What is “Big Data” in healthcare?
“Big data” is commonly and academically defined to have three significant characteristics: volume, variety and velocity. One of the key industries where generated data adheres to these definitions to a large extent is the healthcare industry. Tellingly, at the onset of the Covid-19 pandemic, big data originating from the health sector was instrumental in developing models used as critical inputs for governments worldwide to devise their response strategy.
Even under non-pandemic conditions, the medical industry produces a myriad of health-related data, including electronic health records, scanned imagery, sensor data, and financial information.. Analysing these types of data allows for a plethora of enhanced services such as disease prediction and prevention, optimised treatment plans, the development of new drugs, telemedicine (remote diagnosis of disease), real-time alerting, insurance fraud detection, as well as human resource and supply chain management,
Health data science
However, despite the increasing availability of massive health data sets, even on national and international levels, the complexity of Big Data analysis arises from combining these different information types. Traditional health sector analytics tools have proven incapable to collectively analyse data from electronic healthcare records, pharmaceutical data, clinical trials, patient summaries, genomic data, telemedicine, mobile applications and sensor data, as well as from social media and information on well-being, behaviour and socio-economic indicators.
Health data science is an emerging discipline which combines mathematics, statistics, epidemiology and informatics in order to mitigate these challenges to effectively harness opportunities presented by Big Data in healthcare. The Matogen Applied Insights’ (MAI) data science team has successfully completed a range of projects within the healthcare sector, ranging from oncology to orthopaedics.
Non-communicable and communicable disease
Advances in Big Data analytics are providing researchers powerful new ways to extract value from diverse sources of data in the fight against cancer in particular, one of the most impactful non-communicable diseases worldwide, especially as the world population grows and ages. MAI completed a project for a US client to model, monitor and visualise cancer data in order to recommend optimal treatment regimens. Similarly, the team also modelled orthopaedic data, to predict adverse events related to surgical procedures for patients suffering from dismineral disease. Mental health disorders constitute another related major non-communicable category for which MAI is collaborating with a team of neuroscientists as part of an ongoing project. On the infectious disease front, MAI has participated in advanced modelling of the COVID pandemic for a major international mining company, as well as building a pipeline to wrangle and visualise tuberculosis patient monitoring data for a US client using data from a large national database consisting of millions of records.
