What are some of the ethical and social implications of predictive informatics in health care?
Over the past decade, predictive informatics has emerged as the potential future of healthcare globally. Yet, it still remains imperative to consider key ethical and social implications of this new reliance upon predictive informatics. A major ethical concern associated with this new development is the privacy and confidentiality of patient data. This typically includes issues surrounding the granular control over data provided to a specific healthcare provider, evaluation of the data, and social networking reliance (Meek, 2016). Similarly, decision support is also a key social implication of predictive informatics in healthcare. This is primary due to the fact patients have previously been known to access such data devoid of any qualified clinical intermediaries within a healthcare setting.
Is predictive informatics that uses genomics racist, sexist, or homophobic?
Although predictive informatics using genomics has recently been criticized for being racist, sexist, or homophobic, I firmly contend that this assertion is erroneous and unfounded. Predictive informatics essentially seeks to address medical actualities of the day with the primary aim of introducing viable solutions through new developments in personalized medicine. This now involves identifying the susceptibility of a particular race, ethnic group, sex, or gender with the primary objective of bolstering disease prevention and reducing specific risks associated with a particular. For instance, susceptibility to cardiovascular disease and obesity varies starkly from one individual to the next based solely on biological and sociocultural factors (Srivastava et al., 2020). Thus, predictive informatics through genome-based knowledge is responsible for a deeper comprehension of distinctions within a given population and bound to ultimate result in positive outcomes for vulnerable individuals.
How can genomics and data analytics change how healthcare and coverage could be approached?
Genomics and data analytics represent a revolution undoubtedly bound to have far-reaching impacts on healthcare and an alternative approach to coverage. This is mainly because their application increases the accuracy rate of diagnoses. The subsequent application of predictive algorithms will play a central role in informing a physician’s decision concerning whether a patient should be discharged as opposed to relying solely on their clinical judgment (Yoshihashi & Hoyt, 2017, p. 56). Furthermore, it will also bolster preventive medicine within the healthcare sector through early intervention and health promotion through well-living. Predictive analytics is also bound to transform coverage by according healthcare providers with accurate predictions of individual costs associated with insurance products. This may also go a long way in allowing employers to make accurate future predictions of healthcare costs likely to be incurred by employees covered by the organization.
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