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Population Health Analytics: Technology and Healthcare

With the rise of digital devices and the Internet, many industries crucial for humanity and the quality of human living have also begun to transform. One of the recent approaches that combine IT, remote monitoring, and other technologies (i.e., electronic devices, the Internet) is the population health analytics that becomes more widespread as it involves.

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Technology and Healthcare

Gerst (2016) mentions that the burden on the health care system in the USA is severe enough and should be appropriately addressed, for example, with the help of technologies. Gerst (2012) argues that because the penalties for unnecessary readmissions are high, another approach should be used to get real-time alerts on the patient condition to ensure that they will not experience additional problems. Although this approach might seem to be risky or doubtful to some, it actually presents new possibilities for medicine and cares for patients at home, whereby they will also be treated at the right time. As Gerst (2012) mentions, the ICU hospital beds are already installed in some houses in Australia together with the needed equipment. This approach will not only help the healthcare system eliminate the burden that it is currently experiencing but also provide different patients with needed care for the right cost.

Other technologies that Gerst (2012) discusses are telemedicine and telehealth, also called video visits. The potential of these technologies is astonishing because they are not only capable of providing treatment to those who live in remote and rural areas but also ensure that physicians will not get sued for doing not enough. Nevertheless, the proposed technologies that are mandated by the Government cannot work as efficiently as expected because a proper “ecosystem” or a global electronic record does not exist yet. As Gerst (2012) notices, this is the next significant change that is being anticipated.

Health Analytics

Shillingstad (n.d.) provides other information about health analytics and the potential of records. He notices that public and individual health need to be divided, and data gathered from population health analytics is capable of providing specific data that can indicate which environmental and social factors need to be regarded (Shillingstad, n.d.). What is more, with the help of machine learning, high-quality retrospective studies become possible as well. If this analysis is conducted by a group of scientists, it will demand intellectual severe investment and time. However, machine learning provides the healthcare system with a unique possibility to analyze thousands of cases and extract all necessary data from it.

While some can be skeptical about this type of technology, it can help doctors identify specific correlations exceptionally quickly and accurately predict complications and reactions to different kinds of patients (Raghupathi & Raghupathi, 2013). What is more, analytics can also be used during complex clinical decisions, finance, and resource allocation, and risk assessment (Simpao, Ahumada, Gálvez, & Rehman, 2014).

While population health surveillance is expected to help the healthcare system provide medical care to patients when they need it and assess the outcomes to improve this treatment, it also poses specific dangers to the population of the USA and any other country (Klann et al., 2014). If patients’ data is used to assess and evaluate treatment, does that mean that all patients need to give consent to this utilization of their private data? Can health surveillance be considered as a violation of privacy, even if it is exploited for practical reasons? The problem with the big data is actively discussed by the media and the public; it would be unreasonable to assume that all people are willing to share their private data for “the greater good.” Therefore, population health analytics and health surveillance need to be considered from an ethical standpoint as well, and specific regulations need to be developed.


Gerst, S. (2016). Remote patient monitoring and analysis. [Program transcript].

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Klann, J. G., Buck, M. D., Brown, J., Hadley, M., Elmore, R., Weber, G. M., & Murphy, S. N. (2014). Query Health: Standards-based, cross-platform population health surveillance. Journal of the American Medical Informatics Association, 21(4), 650-656.

Raghupathi, W., & Raghupathi, V. (2013). An overview of health analytics. J Health Med Informat, 4(132), 1-43.

Shillingstad (n.d.). Quality improvement. [Video file].

Simpao, A. F., Ahumada, L. M., Gálvez, J. A., & Rehman, M. A. (2014). A review of analytics and clinical informatics in health care. Journal of Medical Systems, 38(4), 45-51.

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