Data science changing the thinking pattern of Medical Science

Now that data scientists are supplementing doctors, it’s not so far that they might be put in prime positions. Time and again, when doctors mandate a treatment, whether its surgery or an over-the-counter medication, they are using a typical treatment or some variation that is centered on their own intuition, hoping for the best. The sad reality of medicine is that we don’t really understand the relationship between treatments and outcomes.

We have studies to show that various treatments will work more often than try-ons; but, we know that much of our medicine doesn’t work for half or our patients, we just don’t know which half. At least, not in advance. One of data science’s many promises is that, if we can collect data about medical treatments and use that data effectively, we’ll be able to predict more accurately which treatments will be effective for which patient, and which treatments won’t.

We believe that data science has the potential to revolutionize health care. There are big changes happening in healthcare right now, and the implementation of EHR (electronic health records) in particular is a great example of how data scientists will be working with doctors in the future. All of these electronic patient records spell out Big Data for the healthcare fields, and data scientists — like all quantitative folks – love data. These medical data could not only offer tremendous insights that change the face of modern medicine, but also offer rewarding opportunities to the data scientists who must decipher the data. Patient care also stands to receive enormous benefits from data science. While a doctor may be trained to look for many factors when diagnosing an ailment, some of these diseases are impossibly complex, and patients could stand to gain faster, safer treatment if left in the hands of a well-developed machine, or even a physician aided by one.

Two factors lie behind this new approach to medicine: a different way of using data, and the availability of new kinds of data. It’s not just stating that the drug is effective on most patients, based on trials (indeed, 80% is an enviable success rate); it’s using artificial intelligence techniques to divide the patients into groups and then decide the difference between those groups. We’re not asking whether the drug is effective; we’re asking a fundamentally different question: “for which patients is this drug effective?” We’re asking about the patients, not just the treatments. A drug that’s only effective on 1% of patients might be very valuable if we can tell who that 1% is, though it would indeed be rejected by any traditional clinical trial.

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.

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.

Clinical data : These include patient medical records and images gathered during examinations or procedures, as well as doctors’ notes.

Pharmaceutical R&D data : Over the last few years a large number of partnerships have sprung up between pharmaceutical companies – becoming aware of the huge benefits of pooling their knowledge.

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.

What are the Paybacks ?

Personalized Medicine : One of the top goals is to create a personalized treatment plan based on individual biology. Instead of treating your patient with a drug that works 80% of the time (e.g., the breast cancer drug, Tamoxifen), you can employ data science to custom-tailor a regimen just for her.

Genomics : Inexpensive DNA sequencing and next-generation genomic technologies are changing the way health care providers do business.

Self-Motivated Care : It’s a “patient heal thyself” world, now. Developments like personal genetic testing (e.g., 23andMe.com), online patient networks and behavioural apps like Be Well are allowing individuals to take control of their own health.

Disease Modelling and Mapping : One of the flashiest uses of data science in the past few years has been in tracking (and finding ways to halt or prevent) diseases.

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.

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.

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