Theapplication of technology in healthcare has become ubiquitous similarto its use in other social settings. The 2014, Mobile technologysurvey by the HIMSS revealed that 83% of physicians utilizeelectronic devices in the provision of care. Nurses and cliniciansuse mobile gadgets to update, search for clinical information,updating data and connecting with their colleagues (Andrejevic, M. &Gates, 2014). On the other hand, patients use devices to track theirhealth indicators, search for health information and communicate withtheir health providers (Olszak & Bartuś, 2013). The use of smartphones and related gadgets in healthcare is a critical step towardspatient-centered care and value-based reimbursement. The applicationspromise to improve efficiency (Raghupathi & Raghupathi, 2014).However, there is the need to provide solutions that protect patientinformation that is accessed through the apparatus. Besides,mobile-based applications call for interoperability of mobile deviceswith the Electronic Health Records (EHR’s) as well as determiningthe safest and most practical applications (Olszak & Bartuś,2013).
Cloudcomputing has significantly contributed to the development of currenttrends in business analytics. The strategies enhance the provision ofinformation without delay (Fan & Gordon, 2014). The new tendencyis a shift from business intelligence systems towards computeralgorithms that are instrumental in decisions made by policy makers(Barnhill et al., 2015). For example, the systems can provideinformation on the amount of widgets required by a business in itsvarious stores. In addition, businesses utilize the ubiquitous natureof cloud computing to obtain real-time information. The facts areused to predict the most likely outcomes of multiple courses ofactions. Consequently enterprises can provide accurate, economicaland objective reports to enhance effective decision-making (Halper,2011).
Andrejevic,M. & Gates. (2014). Big Data Surveillance: Introduction.Surveillance &Society12 (2): 185-196.http://www.surveillance-and-society.org
Barnhill, M.,Godin, J., Madison, M. & Napier, C. (2015). NoSQL DatabaseTechnologies. Journal ofInternational Technology and Information Management,24(1)1-13 http://scholarworks.lib.csusb.edu/jitim/vol24/iss1/1
Fan, W., &Gordon, M.D. (2014). The power of social media analytics.Communications of the ACM, 57 (6), 74-81.
Halper, F .(2011). The top 5 trends in predictive analytics: Information&management, 2(16), 16-18
Olszak C. &Bartuś, T. (2013).Multi-Agent Framework for Social CustomerRelationship Management Systems. Issues in Informing Science andInformation Technology, 10(1) 368-387http://iisit.org/Vol10/IISITv10p367-387Olszak0055.pdf
Raghupathi &Raghupathi (2014) Big Data Analytics in Healthcare: Promise andPotential http://www.hissjournal.com/content/2/1/3