Variation in Family Involvement on Rounds Between English-Speaking and Spanish-Speaking Families

BACKGROUND: Family-centered rounds (FCR) is the standard of care in inpatient pediatrics. Results of studies have revealed that Spanish-speaking families can experience communication challenges and decreased empowerment on rounds. In our study, we aim to identify variation in FCR practices for Spanish-speaking compared to English-speaking families and factors contributing to these disparities. METHODS: This is a cross-sectional observational study performed by secondary analysis of a quality improvement initiative conducted at a quaternary children’s hospital. Data were collected from June 2019 to March 2020 by using observational audits. Encounters were analyzed to compare key elements of FCR (including rounds location, elicitation of family questions, involvement in discharge planning) for English-speaking and Spanish-speaking families. Multivariable logistic regression was used to compare family involvement in FCR. A sensitivity analysis was conducted to evaluate unmeasured confounding. RESULTS: Rounding encounters included 394 families (261 English-speaking and 133 Spanish-speaking). Fewer Spanish-speaking families were included in the medical team’s discussion on rounds (64.7% vs 76.3%, P = .017), were asked about questions at the start of rounds (44.4% vs 56.3%, P = .025), or were involved in discussion of discharge criteria (72.2% vs 82.8%, P = .018) when compared to English-speaking families. These differences were magnified for resident teams rounding with subspecialists. The finding of decreased family involvement in the discussion on rounds persisted after adjusting for patient age and team type. CONCLUSIONS: Spanish-speaking families were less likely to be involved in FCR compared to English-speaking families. Further investigation is needed to explore the root causes of this practice variation and to develop interventions to address disparities.

The impact of post-hospital remote monitoring of COVID-19 patients using pulse oximetry: a national observational study using hospital activity data

Background There was a national roll out of "COVID Virtual Wards" (CVW) during England's second COVID-19 wave (Autumn 2020 - Spring 2021). These services used remote pulse oximetry monitoring for COVID-19 patients following discharge from hospital. A key aim was to enable rapid detection of patient deterioration. It was anticipated that the services would support early discharge and avoid readmissions, reducing pressure on beds. This study is an evaluation of the impact of the CVW services on hospital activity. Methods Using retrospective patient-level hospital admissions data, we built multivariate models to analyse the relationship between the implementation of CVW services and hospital activity outcomes: length of COVID-19 related stays and subsequent COVID-19 readmissions within 28 days. We used data from more than 98% of recorded COVID-19 hospital stays in England, where the patient was discharged alive between mid-August 2020 and late February 2021. Findings We found a longer length of stay for COVID-19 patients discharged from hospitals where a CVW was available, when compared to patients discharged from hospitals where there was no CVW (adjusted IRR 1.05, 95% CI 1.01 to 1.09). We found no evidence of a relationship between the availability of CVW and subsequent rates of readmission for COVID-19 (adjusted OR 0.95, 95% CI 0.89 to 1.02). Interpretation We found no evidence of early discharges or reduced readmissions associated with the roll out of COVID Virtual Wards across England. Our analysis made pragmatic use of national-scale hospital data, but it is possible that a lack of specific data (for example, on which patients were enrolled) may have meant that true impacts, especially at a local level, were not ultimately discernible. Funding This is independent research funded by the National Institute for Health Research, Health Services & Delivery Research programme and NHSEI.

COVID-19 related outcomes among individuals with neurodegenerative diseases: a cohort analysis in the UK biobank

Background An increased susceptibility to COVID-19 has been suggested for individuals with neurodegenerative diseases, but data are scarce from longitudinal studies. Methods In this community-based cohort study, we included 96,275 participants of the UK Biobank who had available SARS-CoV-2 test results in Public Health England. Of these, 2617 had a clinical diagnosis of neurodegenerative diseases in the UK Biobank inpatient hospital data before the outbreak of COVID-19 (defined as January 31st, 2020), while the remaining participants constituted the reference group. We then followed both groups from January 31st, 2020 to June 14th, 2021 for ascertainment of COVID-19 outcomes, including any COVID-19, inpatient care for COVID-19, and COVID-19 related death. Logistic regression was applied to estimate the association between neurogenerative disease and risks of COVID-19 outcomes, adjusted for multiple confounders and somatic comorbidities. Results We observed an elevated risk of COVID-19 outcomes among individuals with a neurodegenerative disease compared with the reference group, corresponding to a fully adjusted odds ratio of 2.47 (95%CI 2.25–2.71) for any COVID-19, 2.18 (95%CI 1.94–2.45) for inpatient COVID-19, and 3.67 (95%CI 3.11–4.34) for COVID-19 related death. Among individuals with a positive test result for SARS-CoV-2, individuals with neurodegenerative diseases had also a higher risk of COVID-19 related death than others (fully adjusted odds ratio 2.08; 95%CI 1.71–2.53). Conclusion Among UK Biobank participants who received at least one test for SARS-CoV-2, a pre-existing diagnosis of neurodegenerative disease was associated with a subsequently increased risk of COVID-19, especially COVID-19 related death.

Evaluation System of Smart Logistics Comprehensive Management Based on Hospital Data Fusion Technology

With the acceleration of the informatization process, but because of the late start of the informatization construction of logistics management, the current digital system construction of logistics management has not been popularized, and the intelligent logistics integrated management evaluation system is also extremely lacking. In order to solve the lack of existing intelligent logistics comprehensive management evaluation system, this paper introduces the research of intelligent logistics comprehensive management evaluation system based on hospital data fusion technology. This paper analyzes and utilizes the Kalman filter and adaptive weighted data fusion technology in data fusion technology and then analyzes the evaluation index and system design principles of the intelligent logistics comprehensive management evaluation system and then designs the application layer from the application layer. Design the application layer from the application layer. Then design the framework of the intelligent logistics comprehensive management evaluation system at the network layer and the data layer. The system is finally tested, and the test results show that the evaluation accuracy of the system reaches 80%.

Therapeutic itinerary of patients with chronic low-back pain attending outpatient physiotherapy clinic

Objective: to assess therapeutic itinerary of patients with chronic low-back pain. Methods: qualitative study involving the use of an in-depth semi structured interviews were used to collect data on therapeutic itinerary among ten patients with chronic low-back pain who from physiotherapy clinic of a tertiary hospital. Data was analysed using thematic content analysis. Results: emerging themes were as follows: onset of low-back pain, symptoms that initiated the journey for care, steps taken to find pain relief, respondent’s perception on care received, interference of chronic low-back pain with normal life, family and relations, multiple practices in which chronic low-back pain patients are involved, perception of effectiveness of the multiple practices, and hurdles to seeking care. Conclusion: patients with chronic low-back pain take different routes in search of care. Chronic low-back pain markedly altered patients’ life and made them prone to practicing medical pluralism.

ML-Enabled Informed Intervention for Crowdsourcing-Based Optimization of Medical Resources

It is said that every adversity presents the opportunity to grow. The current pandemic is a lesson to all healthcare infrastructure stakeholders to look at existing setups with an open mind. This chapter's proposed solution offers technology assistance to manage patient data effectively and extends the hospital data management system's capability to predict the upcoming need for healthcare resources. Further, the authors intend to supplement the proposed solution with crowdsourcing to meet hospital demand and supply for unprecedented medical emergencies. The proposed approach would demonstrate its need in the current pandemic scenario and prepare the healthcare infrastructure with a more streamlined and cooperative approach than before.

Experiences of Nurse Managers during the COVID-19 Outbreak in a Selected District Hospital in Limpopo Province, South Africa

The South African health care system was hard hit by the second wave of Coronavirus disease (COVID-19), which affected nurse managers as healthcare facilities became overwhelmed due to an increased workload emanating from the overflow of admissions. Therefore, this study sought to explore and describe the nurse managers’ experiences during COVID-19 in order to identify gaps and lessons learnt. A descriptive phenomenological research approach was used to explore the experiences of ten nurse managers who were purposively selected from different units of a selected district hospital. Data was collected through telephonic unstructured individual interviews and analysed using Colaizzi’s seven steps method. The study revealed that nurse managers experienced human resource related challenges during COVID-19, worsened by the fact that vacant posts were frozen. It also emerged that there was a shortage of material resources that affected patient care. Nurse managers also indicated that COVID-19 brought a lot of administrative duties plus an additional duty of patient care. Also, nurse managers who had previously contracted COVID-19 experienced stigma and discrimination. The government needs to address resource related challenges in rural public hospitals and provide continuous support to nurse managers, particularly during a pandemic like COVID-19.

Comparison of the Logical Use of Parenteral Antibiotics Before and After the Implementation of the Stewardship Program

Background: Excessive use of antibiotics has led to drug resistance. As such, stewardship programs are implemented to control antibiotic use in hospitalized patients. Objectives: The present study aimed to evaluate the frequency of antibiotic use after the implementation of the stewardship program. Methods: This retrospective study was conducted on the patients admitted to Imam Reza Hospital in Kermanshah, Iran during 2017 - 2018. The required data were extracted from the pharmaceutical ward of the hospital. Data analysis was performed in SPSS version 24. Results: The median per capita numerical and Rial consumption of caspofungin and linezolid antibiotics increased after the implementation of the stewardship plan, while the consumption of imipenem, amphotericin, teicoplanin, colistin, meropenem, voriconazole, and vancomycin was observed to decrease. In addition, the median per capita of the numerical use of caspofungin and linezolid increased in the surgery ward, intensive care unit (ICU), and internal medicine ward after the implementation of the stewardship plan. On the other hand, a reduction was observed in the use of amphotericin, colistin, voriconazole, and vancomycin in only one ward. The use of meropenem and teicoplanin also increased in the surgery ward and decreased in the other wards, while the use of imipenem increased in the ICU after implementing the plan and decreased in the other wards. The median use of antibiotics was not considered significant in the internal medicine ward, surgery ward, and ICU before and after implementing the stewardship program (P > 0.05). Conclusions: According to the results, antibiotic use slightly decreased after the implementation of the stewardship program. However, an increase was also observed in antibiotic prescription in some cases, which indicated that the stewardship program was not implemented properly, and no changes occurred in antibiotics prescription.

DR-IIXRN : Detection Algorithm of Diabetic Retinopathy Based on Deep Ensemble Learning and Attention Mechanism

Diabetic retinopathy (DR) is one of the common chronic complications of diabetes and the most common blinding eye disease. If not treated in time, it might lead to visual impairment and even blindness in severe cases. Therefore, this article proposes an algorithm for detecting diabetic retinopathy based on deep ensemble learning and attention mechanism. First, image samples were preprocessed and enhanced to obtain high quality image data. Second, in order to improve the adaptability and accuracy of the detection algorithm, we constructed a holistic detection model DR-IIXRN, which consists of Inception V3, InceptionResNet V2, Xception, ResNeXt101, and NASNetLarge. For each base classifier, we modified the network model using transfer learning, fine-tuning, and attention mechanisms to improve its ability to detect DR. Finally, a weighted voting algorithm was used to determine which category (normal, mild, moderate, severe, or proliferative DR) the images belonged to. We also tuned the trained network model on the hospital data, and the real test samples in the hospital also confirmed the advantages of the algorithm in the detection of the diabetic retina. Experiments show that compared with the traditional single network model detection algorithm, the auc, accuracy, and recall rate of the proposed method are improved to 95, 92, and 92%, respectively, which proves the adaptability and correctness of the proposed method.