An Exploratory Study of the Utiva Data Analytics Fellow

How will Data Analytics and its science change the space of Pharmaceutical and Health Service Delivery? Read this piece from a Utiva Data Fellow!

Data analytics is a process of inspecting, cleansing, transforming and modelling data with the goal of discovering useful information, informing conclusion and supporting decision-making. In today’s business world, data analysis plays a role in making decisions more scientific and helping
businesses operate more effectively (Xia and Gong 2015).

Although the advantages of raw data are many but, you can’t access these benefits without proper data analytics tools and processes.

Data analytics helps both in disease prevention and control by helping pharmaceuticals in accelerating drug discovery and better understand patient trends and behaviour. Big data holds great promise for those companies looking to tap its potential. Here are some of the potentials of data analytics for pharmaceuticals and healthcare.

Historical data analytics helps to recognize risks and recommend prevention plans before health risks become a major issue. Through wearables and other tracking devices that take into account historical patterns and genetic information, it’s possible to recognize a problem before it gets out
of hand. It is now possible using quantified health to integrate data directly from consumer wearables (pedometers, Fitbit, muse headbands, etc.), blood pressure cuffs, glucometers, and scales into EMRs through smartphones, and can pick up on warning signs faster by tracking changes in behaviour and vital signs.

Big data allows scientists to simulate the reaction of a drug with body proteins and different types of cells and conditions so that it has a much higher likelihood of curing diverse patients (e.g. people with certain mutation profiles). According to the 2013 Forbes analysis, there are huge benefits
to be had by anything that is able to accelerate the process of drug discovery and development.

Being able to intelligently search vast data sets of patents, scientific publications, and clinical trials data analytics would help accelerate the discovery of new drugs by enabling researchers to examine previous results of tests. Applying predictive analytics to the search parameters should help them
hone in on the relevant information and also get insight into which avenues are likely to yield best results.


One of the most effective uses of data science in healthcare is medical imaging. Computers can learn to interpret MRIs, X-rays, mammographies, and other types of images, identify patterns in the data and detect tumours, artery stenosis, organ anomalies, and more. Researchers have also
developed data-driven models to diagnose irregular heart rhythms from ECGs more quickly than a cardiologist and distinguish between images showing benign skin marks and malignant lesions.

With more data on individual patient characteristics, it is now possible to deliver more precise prescriptions and personalized care. Data science is also helping with the emerging field of gene therapy, which involves inserting genetic material into cells instead of traditional drugs to
compensate for abnormal genes.

Greater amounts of data that companies can tap — including information from remote sensor devices coupled with advanced analytic models, mean that pharmaceutical manufacturers can gain much greater insight into existing patient behaviour. The company can then use that information to design services targeted to different demographics or at-risk patient groups in order to improve the efficacy of treatment.

With increasing competition from generics, Big Pharma is getting smarter about analysing and driving effectiveness in its sales and marketing operations. New niche and underserved markets may be spotted by analysing information from social media, demographics, electronic medical records and other sources of data. Equally, analysing the effectiveness of sales efforts and
capturing the feedback received by the sales force during client visits and using it effectively can help pharmaceutical companies get an edge on their competition.

Hospitals are cost-sensitive and face complex operational problems, such as how many staff to assign at certain hours to maximize efficiency, how to ensure enough hospital beds are available to meet patient demand, and how to enhance utilization in the operating room. Predictive analytics can optimize scheduling and even go so far as to tell hospital staff which beds should be cleaned first and which patients may face challenges during the discharge process.

Analytics software can streamline emergency room operations, ensuring that each admitted patient goes through the most efficient order of operations. Data science can be used to predict the demand for different types of lab tests thereby reducing waiting time. Furthermore, business intelligence can streamline billing, identify patients who are at risk of late
payments or financial difficulties, and coordinate with financial, collections, and insurance departments. Hospitals are able to save money by applying big data analytics in fraud prevention.

Now is the right time for the data-driven healthcare industry and many players are participating in this change, including large biotech and pharmaceutical companies, payers and providers, hospitals, university research centres, and venture-backed startups. Data science can save lives by
predicting the probability that patients will suffer from certain diseases, providing AI-powered medical advice in rural and remote areas in underserved communities, customizing therapies for different patient profiles, and finding cures to cancer, AIDS, Ebola, and other terminal diseases.


Data analytics has the potential to transform the way healthcare providers use sophisticated technologies to gain insight from their clinical and other data repositories and make informed decisions. It will also
accelerate the drug discovery process and aid sales and marketing in the pharmaceuticals. In the future, we’ll see the rapid, widespread implementation and use of big data analytics across the healthcare organization and the healthcare industry.



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