Big data, bioinformatics, predictive analytics or genomic medicine are all buzzwords that get tossed around often. It is true that more data is generated every day than we can possibly assimilate, but predictive analysis is what helps us to use this data appropriately. It, in fact, is no stranger to health care and has been used in many breakthroughs through the years, one among them being the Human Genome Project. This example changed sequencing from a manual to an automated process and saved time, money and increased accuracy – and that’s predictive analytics benefits in a nutshell.
There is a rich source of data buried in patient medical records and scientific research that can be used to improve treatment in cancer. It is important to make the different systems where this data is stored communicate with each other. This is where the challenge lies in predictive analytics. However, the benefits of this technology, in oncology in particular, is why this challenge needs to be addressed.
1. Genomics – Focusing on the patient at the right time
Angelina Jolie’s double mastectomy grabbed media headlines and was a decision that showcased the role of predictive analytics in oncology. After her genetic tests indicated that she was predisposed for breast cancer, genetic testing and genomics have caught the attention of people who had not heard much about it before.
The application of machine learning to cancer databases can be used to predict outcomes. Data that is routinely collected can be used to understand which patients could be at high risk for specific types of cancer and how to take preventive steps. With predictive analytics, prediction and prevention go hand in hand.
Risk scores can be created from real-time data combined with historical data to help predict probable outcomes. Risk scores make use of genetic testing, as well as biometric and health data of a larger patient database, to create possible patterns against which an individual is mapped. This can raise red flags or help healthcare providers to be better armed with information to advise individuals on wellness activities or provide enhanced healthcare services.
2. Predictive Analytics Helps Make Better Decisions in Cancer Treatment
A recent study by Regis College revealed that 43% of U.S. Healthcare Organizations have already adopted predictive analytics. With the trend we are seeing today, this will soon become the norm rather than an exception. While this finding was for healthcare organizations, who offer a range of medical treatments, the effectiveness of PA is highlighted in cancer care.
Predictive analysis of data gathered from a mind-boggling number of patients from diverse groups and ethnicities show us possible long-term outcomes. This is now part of the diagnostic process on how aggressively to treat different cancers. It also helps medical teams to map out effective post-cancer treatment processes – advising additional treatments or even understanding which patients have a good prognosis and when treatment can be stopped.
Another outcome is in diagnosis. Specialization in medicine comes with a few drawbacks. With increased specialization, doctors can sometimes miss out on disorders that they are not familiar with, while it might be more easily recognized by another field of specialization. This is where big data can flourish. With information of every disease, condition, symptom and outcomes, it can quickly compare a patient’s symptoms to other cases and alert doctors to what they might have missed.
3. Predictive Analytics help in Clinical Trial Outcomes
A large number of tumors have now been genetically sequenced, providing researchers with a better understanding of the genetic changes in each type of cancer. This is invaluable to a scientist working on new medicines that can target these changes. Big data is also helping to identify a combination of drugs that can move forward to clinical trials.
Clinical trials, while invaluable, only have about 2% of cancer patients actually taking part. On the other hand, thousands are being diagnosed and treated for these deadly diseases every day. Juxtaposing these two realities means that there is a large amount of data out there that can help doctors make better decisions. While nothing can replace clinical trials for drug efficacy and safety standards, big data can certainly help screen drug candidates for toxicity before the start of a clinical trial. With predictive analytics, these giant datasets will also help drive research and treatment forward.
