Both (AI) and (ML) have the potential to enhance our understanding of data and therefore our ability to accurately diagnose and treat patients. With the sheer volume of data generated in life sciences, advanced data analytics is becoming essential to efficiently discover valuable patterns by analysing large amounts of unstructured, heterogeneous, non-standard, and incomplete healthcare data.
Medical data obtained from Electronic Health records of patients themselves (“Big Data”) which has been mined using AI, is a growing data set, which is high in variety and velocity, and is difficult to handle using traditional tools and techniques.
Value can be provided using big data analytics, which is the application of advanced analytics techniques on large data sets to extract value. Big data analytics can be used in large-scale genetic studies, public health, personalised and precision medicine, new drug development, etc.
The Effect
The increased amount of reliable healthcare data increases the demand to develop an efficient, sensitive, and cost-effective solution for disease prevention. Due to the rise of healthcare expenditures, data science and process improvement can be expected to play a role in resource planning and operational efficiency for optimal patient-centred care.
It does not only identify similarity in genetic variants and biomarkers, but the availability of relevant and reliable data will help in decision making where the goal is to improve the quality of patient care and reduce the healthcare cost.
The benefits are clear to see, but at what cost? What will be the impact on healthcare costs as treatments advance, and how are the rights of the individual protected? We are fascinated by the advancements, and have more to say – watch this space!