Background:The indications for cesarean sections have been undergoing a gradual change over the last few decades. Besides the obstetric causes, several other medical, social, ethical, economic and medico legal factors play a role in this rising trend of cesarean section. Initially it was performed mainly for maternal interest but recently the health of the fetus has played a significant role in making the decision for a cesarean birth. This study was conducted to determine the rate, indications and complications of primary cesarean section in primi and multiparae. Methods: This was a cross sectional study carried out on primary cesarean section in the Department of Obstetrics and Gynecology, P.D.U. Medical College and Hospital, Rajkot during one year period from April 1, 2014 to March 31, 2015. During this study, patients (booked or unbooked) attending the labour room, undergoing primary cesarean section in the department will be selected according to the inclusion criteria. Their intraoperative and postoperative complications would be noted and also maternal and fetal morbidities and complications if any. The study does not include women who had Gestational age of less than 28 weeks, who underwent cesarean section in previous pregnancy, previous uterine surgery or hysterotomy and who had multiple gestations. Results: Out of 7295 deliveries performed during one year period, overall 1349 CS were performed with an incidence of 18.5%. The frequency of primary cesarean section was higher in primipara (72.4%) than multipara (27.6%) women. Emergency cesarean section (79.3%) was performed more than elective cesarean section (20.7%) in both the study group and most of them, did not taken ANC. 28.5% of the patients were unbooked and 46.6% of the patients were referred. Illiteracy was present in 31.7% cases and 62.6% cases belonged to rural areas. Anemia, malpresentations, oligohydramnios, eclampsia and preeclampsia were more common antenatal complications in primipara patients than multipara patients, while APH was more common complication in multiparity. In unbooked patients, anemia was found more commonly. Malpresentation (34.3%) was the commonest indication for cesarean sections in both the study groups. Intraoperative complications like extension of uterine incision, placenta praevia, PPH were found more in caesarean section done in emergency than electively and more in multipara patients. Maternal morbidity was seen in 18.5% of cases and more in multipara (25.4%) than primipara (15.9%). Abdominal distension, wound gap and fever were the common morbidities while no maternal mortality in the present study. 330 out of 685 babies were admitted in NICU and majority of them were for jaundice, preterm care and birth asphyxia. Perinatal mortality in the study was 6.6% and among them antepartum hemorrhage has the highest perinatal mortality rate. Prematurity was the most common cause of neonatal deaths. Conclusions: From this tudy, we concluded that overall CS rate is 18.5% and rate of vaginal delivery is 82.1% of t...

With a population of more than 1.3 billion people, India has a rapidly growing need for health care, particularly in rural areas. Along with a widespread increase in the incidence of cancer, diabetes, and cardiovascular disease, the nation also is suffering from a shortage of health-care professionals. At Apollo Hospitals Enterprise Limited, the use of robotic surgery and artificial intelli-

Purpose Intervention of artificial intelligence (AI) has brought up the issue of future job prospects in terms of the employability of the professionals and their readiness to harness the benefits of the AI. The purpose of this study is to recognize the implications of AI on employability by analyzing the issues in the health-care sector that if not addressed, can dampen the possibilities offered by AI intervention and its pervasiveness (Cornell University, INSEAD, and WIPO, 2019). Design/methodology/approach To get an insight on these concerns, an approach of total interpretive structural modelling, cross impact matrix multiplication applied to classification and path analysis have been used to understand the role of the critical factors influencing employability in the health-care sector. Findings This study primarily explores the driving-dependence power of the critical factors of the employability and displays hierarchical relationships. It also discusses measures which, if adopted, can enhance employability in the health-care sector with the intervention of AI. Research limitations/implications Employability also has an impact on the productivity of the health-care service delivery which may provide a holistic opportunity to the management in health-care organizations to forecast the allocation and training of human resources and technological resources. Originality/value The paper attempts to analyze AI intervention and other driving factors (operational changes, customized training intervention, openness to learning, attitude toward technology, job-related skills and AI knowledge) to analyze their impact on employability with the changing needs. It establishes the hierarchical relationship among the critical factors influencing employability in the health-care sector because of the intervention of AI.

PurposeThis study explores the variables that drive the impact of artificial intelligence (AI) on the competitiveness of a tourism firm. The relationship between the variables is established using the modified total interpretive structural modelling (m-TISM) methodology. The factors are identified through literature review and expert opinion. This study investigates the hierarchical relationship between these variables.Design/methodology/approachThe modified total interpretive structural modelling (m-TISM) method is used to develop a hierarchical interrelationship among variables that display direct and indirect impact. The competitiveness of a tourism firm is measured by investigating the effect of variables on the firm's financial performance.FindingsThe study identifies ten key factors essential for analysing the impact of AI on a firm's competitiveness. The m-TISM methodology gave us the hierarchical relationship between the factors and their interpretation. A theoretical TISM model has been constructed based on the hierarchy and relationship of the elements. The elements that fall in Level V are “AI Skilled Workforce”, “Infrastructure” and “Policies and Regulations”. Level IV includes the elements “AI Readiness”, “AI-Enabled Technologies” and “Digital Platforms”. Elements that fall under Level III are “Productivity” and “AI Innovation”. Level II and Level I comprise “Tourist Satisfaction” and “Financial Performance”, respectively. The levels indicate the elements' hierarchical level, with Level I the highest and Level V the lowest.Research limitations/implicationsTourism and AI scholars can analyse the given variables by including the transitive links and incorporate new variables depending upon future research. The m-TISM model constructed from literature review and expert opinion can act as a theoretical base for future studies to be conducted by researchers.Practical implicationsManagement/Practitioners can focus on the available characteristics and capitalise on them while working on the factors lacking in their organisation to enhance their competitiveness. Entrepreneurs starting their own business can utilise the elements in understanding the ecosystem of strengthening a firm's competitiveness. They can work to improve on the aspects which are crucial and trigger the impact on competitiveness. The government and management can devise policies and strategies that encompass the essential factors that positively impact the competitiveness of the firms. The approach can then be looked at with a holistic approach to cater to the other related components of the tourism industry.Originality/valueThis study is the first of its kind to use the modified TISM methodology to understand the impact of AI on the competitiveness of tourism firms.

Maternal mortality is still a big health concern in developing world. The state of maternal health represents overall state of women's right in health, social and economic realms. If the causes of death can be addressed and are found to be preventable it indicates negligence on the part of government and the entities that have the power to implement the changes. To determine factors and causes of maternal mortality this retrospective observational study was conducted in the department of obstetrics and gynaecology, BJ medical college. Maternal mortalities from October 2016 to March 2018 (18 months) were analysed with respect to factors like locality, literacy, parity, ANC care, time after admission, type of delay (according to maternal death review form) and causes of death (direct and indirect causes of MMR). Total 92 maternal deaths were studied and that gave a MMR of 468 per 1,00,000 live births. Maximum deaths occurred in patients who were in the age group of 20-34 (83.1%), rural locality (71%), Irregular with ANC visits (74%), multigravida (56.5%), within 24 hours of delivery (46.7%) and those who had type 1 delay (delay in decision making to seek help, 78.3%). More of deaths occurred in post partum (84.8%) and in vaginal mode of delivery (47.4) Vs Caesarean section (46.3). Direct obstetric causes of death (57.6%) were of deaths which included hypertensive disorders (29.3%), haemorrhage (19%) and others. Indirect causes accounted for 42.4% deaths which included hepatitis (21.7%), heart diseases (9.8%) etc. Early intervention and diagnosis with timely management are essential to reduce morbidity and hence maternal mortality. Special attention should be given to Routine Antenatal care (RANC) and more specifically to Focussed Antenatal Care (FANC)). Lack of expertise at primary level with lack of proper referral system adds to the cause.