New scientific methods, such as genome sequencing for the next decade, produce enormous amounts of biological data that leads us to scientific breakthroughs through careful study and interpretation, However, scholars fail to keep up with all the enormous data blocks. As the generation of salient Information is rising with each day, we can use it in a productive way. The medical data can be useful in fields of personalized medicine whose demand is growing exponentially. The term emerged in 2012, along with the “Industry 4.0” concept, Famous, and refers to big data, in the marketing of IT, in which industrial equipment-generated data could hold more Potential benefit for sector. Large Data Industrials refers to many Diverse produced time series by industrial equipment at a high speed. Preventive medicine for individual patients with chronic conditions, Big Data and healthcare are critical to address the risk of hospitalization. The current review discusses about application of big data in gene sequencing, healthcare, electronic health reports (EHRs), medical scans, genomic sequencing, reports of payers, pharmacy studies, wearables, and medical devices, gathered ample health data, to name a few. This paper mainly talks about the ways in which big data can be used and the tools for analyzing big data. Big data analytics aims to provide innovations that optimize patient care and generate value for healthcare institutions with improvements in medicine, infrastructure and funding.
In India, any prescription generally has an NSAID. According to our knowledge many of the studies were done on NSAID's hence, the present study is carried out to know the usage and prescribing patterns of NSAID's. The information generated from the study might be supportive to communicate with the prescribers and advice the various gaps noticed for improving the prescribing patterns for best patient outcomes, and the information also helps in outlining further studies. Thus it ultimately benefits the patients in minimizing the incidences of drug interactions with NSAID's if any and adverse effects caused due to NSAID's. The aim of the paper is to conduct a retrospective observational study in a tertiary care teaching hospital to assess demographic and clinical variables like drug interactions associated with NSAID administration, for the 6 months in all departments except Paediatrics. All patients of various age groups who have received NSAID's therapy from all departments were included by giving pre-informed consent in our study. Prescribing patterns were observed and analyzed in 300 patients during the study period. Among 300, 162 were males and 138 were females. Out of all, 36 patients were from age group >70, 50 patients were from age 61-70, 51 patients from age 51-60, 49 patients from 41-50, 48 patients from 31-40, 56 patients from age 21-30 and 10 patients from age 11-20. A social history of every patient was collected and analyzed where 56 were smokers, 244 were non-smokers and 72 were alcoholics, 228 were non-alcoholics. Paracetamol was the most widely used drug of about 42%. We found 11.22% major drug interactions, 37.75% moderate interactions and 51% minor interactions. Statistical analysis one way ANOVA was done in SPSS version 21software, and the result was of significant difference between NSAID's treatments. Two way ANOVA was done which showed variability in gender groups with treatment regard to NSAID's is due to chance attributed to effects of gender or effect of treatment. Though many drugs are prescribed lucidly in India, not much differences are found in prescriptions given by healthcare professionals, and in prescribing patterns. To promote better patient compliance and to detect the adverse drug reactions early, involvement of clinical pharmacist helps more in prescribing the drugs rational.
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