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The purpose of this paper is to increase understanding of how patient deterioration is detected and how clinical care escalates when early warning score (EWS) systems are used.

The authors critically review a recent National Early Warning Score paper published in IJHCQA using personal experience and EWS-related publications, and debate the difference between detection and escalation.

Incorrect EWS choice or poorly understood EWS escalation may result in unnecessary workloads forward and responding staff.

EWS system implementers may need to revisit their guidance materials; medical and nurse educators may need to expand the curriculum to improve EWS system understanding and use.

The paper raises the EWS debate and alerts EWS users that scrutiny is required.

Failure to rescue events, or events involving preventable deaths from complications, are a significant contributor to inpatient mortality. While many interventions have been designed and implemented over several decades, this patient safety issue remains at the forefront of concern for most hospitals. In the first part of this study, the development and implementation of one type of highly studied and widely adopted rescue intervention, algorithm-based patient assessment tools, is examined. The analysis summarizes how a lack of systems-oriented approaches in the design and implementation of these tools has resulted in suboptimal understanding of patient risk of mortality and complications and the early recognition of patient deterioration. The gaps identified impact several critical aspects of excellent patient care, including information-sharing across care settings, support for the development of shared mental models within care teams, and access to timely and accurate patient information.

This chapter describes the use of several system-oriented design and implementation activities to establish design objectives, model clinical processes and workflows, and create an extensible information system model to maximize the benefits of patient state and risk assessment tools in the inpatient setting. A prototype based on the product of the design activities is discussed along with system-level considerations for implementation. This study also demonstrates the effectiveness and impact of applying systems design principles and practices to real-world clinical applications.

The purpose of this paper is to evaluate staff opinion on the impact of the National Early Warning Score (NEWS) system on surgical wards. In 2012, the NEWS system was introduced to Irish hospitals on a phased basis as part of a national clinical programme in acute care.

A modified established questionnaire was given to surgical nursing staff, surgical registrars, surgical senior house officers and surgical interns for completion six months following the introduction of the NEWS system into an Irish university hospital.

Amongst the registrars, 89 per cent were unsure if the NEWS system would improve patient care. Less than half of staff felt consultants and surgical registrars supported the NEWS system. Staff felt the NEWS did not correlate well clinically with patients within the first 24 hours (Day zero) post-operatively. Furthermore, 78-85 per cent of nurses and registrars felt a rapid response team should be part of the escalation protocol.

Senior medical staff were not convinced that the NEWS system may improve patient care. Appropriate audit proving a beneficial impact of the NEWS system on patient outcome may be essential in gaining support from senior doctors. Deficiencies with the system were also observed including the absence of a rapid response team as part of the escalation protocol and a lack of concordance of the NEWS in patients Day zero post-operatively. These issues should be addressed moving forward.

Appropriate audit of the impact of the NEWS system on patient outcome may be pertinent to obtain the support from senior doctors. Deficiencies with the system were also observed including the absence of a rapid response team as part of the escalation protocol and a lack of concordance of the NEWS in patients Day zero post-operatively. These issues should be addressed moving forward.

The purpose of this paper is to determine associations between initially recorded deviations in individual bedside vital parameters that contribute to total Modified Early Warning Score (MEWS) levels 2 or 3 and further clinical deterioration (MEWS level=4).

This was a prospective study in which 27,504 vital parameter values, corresponding to a total MEWS level⩾2, belonging to 1,315 adult medical and surgical inpatient patients admitted to a 90-bed study setting at a university hospital, were subjected to binary logistic and COX regression analyses to determine associations between vital parameter values initially corresponding to total MEWS levels 2 or 3 and later deterioration to total MEWS level ⩾4, and to evaluate corresponding time intervals.

Respiratory rate, heart rate and patient age were significantly (p=0.012, p<0.001 and p=0.028, respectively) associated with further deterioration from a total MEWS level 2, and the heart rate also (p=0.009) from a total MEWS level 3. Within 24 h from the initially recorded total MEWS levels 2 or 3, 8 and 17 percent of patients, respectively, deteriorated to a total MEWS level=4. Patients initially scoring MEWS 2 had a 27 percent 30-day mortality rate if they later scored MEWS level=4, and 8.7 percent if they did not.

It is important to observe all patients closely, but especially elderly patients, if total MEWS levels 2 or 3 are tachypnoea and/or tachycardia related.

Findings might contribute to patient safety by facilitating appropriate clinical and organizational decisions on adequate time spans for early warning scoring in general ward patients.

The purpose of ths paper is to report that timely interventions to facilitate medical patient flow and reduce medical outliers may be associated with a reduction in hospital mortality.

Interventions to improve the flow of medical patients were used to unblock and facilitate the discharge process allowing a reduction in medical outliers. SPC run charts of mortality were used to quality control the changes.

Timeliness in daily senior medical review and discharge planning, a level 1 medical ward, and outreach including ALERT training and early warning scoring allowed a rationalisation in medical beds and a reduction in mortality for emergency medical admissions, reflected in a lower hospital standarised mortality rate (HSMR).

Interventions to improve flow can also lead to a reduction in mortality.

This paper emphasises how quantitative flow improvements can also generate qualitative improvements.

Although it is widely acknowledged that health care delivery systems are complex adaptive systems, there are gaps in understanding the application of systems engineering approaches to systems analysis and redesign in the health care domain. Commonly employed methods, such as statistical analysis of risk factors and outcomes, are simply not adequate to robustly characterize all system requirements and facilitate reliable design of complex care delivery systems. This is especially apparent in institutional-level systems, such as patient safety programs that must mitigate the risk of infections and other complications that can occur in virtually any setting providing direct and indirect patient care. The case example presented here illustrates the application of various system engineering methods to identify requirements and intervention candidates for a critical patient safety problem known as failure to rescue. Detailed descriptions of the analysis methods and their application are presented along with specific analysis artifacts related to the failure to rescue case study. Given the prevalence of complex systems in health care, this practical and effective approach provides an important example of how systems engineering methods can effectively address the shortcomings in current health care analysis and design, where complex systems are increasingly prevalent.

The aim of this study is to analyze the influence mechanism of real estate enterprises' status on debt default risk and explore the heterogeneity effect of the characteristics of enterprises.

Against the background of the “three red lines” regulation of the financing of real estate enterprises and the COVID-19 pandemic, the authors select 123 real estate enterprises listed on China's Shanghai and Shenzhen A-shares markets from the first quarter of 2021 to the second quarter of 2022 as a research sample. The social network analysis method and Z-score financial risk early warning model are used to measure real estate enterprises' status and debt default risk. The authors construct a panel regression model to analyze how the status of real estate enterprises influences their debt default risk.

The results show that the status of real estate enterprises negatively and significantly affects their debt default risk. Economic policy uncertainty and financing constraints play negative moderating and mediating roles, respectively. Further research has found that the effect of real estate enterprises' status on debt default risk is characterized by heterogeneity in equity characteristics, i.e. it is significant in the sample of nonstate-owned enterprises but not in the sample of state-owned enterprises.

It is helpful for real estate enterprises to attach importance to the value of social networks, and the authors provide policy suggestions for real estate enterprises to constantly improve their risk management systems.

Using economic policy uncertainty as the moderating variable and financing constraints as the mediating variable, the authors analyze how the status of real estate enterprises influences debt default risk, which contributes to a better understanding of the formation of the debt default risk of real estate enterprises.

The purpose of this study is to develop an efficient prediction model using vital signs and standard medical score systems, which predicts the clinical severity level of the patient in advance based on the quick sequential organ failure assessment (qSOFA) medical score method.

To predict the clinical severity level of the patient in advance, the authors have formulated a training dataset that is constructed based on the qSOFA medical score method. Further, along with the multiple vital signs, different standard medical scores and their correlation features are used to build and improve the accuracy of the prediction model. It is made sure that the constructed training set is suitable for the severity level prediction because the formulated dataset has different clusters each corresponding to different severity levels according to qSOFA score.

From the experimental result, it is found that the inclusion of the standard medical scores and their correlation along with multiple vital signs improves the accuracy of the clinical severity level prediction model. In addition, the authors showed that the training dataset formulated from the temporal data (which includes vital signs and medical scores) based on the qSOFA medical scoring system has the clusters which correspond to each severity level in qSOFA score. Finally, it is found that RAndom k-labELsets multi-label classification performs better prediction of severity level compared to neural network-based multi-label classification.

This paper helps in identifying patient' clinical status.

Floods have been identified as the most frequent and threatening disaster in Sri Lanka amidst an increasing trend of natural and man-made disasters in the world. Subject experts state that disaster risk management should be based on the results of risk assessments, but flood risk management in Sri Lanka is seemingly not based on community-level flood risk assessments. Accordingly, the purpose of this paper is to introduce a community-level flood risk assessment method to the local context of Sri Lanka.

The sample (n = 425) for the study was selected using the stratified random sampling method, and the Deduru Oya basin was selected as the study area. The risk assessment model introduced by Bollin et al. (2003) was used for the current study, but with some modifications. Accordingly, 16 variables were selected for the risk assessment. Descriptive data analysis methods were used in the study.

Community-level flood risk assessment method was introduced. Variable index, flood risk index and flood risk map were developed for the study area. The Grama Niladari Divisions (GNDs) were grouped into five categories from very high risk to very low risk. The GNDs named Wirakumandaluwa, Thimbilla, Deduru Oya, Bangadeniya and Elivitiya were ranked as the most flood-risk GNDs, respectively.

This paper produces a flood risk assessment method for the local context. Flood risk in the study area was assessed based on people’s perceptions. Accordingly, the flood risk index and flood risk map for the study area were developed based on the empirical data. GNDs were ranked based on the flood risk index.