Objective: To analyse and redesign the care process for patients with severe post-partum haemorrhage using simulation and a human factors approach.
Methods: The project was developed between June 2016 and May 2017. The working group was created following the participatory leadership method and included professionals with knowledge and position to influence the organisation. The existing process and clinical protocols were reviewed. An in situ simulation was used to observe team performance in the hospital recovery room. Information was expanded through an open and structured interview with professionals selected for their experience in the subject matter. Task analysis was used to document the process, and a failure mode and effects analysis was made to assess system vulnerabilities. Results were mapped using a flow chart.
Results: The analysis identified six groups of people working on different tasks, their activities and sequence of action, the importance of naming an explicit coordinator, the way in which information is disseminated and transformed, and the stages where it is necessary to share information and make key clinical decisions. The existing clinical protocols and the aids established in order to use the available resources were integrated, including blood draws and haemostatic agents, as well as an administration guide.
Conclusions: The analysis of the patient care process in post-partum haemorrhage using in situ simulation with a human factors perspective, including an analysis of the interaction between professionals and the system where they work, established a detailed and personalised map of the components that determine how work flows through the organisation.
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http://dx.doi.org/10.1016/j.jhqr.2018.01.002 | DOI Listing |
Sci Rep
December 2024
Department of Computer Science and Digital Technologies, University of East London, London, UK.
Nursing activity recognition has immense importance in the development of smart healthcare management and is an extremely challenging area of research in human activity recognition. The main reasons are an extreme class-imbalance problem and intra-class variability depending on both the subject and the recipient. In this paper, we apply a unique two-step feature extraction, coupled with an intermediate feature 'Angle' and a new feature called mean min max sum to render the features robust against intra-class variation.
View Article and Find Full Text PDFSci Rep
December 2024
Department of Applied Mathematics, Faculty of Mathematical Science, Ferdowsi University of Mashhad, Mashhad, Iran.
This study presents a web application for predicting cardiovascular disease (CVD) and hypertension (HTN) among mine workers using machine learning (ML) techniques. The dataset, collected from 699 participants at the Gol-Gohar mine in Iran between 2016 and 2020, includes demographic, occupational, lifestyle, and medical information. After preprocessing and feature engineering, the Random Forest algorithm was identified as the best-performing model, achieving 99% accuracy for HTN prediction and 97% for CVD, outperforming other algorithms such as Logistic Regression and Support Vector Machines.
View Article and Find Full Text PDFNat Commun
December 2024
Institute of Environment and Ecology, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China.
Record breaking atmospheric methane growth rates were observed in 2020 and 2021 (15.2±0.5 and 17.
View Article and Find Full Text PDFAlzheimers Dement
December 2024
Department of Pathology, Molecular, and Cell-Based Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
Introduction: Alzheimer's disease (AD), primary age-related tauopathy (PART), and chronic traumatic encephalopathy (CTE) all feature hyperphosphorylated tau (p-tau)-immunoreactive neurofibrillary degeneration, but differ in neuroanatomical distribution and progression of neurofibrillary degeneration and amyloid beta (Aβ) deposition.
Methods: We used Nanostring GeoMx Digital Spatial Profiling to compare the expression of 70 proteins in neurofibrillary tangle (NFT)-bearing and non-NFT-bearing neurons in hippocampal CA1, CA2, and CA4 subregions and entorhinal cortex of cases with autopsy-confirmed AD (n = 8), PART (n = 7), and CTE (n = 5).
Results: There were numerous subregion-specific differences related to Aβ processing, autophagy/proteostasis, inflammation, gliosis, oxidative stress, neuronal/synaptic integrity, and p-tau epitopes among these different disorders.
JMIR Form Res
December 2024
Department of Psychiatry & Behavioral Sciences, University of Minnesota, Minneapolis, MN, United States.
Background: Anxiety disorders are common in alcohol use disorder (AUD) treatment patients. Such co-occurring conditions ("comorbidity") have negative prognostic implications for AUD treatment outcomes, yet they commonly go unaddressed in standard AUD care. Over a decade ago, we developed and validated a cognitive behavioral therapy intervention to supplement standard AUD care that, when delivered by trained therapists, improves outcomes in comorbid patients.
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