The Role of Big Data in Medicine

Pharmaceutical companies are making a conscious decision to embrace digitalization and and the information revolution. This is because of the direct patient benefit from adopting data-driven approaches to research. The technology now has the potential to transform medicine and the overall healthcare system.

The Revolution of Data in Medicine 

The three main areas where big data differs from the conventional analysis of data samples:

1. Data can be captured more comprehensively in relation to the circumstance, consequently reducing some bias. 

2. Data is often analysed using machine learning tools rather than the traditional statistical methods, resulting in systems that over time capture insights implicit in data.

3. The purpose of the analysis of data is no longer solely to answer simple questions but to to generate promising new hypotheses (Mayer-Schonberger, V. Ingelsson. E, 2018).

Uses of Data in Medicines

Big Data comes into play around collecting more and more information for what forms a disease, from the DNA, proteins and metabolites to cells, tissues, organs, organism and ecosystems (McKinsey & Company, 2015).

Wearable Devices Transforming Medicine:

Through the use of mobile health apps we can integrate a longitudinal monitor of someone’s state with respect to many different dimensions of your health. This helps to provide a much better profile of who you are, what your medical baseline is. This enables the ability to measure how deviations from that baseline may predict a disease. 

Data for Informed Strategic Planning:

Data in healthcare allows for strategic planning thanks to better insights into people’s motivations. Through the analysis of check-up results among people in different demographic groups and identify which factors discourage people from taking up treatment.

Predictive Analysis:

Predictive analytics in healthcare can help to identify at risk patients and doctors can make data-driven decisions within seconds and improve patient treatments. The algorithms fed with real-time and historical data are used to make meaningful predictions. 

Data and Medical Imaging:

The term medical imaging, includes various radiological imaging techniques such as X-ray radiography. Algorithms are used to analyze hundreds of thousands of images to identify specific patterns in the pixels and convert it into a number to help the physician with the diagnosis.

How PatientMetRx Connects Data with Medicine:

The PatientMetRx data service uses AI, ML and NLP to capture and analyse the voice of the patient from multiple social sources, mapped to a curated database of the full set of 130,000 regulated global medicines. Successfully providing a systematic way of measuring patient confidence by medicine. 

To find out more book a PatientMetRx demo using the link below:

Book a PatientMetRx Demo

References:

Mayer-Schonberger, V. Ingelsson. E, (2018). Big Data and medicine: a big deal?. Journal of Internal Medicine [Online] Available at: https://onlinelibrary.wiley.com/doi/pdf/10.1111/joim.12721 [Accessed 5th March 2021]. 

McKinsey & Company, (2015) The Role of Big Data in Medicines. [Online] Available at: https://www.mckinsey.com/industries/pharmaceuticals-and-medical-products/our-insights/the-role-of-big-data-in-medicine [Accessed 5th March 2021].

Durcevic, S. (2020) 18 Examples Of Big Data Analytics In Healthcare That Can Save People. Data pine [Online] Available at: https://www.datapine.com/blog/big-data-examples-in-healthcare/#:~:text=Medical%20researchers%20can%20use%20large,up%20with%20patient%20treatment%20records.[Accessed 5th March 2021]. 

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