Assessment of COVID-19 prevention and protection measures in hospitals

Changes to women's childbirth plans during the COVID-19 pandemic and posttraumatic stress symptoms: a cross-national study

A considerable number of women giving birth during COVID-19 pandemic reported being concerned about changes to their childbirth plans and experiences due to imposed restrictions. Research prior to the pandemic suggests that women may be more at risk of post-traumatic stress symptoms (PTSS) due to unmet expectations of their childbirth plans. Therefore, this study aimed to examine if the mismatch between women's planned birth and actual birth experiences during COVID-19 was associated with women's postpartum PTSS. Women in the postpartum period (up to 6 months after birth) across 11 countries reported on childbirth experiences, mental health, COVID-19-related factors, and PTSS (PTSD checklist DSM-5 version) using self-report questionnaires (ClinicalTrials.gov: NCT04595123). More than half (64%) of the 3532 postpartum women included in the analysis reported changes to their childbirth plans. All changes were significantly associated with PTSS scores. Participants with one and two changes to their childbirth plans had a 12% and 38% increase, respectively, in PTSS scores compared to those with no changes (Exp(β) = 1.12; 95% CI [1.06-1.19]; p < 0.001 and Exp(β) = 1.38; 95% CI [1.29-1.48]; p < 0.001). In addition, the effect of having one change in the childbirth plan on PTSS scores was stronger in primigravida than in multigravida (Exp(β) = 0.86; 95% CI [0.77-0.97]; p = 0.014). Changes to women's childbirth plans during the COVID-19 pandemic were common and associated with women's postpartum PTSS score. Developing health policies that protect women from the negative consequences of unexpected or unintended birth experiences is important for perinatal mental health.

Identification of key potential infection processes and risk factors in the computed tomography examination process by FMEA method under COVID-19

PURPOSE

To identify the key infection processes and risk factors in Computed Tomography (CT) examination process within the standard prevention and control measures for the COVID-19 epidemic, aiming to mitigate cross-infection occurrences in the hospital.

METHOD

The case hospital has assembled a team of 30 experts specialized in CT examination. Based on the CT examination process, the potential failure modes were assessed from the perspective of severity (S), occurrence probability (O), and detectability (D); they were then combined with corresponding risk prevention measures. Finally, key infection processes and risk factors were identified according to the risk priority number (RPN) and expert analysis.

RESULTS

Through the application of RPN and further analysis, four key potential infection processes were identified, including "CT request form (A1)," "during the scan of CT patient (B2)," "CT room and objects disposal (C2)," and "medical waste (garbage) disposal (C3)". In addition, eight key risk factors were also identified, including "cleaning personnel does not wear masks normatively (C32)," "nurse does not select the vein well, resulting in extravasation of the peripheral vein for enhanced CT (B25)," "patient cannot find the CT room (A13)," "patient has obtained a CT request form but does not know the procedure (A12)," "patient is too unwell to continue with the CT scan (B24)," "auxiliary staff (or technician) does not have a good grasp of the sterilization and disinfection standards (C21)," "auxiliary staff (or technician) does not sterilize the CT machine thoroughly (C22)," and "cleaning personnel lacks of knowledge of COVID-19 prevention and control (C33)".

CONCLUSION

Hospitals can publicize the precautions regarding CT examination through various channels, reducing the incidence of CT examination failure. Hospitals' cleaning services are usually outsourced, and the educational background of the staff employed in these services is generally not high. Therefore, during training and communication, it is more necessary to provide a series of scope and training programs that are aligned with their understanding level. The model developed in this study effectively identifies the key infection prevention process and critical risk factors, enhancing the safety of medical staff and patients. This has significant research implications for the potential epidemic of major infectious diseases.

Utility and Utilization of Patient-Reported Experience Measures for the Supplementary COVID-19 Protective Actions at the Ovidius Clinical Hospital in Romania

Patient-reported experience measures (PREMs) provide assessments of patients' subjective experiences and perceptions regarding their interactions with the healthcare system and its services. We present a cross-sectional study of the patient perception and evolution of COVID-19 cases performed at Ovidius Clinical Hospital in Romania during the COVID-19 pandemic. The study objective is to explore the utility and the utilization of PREMs in monitoring patient perceptions of the supplementary protective actions. During the pandemic, the hospital implemented early supplementary protective actions, like PCR and lung CT, to all surgically admitted patients in the hospital alongside government-recommended actions. At the same time, functional PREMs were used to evaluate patient perceptions regarding these supplementary actions. The research was carried out for 19 months between June 2020 and December 2021. The findings revealed that opinions about the severity of the COVID-19 pandemic, the personal risk of infection, and the perception of protective actions in the hospital were not correlated. Conclusions: The patients' appreciation of the COVID-19 protective actions taken by the hospital is related more to the general perceptions induced by the number of cases presented in the mass media and less by perceptions of the gravity of the problem or the risk of infection. In a hospital, the primary mission of patient safety is essential, and it must be fulfilled even if the patients are not sure or fully convinced that this is for their benefit. For management decisions and monitoring, using PREMs can be essential in a situation when general evidence is not conclusive.

Temporary COVID-19 Specialized Hospital: Management Strategies for Public Health Emergencies

During the coronavirus disease 2019 (COVID-19) outbreak in Shanghai with the Omicron variant in March 2022, locally accessible hospitals and healthcare centres encountered difficulties quickly responding to a demand for hospitals that were rapidly increasing, optimizing clinical results and controlling the infection. In this commentary, we summarize the management strategies of patients in a temporary COVID-19 specialized hospital during the outbreak in Shanghai, China. The present commentary was considered eight characteristics of management system, including general idea, infection prevention team, and efficient time management, and preventive and protective measures management, strategies for the management of infected patients, disinfection management, drug supply management strategies, and medical waste management. Following eight characteristics, the temporary COVID-19 specialized hospital operated effectively for 21 days. A total of 9674 patients were admitted, 7127 cases (73.67%) were cured and discharged, and 36 were transferred to designate hospitals for better treatment. Twenty-five management staff, 1130 medical, nursing staff, 565 logistics staff, and 15 volunteers participated in the temporary COVID-19 specialized hospital, and no infection prevention team member was infected. We speculated that these management strategies could be potential references for public health emergencies.

Clinical Outcome and Prognosis of a Nosocomial Outbreak of COVID-19

Nosocomial coronavirus disease 2019 (COVID-19) outbreaks have been reported despite widespread quarantine methods to prevent COVID-19 in society and hospitals. Our study was performed to investigate the clinical outcome and prognosis of a nosocomial outbreak of COVID-19. We retrospectively analyzed the medical records of patients diagnosed with nosocomial COVID-19 of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) at a university teaching hospital between 1 November 2021 and 31 April 2022. Nosocomial COVID-19 was defined as a positive SARS-CoV-2 polymerase chain reaction (PCR) test result 4 or more days after admission in asymptomatic patients who had a negative SARS-CoV-2 PCR test on admission. In this study, 167 patients were diagnosed with nosocomial COVID-19 (1.14%) among a total of 14,667 patients admitted to hospital during the study period. A total of 153 patients (91.6%) survived, but 14 patients (8.4%) died. The median time between admission and COVID-19 diagnosis was 11 days, and the median duration of hospital stay was 24 days. After adjusting for other factors, no vaccination (adjusted HR = 5.944, 95% CI = 1.626–21.733, p = 0.007) and chronic kidney disease (adjusted HR = 6.963, 95% CI = 1.182–41.014, p = 0.032) were found to increase mortality risk. Despite strict quarantine, a significant number of nosocomial COVID-19 cases with a relatively high mortality rate were reported. As unvaccinated status or chronic kidney disease were associated with poor outcomes of nosocomial COVID-19, more active preventive strategies and treatments for patients with these risk factors are needed.

Remote Monitoring of COVID-19 Patients Using Multisensor Body Area Network Innovative System

As of late 2019, the COVID19 pandemic has been causing huge concern around the world. Such a pandemic posed serious threats to public safety, the well-being of healthcare workers, and the overall health of the population. If automation can be implemented in healthcare systems, patients could be better cared for and health industries could be less burdened. To combat such challenges, e-health requires apps and intelligent systems. Using WBAN sensors and networks, a doctor or medical professional can advise patients on the best course of action. Patients’ fitness could be assessed using WBAN sensors without interfering with their daily activities. When designing a monitoring system, system performance reliability for competent healthcare is critical. Existing research has failed to create a large device capable of handling a large network or to improve WBAN topologies for fast transmitting and receiving patient data. As a result, in this research, we create a multisensor WBAN (MSWBAN) intelligent system for transmitting and receiving critical patient data. To gather information from all cluster nodes and send it to multisensor WBAN, a novel additive distance-threshold routing protocol (ADTRP) is proposed. In small networks where data are managed by the transmitting node and the best data route is determined, this protocol has less redundancy. An edge-cutting-based routing optimization (ES-EC-R ES-EC-RO) is used to find the best route. The Trouped blowfish MD5 (TB-MD5) algorithm is used to encrypt and decrypt data, and the encrypted data are stored in a cloud database for security. The performance metrics of our proposed model are compared to current techniques for the best results. End-to-end latency is 63 ms, packet delivery is 95%, security is 95.7%, and throughput is 9120 bps, according to the results. The purpose of this article is to encourage engineers and front-line workers to develop digital health systems for tracking and controlling virus outbreaks.