Please reply to the two-discussion post below.
· APA Format with intext citation
· Each post must have two scholarly references
· 180-to-200-word count minimal
· Make it sound personal
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Under the healthcare context, big data (BD) signifies immense volumes of data resulting from the adoption of digital tools that gather patients’ data and help direct hospital performance. Globally, healthcare systems are increasingly facing incredible challenges due to disability and the aging population, patients’ expectations, and increased technology use. The increasing use of BD can help clinicians meet these goals unprecedentedly. The potential of BD in the medical industry relies on the ability to turn high data volumes into actionable knowledge and detect patterns for decision-maker and precision medicine. The use of BD in healthcare contributes towards ensuring patients’ safety in several contexts. Evidence bolsters that EHRs can become a vital tool for communication across healthcare teams and a valuable information hub when implemented well (Pastorino et al., 2019). However, the process towards the use of BD requires interdisciplinary collaboration and adapt performance and design of the systems. Additionally, the proliferating use of big data requires the healthcare teams to build technological infrastructure to invest in human capital and cover and house the massive volumes of medical care data to guide people into the novel frontier of health and wellbeing. Therefore, appropriate data analysis tools and software can be employed to determine themes and trends in BD. It would then serve as actionable information to guide multiple advancements, including value-based healthcare, better care outcomes, improved safety, and cost reduction.
I have noticed the massive use of BD in our medical facility. While human factors, staff training, and workflow strategies play a pivotal role in helping the hospitals prevent medication errors, pressure ulcers, falls, and infections, BD analytical tools are gaining prominence in the digital care age. Prevention and prediction are the primary goals for patient safety experts to reduce the pervasiveness of hospital-acquired conditions and avoid adverse events (Catalyst, 2018). The healthcare team realized that when machine learning and predictive analysis are input from bedside devices and applied to electronic health records data, the clinicians can access powerful clinical decision support to prevent costly adverse events and catch up with human errors. However, the use of BD comes with multiple challenges, including budget constraints, health information privacy, data security, diversity in data contexts, and data siloes. The target approaches can help address these challenges. For instance, presenting the data in the same format, type, and context can facilitate the process efforts (Suter-Crazzolara, 2018). Despite these risks, BD is crucial for the medical system to deliver evidence-based information to inform clinical decision-making and improve the clinical systems’ performances.
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Big data takes part in an extensive process of how health care is practiced today, and by gaining a greater understanding of how it affects all
aspects of provided care, it can be an asset to patients(Wang et al., 2018). One example of the benefits of using big data in health care today is
monitoring the use of opioids by preventing overdoses and addiction. By integrating information from insurance companies and pharmacies on
opioid use and prescribing histories, there is the ability to create a risk assessment and predictor of an individual who may misuse or are at risk of
overdose (datapine, 2020). A few assets of this gathered data on opioids, there are better predictions of patients predisposed to addiction, who
are currently misusing opioids and prescribing practices that can be followed (Eckardt et al., 2020).
A challenge or risk for big data is the necessity of keeping patient information private. Current systems monitor the secure data where patient
information is stored and used. However, there are still possibilities of one’s data being compromised, and the inability to share information
across state lines is difficult in direct patient care (Murdoch, 2021).
One potential challenge in my experience and living in Phoenix, Az, is the snowbird population. Patients live part-time here and part-time in
other states. This creates a disconnect between state lines and continuity of care. Creating an infrastructure database that crosses state lines and
promotes more efficient and current medical records of the traveling patient enables a more accurate “real-time” snapshot of the patient needing
care (Bourgeois et al., 2018). Although this can have a high cost associated with creating such files, in the long run, it will be cost-efficient and
make an impact on the ease of patient care across state lines.
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