Background: Examining the knowledge, attitude, perceptions and practices (KAP) of the medical students regarding antibiotic resistance (ABR) and use can help us in devising suitable educational interventions for them, tailored according to their earlier held knowledge, beliefs, capabilities and experience.
Big data concept provides opportunity to exchange patient's medical information to the different healthcare providers. Health Information System (HIS) has created the ability to electronically store, maintain and move data across the world in a matter of seconds and has the potential to provide healthcare with tremendous increasing productivity and quality of services. Big data analytics is a growth area with the potential to provide useful insight in health information system. Big Data can unify all patient related data to get more option to view patient records to analyze and predict early disease detection. Big data supports and improve clinical practices, new drug development and health care financing process. Implementation of Health Information system (HIS) continues to expand infrastructure in Medical field due to enormous number of patient comes across to store medical data. In this paper we focus the Big data concept to increase and store patients details in Saudi public hospitals with maximum utilization. Most of the Saudi public and private hospitals Health information system locally connected and maintained by own hospital admin. There is no system implemented to share the patient health record, treatment details and medical prescription data to other hospital. The main problem in the Saudi hospital, Health information is not centralized due to unstructured, semi structured data maintain by the Saudi hospital. Proper Health information system is able to offer correct and complete personal health and medical summary through the Big data methods. This paper introduces the Big Data concept and characteristics, health care data and some major issues of Big Data. Big Data methods and challenges in medical applications and health information system are also discussed in this study. This study provides a base model to increase the use of big data in health information system and can assist to understand the breadth of big data applications.
Background: Prescribing during pregnancy requires careful estimation of risk/benefit ratio for the mother and her baby. Both, underestimation and overestimation can produce undesirable maternal and fetal outcomes hence the present study was undertaken to assess the knowledge, attitude and practices (KAP) of medical practitioners related to the prescribing of medications during pregnancy. Methods: A preformed structured questionnaire was administered to 41 medical practitioners. Multiple choice questions (MCQs) and yes/no type questions were used to assess their knowledge. Likert type questions studied their attitude and practices. Their perception of teratogenic risk of medications was evaluated using a visual analogue scale. The data so obtained was analyzed using descriptive statistics. Results: Completed questionnaires were returned by 76% of the survey participants. Among whom only 24% felt highly confident while others were less confident or unsure regarding their prescribing ability during pregnancy. 90% of the respondents reported that a disease should not be untreated or undertreated due to fear of teratogenicity. 81% exhibited good knowledge of the FDA pregnancy categorization of drugs and more than 80% were aware of the teratogenic risk associated with valproic acid, lithium, isotretinoin, warfarin & thalidomide. The perceived risk of teratogenicity (mean) suggested for them was 33, 52, 52, 43 & 62 percentage respectively. Conclusions: Most respondents exhibited a healthy attitude and a fairly sound knowledge, except for their perception of risk associated with individual drugs which was much higher than what is mentioned in the literature. Educational interventions may help to increase the awareness leading to better and confident prescribing.
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