How can predictive analytics reshape the future of healthcare?

WORDS: Mr. Kunal Sawhney CEO, Kalkine PHOTOGRAPHY Supplied

The robust transformation of the healthcare sector over the last two decades has been a significant feat, given the increasing chronic conditions that have developed among patients. One of the prime factors pushing this change in the healthcare sector has been technology. Predictive data analytics is one such major technological pathway that has broadened the horizons of modern caregiving.  

For the uninitiated, predictive analytics is a kind of technology that makes predictions about specific unknowns in the future. Predictive analytics has been implemented across health organisations to enhance patient care, reduce costs, and ensure efficient use of medical equipment and professional care. It broadly involves limiting the unknown future to potentially known circumstances using the previously existing data.

The integration of predictive analytics into the healthcare space has opened the doors to several new possibilities. This enhancement has allowed organisations to build novel models that better fit specific situations. Additionally aided by Artificial Intelligence (AI), data analytics is helping organisations predict fatalities among critical patients and prevent readmissions. This far-reaching technology is averting readmissions by assigning optimal care to each patient such that one need not come back to the doctor.

The unique combination of technology and logical deductions has made predictive analytics a perfect augmentation tool for the healthcare sector. Having said that, let us quickly understand how the data-intensive field has reshaped the medical sector:

A closer look at predictive analysis

While predictive analytics is certainly a cutting-edge tool, it merely supports the decision-making process conducted by doctors based on their expertise. However, healthcare professionals can perform the decision-making process in a much more efficient and quicker way with the help of this innovative technology.

Just as the doctors anticipate the probable causes of a patient’s illness, predictive analytics utilises past information to make useful decisions, though with a much larger database. In its essence, predictive analytics provides highly aligned information that is already known, allowing the decision-making process to become much more valuable for professionals.

Meanwhile, it widens the training data set for medical practitioners, enabling them to provide better personalised medical attention to patients. While it is hard to mimic the minds of medical professionals, the latest software and tools have been known to deliver promising results.

The link between data and healthcare

Analysing past data through high-tech tools and digital software is known to provide several significant benefits to the healthcare space. Some of these advantages are discussed below:

Allows accurate reading of symptoms

Human judgement might be subject to errors. However, the same cannot be said for technology. Tool-based analysis helps aid the doctor’s own judgement about the medical condition of a patient. Moreover, it supports the doctor in deciding whether a treatment is required in a specific medical condition.

Reduces costs

Predictive analysis not only anticipates the future cost of a procedure but also helps in cost-cutting by avoiding unnecessary actions. Meanwhile, analysis of data supports in predicting the trends in the medical industry and providing the best financial model for the changing number of patients.

Supports in determining illness

Medical researchers can utilise large scale data for determining illnesses prevalent in certain groups of individuals. On a macro level, scientists and researchers can segregate healthcare data based on location, age, gender, place of birth and family history, which can support them in the development of medicines.

Helps to develop preventive medicine

Medical researchers can devise different medicines that may help prevent chronic diseases while coming to newer findings using predictive analytics. Since data on healthcare would be highly reflective of inbound trends and lifestyle habitats, scientists can stay up to date in their research.

The challenges accompanying data analytics

Apart from the risks regarding the soundness of statistical modelling, the predictive analysis also poses threats associated with the increased pace of the decision-making process. The rapidly changing results provided by predictive analysis can alter how the decision is handed over from a machine to a human.

Moreover, the underlying coding that is used to develop the predictive model also plays a key role in determining how robust the analytics is. In a nutshell, a predictive tool is only as accurate as the underlying technological expertise that is used to develop it.

Other challenges accompanying data analytics include privacy concerns, regulatory issues, and the possibility of human intervention in the machine-based process.

Overall, the combination of predictive analytics and the healthcare sector has been redefining how the medical industry functions. With this unique amalgamation, medical professionals are looking at an advanced healthcare space that is personalised and better suited for each individual patient. It would be interesting to see how well-oiled the medical sector will become with this progressing development.