Health care

Health Care
Student’s Name
Institutional Affiliation
Health Care
Real-time Data Analytics
Abstract
Big data is a good strategy for extracting, keeping and performing an analysis of data to reveal information unknown in the past. This is the age of big data, and the healthcare sector must keep stride with the growth in the data produced by the industry. Data analytics in the health sector refers to the need to take advantage of the expanding patient and health sector systems’ data accessibility to produce healthcare novelty (Wang & Hajli, 2017). By the development of new ways of interpreting data, it can be possible to obtain new insights. In healthcare, such eventualities could have a more substantial implication rather than just mining the records of patients, diagnostic reports or medical images. Useful ideas can involve diagnoses, decision-support systems and endless analyses of the data channels generated by patients in a health institution, at home, or even perhaps through mobile services (Lv et al., 2017). Today, a large part of healthcare analytics occurs through regular refreshing of data using distributed databases that generate pre-developed reporting documents. There is a gaping hole concerning events such as lab test reports, which can be available after a certain number of days. The future of data analytics in healthcare requires spot moments and not reports in pre-processed formats.
Aims/Goals
The health sector is demanding a lot from the efforts of data analytics as it requires instant insights that can be instrumental in driving critical decisions.

In response, health sector players are keen to adopt new technologies, such as the development of machine and deep learning or natural language processing. Advances in technologies in the future should be able to have a profound impact on the analysis of data, which can provide more rounded alerts and customized recommendations for health providers and patients.
Personalized Medicine
Abstract
The field of personalized medicine has made big steps with groundbreaking discoveries such as biochips, microarray, and nucleotide. Such progress, among the others in the future will be important for plans, patients, and the society in general. The medical community’s knowledge of the individual has been strengthened by the achievements in human genome research (Visser et al., 2017). These developments of the human genome have revealed that even though human beings’ genetic makeup is slightly ninety-nine percent identical, only one percent of inter-individual genetic variations represents the extensive changes that occur within the homo sapiens species.
The medical world has long taken notice of the unique characteristics of patients as demonstrated by the pervasiveness of distinct disease categories within different units such as in a family or an ethnic group, varying reactions to medications and varied manifestations of just a single internal medicine. Nevertheless, medical therapy has commonly relied on a comprehensive treatment approach to a heterogeneous population compared to unique treatment approach provided to an individual patient (Pritchard et al., 2017). Personalized medicine is today changing the landscape of medicine as technological advancement is enabling medical practitioners to identify and treat those under their care, based on the unique individualities.
Goals
Future research in personalized medicine will be developed to explain the variations among individuals for the different outcomes from treatments. The cornerstone of personalized medicine depends on transcriptomic epigenomic and microbiota. Variations in transcriptomic profiles are entangled in the development of various diseases. Today, cell-free transcriptomic have shown great potential as innovative biomarkers for different health conditions. Consequently, this will be important for future research in this field.
Public Health Informatics (PHI)
Abstract
The continuing growth of communication technologies and the internet, in particular, has led to newer dimensions of healthcare systems. It is essential to reduce the gap between advances in health information with an eye for future applications in this field. Surprises are never desirable in public health (Ravì et al., 2017). Public health informatics can be considered as fortune telling to the medical world. This field is a relevant and rapidly expanding discipline for health practitioners and providers to know and to be participants. There is a need for distinct medical care systems and the public health information technology systems to integrate.
Presently, there are insufficient public hospitals that are sharing their electronic health records (EHR) data with public health databases to generate a sound output of population health informatics. In future, there is an expectation that cloud-based health will be a reality and this will be critical in integrating various public health IT systems (Leider et al., 2017). Moreover, there is an expectation that more mergers and partnerships will happen between electronic health records suppliers and other sources of data, which include those who pay. The future advances shall usher in a new age in data analytics, alliances, and public health.
Goal
The primary goal of new technologies in public health informatics is the development of telemedicine that may mitigate against the present difficulties players are facing in the healthcare sector. Mature economies such as the US are continuously facing problems that relate to an aging population, scarcity of physicians and an expanding need to manage terminal illnesses. Telemedicine would be prominent in data analytics, patient care, and epidemiology, amongst others.
References
Leider, J. P., Shah, G. H., Williams, K. S., Gupta, A., & Castrucci, B. C. (2017). Data, staff, and money: leadership reflections on the future of public health informatics. Journal of Public Health Management and Practice, 23(3), 302-310.
Lv, Z., Song, H., Basanta-Val, P., Steed, A., & Jo, M. (2017). Next-generation big data analytics: State of the art, challenges, and future research topics. IEEE Transactions on Industrial Informatics, 13(4), 1891-1899
Pritchard, D. E., Moeckel, F., Villa, M. S., Housman, L. T., McCarty, C. A., & McLeod, H. L. (2017). Strategies for integrating personalized medicine into healthcare practice. Personalized Medicine, 14(2), 141-152.
Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., & Yang, G. Z. (2017). Deep learning for health informatics. IEEE Journal of Biomedical and Health Informatics, 21(1), 4-21
Visser, J. C., Woerdenbag, H. J., Hanff, L. M., & Frijlink, H. W. (2017). Personalized medicine in pediatrics: the clinical potential of orodispersible films. AAPS PharmSciTech, 18(2), 267-27
Wang, Y., & Hajli, N. (2017). Exploring the path to big data analytics success in healthcare. Journal of Business Research, 70, 287-299.