Curing healthcare challenges with Big Data posted on Mon, January 14 2013 in Analytics, Big Data, Data Scientists

These are trying times for those in the healthcare industry, which no longer profit from their former status of seemingly sacred immunity. Healthcare providers of today need to continually prove that they are worth soliciting by delivering high-quality and efficient care services. For the sake of the patient but also to drive their own revenue and minimize expenses. To heighten the challenge, healthcare is turning into a consumerized market, with patients transformed into highly informed, critical and aware customers. If they check out a medical provider and they don’t like it, they will shop elsewhere.

Our changing environment influences the healthcare sector very deeply. It affects governments, which, as ever, are eager to reduce costs, insurance companies that want to offer tailored services and benefit from a healthier client base, but also researchers at academic institutions or pharmaceutical companies looking to up their revenue.

Healthcare needs Big Data solutions. It is how healthcare professionals will truly learn to know patients, beyond their medical records, how they will refine their risk management, how medical breakthroughs will be initiated and new cures will be found. The information that is at hand is truly gargantuan: the global size of Big Data in healthcare is estimated at 150 exabytes in 2011 and is increasing at between 1.2 and 2.4 exabytes a year. It comes from medical care providers, public and private players, ancillary service providers and the healthcare consumers who personally monitor health indicators.

And yet, so much of that information is still untapped: healthcare providers discard no less than 90% of the data they generate. If they were able to keep and store all their own useful information and interconnect it with data from other industry players, their intelligence could grow to unknown heights. What’s more, healthcare data can only offer truly groundbreaking insights if it can be combined with other, unstructured data sources, such as social media data, Internet search data, financial data, census data, shopping data, cellphone data, and the list goes on. Only when leveraging such a truly massive amount of data can patterns be found and the insights, thus gathered, be truly predictive and actionable. By connecting all these data sources, Big Data tools can create added value by demonstrating transparency, enabling experimentation, identifying population-specific needs, supporting human decision with automated algorithms, and promoting new business models and technologies.

Improvement of patient care and engagement

Big Data can drive a solid improvement of patient care. Just to give a small example, if a critically wounded person’s medical data - including blood type, allergies and previous medical complications - are known even before the ambulance reaches the hospital, this can mean the difference between life and death.

Big Data can also heighten patient involvement. Big Data-driven apps in smartphones could remind people to take their medication, to practice a certain movement or take their temperature at designated intervals. This could greatly improve the efficiency of healthcare when we realize that 50% of prescription medications are not taken as directed.

A better and faster R&D process

The average cost of developing a successful drug is in the range of 1.5 billion to 2 billion dollars. Companies that research and manufacture drugs (but also therapies and devices) have been using Big Data solutions for several years now so that they can conduct this process faster and more efficiently. Drug effectiveness in the field is, essentially, a Big Data application, especially when correlated with other health and treatment information, demographics, etc. The more data one gets from more sources, the better the insight. Perhaps the most extreme example of this is the new wave of genomics companies. An enormous amount of data is generated by sequencing genomes, but that specific data is of limited use unless it’s correlated correctly with actual life histories, therapeutic results, etc.

When personalization rhymes with efficiency

We are living in a hyperpersonalized world, but healthcare seems to be one of the last sectors still using generalized approaches. When someone is diagnosed with cancer they usually undergo one therapy, and if that doesn’t work, the doctors try another, etc. But what if a cancer patient could receive medication that is tailored to his individual genes? This would result in a better outcome, less cost, less frustration and less fear.

With human genome mapping and the reduction in costs for the soft- and hardware required for doing so, it will soon be commonplace for everyone to have their genes mapped as part of their medical record. This brings medicine closer than ever to finding the genetic determinants that cause a disease and developing drugs expressly tailored to treat those causes — in other words, personalized medicine.

Cutting costs

One of the problems in healthcare is that healthcare consumers have no idea of what healthcare actually costs. Providing patients with more data will help them make better and more economical choices. With analytics, those consumer choices will include new insurance options.

If patients are helped more efficiently and effectively in the healthcare cycle, healthcare becomes

cheaper. Keeping patients out is a big ambition of any hospital. Taking a closer look at administrative costs and ever-cheaper genome sequencing (with today’s technology) are two other ways of deploying Big Data to reduce cost pressures on the healthcare system. The McKinsey Global Health Institute has reported that, in Europe, savings through Big Data analytics could be high as 205 billion euros.

Limiting risks

A readmission to a hospital within 30 days of being discharged typically causes a lot of pain

and suffering for the patient and sends bills skyrocketing. It is, however, a major problem in the healthcare system. Readmissions could be severely reduced by means of Big Data which could allow hospitals to flag patients in danger of readmission, via certain key indicators.

The benefits described above are just some of the benefits of Big Data analytics for the healthcare industry. If you want to know more, read Greenplum’s The Age of Data-driven Medicine.

 

Comments

by Ilham on September 30, 2014

"Hi Florian,I am very happy to be the first commenter on your blog. Interesting post, renmids me of our visit at GP a few years back. I wonder if your team thought something similar about our research back then :)I guess it’s a natural implication of your discussion of the compromise between conflicting requirements that we now see quite a number of specialized engines doing fewer things better. At the same time, I believe it’s not only the conflicting requirements making various aspects of elephant’ databases suboptimal. The progress in research, improvements in hardware, and changes of requirements simply allow and require the level of change that are extremely hard for these systems. Hence, new players not only can focus on fewer aspects of the system, but they can also try new approaches, that haven’t been tried before. So, perhaps your visitor was an extreme case of that   very narrow but cutting edge?Anyway, keep it coming, we’ll be watching!Best,Marcin"

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