Adoption of digital tools that gather patients

Adoption of digital tools that gather patients

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Keyandra W

Discussion 1

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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.