What Are the Benefits of Big Data in Clinical Trials?

When a clinical trial begins for a new and improved medicine, the researchers opt for big data. It refers to the number of data they collect from either patients or healthy volunteers for the experimental procedure. You may wonder, what are the benefits of big data in clinical trials? 

The trial can be done within a small group of 20-50 people. Whether the drug shows satisfactory results or not depends on the health condition of that small group.

This is why the effectiveness of the new drug delivery system cannot be justified in the small group; the number of variables is quite small.

Big data reduces the chance of failure of a drug trial by broadening the range of different health conditions of patients. Continue reading the article to learn more about the benefits. 

What is Big Data in Health Care?

A large number of data sets that contain both structured or organized data, as well as unstructured or unprocessed data, is called big data.

Big data in health care helps researchers analyze trends, patterns, and insights regarding a disease condition, progression, or effectiveness of a new drug after a clinical trial. 

To define Big data in clinical trials, scientists use three Vs: volume, variety, and velocity. Volume represents the high amount of data stored in the system, velocity represents the quick speed, and variety represents how the data is collected and organized, for example, images, hospital charts, surveys, videos, or graphs. 

With the help of advanced technology, data collection in healthcare has become much easier and faster. Researchers use electronic medical records(EMRs), electronic health records(EHRs), personal health records(PHRs), or medical devices included in your personal devices. Researchers of a clinical trial or any other health experiment collect these data to discover the following trends:

1. Improved decision-making regarding the result of clinical trials, 
2. Decreased healthcare costs with personalized treatment options, 
3. Better healthcare is provided to the patient.

There are more benefits of using big data in the healthcare industry. They are:

• Creating a Performance Metric for Pharma Investors

Big clinical data is a key point in determining the performance metric for any pharmaceutical investor. Pharmaceutical investors need to collaborate with health technology and healthcare facilities to initiate the production and launch of a new product. The success ratio of big clinical data helps them decide whether to start producing the new drug or whether it needs more modification. 

With the help of data analytics, collecting and analyzing the massive and diverse data set becomes easier, and the data can help in following steps before launching a new drug in the market-

1. Select the drug pipeline according to the predictive drug modeling 
2. Researchers can determine whether to increase or decrease the patient number for the trial and conduct the trial again, 
3. Enhancing monitoring to record the adverse effects of the drug and implement them to improve the new drug. 

Collecting and analyzing massive data serves as new catalyst events in pharmaceutical and biotech markets which makes the importance of data undeniable. 

• Determining After-Trial Work Plan

After clinical trials, researchers use tools, algorithms, and AI to analyze and process unstructured, complex, and collective data. The result helps provide reports on drug development, treatment outcomes, drug discovery, and disease progression.

Analyzing the research reports reveals new ways to improve the drug delivery system and the effectiveness of the new drug. 

The big-data-generated study reports support better decision-making and provides a more individualized approach to ensuring better treatment for acute and complex diseases. 

• Determining the Commercial Value of the Treatment

Data from a clinical trial includes chemical, pharmacological, biological, and clinical sources. You may think only the result of the trial matters to determine whether the drug is suitable for use against a disease or not.

However, in the real case scenario, the attributes of all the collected data are of immense importance to the medical world. 

The data not only ensures the determination of the commercial value of the drug but also defines fast-producing, high-value, and large-size data for the whole medical world. 

• Reduces the Number and Cost of Clinical Trials

Clinical trials are usually expensive and high-maintenance experiments. This is why it is difficult to manage the funds for every single clinical trial with the arrangements that need to be made for patients.

However, it can be reduced by using advanced AI. The high-value data helps researchers predict related potential drug molecules.

If you input a number of clinical data to the server or system, scientific tools, and computer-based systems analyze the trial data and predict the next trial result so that you do not have to conduct another clinical trial. 

For example, if you want to find the best therapeutic massage therapy near your area, you can search Google, which will show you the search result after analyzing similar searches and posts around the internet.

Similarly, predictive data modeling helps scientists theoretically obtain clinical results without actually conducting the trial. 

The chances of success for drug trials are high in this manner because the predictive model merges clinical and molecular data to identify a candidate molecule that has the potential to effectively attack and eradicate a disease particle. 

• Reduces Time of Clinical Trial

A drug will only work in your body when the drug molecule successfully attaches to the disease molecule and deactivates it.

With the help of a computer-aided drug delivery system, you can see whether your candidate drug molecule will attach to the targeted site of the disease molecule or not.

The system has a large amount of clinical data so that it can compare other data and provide you with a more accurate and faster response. 

If the response is good and the candidate molecule shows a perfect match with the target site, scientists can confidently proceed with the clinical trial with absolute success.

However, if the match is not as perfect as it seems, researchers will try to modify the molecule to make the match perfect and increase the drug’s efficacy. 

• Big Data Cloud for Academic Purposes

All the research work in the healthcare industry does not necessarily need to be done in the laboratory.

Scientists and masterminds in this field allow students and other researchers to join, observe what they have discovered, and share their experiences of discovering new drugs or drug development. 

Hence, if you are a student conducting research and need support from big data, you can collect it from the big data cloud provided by researchers around the world on various topics. 

• Reduced Healthcare Cost 

Clinical trials allow physicians to learn about the drug’s toxicity, lethality, and adverse effects, along with the efficacy and potency of treatment.

Hence, personalized treatment is possible using big data after analyzing which drug molecule is required to cure a certain patient under expert observation.

Since big clinical data includes thousands of data regarding patient conditions and drug effects, they help physicians and healthcare providers to select and make a treatment decision suitable for the patient. 

• Better Accuracy During Clinical Trial

Physicians consider big data a revolution in the clinical trial sector because it helps to accurately detect a potential drug molecule and, at the same time, better requires volunteers.

The traditional method for clinical trials was time-consuming and challenging, and the diversity of participants was low. Therefore, the accuracy of the trial was also low. 

At present, with the help of big data, researchers can select the patient who has a better chance to withstand the clinical trial, which increases the ratio of success of the new drug.

As hospitals and other healthcare institutions now collect their patients’ information in online-based software, the person responsible for selecting the volunteers can analyze the patient data and recruit them as per requirement. 

The whole system of clinical trials is now optimized by analyzing big data.

Lifesaving treatments are now possible, and the process of a clinical trial is more accurate and accelerated by big data analyzing, ensuring trial-based required participants and diversity of volunteer’s physical condition. 

Conclusion

Hopefully, this article has answered the question of what the benefits of big data are in clinical trials.

Although big data has accelerated the clinical process, there are still a few challenges that make it difficult to implement big data everywhere in the clinical sector. For example, the privacy of personal data concerns and the inability to integrate old systems into the new modernized system. 

Furthermore, more people need to be trained in data analytics to help organize and integrate the data.

If we can ensure skilled and experienced manpower in the clinical trial sector regarding big data analysis, it is possible to overcome the challenges and get the best results that scientists desire in clinical trials.