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Waiting time, as an important predictor of queue abandonment and patient satisfaction, is important for resource utilization and patient experience management. Medical institutions have given top priority to reforming the appointment system for many years; however, whether the increased information transparency brought about by the appointment scheduling mechanism could improve patient waiting time is not well understood. In this study, the authors examine the effects of information transparency in reducing patient waiting time from an uncertainty perspective.

Leveraging a quasi-natural experiment in a tertiary academic hospital, the authors analyze over one million observational patient visit records and design the propensity score matching plus the difference in difference (PSM-DID) model and hierarchical linear modeling (HLM) to address this issue.

The authors confirm that, on average, improved information transparency significantly reduces the waiting time for patients by approximately 6.43 min, a 4.90% reduction. The authors identify three types of uncertainties (resource, process and outcome uncertainty) in the patient visit process that affect patients' waiting time. Moreover, information transparency moderates the relationship between three sources of uncertainties and waiting time.

The authors’ work not only provides important theoretical explanations for the patient-level factors of in-clinic waiting time and the reasons for information technology (IT)-enabled appointment scheduling by time slot (ITASS) to shorten patient waiting time and improve patient experience but also provides potential solutions for further exploration of measures to reduce patient waiting time.

Medical uncertainty is recognized as a critical issue in the sociology of diagnosis and medical sociology more generally, but a neglected focus of this concern is the question of patient decision making. Using a mixed methods approach that draws upon autoethnographic accounts and third-party interviews, we aim to illuminate the dilemmas of patient decision making in the face of uncertainty. How do patients and supportive caregivers go about navigating this state of affairs? What types of patient–doctor/healthcare professional relationships hinder or enhance effective patient decision making? These are the themes we explore in this study by following patients through the sequence of experiencing symptoms, seeking a diagnosis, evaluating treatment protocols, and receiving treatments. In general, three genres of culturally available narratives are revealed in the data: strategic, technoluxe, and unbearable health narratives.

The institutional conditions of primary care provision remain understudied in low- and middle-income countries. This study analyzes how primary care doctors cope with medical uncertainty in municipal clinics in urban India. As street-level bureaucrats, the municipal doctors occupy two roles simultaneously: medical professional and state agent. They operate under conditions that characterize health systems in low-resource contexts globally: inadequate state investment, weak regulation and low societal trust. The study investigates how, in these conditions, the doctors respond to clinical risk, specifically related to noncommunicable diseases (NCDs).

The analysis draws on year-long ethnographic fieldwork in Pune (2013–14), a city of three million, including 30 semi-structured interviews with municipal doctors.

Interpreting their municipal mandate to exclude NCDs and reasoning their medical expertise as insufficient to treat NCDs, the doctors routinely referred NCD cases. They expressed concerns about violence from patients, negative media attention and unsupportive municipal authorities should anything go wrong clinically.

The study contextualizes street-level service-delivery in weak institutional conditions. Whereas street-level workers may commonly standardize practices to reduce workload, here the doctors routinized NCD care to avoid the sociopolitical consequences of clinical uncertainty. Modalities of the welfare state and medical care in India – manifest in weak municipal capacity and healthcare regulation – appear to compel restraint in service-delivery. The analysis highlights how norms and social relations may shape primary care provision and quality.

Advertisements often use fear appeals to encourage prevention focused behaviors. This approach has been somewhat successful in changing attitudes and behaviors, often encouraging consumers to secede from behaviors such as smoking or to adopt preventative behaviors such as engaging in health screenings. However, health-care marketers have been less successful in efforts to reduce obesity. The obesity crisis has led to an abundance of marketing communications designed to influence weight loss. Many of these focus on fear of physical health risks associated with being overweight which have a certain degree of uncertainty surrounding them. This study aims to examine financial threats that have lower perceptions of uncertainty, and the differential impact this type of threat has on elements of the Extended Parallel Process Model (EPPM).

A 2 × 2 experimental design is used to examine the differential impact of messages communicating threat of financial and physical risk on evoked fear, perceived uncertainty, perceived susceptibility, efficacy and intention to lose weight.

Overall results indicate that response to weight loss advertising varies given the type of threat presented. Results indicate that there is a greater level of uncertainty associated with physical health threats than that with financial threats. Moreover, even though individuals were more fearful of and felt more susceptible to physical threats, when they believed that the recommended behavior was feasible, financial threat was more influential.

To encourage weight loss and intentions to lose weight advertising in practice and advertising research primarily focus on the physical health risks associated with being overweight as a motivating factor. Current research explores the impact of financial threats on attitudes and behavioral intention and finds that financial threats are perceived as more certain than physical threats, and the communication of financial threats is more salient in its effect on weight loss intentions. An opportunity for future research is to further explore the impact of uncertainty in relation to components of EPPM and how threats varying in degrees of uncertainty may impact weight loss intentions.

A dynamic model of the evolution of health for those over the age of 50 is embedded in a structural, econometric model of retirement and saving. Effects of smoking, obesity, alcohol consumption, depression, and other proclivities on medical conditions are analyzed, including hypertension, diabetes, cancer, lung disease, heart problems, stroke, psychiatric problems, and arthritis. Compared to a population in good health, the current health of the population reduces retirement age by about one year. Including detailed health dynamics in a retirement model does not influence estimates of the marginal effects of economic incentives on retirement.

To examine the ways in which sports-related brain injury (concussion and subconcussion) is both similar to and different from other injuries and to set out a sociological understanding of the injury, its manifestation and management.

There is a broad contextualization of the ‘issue’ of concussion and the processes that have brought this to the fore, an examination of the ways in which concussion has been figuratively clouded from plain view, and an outline of the main contributions of the social sciences to understanding this injury – the culture of risk and the mediating effect of social relationships. The chapter concludes by questioning whether the emergence of concerns over chronic traumatic encephalopathy has stimulated a fundamental change in attitudes towards sport injuries, and if this has had a significant impact on the social visibility of concussion.

The two available sociological studies of the lived experiences of concussion are situated within a broader analysis of the politicization of sports medicine and the emergence of a particular social discourse around sports-related brain injury.

The difficulties emanating from the dominance of a biomedical approach to concussion are discussed along with the need for further research, incorporating a more holistic view of concussion, as a bio-psycho-social phenomenon.

This paper develops a robust hospital readmission prediction framework by combining the feature selection algorithm and machine learning (ML) classifiers. The improved feature selection is proposed by considering the uncertainty in patient's attributes that leads to the output variable.

First, data preprocessing is conducted which includes how raw data is managed. Second, the impactful features are selected through feature selection process. It started with calculating the relational grade of each patient towards readmission using grey relational analysis (GRA) and the grade is used as the target values for feature selection. Then, the influenced features are selected using the Least Absolute Shrinkage and Selection Operator (LASSO) method. This proposed method is termed as Grey-LASSO feature selection. The final task is the readmission prediction using ML classifiers.

The proposed method offered good performances with a minimum feature subset up to 54–65% discarded features. Multi-Layer Perceptron with Grey-LASSO gave the best performance.

The performance of Grey-LASSO is justified in two readmission datasets. Further research is required to examine the generalisability to other datasets.

In designing the feature selection algorithm, the selection on influenced input variables was based on the integration of GRA and LASSO. Specifically, GRA is a part of the grey system theory, which was employed to analyse the relation between systems under uncertain conditions. The LASSO approach was adopted due to its ability for sparse data representation.

Untimely responses to emergency situations in urban areas contribute to a rising mortality rate and impact society's primary capital. The efficient dispatch and relocation of ambulances pose operational and momentary challenges, necessitating an optimal policy based on the system's real-time status. While previous studies have addressed these concerns, limited attention has been given to the optimal allocation of technicians to respond to emergency situation and minimize overall system costs.

In this paper, a bi-objective mathematical model is proposed to maximize system coverage and enable flexible movement across bases for location, dispatch and relocation of ambulances. Ambulances relocation involves two key decisions: (1) allocating ambulances to bases after completing services and (2) deciding to change the current ambulance location among existing bases to potentially improve response times to future emergencies. The model also considers the varying capabilities of technicians for proper allocation in emergency situations.

The Augmented Epsilon-Constrained (AEC) method is employed to solve the proposed model for small-sized problem. Due to the NP-Hardness of the model, the NSGA-II and MOPSO metaheuristic algorithms are utilized to obtain efficient solutions for large-sized problems. The findings demonstrate the superiority of the MOPSO algorithm.

This study can be useful for emergency medical centers and healthcare companies in providing more effective responses to emergency situations by sending technicians and ambulances.

In this study, a two-objective mathematical model is developed for ambulance location and dispatch and solved by using the AEC method as well as the NSGA-II and MOPSO metaheuristic algorithms. The mathematical model encompasses three primary types of decision-making: (1) Allocating ambulances to bases after completing their service, (2) deciding to relocate the current ambulance among existing bases to potentially enhance response times to future emergencies and (3) considering the diverse abilities of technicians for accurate allocation to emergency situations.