Re-Imagining Hospital Patient Room Design for People After stroke: A Randomized Controlled Study Using Virtual Reality.

BACKGROUND: The hospital's physical environment can impact health and well-being. Patients spend most of their time in their hospital rooms. However, little experimental evidence supports specific physical design variables in these rooms, particularly for people poststroke. The study aimed to explore the influence of patient room design variables modeled in virtual reality using a controlled experimental design. METHODS: Adults within 3 years of stroke who had spent >2 nights in hospital for stroke and were able to consent were included (Melbourne, Australia). Using a factorial design, we immersed participants in 16 different virtual hospital patient rooms in both daytime and nighttime conditions, systematically varying design attributes: patient room occupancy, social connectivity, room size (spaciousness), noise (nighttime), greenery outlook (daytime). While immersed, participants rated their affect (Pick-A-Mood Scale) and preference. Mixed-effect regression analyses were used to explore participant responses to design variables in both daytime and nighttime conditions. Feasibility and safety were monitored throughout. Australian New Zealand Clinical Trials Registry, Trial ID: ACTRN12620000375954. RESULTS: Forty-four adults (median age, 67 [interquartile range, 57.3-73.8] years, 61.4% male, and a third with stroke in the prior 3-6 months) completed the study in 2019-2020. We recorded and analyzed 701 observations of affective responses (Pick-A-Mood Scale) in the daytime (686 at night) and 698 observations of preference responses in the daytime (685 nighttime) while continuously immersed in the virtual reality scenarios. Although single rooms were most preferred overall (daytime and nighttime), the relationship between affective responses differed in response to different combinations of nighttime noise, social connectivity, and greenery outlook (daytime). The virtual reality scenario intervention was feasible and safe for stroke participants. CONCLUSIONS: Immediate affective responses can be influenced by exposure to physical design variables other than room occupancy alone. Virtual reality testing of how the physical environment influences patient responses and, ultimately, outcomes could inform how we design new interventions for people recovering after stroke. REGISTRATION: URL: https://anzctr.org.au; Unique identifier: ACTRN12620000375954.

External Validation of Deep Learning-Based Cardiac Arrest Risk Management System for Predicting In-Hospital Cardiac Arrest in Patients Admitted to General Wards Based on Rapid Response System Operating and Nonoperating Periods: A Single-Center Study.

OBJECTIVES: The limitations of current early warning scores have prompted the development of deep learning-based systems, such as deep learning-based cardiac arrest risk management systems (DeepCARS). Unfortunately, in South Korea, only two institutions operate 24-hour Rapid Response System (RRS), whereas most hospitals have part-time or no RRS coverage at all. This study validated the predictive performance of DeepCARS during RRS operation and nonoperation periods and explored its potential beyond RRS operating hours. DESIGN: Retrospective cohort study. SETTING: In this 1-year retrospective study conducted at Yonsei University Health System Severance Hospital in South Korea, DeepCARS was compared with conventional early warning systems for predicting in-hospital cardiac arrest (IHCA). The study focused on adult patients admitted to the general ward, with the primary outcome being IHCA-prediction performance within 24 hours of the alarm. PATIENTS: We analyzed the data records of adult patients admitted to a general ward from September 1, 2019, to August 31, 2020. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Performance evaluation was conducted separately for the operational and nonoperational periods of the RRS, using the area under the receiver operating characteristic curve (AUROC) as the metric. DeepCARS demonstrated a superior AUROC as compared with the Modified Early Warning Score (MEWS) and the National Early Warning Score (NEWS), both during RRS operating and nonoperating hours. Although the MEWS and NEWS exhibited varying performance across the two periods, DeepCARS showed consistent performance. CONCLUSIONS: The accuracy and efficiency for predicting IHCA of DeepCARS were superior to that of conventional methods, regardless of whether the RRS was in operation. These findings emphasize that DeepCARS is an effective screening tool suitable for hospitals with full-time RRS, part-time RRS, and even those without any RRS.

Wireless continuous single-lead ST-segment monitoring to detect new-onset myocardial injury at the general ward-An exploratory subanalysis.

Patients admitted for acute medical conditions and major noncardiac surgery are at risk of myocardial injury. This is frequently asymptomatic, especially in the context of concomitant pain and analgesics, and detection thus relies on cardiac biomarkers. Continuous single-lead ST-segment monitoring from wireless electrocardiogram (ECG) may enable more timely intervention, but criteria for alerts need to be defined to reduce false alerts. This study aimed to determine optimal ST-deviation thresholds from wireless single-lead ECG for detection of myocardial injury following major abdominal cancer surgery and during acute exacerbation of chronic obstructive pulmonary disease. Patients were monitored with a wireless single-lead ECG patch for up to 4 days and had daily troponin measurements. Single-lead ST-segment deviations of <0.255 mV and/or >0.245 mV (based on previous study comparison with 0.1 mV 12-lead ECG and variation in single-lead ECG) were analyzed for relation to myocardial injury defined as hsTnT elevation of 20-64 ng/L with an absolute change of ≥5 ng/L, or a hsTnT level ≥ 65 ng/L. In total, 528 patients were included for analysis, of which 15.5% had myocardial injury. For corrected ST-thresholds lasting ≥10 and ≥ 20 min, we found specificities of 91% and 94% and sensitivities of 17% and 13% with odds ratios of 2.0 (95% CI: 1.1; 3.9) and 2.4 (95% CI: 1.1; 5.1) for myocardial injury. In conclusion, wireless single-lead ECG monitoring with corrected ST thresholds detected patients developing myocardial injury with specificities >90% and sensitivities <20%, suggesting increased focus on sensitivity improvement.

Visibility-based layout of a hospital unit - An optimization approach.

A patient fall is one of the adverse events in an inpatient unit of a hospital that can lead to disability and/or mortality. The medical literature suggests that increased visibility of patients by unit nurses is essential to improve patient monitoring and, in turn, reduce falls. However, such research has been descriptive in nature and does not provide an understanding of the characteristics of an optimal inpatient unit layout from a visibility-standpoint. To fill this gap, we adopt an interdisciplinary approach that combines the human field of view with facility layout design approaches. Specifically, we propose a bi-objective optimization model that jointly determines the optimal (i) location of a nurse in a nursing station and (ii) orientation of a patient's bed in a room for a given layout. The two objectives are maximizing the total visibility of all patients across patient rooms and minimizing inequity in visibility among those patients. We consider three different layout types, L-shaped, I-shaped, and Radial; these shapes exhibit the section of an inpatient unit that a nurse oversees. To estimate visibility, we employ the ray casting algorithm to quantify the visible target in a room when viewed by the nurse from the nursing station. The algorithm considers nurses' horizontal visual field and their depth of vision. Owing to the difficulty in solving the bi-objective model, we also propose a Multi-Objective Particle Swarm Optimization (MOPSO) heuristic to find (near) optimal solutions. Our findings suggest that the Radial layout appears to outperform the other two layouts in terms of the visibility-based objectives. We found that with a Radial layout, there can be an improvement of up to 50% in equity measure compared to an I-shaped layout. Similar improvements were observed when compared to the L-shaped layout as well. Further, the position of the patient's bed plays a role in maximizing the visibility of the patient's room. Insights from our work will enable understanding and quantifying the relationship between a physical layout and the corresponding provider-to-patient visibility to reduce adverse events.

Risk factors for thromboembolic events in patients hospitalized for COVID-19 pneumonia in a general ward and requiring treatment with oxygen.

PURPOSE: To assess risk factors for arterial and venous thromboses (AVT) in patients hospitalized in general wards for COVID-19 pneumonia and requiring oxygen therapy. METHODS: Our study was based on three randomized studies conducted as part of the CORIMUNO-19 platform in France between 27 March and 26 April 2020. Adult inpatients with COVID-19 pneumonia requiring at least 3 l/min of oxygen but not ventilation were randomized to receive standard care alone or standard care plus biologics. Patients were followed up for 3 months, and adverse events were documented. Risk factor for AVT and bleeding was identified by analyzing clinical, laboratory, and treatment data at baseline among the 315 patients with complete datasets. A Fine and Gray model was used to take account of competing events. RESULTS: During the 3-month follow-up period, 39 AVT occurred in 38 (10%) of the 388 patients: 26 deep vein thromboses and/or pulmonary embolisms in 25 (6%) patients, and 14 arterial thrombotic events in 13 (3%) patients. A history of diabetes at inclusion [sHR (95% CI) = 2.65 (1.19-5.91), P = .017] and the C-reactive protein (CRP) level (sHR = 1 [1-1.01], P = .049) were significantly associated with an elevated risk of thrombosis. Obesity was not associated with a higher risk of thrombosis (sHR = 1.01 [0.4-2.57], P = .98). The CRP level and diabetes were not risk factors for hemorrhage. CONCLUSION: Among patients hospitalized in general wards for COVID-19 pneumonia during the first wave of the epidemic, diabetes (but not obesity) and a high CRP level were risk factors for AVT. The use of higher doses of anticoagulant in these high-risk patients could be considered.

The silent threat: investigating sleep disturbances in hospitalized patients.

Sleep disruptions in the hospital setting can have adverse effects on patient safety and well-being, leading to complications like delirium and prolonged recovery. This study aimed to comprehensively assess the factors influencing sleep disturbances in hospital wards, with a comparison of the sleep quality of patients staying in single rooms to those in shared rooms. A mixed-methods approach was used to examine patient-reported sleep quality and sleep disruption factors, in conjunction with objective noise measurements, across seven inpatient wards at an acute tertiary public hospital in Sydney, Australia. The most disruptive factor to sleep in the hospital was noise, ranked as 'very disruptive' by 20% of patients, followed by acute health conditions (11%) and nursing interventions (10%). Patients in shared rooms experienced the most disturbed sleep, with 51% reporting 'poor' or 'very poor' sleep quality. In contrast, only 17% of the patients in single rooms reported the same. Notably, sound levels in shared rooms surpassed 100 dB, highlighting the potential for significant sleep disturbances in shared patient accommodation settings. The results of this study provide a comprehensive overview of the sleep-related challenges faced by patients in hospital, particularly those staying in shared rooms. The insights from this study offer guidance for targeted healthcare improvements to minimize disruptions and enhance the quality of sleep for hospitalized patients.

Effectiveness of Implementing Modified Early Warning System and Rapid Response Team for General Ward Inpatients.

This retrospective study assessed the effectiveness and impact of implementing a Modified Early Warning System (MEWS) and Rapid Response Team (RRT) for inpatients admitted to the general ward (GW) of a medical center. This study included all inpatients who stayed in GWs from Jan. 2017 to Feb. 2022. We divided inpatients into GWnon-MEWS and GWMEWS groups according to MEWS and RRT implementation in Aug. 2019. The primary outcome, unexpected deterioration, was defined by unplanned admission to intensive care units. We defined the detection performance and effectiveness of MEWS according to if a warning occurred within 24 h before the unplanned ICU admission. There were 129,039 inpatients included in this study, comprising 58,106 GWnon-MEWS and 71,023 GWMEWS. The numbers of inpatients who underwent an unplanned ICU admission in GWnon-MEWS and GWMEWS were 488 (.84%) and 468 (.66%), respectively, indicating that the implementation significantly reduced unexpected deterioration (p < .0001). Besides, 1,551,525 times MEWS assessments were executed for the GWMEWS. The sensitivity, specificity, positive predicted value, and negative predicted value of the MEWS were 29.9%, 98.7%, 7.09%, and 99.76%, respectively. A total of 1,568 warning signs accurately occurred within the 24 h before an unplanned ICU admission. Among them, 428 (27.3%) met the criteria for automatically calling RRT, and 1,140 signs necessitated the nursing staff to decide if they needed to call RRT. Implementing MEWS and RRT increases nursing staff's monitoring and interventions and reduces unplanned ICU admissions.

[Changes in Patient Safety Culture Attitudes Among Emergency, Intensive Care, and General Ward Nurses During the COVID-19 Pandemic].

BACKGROUND: Patient safety culture is an indicator of healthcare quality and a topic of global importance in medical care. PURPOSE: In this study, the attitudes towards patient safety culture of nursing staff working in the emergency, intensive care, and general wards are compared before and during the COVID-19 pandemic. METHODS: A retrospective research design was utilized and an anonymous questionnaire survey conducted on the Taiwan Patient Safety Culture Survey web-based platform system was used to collect the data. The survey was administered in a regional hospital in northern Taiwan between 2018 and 2020. The 1,540 nursing personnel who participated in this study worked in the emergency, intensive care units, or general adult ward. The analysis focused on assessing participant attitudes towards patient safety culture in terms of both the overall score and sub-dimensions. RESULTS: The participants were mostly female and between 21 and 30 years old. A majority had completed a diploma or university education. The two analyses revealed the highest and lowest average scores were earned, respectively, in the "teamwork" and "resilience" dimensions of patient safety culture. In 2020, the average scores for all dimensions were lower than in 2018, and the average scores for the emergency and critical care group were lower than those for the general adult ward group. Sub-dimension analysis showed that the general adult ward group earned significantly higher scores in "teamwork" across all three sub-dimensions compared to the emergency and critical care groups. The general ward group exhibited the most significant score decline between the two surveys. CONCLUSIONS / IMPLICATIONS FOR PRACTICE: Overall scores were found to have decreased during the COVID-19 pandemic period (2020). Notably, emergency and intensive care nurses earned consistently lower scores, likely due to the severity of patient conditions and increased pandemic-related workloads and stress. "Resilience" scores were the lowest among all nursing staff, with the most significant drop seen in general ward nurses. Enhancing nursing staff education and training as well as addressing their psychological well-being will be crucial to improving patient safety culture attitudes. Managers should provide infection control, resilience training, and psychological counseling to help nurses manage the challenges posed by infectious diseases effectively and enhance patient safety culture.

External Validation and Comparison of a General Ward Deterioration Index Between Diversely Different Health Systems.

OBJECTIVES: Implementing a predictive analytic model in a new clinical environment is fraught with challenges. Dataset shifts such as differences in clinical practice, new data acquisition devices, or changes in the electronic health record (EHR) implementation mean that the input data seen by a model can differ significantly from the data it was trained on. Validating models at multiple institutions is therefore critical. Here, using retrospective data, we demonstrate how Predicting Intensive Care Transfers and other UnfoReseen Events (PICTURE), a deterioration index developed at a single academic medical center, generalizes to a second institution with significantly different patient population. DESIGN: PICTURE is a deterioration index designed for the general ward, which uses structured EHR data such as laboratory values and vital signs. SETTING: The general wards of two large hospitals, one an academic medical center and the other a community hospital. SUBJECTS: The model has previously been trained and validated on a cohort of 165,018 general ward encounters from a large academic medical center. Here, we apply this model to 11,083 encounters from a separate community hospital. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: The hospitals were found to have significant differences in missingness rates (> 5% difference in 9/52 features), deterioration rate (4.5% vs 2.5%), and racial makeup (20% non-White vs 49% non-White). Despite these differences, PICTURE's performance was consistent (area under the receiver operating characteristic curve [AUROC], 0.870; 95% CI, 0.861-0.878), area under the precision-recall curve (AUPRC, 0.298; 95% CI, 0.275-0.320) at the first hospital; AUROC 0.875 (0.851-0.902), AUPRC 0.339 (0.281-0.398) at the second. AUPRC was standardized to a 2.5% event rate. PICTURE also outperformed both the Epic Deterioration Index and the National Early Warning Score at both institutions. CONCLUSIONS: Important differences were observed between the two institutions, including data availability and demographic makeup. PICTURE was able to identify general ward patients at risk of deterioration at both hospitals with consistent performance (AUROC and AUPRC) and compared favorably to existing metrics.

Nutritional issues concerning general medical ward patients: feeding patients recovering from critical illness.

PURPOSE OF REVIEW: ICU survivors often spend long periods of time in general wards following transfer from ICU in which they are still nutritionally compromised. This brief review will focus on the feeding of patients recovering from critical illness, as no formal recommendations or guidelines on nutrition management are available for this specific situation. RECENT FINDINGS: While feeding should start in the ICU, it is important to continue and adapt nutritional plans on the ward to support individuals recovering from critical illness. This process is highly complex - suboptimal feeding may contribute significantly to higher morbidity and mortality, and seriously hinder recovery from illness. Recently, consensus diagnostic criteria for malnutrition have been defined and large-scale trials have advanced our understanding of the pathophysiological pathways underlying malnutrition. They have also helped further develop treatment algorithms. However, we must continue to identify specific clinical parameters and blood biomarkers to further personalize therapy for malnourished patients. Better understanding of such factors may help us adapt nutritional plans more efficiently. SUMMARY: Adequate nutrition is a vigorous component of treatment in the post-ICU period and can enhance recovery and improve clinical outcome. To better personalize nutritional treatment because not every patient benefits from support in the same manner, it is important to further investigate biomarkers with a possible prognostic value.

Neurological and (neuro)psychological sequelae in intensive care and general ward COVID-19 survivors.

BACKGROUND AND PURPOSE: Coronavirus disease 2019 (COVID-19) affects the brain, leading to long-term complaints. Studies combining brain abnormalities with objective and subjective consequences are lacking. Long-term structural brain abnormalities, neurological and (neuro)psychological consequences in COVID-19 patients admitted to the intensive care unit (ICU) or general ward were investigated. The aim was to create a multidisciplinary view on the impact of severe COVID-19 on functioning and to compare long-term consequences between ICU and general ward patients. METHODS: This multicentre prospective cohort study assessed brain abnormalities (3 T magnetic resonance imaging), cognitive dysfunction (neuropsychological test battery), neurological symptoms, cognitive complaints, emotional distress and wellbeing (self-report questionnaires) in ICU and general ward (non-ICU) survivors. RESULTS: In al, 101 ICU and 104 non-ICU patients participated 8-10 months post-hospital discharge. Significantly more ICU patients exhibited cerebral microbleeds (61% vs. 32%, p < 0.001) and had higher numbers of microbleeds (p < 0.001). No group differences were found in cognitive dysfunction, neurological symptoms, cognitive complaints, emotional distress or wellbeing. The number of microbleeds did not predict cognitive dysfunction. In the complete sample, cognitive screening suggested cognitive dysfunction in 41%, and standard neuropsychological testing showed cognitive dysfunction in 12%; 62% reported ≥3 cognitive complaints. Clinically relevant scores of depression, anxiety and post-traumatic stress were found in 15%, 19% and 12%, respectively; 28% experienced insomnia and 51% severe fatigue. CONCLUSION: Coronavirus disease 2019 ICU survivors had a higher prevalence for microbleeds but not for cognitive dysfunction compared to general ward survivors. Self-reported symptoms exceeded cognitive dysfunction. Cognitive complaints, neurological symptoms and severe fatigue were frequently reported in both groups, fitting the post-COVID-19 syndrome.

Comparison of Methicillin-Resistant Staphylococcus aureus Nasal Screening Predictive Value in the Intensive Care Unit and General Ward.

BACKGROUND: The clinical utility of methicillin-resistant Staphylococcus aureus (MRSA) nasal screening appears promising for antimicrobial stewardship programs. However, a paucity of data remains on the diagnostic performance of culture-based MRSA screen in the intensive care unit (ICU) for pneumonia and bacteremia. OBJECTIVE: The objective of this study was to compare the predictive value of culture-based MRSA nasal screening for pneumonia and bacteremia in ICU and general ward patients. METHODS: This multicenter, retrospective study was conducted over a 23-month period. Adult patients with MRSA nasal screening ≤48 hours of collecting a respiratory and/or blood culture with concurrent initiation of anti-MRSA therapy were included. The primary endpoint was to compare the negative predictive value (NPV) associated with culture-based MRSA nasal screening between ICU and general ward patients with suspected pneumonia. RESULTS: A total of 5106 patients representing the ICU (n = 2515) and general ward (n = 2591) were evaluated. The NPV of the MRSA nares for suspected pneumonia was not significantly different between ICU and general ward patient populations (98.3% and 97.6%, respectively; P = 0.41). The MRSA nares screening tool also had a high NPV for suspected bacteremia in ICU (99.8%) and general ward groups (99.7%) (P = 0.56). The overall positive MRSA nares rates in the ICU and general ward patient populations were 9.1% and 8.2%, respectively (P = 0.283). Moreover, MRSA-positive respiratory and blood cultures among ICU patients were 5.8% and 0.8%, respectively. CONCLUSION AND RELEVANCE: Our findings support the routine use of MRSA nasal screening using the culture-based method in ICU patients with pneumonia. Further research on the clinical performance for MRSA bacteremia in the ICU is warranted.

Prospective, multicenter validation of the deep learning-based cardiac arrest risk management system for predicting in-hospital cardiac arrest or unplanned intensive care unit transfer in patients admitted to general wards.

BACKGROUND: Retrospective studies have demonstrated that the deep learning-based cardiac arrest risk management system (DeepCARS™) is superior to the conventional methods in predicting in-hospital cardiac arrest (IHCA). This prospective study aimed to investigate the predictive accuracy of the DeepCARS™ for IHCA or unplanned intensive care unit transfer (UIT) among general ward patients, compared with that of conventional methods in real-world practice. METHODS: This prospective, multicenter cohort study was conducted at four teaching hospitals in South Korea. All adult patients admitted to general wards during the 3-month study period were included. The primary outcome was predictive accuracy for the occurrence of IHCA or UIT within 24 h of the alarm being triggered. Area under the receiver operating characteristic curve (AUROC) values were used to compare the DeepCARS™ with the modified early warning score (MEWS), national early warning Score (NEWS), and single-parameter track-and-trigger systems. RESULTS: Among 55,083 patients, the incidence rates of IHCA and UIT were 0.90 and 6.44 per 1,000 admissions, respectively. In terms of the composite outcome, the AUROC for the DeepCARS™ was superior to those for the MEWS and NEWS (0.869 vs. 0.756/0.767). At the same sensitivity level of the cutoff values, the mean alarm counts per day per 1,000 beds were significantly reduced for the DeepCARS™, and the rate of appropriate alarms was higher when using the DeepCARS™ than when using conventional systems. CONCLUSION: The DeepCARS™ predicts IHCA and UIT more accurately and efficiently than conventional methods. Thus, the DeepCARS™ may be an effective screening tool for detecting clinical deterioration in real-world clinical practice. Trial registration This study was registered at ClinicalTrials.gov ( NCT04951973 ) on June 30, 2021.

Diagnostic accuracy of the 3-minute diagnostic interview for confusion assessment method-defined delirium in delirium detection: a systematic review and meta-analysis.

BACKGROUND: Delirium is a common complication clinically and is associated with the poor outcomes, yet it is frequently unrecognised and readily disregarded. Although the 3-minute diagnostic interview for confusion assessment method-defined delirium (3D-CAM) has been used in a variety of care settings, a comprehensive evaluation of its accuracy in all available care settings has not been performed. OBJECTIVE: This study aimed to evaluate the diagnostic test accuracy of the 3D-CAM in delirium detection through a systematic review and meta-analysis. METHODS: We systematically searched PubMed, EMBASE, the Cochrane Library, Web of Science, CINAHL (EBSCO) and ClinicalTrials.gov published from inception to 10 July 2022. The quality assessment of the diagnostic accuracy studies-2 tool was applied to evaluate methodological quality. A bivariate random effects model was used to pool sensitivity and specificity. RESULTS: Seven studies with 1,350 participants and 2,499 assessments were included, which were carried out in general medical wards, intensive care units, internal medical wards, surgical wards, recovery rooms and post-anaesthesia care units. The prevalence of delirium ranged from 9.1% to 25%. The pooled sensitivity and specificity were 0.92 (95% confidence interval [CI] 0.87-0.95) and 0.95 (95% CI 0.92-0.97), respectively. The pooled positive likelihood ratio was 18.6 (95% CI 12.2-28.2), the negative likelihood ratio was 0.09 (95% CI 0.06-0.14) and the diagnostic odds ratio was 211 (95% CI 128-349). Moreover, the area under the curve was 0.97 (95% CI 0.95-0.98). CONCLUSIONS: The 3D-CAM has good diagnostic accuracy for delirium detection in different care settings. Further analyses illustrated that it had comparable diagnostic accuracy in older adults and patients with dementia or known baseline cognitive impairment. In conclusion, the 3D-CAM is recommended for clinical delirium detection.

Outcomes of de-prescribing proton pump inhibitors during acute admissions in a general medical unit: a pilot study.

The study describes the feasibility and short-to-medium-term efficacy of an evidence-based proton pump inhibitor (PPI) de-prescribing initiative undertaken as part of routine clinical care during acute admissions in a general medical unit. Of the 44 (median (IQR) age 75.5 (13.75) years; females 25 (57%)) who participated in the study, de-prescription was maintained in 29 (66%) and 27 (61%) patients at 12 and 26 weeks respectively.

Comparison of experiences of nursing staff and patients before and after move to 100% single-bed room hospital in Australia: mixed methods.

BACKGROUND: There is sufficient and consistent international evidence of issues reported by nurses working in single-bed room environments, requiring a design that is not only comfortable for patients but meets nurses working needs. This paper presents a comparison of nursing staff and patients experience prior to a move to 100% single-bed room hospital in 2016 (Stage 1) and actual experiences after the move in 2021 (Stage 2) in South Australia. METHOD: Mixed method case study design. Survey sample of forty-two nursing staff; twelve patient interviews of their experiences of current environment and; thirteen nursing staff interviews of their experiences delivering nursing care in 100% single bed-room environment. RESULTS: Nurses and patients highlighted single-bed rooms contributed to patients' privacy, confidentiality, dignity and comfort. As anticipated in Stage 1, nurses in Stage 2 reported lack of patient and staff visibility. This impacted workload, workflow and concern for patient safety. CONCLUSION: Patient and nursing staff experiences are interdependent, and implications of single-bed room accommodation are complicated. Future impacts on the health system will continue to affect hospital design, which must consider nurses working needs and patient safety and comfort.

Framework for cyber risk loss distribution of hospital infrastructure: Bond percolation on mixed random graphs approach.

Networks like those of healthcare infrastructure have been a primary target of cyberattacks for over a decade. From just a single cyberattack, a healthcare facility would expect to see millions of dollars in losses from legal fines, business interruption, and loss of revenue. As more medical devices become interconnected, more cyber vulnerabilities emerge, resulting in more potential exploitation that may disrupt patient care and give rise to catastrophic financial losses. In this paper, we propose a structural model of an aggregate loss distribution across multiple cyberattacks on a prototypical hospital network. Modeled as a mixed random graph, the hospital network consists of various patient-monitoring devices and medical imaging equipment as random nodes to account for the variable occupancy of patient rooms and availability of imaging equipment that are connected by bidirectional edges to fixed hospital and radiological information systems. Our framework accounts for the documented cyber vulnerabilities of a hospital's trusted internal network of its major medical assets. To our knowledge, there exist no other models of an aggregate loss distribution for cyber risk in this setting. We contextualize the problem in the probabilistic graph-theoretical framework using a percolation model and combinatorial techniques to compute the mean and variance of the loss distribution for a mixed random network with associated random costs that can be useful for healthcare administrators and cybersecurity professionals to improve cybersecurity management strategies. By characterizing this distribution, we allow for the further utility of pricing cyber risk.

Unintended Consequences of the Neonatal Intensive Care Unit Environment: Integrative Review of Single-Family Room Unit Design.

BACKGROUND: Recent trends in neonatal intensive care unit design have been directed toward reducing negative stimuli and creating a more developmentally appropriate environment for infants who require intensive care. These efforts have included reconfiguring units to provide private rooms for infants. PURPOSE: The purpose of this integrative review was to synthesize and critically analyze negative outcomes for patients, families, and staff who have been identified in the literature related to single-family room (SFR) care in the neonatal intensive care unit. METHODS/SEARCH STRATEGY: The electronic databases of CINAHL, ProQuest Nursing & Allied Health, and PubMed databases were utilized. Inclusion criteria were research studies in English, conducted from 2011 to 2021, in which the focus of the study was related to unit design (SFRs). Based on the inclusion criteria, our search yielded 202 articles, with an additional 2 articles found through reference list searches. After screening, 44 articles met our full inclusion/exclusion criteria. These studies were examined for outcomes related to SFR unit design. FINDINGS/RESULTS: Our findings revealed both positive and negative outcomes related to SFR unit design when compared with traditional open bay units. These outcomes were grouped into 4 domains: Environmental Outcomes, Infant Outcomes, Parent Outcomes, and Staff Outcomes. IMPLICATIONS FOR PRACTICE AND RESEARCH: Although SFR neonatal intensive care unit design improves some outcomes for infants, families, and staff, some unexpected outcomes have been identified. Although these do not negate the positive outcomes, they should be recognized so that steps can be taken to address potential issues and prevent undesired outcomes.

Examining Efficiency in Open-Bay and Single-Family Room NICU Designs.

BACKGROUND: There is growing awareness of the relationship between physical work environments and efficiency. Two conflicting factors shape efficiency in the neonatal intensive care unit (NICU) environment: the move to single-family rooms (SFRs) and increased demand for care, requiring growth in unit size. PURPOSE: The goal of this research was to understand the impact of SFR NICUs on efficiency factors such as unit design, visibility and proximity, staff time, and workspace usage by various health professionals. METHODS: A pre-/postoccupancy evaluation assessed a NICU moving from an open-bay to an SFR unit composed of 6 neighborhoods. A NICU patient care manager and researchers in design and communication implemented a multimethodological design using staff surveys, observations, and focus groups. RESULTS: Outcomes revealed SFR NICUs contribute to increased efficiency and overall satisfaction with design. Outside of staff time spent in patient rooms, decentralized nurse stations were the most frequented location for staff work, followed by huddle stations, medication and supply rooms, and corridors. Work at the observed locations was largely performed independently. Survey outcomes reported increased feelings of isolation, but focus groups revealed mixed opinions regarding these concerns. IMPLICATIONS FOR PRACTICE AND RESEARCH: Design solutions found to enhance efficiency include a neighborhood unit design, standardized access to medications and supplies, and proximity of supplies, patient rooms, and nurse workstations. Although feelings of isolation were reported and most staff work was done independently in the patient room, the SFR unit might not be the culprit when considered alongside staff's desire to be closer to the patient room.

Relating clustered noise data to hospital patient satisfaction.

Hospital noise can be problematic for both patients and staff and consistently is rated poorly on national patient satisfaction surveys. A surge of research in the last two decades highlights the challenges of healthcare acoustic environments. However, existing research commonly relies on conventional noise metrics such as equivalent sound pressure level, which may be insufficient to fully characterize the fluctuating and complex nature of the hospital acoustic environments experienced by occupants. In this study, unsupervised machine learning clustering techniques were used to extract patterns of activity in noise and the relationship to patient perception. Specifically, nine patient rooms in three adult inpatient hospital units were acoustically measured for 24 h and unsupervised machine learning clustering techniques were applied to provide a more detailed statistical analysis of the acoustic environment. Validation results of five different clustering models found two clusters, labeled active and non-active, using k-means. Additional insight from this analysis includes the ability to calculate how often a room is active or non-active during the measurement period. While conventional LAeq was not significantly related to patient perception, novel metrics calculated from clustered data were significant. Specifically, lower patient satisfaction was correlated with higher Active Sound Levels, higher Total Percent Active, and lower Percent Quiet at Night metrics. Overall, applying statistical clustering to the hospital acoustic environment offers new insights into how patterns of background noise over time are relevant to occupant perception.