Big Data is a term for data sets that are so large or complex that traditional data processing applications are inadequate. Challenges include analysis, capture, data curation, search, sharing, transfer, visualization, querying, updating and information privacy. The term often refers simply to the use of predictive analytics, user behavior analytics, or certain other advanced data analytics methods that extract value from data.

As this concerns healthcare, accuracy in big data may lead to more confident decision-making, where better decisions can result in improved efficiency in healthcare delivery models, cost reduction and reduced risk.

Analysis of data sets can find new correlations to prevent diseases in addition to promoting the overall delivery of higher quality healthcare. Medical practitioners regularly meet difficulties with large data sets and the processes of extracting value from data that is consistently being made available as patients are being cared for.

With the advent of the Electronic Health Record (EHR) and its increasing rate of adoption, strides have been and will continue to be made in the direction where value can consistently be extracted from healthcare big data.

As noted in Competing on Healthcare Analytics, J. Bryan Bennett, 2016, healthcare data analytics can be depicted by the following 4 categories:

• Descriptive Analytics where the question is “What” Happened?
  Example: How many patients developed Hypertension?
  
  
• Diagnostic Analytics where the question is “Why” did it happen?
  Example: Why did these patients develop Hypertension?
  
  
• Predictive Analytics where the question is What “Will” happen?
  Example: What are the chances that Hypertension will lead to a stroke in these patients?
  
  
• Prescriptive Analytics where the question is “How” can we make it happen?
  Example: What meds should be prescribed to prevent these patients from experiencing strokes?

EHRs have clearly demonstrated the ability to address the Descriptive and Diagnostic (present tense) questions, however, the challenge lies in addressing and answering the Predictive and Prescriptive (future tense) questions.

In order for value to be extracted from healthcare big data that is of Predictive and Prescriptive relevance, there has to be a means established by which the process can be achieved consistently and seamlessly. Tools have to be developed to address these Predictive and Prescriptive questions in real-time, during the patient-healthcare provider encounter in order for this data to be optimally useful.

NEXA, LLC Custom Medical Technologies® aims to design such custom tools that will, in real-time, execute Predictive and Prescriptive Data Analysis, extracting value from data while promoting clearer interpretations of both the clinical and non-clinical patient data in order to establish more efficient Quality Outcome Measures.

NEXA intends to develop a scalable means as it regards the processes of Preventive and Prescriptive data analysis with the hope of employing a model that could be used on a national level, and compatible with multiple EHR systems. There has to be better interpretation of the endless amount of healthcare Big Data that will continue to be generated through patient-healthcare provider interactions.

NEXA’s goal is to develop custom software tools that will serve to support Predictive and Prescriptive data analysis of Healthcare Big Data across multiple health care delivery platforms, both nationwide and globally.