The cost of clinical research trials is increasing. According to a report published in June 2020, the estimated cost of a trial at Phase III is $255 million.
There are several reasons for this, one of which could be the stringent regulations in place. Local governments and medical industries have their own requirements and abiding by these regulations can drive costs up. Moreover, there are different research trials depending on the objective in question, which means there are different procedures too.
There are also other factors to consider, like reducing volunteer enrollment. For most clinical researchers, finding the patients they need is becoming more difficult. One reason for this could be because of regulations that require extensive information on volunteers, making it more difficult to get people who are willing to provide this information. The other reason could be that patients have little to no reward for participating in these studies.
All these challenges make it difficult to conduct clinical research trials that are economically feasible while meeting compliance regulations. To resolve some of these issues (or mitigate their severity) you need to invest in a solution that can help you collect and analyse data efficiently.
This is where clinical research data analytics becomes an invaluable resource.
First, it’s important to answer the question—how can analysing data reduce the cost of clinical trials?
Clinical research data provides useful insights into the research process. When analysed properly, you can find several ways to optimise the work you do including maintaining consistency in research outcomes, streamlining complex procedures, and having a more solid framework for creating backup plans.
If you are looking to take clinical research trials to a new level, then your data holds the answer.
Yet, we face a problem. Research data is incredibly complex to deal with. At any point in the research process, you might be dealing with petabytes of data in different formats. These formats can include structured data (medical forms) and unstructured data (readings from Fitbit devices).
Analysing data from diverse sources to find common trends is not the most straightforward process. Not to mention, there is the issue of third-party data; how can you merge third-party information with your own findings?
Fortunately, clinical research data analytics platforms can help you tackle these challenges. These solutions help you leverage research data to reduce the cost of clinical research, launch your product to the market faster, and make compliance regulation an easier process.
Research analytics platforms can help you reduce the cost of clinical research because these are equipped with different technologies, like artificial intelligence, to aid your analysis. Your researchers will have a much easier time optimising the research process, including creating research trials. Analytics platforms can also automatically generate a trial model based on your data, lowering your researchers’ workload and reducing costs.
When your research programmes fall short of the required volunteer numbers, it can throw you into a lurch.
Research analytics can predict the number of volunteers you are likely to get for each trial. You can even compare enrollment models against simulations that emulate different situations. This means you will know if you are falling short of the required number of patients needed for each trial. The extra insight can help you draw up backup plans should you fall short of the required numbers.
Data analytics allows you to incorporate data from a wide range of sources, including third parties. In fact, your researchers will have access to a flexible analytical environment, making it easier to study clinical data.
Streaming analytics and machine learning can optimise the process by collecting data “close to the source” and extracting new insights from it quickly. Moreover, researchers gain free access to data due to the transparency of the platform. All these features come together to make the research process easier to optimise, reducing costs and accelerating the rate at which you get results.
Research analytics can help you make radical changes to your clinical research process. These adjustments help you reduce costs and access insights at a faster rate than before. Moreover, analytics makes it much easier to share data with third parties for secondary research.
If you are looking for a solution that can help you optimise clinical research, consider investing in SAS© Clinical Research Data Analytics.
To find out more about SAS Analytics, explore the Selerity blog for more information.