Those within that industry that have yet to adopt such solutions – and are considering doing so – need to look at it from a number of perspectives. First off, it’s best to closely examine the big picture, to look at the areas of health care where analytics and data have the most valuable applications. Then, it will be important to look at a BI solution in terms of its adaptability – what different tasks can it handle? Will it take shape and evolve alongside your organization, and roll with the punches of a changing industry?
Reviewing the basics of BI in health care
According to research firm McKinsey & Company, health care big data can be segmented into four categories. These include pharmaceutical research and clinical trial data, patient records, activity or cost data and patient behavior information. As a Salon post pointed out, these data types are most commonly used for a number of crucial purposes. One of these is prediction – determining patients’ likelihood of suffering an accident or contracting an ailment. Insurance firms can derive major benefits from this, and clinics or other care providers can intervene early if the data bears out the probability of a serious affliction.
Cost efficiency is also important to all players in this industry, and proper examination of BI can help achieve this, looking over how much specific procedures cost and paring away non-essential steps. In turn, medical facilities can focus on providing patients with the most important care and treatments.
Looking at common levels of health care BI implementation
A recent piece from InformationWeek points out that many organizations aren’t working with BI solutions to their best possible potential. According to the source, the top-tier organizations are using real-time big data analysis to improve operations on a massive scale, but this is more of an anomaly. Others are only using analytics and BI to report on meaningful use metrics and other essentially basic tenets.
It’s important for health care providers to at least strike a middle ground between those two levels. Higher-level tasks worth focusing on include using predictive and prescriptive analytics for the benefit of patients and having an organized data infrastructure.
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