Search results

Healthcare-associated infections (HAIs) are a major cause of concern because of the high levels of associated morbidity, mortality, and cost. In addition, children and intensive care unit (ICU) patients are more vulnerable to these infections due to low levels of immunity. Various medical interventions and statistical process control techniques have been suggested to counter the spread of these infections and aid early detection of an infection outbreak. Methods such as hand hygiene help in the prevention of HAIs and are well-documented in the literature. This chapter demonstrates the utilization of a systems methodology to model and validate factors that contribute to the risk of HAIs in a pediatric ICU. It proposes an approach that has three unique aspects: it studies the problem of HAIs as a whole by focusing on several HAIs instead of a single type, it projects the effects of interventions onto the general patient population using the system-level model, and it studies both medical and behavioral interventions and compares their effectiveness. This methodology uses a systems modeling framework that includes simulation, risk analysis, and statistical techniques for studying interventions to reduce the transmission likelihood of HAIs.

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.

This chapter shows how to identify the characteristics of borrowers that are part of a credit scoring model. The credit risk scoring model is an important tool for evaluating credit risk associated with customer characteristics that affect defaults. This research was conducted at a financial institution, a subsidiary of a commercial bank in Indonesia, to answer the challenge of determining the feasibility of providing financing quickly and accurately. This model uses a logistic regression method based on customer data with indicators of demographic characteristics, assets, occupations, and financing payments. This study identifies nine variables that meet the goodness of fit criteria, which consist of WOE, IV, and p-value. The nine variables can be used as predictors of default probability: type of work, work experience, net finance value, tenor, car brand, asset price, percentage of down payment (DP), interest, and income. The results of the study form a risk assessment model to identify variables that have a significant effect on the probability of default.

We examine explicitly priced financial distress risk in post-1990 equity markets. We add a financial distress risk factor to Fama and French's (1993) three-factor model, based on Griffin and Lemmon's (2002) findings that financial distress is not fully captured by the book-to-market factor. We test three-factor and four-factor capital asset pricing models using both annual buy-and-hold analysis and monthly time series analysis across portfolios adjusted for common book-to-market, size, and financial distress factors. We find empirical support for an Ohlson (1980) O-score-based financial distress risk four-factor asset pricing model in the U.S. and Japanese markets.

Lai, Li, Conover, and Wu (2010) propose a four-factor financial distress model to explain stock returns in the U.S. and Japanese markets. We examine this model in the stock markets of Australia, and six Asian markets (Hong Kong, Indonesia, Korea, Malaysia, Singapore, and Thailand). We find broad empirical support for the four-factor financial distress risk asset-pricing model in those markets. The four-factor financial distress asset pricing model improves explanatory power beyond the Fama–French (1993) three-factor asset pricing model in six of the seven Asian-Pacific markets (12 of 14 portfolio groupings), while the Carhart (1997) momentum-based asset pricing model only improves explanatory power beyond the Fama–French model in three of the seven markets (4 of 14 portfolio groupings).

We investigate the link between firm volatility and risk-taking (RT) among 4232 institutions across 11 countries during the period of 2000–2017 and find RT is negatively correlated with volatility measures. Second, a decomposition of the primary risk measure, the Z score and Merton distance-to-default, reveals that high RT contributed to lower stock return volatility mainly through better corporate governance, firm size, higher information efficiency, and strong BOD. Third, Australia firms engage in more RT compared to other countries. Finally, majority of the selected countries show the negative impact of RT in firm volatility in the pre-crises period (2002–2006) and during the crises period (2007–2009) but not in the post-crises period (2010–2014).

In the past decade, firms have become more aware of supply chain disruptions and their impact on the firm. Developing a supply chain resilience organizational culture has been proposed as an effective way to manage supply chain risks. This study intends to explore how the geographical location risks impact the decision to develop a supply chain resilience strategy, in particular, to anticipate the disruption proactively and have a business continuity plan in place. Using a unique database including thousands of manufacturing locations that belong to over 7,000 firms across 102 countries, we test three hypotheses to understand if geographical location risks, frequency of disruptive events, and the region in which a site is located are factors for the likelihood of a firm having a business continuity plan at their locations. The study also seeks to understand if there are regional effects and firm effects affecting the decision to develop resilience. With a particular focus in Latin America and the firms with a manufacturing presence in that region. The main findings of the study are that natural disaster risks do tend to develop a culture of resilience, while macroeconomic risks tend to do the opposite. These results remain stable for firms' effects. The Latin America region shows no observable statistical difference in developing resilience compared to the Asia region. While the Northern America region shows more resilience compared to Asia. We conclude that economic risk is less predictable and harder to develop a plan for than disruptions, such as natural disasters. The findings of this study present an opportunity for governments to develop resilience plans that can make their countries more attractive for investment to multinational firms looking to establish new manufacturing locations around the world.

Both consumer and corporate credit ratings agencies played a major role in the US subprime mortgage crisis. Equifax, Experian, and TransUnion deployed a formalized scoring system to assess individuals in mortgage origination, mortgage pools then were assessed for securitization by Moody's, S&P, and Fitch relying on expert judgment aided by formal models. What can we learn about the limits of formalization from the crisis? We discuss five problems responsible for the rating failures – reactivity, endogeneity, learning, correlated outcomes, and conflict of interest – and compare the way consumer and corporate rating agencies tackled these difficulties. We conclude with some policy lessons.

Since the mid-1990s, enterprise risk management (ERM) has proliferated in both the private and public sector as a holistic, enterprise-wide approach to risk management. In this chapter, we begin by exploring the economic, regulatory and professional context of ERM practices in Norway. To gain an understanding of the current state of ERM practices among Norwegian entities, we have conducted a survey among members of the Institute of Internal Auditors (IIA) Norway. Based on the survey data, we go on to analyse the perceived maturity of risk management practices of the surveyed organizations, as well as their integration of risk management with governance mechanisms and accounting practices. Four main findings emerged from the survey. We firstly observed that a majority of the respondents perceived that they had implemented ERM. Secondly, the average maturity of risk management practice is at a medium level, with ambitions to improve it further in the future. We further observed that a majority of the organizations have established risk management governance structures regarding the roles of risk management. However, there is still work to be done in relation to risk management functions in order for them to gain more attention and influence in the organizations. Finally, we find that risk management is more integrated with reporting processes than with strategic and performance planning processes, suggesting a more reactive than proactive approach to managing risks.