MHA 605 Week 2 – Discussion Healthcare Analytics Framework

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MHA 605 Week 2 – Discussion Healthcare Analytics Framework

MHA 605 Week 2 – Discussion Healthcare Analytics Framework

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Your initial discussion thread is due on Day 3 (Thursday) and you have until Day 7 (Monday) to respond to your classmates. Your grade will reflect both the quality of your initial post and the depth of your responses. Refer to the Discussion Forum Grading Rubric under the Settings icon above for guidance on how your discussion will be evaluated.

 Healthcare Analytics Framework

The use of healthcare analytics is an important step towards providing value to the transformation; this is taking place in the use of big data to assess areas of cost, innovation, productivity, and safety. These areas create value in the healthcare organizations as executives and leaders seek advanced decision-making metrics. Based on the aforementioned benchmarks the use of the Health Analytics Continuum and the Value Life Cycle will create increase in both data quantity and quality in any chosen hospital department (clinical, operations, or financial).

For this discussion you are to choose one area for the Healthcare Value Framework and one from the Value Life Cycle and discuss how each adds to the overall Healthcare Analytics Framework based on your chapter readings and current scholarly articles. Your discussion should be no more than 300 words in length.


The Healthcare Value Framework (Davenport, 2014).


The Value Life Cycle (Davenport, 2014).


The key to greater organization and new advances has been information. 
We can better organize ourselves to offer the greatest results if we have more information. 
As result, data collecting is an essential component of any company. 
We may also utilize this information to forecast present trends in specific metrics as well as future events. 
As we become more conscious of this, we have begun to produce and collect more data on nearly everything by using technical advancements in this regard. 
We are now in scenario where we are inundated with data from every part of our lives, including social activities, research, job, health, and so on. 
In some ways, the current situation resembles data flood. 
Technological advancements have aided us in generating an increasing amount of data, to the point where it is now unmanageable with currently available technology. 
As result, the term “big data” was coined to characterize data that is enormous and unmanageable. 
We need to find innovative techniques to organize this data and derive relevant information in order to meet our current and future social needs. 
Healthcare is one such specific social necessity. 
Healthcare firms, like every other industry, generate significant amount of data, which brings both opportunities and challenges. 
The basics of big data, including its management, analysis, and future prospects, are discussed in this overview, with focus on the healthcare sector.


The data deluge


People working for numerous organizations all over the world generate vast amounts of data every day. 
The term “digital universe” refers to the huge volumes of data that are created, copied, and consumed in single year. 
In 2005, the International Data Corporation (IDC) estimated the size of the digital world to be at 130 exabytes (EB). 
In 2017, the digital universe grew to almost 16,000 EB (16 zettabytes) (ZB). 
By 2020, according to IDC, the digital universe will have grown to 40,000 EB. 
To get sense of scale, we’d have to assign each person about 5200 gigabytes (GB) of data. 
This demonstrates the incredible rate of expansion of the digital cosmos. 
Google and Facebook, for example, have been gathering and storing vast amounts of data. 
Google, for example, may retain range of information, such as the user’s location, advertisement preferences, list of programs used, internet browsing history, contacts, bookmarks, emails, and other required information related to the user, based on our preferences. 
Facebook, likewise, keeps and analyzes around 30 petabytes (PB) of user-generated data. 
‘Big data’ refers to massive volumes of data. 
The IT industry has effectively leveraged big data over the last decade to generate important information that can earn large money.


These observations have become so prominent that they have spawned new field of inquiry known as ‘Data Science.’ 
Data science covers wide range of topics, including data administration and analysis, in order to extract deeper insights and improve system’s performance or services (for example, healthcare and transport system). 
Furthermore, the availability of some of the most innovative and relevant techniques to show big data after analysis has made it easier to comprehend the operation of any complicated system. 
As growing segment of the population becomes aware of and participating in the generation of big data, it is vital to define what big data is. 
As result, we will strive to provide details on the impact of big data on the global healthcare sector’s transformation as well as its impact on our daily lives in this review.


Determining what constitutes “big data”


Big data, as the name implies, refers to massive amounts of data that are unmanageable with standard software or web-based platforms. 
It outperforms standard storage, processing, and analytical capabilities. 
Despite the fact that there are other definitions for big data, Douglas Laney’s is the most common and widely recognized. 
Laney saw that (large) data was expanding in three dimensions: volume, velocity, and variety (also known as the Vs) [1]. 
The ‘huge’ portion of big data refers to the sheer amount of information it contains. 
The term “big data” encompasses not only volume but also velocity and variety. 
Variety refers to the various sorts of organized and disorganized data that any firm or system might gather, such as transaction-level data, video, audio, text, or log files. Velocity refers to the speed or rate at which data is collected and made available for further analysis. 
These three Vs have come to define big data as whole. 
Although some have added additional Vs to this definition [2], the most widely recognized 4th is’veracity.’


In recent years, the term “big data” has gained lot of traction around the world. 
Almost every field of study, whether in industry or academia, generates and analyzes large amounts of data for variety of goals. 
The most difficult task in dealing with this massive pile of data, which can be both organized and unorganized, is managing it. 
We need technically advanced apps and software that can use fast and cost-efficient high-end computational capacity for such activities since huge data is unmanageable with standard software. 
To make sense of this massive volume of data, artificial intelligence (AI) algorithms and unique fusion techniques would be required. 
Indeed, using machine learning (ML) approaches like neural networks and other AI techniques to achieve automated decision-making would be huge accomplishment. 
Big data, on the other hand, can be murky without the right software and hardware. 
Better strategies for dealing with this “endless sea” of data, as well as smart online apps for rapid analysis and actionable insights, are needed. 
The information and insights gained from big data can make important social infrastructure components and services (such healthcare, safety, or transportation) more aware, interactive, and efficient [3] with suitable storage and analysis tools in hand. 
Furthermore, user-friendly presentation of large data will be vital role in societal progress.
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