Data science is potentialising every corner of the industry. It’s allowing organisations to tap into precious insights to optimise their processes and tailor their services to exactly match customer needs and expectations.
The healthcare sector is no different—particularly in the wake of the global pandemic, during which rapid and remote healthcare practices have had to take shape almost overnight. Healthcare software development services and data science solutions have become an integral part of the industry today. In fact, data science in healthcare represents arguably one of the most critical and long-overdue sector revolutions of modern times.
With data science, healthcare institutions can harness analytics to bring about faster and far more accurate diagnoses while providing treatments that carry a higher efficacy and lower risk to patients’ health. Data science can optimise every aspect of the caregiving model, from medical imaging to drug discovery and genomics.
And with over a billion clinical documents being produced every year in the US alone, there’s a deep mine of healthcare data out there to be drilled. Here, we take a look at six of the key trends that are set to drive the implementation of data science services in healthcare.
Six areas where data science in healthcare can make the biggest impact
1. Data-driven clinical decision making
Predictive analytics is a powerful ally for medical professionals, allowing them to gain insights into things like treatment effectiveness weeks or even months earlier than was previously possible. Modern data science platforms can greatly speed up the decision-making process for clinicians, allowing them to switch an ineffective course of treatment for one that’s better suited to a patient’s needs.
This is particularly crucial for promoting better patient outcomes in terms of the treatment of chronic illnesses such as cancer and heart disease. It’s also significantly more cost-effective than the traditional one-size-fits-all trial and error model.
And predictive analytics becomes even more powerful is when combined with machine learning models that take into account the implications of social factors on health and wellbeing. For example, when determining an individual’s risk of cardiovascular disease, researchers have found that the effectiveness of prediction and treatment improves when a machine learning model considers things such as socioeconomic and environmental circumstances.
2. Medical imaging
One of the most promising applications for data science in healthcare is its ability to enhance medical imagining. Currently, there are various imagining methods used to detect different medical conditions, including X-ray, MRI scans, mammography, and so on, and more techniques are being developed.
With data science, healthcare institutions can harness the power of deep-learning-based algorithms to improve the accuracy of imaging by feeding the algorithm previous examples from which it can learn and improve. This, in turn, enables healthcare professionals to prescribe the most appropriate course of treatment.
With data science, healthcare organisations can rapidly and accurately detect conditions such as cancer, arterial complications and organ issues, thus enabling them to treat the condition before it becomes a much bigger and more expensive problem.
3. Genomics and genetics
Genomics, genetics and digital biology are the frontier fields of medicine, and data science is allowing healthcare institutions to deliver tailor-made treatment solutions based on patients’ individual genetic makeup. Data science is helping healthcare professionals integrate multiple strands of data with genetic information to gain crucial insights into the implications of one’s DNA on disease development and drug response. In particular, genetic predisposition to certain diseases is an area of healthcare for which data science holds huge promise.
