocytic infiltration). To our best knowledge, up to date there is no report indicating the presence of TVP in Poland nor in Central and Eastern Europe.

Pay for performance is now a widely adopted quality improvement initiative in health care. One of the largest schemes in primary care internationally is the English Quality and Outcomes Framework (QOF). To obtain a longer term perspective on the implementation of the QOF. Qualitative study with 47 health professionals in 23 practices across England. Method Semi-structured interviews. Pay for performance is accepted as a routine part of primary care in England, with previous more individualistic and less structured ways of working seen as poor practice. The size of the QOF and the evidence-based nature of the indicators are regarded as key to its success. However, pay for performance may have had a negative impact on some aspects of medical professionalism, such as clinical autonomy, and led a significant minority of GPs to prioritise their own pay rather than patients' best interests. A small minority of GPs tried to increase their clinical autonomy with further unintended consequences. Pay for performance indicators are now welcomed by primary healthcare teams and GPs across generations. Almost all interviewees wanted to see a greater emphasis on involving front line practice teams in developing indicators. However, almost all GPs and practice managers described a sense of decreased clinical autonomy and loss of professionalism. Calibrating the appropriate level of clinical autonomy is critical if pay for performance schemes are to have maximal impact on patient care.

Abstract In this chapter an alternative method to make independent component analysis and source separation is introduced. It is based upon Fuzzy Adaptive Simulated Annealing and uses mainly mutual information measures to achieve its final goals. After presenting the central arguments of the method, some experimental results are shown and comparison to previous work is done.KeywordsMutual InformationJoint DistributionOriginal SignalFuzzy Inference SystemIndependent Component AnalysisThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

The design of complex Systems-on-Chips implies to take into account communication and memory access constraints for the integration of dedicated hardware accelerator. In this paper, we present a methodology and a tool that allow the High-Level Synthesis of DSP algorithm, under both I/O timing and memory constraints. Based on formal models and a generic architecture, this tool helps the designer to find a reasonable trade-off between both the required I/O timing behavior and the internal memory access parallelism of the circuit. The interest of our approach is demonstrated on the case study of a FFT algorithm.