Month: May 2015

Transforming Healthcare using Big Data

What if nurses & doctors could remotely monitor their patients through real time notifications based on pre-set thresholds set by the doctor, based on the patient’s condition?

Many doctors and nurses think that Big Data discussions and strategies means more work for them, not less, and that Big Data implementations will take away time from what they see as their key responsibilities such as consulting with patients and providing quality care. The key challenge to using healthcare data smartly is that, ‘Big Data’ brings with it as top of mind….data costs, risks, liabilities & patient privacy, and this thought just scares the key stakeholders as healthcare data is not seen as a source of value, but of additional work.

Another key challenge is that different users imagine data in very different ways. Understanding this key facts about data helps to understand why so-called “big data” solutions are so difficult to implement in practice.The biggest challenge for the use of “big data” in healthcare organisations is not technical. The challenge is figuring out how healthcare professionals such as doctors and nurses, management and technical, will actually use the data in practice.

There is a gap in the understanding of the value of Big Data and how Big Data can help doctors and nurses and free them up from many of the duties and roles that they are currently finding difficult to do, only because, they have so many more patients to take care of in a day, than a few years ago. Using Big Data doctors will be better able to target and understand high risk patients by utilizing patient key biomedical data and natural language processing to extract key elements from unstructured History and Physical, Discharge Summaries, and Consult Notes.

With the silver age, hospitals and clinics are busier than ever. Many times treatments even have to be delayed because of competition for beds or doctor time. The outcome from understanding the time management pain points of doctors and nurses – is by using analytics, we can monitor patient thresholds using a decision support system at the ward reception desk. Real time patient risk models can also help to predict and identify which patients are more at risk and need more effective management to prevent them from getting worse. This way monitoring of patients more efficiently can be done as doctors can have real time updates on how their patients are doing and may reduce their time with patients in control and have more time to focus on high risk patients and help prevent them from getting a disease or at least reducing their their risk.

Healthcare analytics

I would like to have the opportunity to analyse real patient data, to identify patient risk factors for a particular disease. Do you know any healthcare organisation that would like help in this area?

Making the Most of Big Data Now…

When it comes to actually mining Big Data for insights, many companies don’t know where to start or focus on the wrong things and get bogged down…. I say with confidence, Data Visualization is your Key, to making the most of Big Data!

Condensing piles of data to just a few charts is a balancing act of art and science. The visualization should narrate what the next short-term actions should be in order to improve the business outcome.All you need is a few charts with great data visualization – and this is worth 1,000 slides.


The next good tip is –  keep reports easy to understand and don’t forget the actionable steps that need to be taken based on the visualization insights or report insights. Your data visualization should always include recommendations as to what the business user or decision maker need to do.

It’s a 4 step process, ‘DATA – VISUAL INSIGHTS – ACTION – MEASURE’

When it comes to Big Data, it’s important to ask the right questions about what kind of information can empower your business, and your customers.

Identify trends and looking at what people are looking out for during that period. Information can be rendered useless or useful during different periods in your customers’ life.

For example, we found 35 out of 450,000 customers who were at high risk of leaving. That’s a small number, but the loss of those customers would have meant a loss of about $3.75 million dollars.


If the company had waited for a completed data warehouse implementation, this insight would have been missed and the company would have been in danger of closure!