NURS350 Assignment: Week 6 Discussion Research in Nursing
NURS350 Assignment: Week 6 Discussion Research in Nursing
NURS350 Assignment: Week 6 Discussion Research in Nursing
DQ1 Using examples, compare and contrast the sampling strategies of qualitative versus quantitative research designs. Describe some advantages and disadvantages of each.
DQ2 How is data used to evaluate health outcomes within your work environment and at the national level? Provide an example for both.
Big data has changed the way we manage, analyze, and leverage data across industries. One of the most notable areas where data analytics is making big changes is healthcare.
In fact, healthcare analytics has the potential to reduce costs of treatment, predict outbreaks of epidemics, avoid preventable diseases, and improve the quality of life in general. The average human lifespan is increasing across the world population, which poses new challenges to today’s treatment delivery methods. Health professionals, just like business entrepreneurs, are capable of collecting massive amounts of data and look for the best strategies to use these numbers.
In this article, we’re going to address the need for big data in healthcare and hospital big data: why and how can it help? What are the obstacles to its adoption? We will then look at 18 big data examples in healthcare that already exist and that medical-based institutions can benefit from.
But first, let’s examine the core concept of big data healthcare analytics.
What Is Big Data In Healthcare?
Big data in healthcare is a term used to describe massive volumes of information created by the adoption of digital technologies that collect patients’ records and help in managing hospital performance, otherwise too large and complex for traditional technologies.
The application of big data analytics in healthcare has a lot of positive and also life-saving outcomes. In essence, big-style data refers to the vast quantities of information created by the digitization of everything, that gets consolidated and analyzed by specific technologies. Applied to healthcare, it will use specific health data of a population (or of a particular individual) and potentially help to prevent epidemics, cure disease, cut down costs, etc.
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Now that we live longer, treatment models have changed and many of these changes are namely driven by data. Doctors want to understand as much as they can about a patient and as early in their life as possible, to pick up warning signs of serious illness as they arise – treating any disease at an early stage is far more simple and less expensive. By utilizing key performance indicators in healthcare and healthcare data analytics, prevention is better than cure, and managing to draw a comprehensive picture of a patient will let insurance provide a tailored package. This is the industry’s attempt to tackle the siloes problems a patient’s data has: everywhere are collected bits and bites of it and archived in hospitals, clinics, surgeries, etc., with the impossibility to communicate properly.
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