Why don't hospital staff activate the rapid response system (RRS)? How frequently is it needed and can the process be improved?

Abstract Background The rapid response system (RRS) is a process of accessing help for health professionals when a patient under their care becomes severely ill. Recent studies and meta-analyses show a reduction in cardiac arrests by a one-third in hospitals that have introduced a rapid response team, although the effect on overall hospital mortality is less clear. It has been suggested that the difficulty in establishing the benefit of the RRS has been due to implementation difficulties and a reluctance of clinical staff to call for additional help. This assertion is supported by the observation that patients continue to have poor outcomes in our institution despite an established RRS being available. In many of these cases, the patient is often unstable for many hours or days without help being sought. These poor outcomes are often discovered in an ad hoc fashion, and the real numbers of patients who may benefit from the RRS is currently unknown. This study has been designed to answer three key questions to improve the RRS: estimate the scope of the problem in terms of numbers of patients requiring activation of the RRS; determine cognitive and socio-cultural barriers to calling the Rapid Response Team; and design and implement solutions to address the effectiveness of the RRS. Methods The extent of the problem will be addressed by establishing the incidence of patients who meet abnormal physiological criteria, as determined from a point prevalence investigation conducted across four hospitals. Follow-up review will determine if these patients subsequently require intensive care unit or critical care intervention. This study will be grounded in both cognitive and socio-cultural theoretical frameworks. The cognitive model of situation awareness will be used to determine psychological barriers to RRS activation, and socio-cultural models of interprofessional practice will be triangulated to inform further investigation. A multi-modal approach will be taken using reviews of clinical notes, structured interviews, and focus groups. Interventions will be designed using a human factors analysis approach. Ongoing surveillance of adverse outcomes and surveys of the safety climate in the clinical areas piloting the interventions will occur before and after implementation

A study of utilization of sanitary facilities by adolescent girls in an urban slum of Central India

Background: Hygienic sanitation facilities are crucial for public health. Investment on sanitation brings the single greatest return for any development intervention. Poor sanitation, open defecation and lack of awareness about hygiene have detrimental effect on the health of women and children living in slums. Objective: The objective of this study was to perceive/assess the barriers to access of hygienic sanitary facilities for adolescent girls in an urban slum. Methodology: This study included 98 adolescent females (10-19years) living in urban slums Ward no 19 Raipur. Simple random sampling by 'note method' was used to select one administrative division of this area. Result: Mean age of adolescent girls in the present study was 15.44 ±2.2years (Range: 12 to 19 years) and a majority of them were in High School 60 (60.2%). About half (42%) of the study subjects were living in Semi pucca house and only 38% had access to an independent toilet facility, 9% were practicing open defecation and remaining (51%) were using public toilets. Conclusion: The availability of sanitation facility and latrine utilization rate of the households were satisfactory. Privacy is a concern in public toilet, uses of sanitary pad was also less and changing of absorbent material in toilets was also a matter of concern for the girls

Interventional Fairness with Indirect Knowledge of Unobserved Protected Attributes

The deployment of machine learning (ML) systems in applications with societal impact has motivated the study of fairness for marginalized groups. Often, the protected attribute is absent from the training dataset for legal reasons. However, datasets still contain proxy attributes that capture protected information and can inject unfairness in the ML model. Some deployed systems allow auditors, decision makers, or affected users to report issues or seek recourse by flagging individual samples. In this work, we examine such systems and consider a feedback-based framework where the protected attribute is unavailable and the flagged samples are indirect knowledge. The reported samples are used as guidance to identify the proxy attributes that are causally dependent on the (unknown) protected attribute. We work under the causal interventional fairness paradigm. Without requiring the underlying structural causal model a priori, we propose an approach that performs conditional independence tests on observed data to identify such proxy attributes. We theoretically prove the optimality of our algorithm, bound its complexity, and complement it with an empirical evaluation demonstrating its efficacy on various real-world and synthetic datasets