"Data science helps enable more effective care for the patients in many ways", says Rob O'Neill.

Specialists like Rob O'Neill are now making use of vast amounts of data to evaluate what works better. The data science in healthcare approach allows applying data analytics that has been aggregated from various fields to boost the healthcare sector. It is now obvious, that the healthcare data system is ready for change.

Data science can change the health care sector in so many ways. From using technologies like Artificial Intelligence and DevOps for health tracking to scheduling nursing shifts, data analysis backs up a value-based data-driven approach. This, in turn, allows for optimizing the workforce and throughput, improves care recipients' satisfaction, and balances the supply. On top of this, if you implement the right use of data science in healthcare, medical organizations can greatly reduce costs and re-admissions.

The health care sector has been leveraging data science to accelerate operations and aid patient recovery from various ailments, including COVID-19. As the industry continues to navigate through the post-pandemic landscape, this is showing no signs of slowing down.

Some of the biggest data science trends that are occurring in the health care data space:

• The rise of hybrid platforms: Organizations need to consolidate their health care data and analytics capabilities. This requires building a platform strategy where organizations can leverage their internal resources. Think of this as building an analytics hub for big data, where tools are built to manage and process the entire data science life cycle.

• Increased use of Artificial Intelligence:

Artificial Intelligence and machine learning deployments have proved key in accelerating communication and data management within healthcare. There is huge potential for Artificial Intelligence and data science to have a sizeable, positive impact on healthcare shortly. Artificial Intelligence is also being used to help diagnose illnesses. Using computer vision and deep learning to understand images from scans, those illnesses that can be harder to detect, such as certain types of cancers, can be found and treated a lot earlier, meaning a much higher rate of survival for patients.

• DevOps adoption for cost reduction:

DevOps has played a vital role, particularly in the area of pharmaceuticals. DevOps has helped businesses in the space to reduce costs, achieve compliance quicker, and maintain productivity. The health care data industry is heavily regulated to ensure that the drugs created do not cause harm, and this includes monitoring its software and hardware components as much as anything else. Using DevOps instead of computer system validation allows businesses to autonomously reduce the risk of bugs, and avoid bottlenecking all without damaging productivity and reliability.

• Digital twins: Digital twins is another aspect of data science that's been making moves within health care data, helping to drive the sector's post-pandemic recovery. The technology is allowing organizations to make decisions quicker, with the aid of modeling and simulation. Breaking down everyday processes into numerical information – things like waiting times, staff numbers, resource availability, floorplans, and typical timings for surges in demand – is where data science in healthcare is contributing toward healthcare process excellence. This data provides the blocks on which to build out a digital replica – or twin – and it's in this virtual playground that you can experiment and optimize by running simulations.
• Shifting towards preventive treatment: health care's wealth of historic patient data coupled with the steady rise of home monitoring equipment means health services have vast stores of structured data. Yet, to date, the overwhelming majority of treatment is reactive, managing an established condition or responding to medical emergencies. With the right data science tools and patient home equipment, we're learning as an industry how to spot trends and early indicators of conditions, allowing for a more preventive approach to care.

Providing strong security, a community of practice, mentorship, and communications to a whole learning eco-system, data science professionals in the healthcare sector and life sciences can better understand the value and use of data science in healthcare within their industry.