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4 Ways Big Data Is Transforming Healthcare

It’s hard to think of a more worthwhile use for big data than saving lives – and around the world the healthcare industry is finding more ways to do that every day. 

From predicting epidemics to curing cancer and making staying in hospital a more pleasant experience, big data is proving invaluable to improving outcomes.

This is very good news indeed – as the cost of caring has skyrocketed in recent years and is expected to continue to do so as the population ages – to the point where we could be headed for serious trouble.

I’ve spoken before about the hospital unit which found it could detect infections in newborns 24 hours before symptoms showed, by monitoring a live stream of heartbeats and breathing patterns.

And I’ve also mentioned Google’s (disputed but interesting) claims that it could detect outbreaks of flu more accurately than standard prediction methods by monitoring search activity.

But these are just the tip of the iceberg in an industry which generates mountains of data across every area of its operations.

In fact last year a survey by IDC Health Insights found that 50% of the hospitals and healthcare insurers put increasing their analytics capabilities as their top priority for investment over the next year.

And the body of medical literature from which further research evolves continues to grow every day – with an estimated one million records per year added to Medline, the online repository of scientific studies related to medicine.

Efficiency is the great driver here – with the cost of healthcare in the US currently standing at around 18% of GDP and forecast to rise, payment models are changing. While traditionally providers have been paid according to number of patients they treat, a move towards payment based on results and quality of treatment is taking place. These more complex metrics require more data and a different analytical skill set, rather than simply counting the number of patients coming through the door.

McKinsey & Company compiled a report for the Center for US Health System Reform which identified four main sources of big data in the healthcare industry.

They are:

Activity (claims) and cost data.

These are the basic figures showing the amount of care which has been supplied by providers in the system, and the cost of paying for that care. Analysis of this tells us about the spread of diseases, and the priority that should be given to dealing with specific health threats. The most cost-effective treatments for specific ailments can be identified and the number of duplicate or unnecessary treatments can be significantly reduced. In the United States, Methodist Health System has used a tool which analyses Medicare claims data to highlight groups and individuals who may need expensive care in the future, allowing for less costly preventative action at an early stage.

Clinical data

These include patient medical records and images gathered during examinations or procedures, as well as doctors’ notes. For example, the Carilion Clinic, in Virginia, says it used natural language processing algorithms to analyse 350,000 patient records, identifying 8,500 people at risk of heart problems. Similarly, the American Medical Association reported that analysis of patient records found only 26% of children who had recorded three high blood pressure readings at separate visits to their doctors had been diagnosed as suffering hypertension – highlighting a significant number of failures to spot the condition.

Pharmaceutical R&D data

Over the last few years a large number of partnerships have sprung up between pharmaceutical companies – as if they have suddenly become aware of the huge benefits of pooling their knowledge. In the US major firms such as Pfizer and Novartis pool their data from trials into the clinicaltrials.gov website. And in the UK GlaxoSmithKline recently unveiled its partnership with the SAS Institute which aims to increase collaboration based on data from clinical trials. Suitable candidates can be found for trials more effectively by looking into lifestyle information. And comparison of data from multiple trials can throw up surprising results which can lead to new breakthroughs. For example the antidepressant desipramine is being trialled for its potential to destroy cancer cells in patients with small cell lung cancer.

Patient behaviour and sentiment data

This is data from over-the-counter drug sales combined with the latest “wearables” which monitor your activity and heart rates, patient experience and customer satisfaction surveys as well as the vast amount of unstructured information about our lifestyles broadcast every day over social media. At the moment wearable devices are mainly used for personal fitness, but this is set to change – spending on bringing this information from smart watches, wrist bands, running shoes and other wearables is expected to reach $52 million by 2019, according to a study by ABI Research. Services such as ginger.io already allow care providers to monitor their patients through sensor-based applications on their smartphones. And Proteus manufacture an “ingestible” scanner the size of a grain of sand, which can be used to track when and how patients are taking their medication. This gives providers information about “compliance rates” – how often patients follow their doctor’s orders – and can even alert a family member to remind them.

 Of course with medical matters patient privacy is always high priority, and big data brings big challenges in this respect. How insurance companies will act on the vast increase in information about our lives that they are able to glean is a concern – will we see individuals turned down for cover because their running shoes have snitched that they are lazy?

It is plain to see that there are huge benefits to be had from analyzing the data about our health that is out there. The mantra of “prevention is better than cure” has led to a focus on predicting problems in the early stages when they are easier to treat, and outbreaks can be more easily contained.

For example, Global Viral monitors data sources including a network of “listening posts” across Africa and Asia, as well as social media chatter, to detect the spread of disease from wildlife to humans – considered to be the source of 75% of diseases which are harmful to human health.

In the future we are likely to recover more quickly from illness and injury, and we will live longer. New drugs will come into existence and our hospitals and surgeries will operate more efficiently – all thanks to big data.

I hope you found this post useful. I am always keen to hear your views on the topic and invite you to comment with any thoughts you might have.

About : Bernard Marr is a globally recognized expert in strategic metrics and data. He helps companies and executive teams manage, measure, analyze and improve performance.

His new book is: Big Data: Using Smart Big Data, Analytics and Metrics To Make Bette...

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Comment by madhu peram on May 4, 2016 at 2:59am

Great post :) Thanks for sharing such an informative stuff I loved it. Yes, Big data is playing a vital role in present technology. Big data is very useful in every organization. It's a collection of data sets that you can process using conventional database software like SQL. Here I found new tips & tricks in Big data. Further at https://www.excelr.com/big-data-hadoop/

Comment by Ravi on September 21, 2015 at 9:45pm

Good write up....on areas of application in Healthcare

Comment by Howard Fulks on April 16, 2015 at 5:35am

 An interesting post! I'd like to highlight another fast-moving trend in healthcare - the implementation of RTLS for patients, staff and equipment to gather intelligence that improves efficiency across multiple facets of hospital administration. Safety, process improvement, equipment availability and patient flow are the tip of the analytics iceberg.

Comment by Riya Saxena on April 13, 2015 at 1:35am

Thanks for sharing such an informative post! Bigdata has spread everywhere in Education, healthcare, logistics, Finance etc. Organisations are constantly expanding and new technologies are being innovated and then deployed. Leading to not only more data but data unable to communicate with each other, leaving analysts frustrated as they are unable to get a holistic view of organisational data. More at https://intellipaat.com/hadoop-online-training/

Comment by jim r on February 6, 2015 at 6:50am

Healthcare cost as a percent of U.S. GDP has been around 17.4% and is no 17.2%.  Here's a great summary 

If Slow Rate Of Health Care Spending Growth Persists, Projections M...

Also, many of the drivers of healthcare analytics are policy driven -- understanding how your hospital compares to  others, for example requires you to keep and report on various items.  This is the difference between running toward something and being pulled kicking and screaming toward something.

Comment by Sione Palu on February 4, 2015 at 10:07am

Modern medical analytics development had been going on for years (both in academic researches and industry adoptions), such as app for automated diagnosis of cancer solely based on patients medical images using machine learning, signal & image processing techniques. Those types of modern apps appeared in the markets in late 1990s. With the advance of computing technology of today and fast accumulation of data (big data), various new apps related to medical analytics have started appearing in the markets which are very sophisticated.

Medical analytics is not new, however the new techniques & big data of today, have improved & advanced automated medical diagnostic to a sophisticated level now, that have not been realized a few decades ago. The earliest automated diagnostic computing tool for detection of bacterial infection was Mycin which was developed at Stanford University in the 1970s. During the testing of the system at the time, results for Mycin automated diagnosis outperformed those of human specialists.

"Mycin"

http://en.wikipedia.org/wiki/Mycin

Comment by Billy Wong on February 3, 2015 at 1:20am

Very interesting article. Thank you. 

One area that is not mentioned explicitly is process management in healthcare organisations. Using machine learning to monitor and discover anomalies can reduce risks and control costs. Interesting free paper here http://bit.ly/1ztwDKv

Comment by Big Data Queen on February 2, 2015 at 12:37pm

Bernard, at LexisNexis Risk Solutions we are actively engaged in using the open source HPCC Systems data intensive compute platform along with the massive LexisNexis Public Data Social Graph to tackle everything from fraud waste and abuse, drug seeking behavior, provider collusion to disease management and community healthcare interventions. We have invested in analytics that help map the social context of events through trusted relationships to create better understanding of the big picture that surrounds each healthcare event, patient, provider, business, assets and more. For an interesting case study visit: http://hpccsystems.com/Why-HPCC/case-studies/health-care-fraud

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