The healthcare system of India has a major issue of inequality in the accessibility, quality among the public and private sector. These gaps have been addressed through Ayushman Bharat scheme initiated in 2018 to enhance access to healthcare in both sectors. This paper will explore the implication of Universal Health Schemes (UHS) or in this case Ayushman Bharat on the private hospital facilities. It examines impacts on revenues, operational expenses, long term budgetary sustainability and the new phenomenon of voluntary opt-outs provided a comparative approach to other economies. The systematic literature review adopted a PRISMA format to identify 54 relevant pieces of literature published between 2015 and 2024 based on PubMed, Scopus, Web of Science, and Google Scholar. Thematic coding was used to determine the following four themes that comprised of the findings: financial strain, operational burden, growth opportunities and cross-country insights. The findings indicate that Ayushman Bharat has caused growth in the number of patients but not an equivalent increment in revenues because of low reimbursement rates and delayed payment. Costs of operation have also increased. Nevertheless, there are prospects of growing services and nuturing public-private collaboration in case payment deficiencies and administrative obstacles are accommodated by policies. The increasing possibility of hospital opt-outs is a menace to the scheme sustainability. Accordingly, proper reimbursement, payment and low bureaucracies are necessary to sustain the input of the private sector and attain universal health coverage.

Introduction: Due to the high costs of patient hospitalization, the increasing demand for readmission has led to a lack of suitable services provided by hospitals. The present study aimed to predict the risk of readmission among patients admitted to a large subspecialty hospital in Tehran using data mining techniques. Method: This retrospective cohort study employed data mining techniques following the CRISP-DM methodology to identify factors contributing to patient readmission. The study analyzed 47,892 electronic medical records from a large public hospital in Tehran, analyzing data from August 2018 to August 2019. The study utilized demographic and clinical data, extracting patterns to provide insights into readmission risks and support healthcare decision-making. Key algorithms included neural networks and the C5 decision tree. Model evaluation was measured by the accuracy rate in predicting readmission. Results: The findings from the neural network analysis revealed that the type of discharge, inpatient department, and length of stay significantly impacted readmission rates, with coefficients of 0.28, 0.21, and 0.16, respectively. The neural network model achieved an accuracy rate of 61.2% in predicting readmission. The analysis using the C5 decision tree algorithm showed that the length of stay, number of medications prescribed, and type of discharge had the most influence on readmission rates, with coefficients of 0.12, 0.11, and 0.10, respectively. Among the 37,832 patients analyzed, 11.95% experienced readmission, with 8.63% readmitted once, 2.32% twice, and 1% three or more times. Non-emergency admissions, non-surgical treatments, and specific discharge types were notable factors in readmission rates. Conclusion: Findings revealed that key factors, including the type of discharge, inpatient department, length of stay, and the number of medications prescribed, have substantial impacts on readmission likelihood. The data mining models achieved a suitable accuracy to predict readmissions as well as highlighted variables related to readmissions. Implementing strategies to use data mining models with better data management and quality can provide actionable insights for decision-making on patients’ risk of readmission.

Background and objectives Although inhalation of contaminated air and contact with contaminated surfaces were known as main routes of SARS-CoV-2 transmission, the degree of surface contamination in actual hospital environments and the effectiveness of regular disinfection remained crucial questions. The aim of the present study was to investigate the presence of SARS-CoV-2 on various hospital surfaces before and after disinfection in two large hospitals of Urmia megacity, Iran. Methods In this cross-sectional study, a total of 144 samples were collected from high-touch surfaces inside and outside of patient rooms, both before and after disinfection. Samples were taken using sterile swabs and SARS-CoV-2 was identified via real-time reverse-transcriptase polymerase-chain-reaction (rRT-PCR). Results SARS-CoV-2 was found on 38% (8/21) of surfaces within patient rooms in Hospital A and 20% (3/15) in Hospital B before disinfection. Rates of contamination outside patient rooms were 7/21 in Hospital A and 7% (1/15) in Hospital B. Especially, SARS-CoV-2 was positive in 81.8% of ventilation duct dampers (air outlet covers of mechanical ventilation) from Hospital A and 66.7% from Hospital B. Most importantly, no SARS-CoV-2 was found in any samples collected following disinfection (using benzalkonium chloride and 70% ethanol with a 15-minute contact time). Conclusion The results revealed a high likelihood of SARS-CoV-2 being present on surfaces near patients. Many samples from ventilation duct dampers also tested positive, which pointed to the role of airborne transmission. Importantly, after cleaning, no SARS-CoV-2 was detected on any surfaces, showing that standard hospital cleaning practices effectively lower surface contamination.

Introduction Many healthcare workers have been infected with the SARS-CoV-2 virus. These infected healthcare workers had contacts, some of whom later developed COVID-19. We aim to investigate the risks of contact and infection in different departments and professions in the hospital. Methods From April 2020 to September 2021, healthcare workers with COVID-19 were investigated from the follow-up files, respectively. The follow-up files included information on healthcare workers with COVID-19, as well as their contacts, contact patterns, and test results after 7 days of contact follow-up. The relationship between the occupations, the departments, COVID-19 patient care, and the contacts’ degree, types, and test results was investigated. Results A total of 464 healthcare workers were SARS-CoV-2 PCR-positive. While 230 (49.6%) healthcare workers had no contacts, 234 had 1094 contacts. The contact numbers were related to their occupation and department (p 0.028, 0.001). The cleaning staff had the highest contacts per person. Among all contacts, 112 (10.23%) were high risk, and 363 (33.18%) were social contacts. Healthcare workers providing healthcare to COVID-19 patients had a lower risk for high risk and social contacts, and after follow-up, COVID-19 infection (p 0.005, p 0.001, and p 0.017). After social contacts, healthcare workers had more COVID-19 infections than work-related contacts (p 0.00). Conclusion COVID-19 was most prevalent among non-healthcare workers. Non-healthcare departments had the highest contact numbers. Social contacts had a higher risk of infection. Social areas, non-healthcare-related workers, and workers who did not provide healthcare to COVID-19 patients had a higher risk of COVID-19.

The growth of physician assistants/associates (PAs) in hospital medicine is due to increased demand and insufficient physician-house staff. We compared the demographics and practice attributes of 4074 PAs in hospital medicine to 111,430 in all other specialties. Hospitalist PAs were younger (median age, 36 vs. 39), female (73.4% vs. 68.9%), resided in urban regions (93.3% vs. 92.4%), and worked in secondary positions (18.7% vs. 15.1%). However, hospitalist PAs indicated a higher burnout rate than all other specialties. The growth of PAs in hospital medicine makes them part of the strategy to meet the demand for specialized service delivery.

The aim of this study is to determine the effect of nurses’ calling levels on their organizational identification levels. The population of the study consists of nurses working in a training and research hospital in Ankara. Within the scope of the research, it was tried to reach all nurses without sample selection. Questionnaire method was used to obtain research data. As a result of the hypothesis test; it is seen that there is a significant and positive correlation between the identification levels of the participants and their level of calling (R = .62, p < .00). Calling has a significant and positive effect on nurses’ organizational identification levels (F = 125.367; p < .00). This study is unique by examining the effect of calling on nurses’ organizational identification. Moreover, considering the results of the current study, it was determined that nurses’ calling for their profession is important in terms of organizational identification.

Background The elderly population of India has been growing exponentially over the past few decades, caused by a decline in fertility and an increase in life expectancy. The growth eventually has transcended the disease burden on the public healthcare system. This calls for a need to evaluate the healthcare utilization pattern of the elderly based on their socioeconomic and working condition. Methods Study used access to public and private healthcare services to measure healthcare utilization. Descriptive analysis and multivariable logistic regression were used to understand utilization patterns by working status and some selected sociodemographic parameters. All the results were reported at a 95% confidence interval. Results Using the data from the first wave of Longitudinal Ageing Study in India (LASI) with a sample of 22,680 older persons 60 years and above. The study identified that 50% of the working elderly access private services; however, 26% access public healthcare services. It was found that the working status of the elderly alone did not influence access to healthcare services, but education is also an essential indicator for utilizing healthcare services. Further, factors such as gender, marital status, religion, wealth, tobacco usage, self-rated health, ADL and IADL were significant predictors of healthcare services’ utilization for the elderly. Conclusion This study suggests that there are not many differences found among working and non-working status with healthcare utilization, although some sociodemographic indicators are associated with the utilization of healthcare services, highlighting that increasing health needs among the elderly requires strengthening the quality and appropriate public investment in health.

The aim of this study is to assess the levels, relationships, and predictors of resilience and perceived stress among psychiatric nurses working in inpatient units. A descriptive, cross-sectional, predictive design was employed to collect data from 140 psychiatric nurses through a self-reported questionnaire. The questionnaire consisted of sociodemographic and work-related variables, Connor-Davidson Resilience Scale (CD-RISC-25), and perceived stress scale (PSS-10). The total mean score of the perceived stress was 22.73 (SD = 3.54) indicating a moderate level. The total mean score of the resilience was 49.49 (SD = 15.31) indicating a low level. Resilience correlated positively with educational level and job satisfaction; and negatively with intention to leave psychiatric settings. Significant differences were found in resilience with educational level, intention to leave psychiatric settings, and job satisfaction. Three independent variables predicted resilience: educational level, ward/unit, and job satisfaction; and only one variable (intention to leave psychiatric settings) predicted the perceived stress.

Hospitals play a crucial role in providing medical care to patients, but they also have a significant environmental impact due to their high energy consumption and resource use. In recent years, there has been growing interest in the concept of green hospital design, which aims to reduce the environmental footprint of hospitals while simultaneously improving patient outcomes.10 articles were finalized for review and were coded in QDA Miner qualitative analysis software for descriptive and link analysis. Results indicated a strong correlation between green design aspects of hospital and patient well-being, it failed to provide any evidence of concrete relation between relation between green hospital design and lower operation cost.”

This study examined the relationship between patient loyalty and adherence to treatment and evaluated the mediating role of patient satisfaction in this relationship. This study consisted of 386 participants. Correlation and regression analyses were used. A low level of positive correlation was found between loyalty and compliance scores. It was determined that satisfaction did not mediate the relationship between loyalty and compliance. In addition, a moderately positive relationship was found between loyalty and satisfaction. It has been observed that patient loyalty can play a critical role in important health outcomes such as adherence to treatment and increasing satisfaction.