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.

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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.

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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.

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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!

Data Scientists are Making Healthcare More Effective

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I was recently inspired to write this article below, when I read a long while ago about how patients are most times treated as some sort of ‘average’.”Typically, a patient will receive a treatment based on what worked for most people, as reflected in large clinical studies. For example, for a long time, it was thought that Tamoxifen was roughly 80% effective for breast cancer patients. But now we know that it is 100% effective in 70% to 80% of the patients, and ineffective in the rest”.

Today, there is a huge opportunity for Data Scientists to put their Predictive Analytics / Statistical skills to work, as today, we have access to a new kind of data such as ‘DNA sequencing’ to tell whether it’s likely that a drug will be effective or ineffective in any given patient, and we can tell in advance whether to treat with the drug or to try something else.

Today, we can use predictive analytic techniques such as the Logistic Regression or Decision Trees to divide patients into groups and then determine the difference between those groups. With the Logistic Regression Modelling, we can tell who is likely to be cured with a particular treatment and also, the probability of being cured with that particular treatment. Decision Trees also offer good visualisation showing the breakdown on the different segments of patients who are likely to cured by a particular treatment or not.

Many focus on whether a treatment is effective or not. The fundamental question is, “for which patients is this treatment effective”? It’s all about asking the right questions….The question should always include the patient not just the treatment! A treatment that is only effective on 25% of patients might be very valuable if we can tell who that 25%  is.

One of Data Science’s many promises, is that, if we can collect enough data effectively, we will be able to predict more accurately which treatments will be effective for which patients and which treatments won’t.

If you are interested in “What treatments will work and on which type of patients?” or “Whether you are spending money effectively on your treatment?” Why not get help from Data Scientists, like myself, who can apply Predictive Analytics / Statistical Techniques to historical patient and treatment data and help you answer such questions.

7 Items your Business Needs to consider, to Influence Customer Buying Behaviour?

People – Process – Data – Domain Knowledge – Technology – Analytics – Plan

The above 7 items is all you need. But I would like to start off by saying that:

“Every Business is unique! Every business has its own leadership, Culture, Products, Pricing, Marketing, Customers, Technology, Processes, Analytics”.

It therefore makes sense to acknowledge that every business has it’s own needs, it’s own business questions that need to be answered at any specific point in time. Questions such as Which of our customers are buying product A, How can we increase our number of customers by 20%, How do most of our customers buy from us, What is the customer process for buying our products, When can we expect our customers to change their buying behaviour, How can we reduce churn?

Your data can answer all these questions. You may have several sources of data such as

  • Descriptive – demographic, geographic,
  • Attitudinal – preferences, needs,
  • Interaction – Email, web, call centres
  • Behavioural – transactions, payment history, usage
  • Website Activity – Number of customers, Number of conversions,
  • Social Media – Positive Sentiments versus Negative Sentiments

To influence your customer behaviour, that is, to get customers to do what you want them to do, your business needs the right People, the right Processes and the right Plan in place. Talking to your business domain experts and then taking the relevant data necessary to answer your business question through the use of technology and tools such as SPSS, SAS, R, etc businesses can uncover key insights that will greatly assist them with proactive decision making and business strategy.

Now, what do I mean by the right People? The right people refers to the right employees and the right customers. Having the right employees, means that your business must have the right talent who have enough experience in the industry and with your products as well as the right analytic skill set to better understand the data and how to derive business insights from it.

Having the right customers, means that your business understands exactly who are their target customers, what they like, what they dislike, what they perceive as being most important and what are their current needs.

Having the Right Processes in place, is all about having processes in place at each of the customer touch points where by your business can engage or incentivise your customers to buy your product or service and integrate all the data from a single customer as a single view. This is where the power of the analytics lie. By integrating all you know about your customers into one file, you are able to determine relationships between different customer activities that allow you to make offers or to the right customer, at the right time with the right product offer using the right channel.

What about the Right Plan. How does your ensure that they have the Right Plan? I wold say, it all about the 7 items that your business needs to consider and taking each of them into account, your analyst will have enough information to decide how best to answer the business questions. Based on the business question, your analyst will identify the best statistical techniques and tools that need to be used to provide your business with accurate and reliable results to strategy and proactive decide on what is the best approach to successfully achieve the business goal.

Please share any questions you may have at this point…I will follow on from here next time with some common business questions and the statistical technique and approach that may be used.