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The aim of the study was to investigate the incidence and diversity of deformational auricular anomalies in human fetuses based on their morphologic appearances. A total of 100 auricles from 56 formalin-fixed fetuses (32 female and 24 male), gestational ages ranging from 17 to 36 weeks, were examined. The auricles were categorized according to the morphologic deformities outlined in existing literature. Of the samples analyzed, 67% exhibited auricular deformities, while the remaining 33% showed no auricle anomalies. Among the auricles with deformities, 73% displayed a single type of deformity: Conchal crus, 22%; Stahl's ear, 13%; inverted conchal bowl, 13%; lidding ear, 12%; cup ear, 7%; and constricted ear, 6%. In addition, 27% of the auricles exhibited 2 different deformities on the same side: Conchal crus with lidding ear, 10%; conchal crus with Stahl's ear, 5%; conchal crus with the antihelix 3rd crus, 3%; constricted ear with lidding ear, 5%; constricted ear with cup ear, 2%; and constricted ear with inverted conchal bowl, 2%. The most prevalent deformity was conchal crus, whereas cup ear was the least. Among the fetuses, 10 had bilaterally normal auricles, 8 had unilaterally normal auricles, 12 exhibited the same deformity bilaterally, and 14 displayed different deformities on each side. Auricular deformational variations can vary in shape and symmetry, even in morphologically normal fetuses. Identifying these deformities can contribute to accurate diagnosis and treatment planning for such anomalies in newborns.

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Patient Activation Measure (PAM) measures the activation level of patients with chronic conditions and correlates well with patient adherence behavior, health outcomes, and healthcare costs. PAM is increasingly used in practice to identify patients needing more support from the care team. We define PAM levels 1 and 2 as low PAM and investigate the performance of eight machine learning methods (Logistic Regression, Lasso Regression, Ridge Regression, Random Forest, Gradient Boosted Trees, Support Vector Machines, Decision Trees, Neural Networks) to classify patients. Primary data collected from adult patients (n=431) with Diabetes Mellitus (DM) or Hypertension (HT) attending Family Health Centers in Istanbul, Turkey, is used to test the methods. [Formula: see text] of patients in the dataset have a low PAM level. Classification performance with several feature sets was analyzed to understand the relative importance of different types of information and provide insights. The most important features are found as whether the patient performs self-monitoring, smoking and exercise habits, education, and socio-economic status. The best performance was achieved with the Logistic Regression algorithm, with Area Under the Curve (AUC)=0.72 with the best performing feature set. Alternative feature sets with similar prediction performance are also presented. The prediction performance was inferior with an automated feature selection method, supporting the importance of using domain knowledge in machine learning.

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Introduction: The number of people diagnosed with dementia is increasing, creating significant economic burden globally. With the progression of the disease, patients need a caregiver whose wellbeing is important for continuous care. Providing respite as a service, through sharing the responsibility of caregiving or support for the caregiver, is a costly initiative. A peer-to-peer online support platform for dementia caregivers, motivated by the sharing economy, putting exchange of knowhow, resources, and services at its center, has the potential to balance cost concerns with a search for respite. The aim of this research is to assess caregivers' intention to engage in peer-to-peer exchange. Methods: A survey including sociodemographic, technology use, and caregiving variables, structured questionnaires (Zarit caregiver burden, WHO brief quality of life scale, ADCS-ADL and chronic stress scale) were administered, January 2018-May 2019, in the dementia outpatient clinic of a university hospital, to a convenience sample of n = 203 individuals identifying themselves as primary caregivers. A path analysis exploring the drivers of an intention to engage in peer-to-peer service exchange was conducted. Results: In the path model, caregivers experiencing higher caregiver burden showed higher intention to engage (0.079, p < 0.001). Disease stage had no effect while patient activities of daily living, chronic social role related stressors of the caregiver and general quality of life were significant for the effect on the caregiver burden. Existing household support decreased the caregiver burden, affecting the intention to engage. Caregivers who can share more know-how demonstrate a higher intention to engage (0.579, p = 0.021). Caregiver technology affinity (0.458, p = 0.004) and ability and openness to seek professional help for psychological diagnoses (1.595, p = 0.012) also increased intention to engage. Conclusion: The model shows caregiver burden to be a major driver, along with caregiver characteristics that reflect their technology affinity and openness to the idea of general reciprocity. Existing support for obtaining knowhow and exchanging empathy have a direct effect on the intention to engage. Given the scarcity of caregiver support in the formal care channels, the identified potential of enlarging informal support via a peer-to-peer exchange mechanism holds promise.

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Planning treatments of different types of patients have become challenging in hemodialysis clinics during the COVID-19 pandemic due to increased demands and uncertainties. In this study, we address capacity planning decisions of a hemodialysis clinic, located within a major public hospital in Istanbul, which serves both infected and uninfected patients during the COVID-19 pandemic with limited resources (i.e., dialysis machines). The clinic currently applies a 3-unit cohorting strategy to treat different types of patients (i.e., uninfected, infected, suspected) in separate units and at different times to mitigate the risk of infection spread risk. Accordingly, at the beginning of each week, the clinic needs to allocate the available dialysis machines to each unit that serves different patient cohorts. However, given the uncertainties in the number of different types of patients that will need dialysis each day, it is a challenge to determine which capacity configuration would minimize the overlapping treatment sessions of different cohorts over a week. We represent the uncertainties in the number of patients by a set of scenarios and present a stochastic programming approach to support capacity allocation decisions of the clinic. We present a case study based on the real-world patient data obtained from the hemodialysis clinic to illustrate the effectiveness of the proposed model. We also compare the performance of different cohorting strategies with three and two patient cohorts.

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OBJECTIVE: Turkey has implemented family practice on a pilot basis as part of the reform in health care, since 2005. This paper aims to understand and describe the prevalent practice patterns and clinic characteristics during the transition period. DESIGN: A cross-sectional descriptive study design was used. SUBJECTS: An online survey was conducted among Turkish GPs working as primary care doctors (without vocational training) during the reform period. Clinic and GP characteristics are analysed with descriptive statistics. RESULTS: List size is an important factor; larger lists lead to shorter consultation time and a longer wait for patients. GPs are generally satisfied with the reform. CONCLUSION: During the transition to family practice access of patients to health care has improved and GPs are satisfied with their job. KEY POINTS: Patients in Turkey have adequate access to primary health care services. The waiting time for consultation is relatively short. Basic prevention activities occupy the majority of the GPs' time. Reducing the panel size and introducing appointment systems may be useful.

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INTRODUCTION: Turkey's primary health care (PHC) system was established in the beginning of the 1960s and provides preventive and curative basic medical services to the population. This article describes the experience of the Turkish health system, as it tries to adapt to the European health system. It describes the current organization of primary health care and the family medicine model that is in the process of implementation and discusses implications of the transition for family physicians and the challenges faced in meeting the needs for health care staff. In Turkey a trend toward urbanization is evident and more staff positions in rural PHC centers are vacant. Shortages of physicians and an ineffective distribution of doctors are seen as a major problem. Family medicine gained popularity at the beginning of the 1990s, as a specialty with a 3-year postgraduate training program. Medical practitioners who are graduates of a 6-year medical training program and are already working in the PHC system are offered retraining courses. Better working conditions and higher salaries may be important incentives for medical practitioners to sign a contract with the social security institution of Turkey. DISCUSSION: The lack of well-trained primary care staff is an ongoing challenge. Attempts to retrain medical practitioners to act as family physicians show promising results. Shortness of physician and health professionals and lack of time and resources in primary health care are problems to overcome during this process.

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This work is motivated by the recent changes in the health system in Turkey, which is a consolidation of health insurance funds, and its implications on the resource allocations and the flow of patients in the system. Our aim is to provide a model to find the best reallocation of resources between the hospitals and the best patient-hospital match to minimize the costs.

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This work combines and extends previous work on breast cancer screening models by explicitly incorporating, for the first time, aspects of the dynamics of health care states, program outreach, and the screening volume-quality relationship in a service system model to examine the effect of public health policy and service capacity decisions on public health outcomes. We consider the impact of increasing standards for minimum reading volume to improve quality, expanding outreach with or without decentralization of service facilities, and the potential of queueing due to stochastic effects and limited capacity. The results indicate a strong relation between screening quality and the cost of screening and treatment, and emphasize the importance of accounting for service dynamics when assessing the performance of health care interventions. For breast cancer screening, increasing outreach without improving quality and maintaining capacity results in less benefit than predicted by standard models.

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