Data mining is a buzzword widely circulating in all industries dealing with data. The term was coined long ago to suit purposes then, as it required intelligent methods to extract the data. However, the real definition of “data mining” is the “process of identifying various patterns or trends from the available data database.”
The extraction of data has always involved manual processes, and statistical methods were also used to observe certain trends. The advancement of technology and computer systems has led to an increase in the size and complexity of the data. Meanwhile, the applications have become more sophisticated as retrieval and archival have become easier.
The process of data mining plays a pivotal role in business analysis, predictive modelling, bioinformatics, behavior informatics, preventive healthcare, and more. The importance of data in the healthcare industry has grown many folds in the recent times. Now, there is an large push within the industry to meet growing competition that demands precision medicines, meeting unmet medical needs, and personalized healthcare.
Pharmaceutical companies struggle to keep the drug pipeline alive by investing millions of dollars to launch a blockbuster drug. The drug discovery involves various phases, such as detection, pre-clinical and clinical trials, and marketing. The clinical trial phase is the most critical and expensive phase where the drug is tested for its efficacy and safety, and requires a targeted patient population.
Pharma companies and contract research organizations can benefit from data mining techniques as there is plenty of data available from rich databases of completed clinical trials that have been neglected up to now. Trends observed from these databases can provide a great deal of information about recruitment patterns, adverse events throughout trials, drug dosing patterns, and site performance.
This information can help plan a better study design, can lead to relative endpoints, and give insights into the overall management of trials, along with an understanding of the trial budgets as well.
Drug development is an iterative process and you need to keep analyzing the results (end points) at each stage. You start with a primary objective and a molecule that hits the target. You weigh the benefit vs. the risk ratio, further make alterations, and try on a larger scale.
This continuous cycle will remain until your calculations come closer to the desired endpoints. The advances in automated tools and programs have paved the way to a possibility of combining data from different stages and analyzing the patterns closer to the desired endpoints.
Data mining has come of age because of the convergence of key elements. These include the continuous capturing of enormous amounts of data with new technology that allows the storage and retrieval of any kind of data, and lastly the analysis of any given data, be it simple or complex.
Although the applications of data mining are vast, it poses certain challenges, and it’s necessary to delineate its uses within the healthcare industry. Data mining techniques can help you to find a specific pattern when planning, or submitting the claims of given clinical trials. Hence, at times, the data being analyzed has to be filtered, cleaned, and should have logic. You cannot simply feed your data into a tool and expect it to generate valid results.
The collection of data is extremely erratic in medical and academic centers, and many departments are involved in building a case file. We can imagine a separate data set for a clinical trial as per defined protocols and simultaneously ‘n’ number of trials running in that center in varied therapeutic areas.
There is therefore a need to integrate the data registered in a center’s laboratory, diagnostics, and in the records department. There should be a strategy for a central repository before starting data mining.
Clinical data mining is highly encouraged, as theoretically this technique can fill the gap between research and development, investigators/researchers, and patients. This can provide an impetus to innovate new techniques for clinical record analysis and examination, above and beyond restructuring the flaw(s) in the existing systems.
Continuous education and development of healthcare systems can significantly improve healthcare costs, increase the quality and outcomes of any clinical study, and augment drug discovery.