How Digitalization optimizes Clinical Trials

^{Created on: 10/14/2021}

Clinical trials are time-consuming and costly. These digital innovations offer opportunities for the future.

 

The approval of medicines not only costs a lot of time, but also a lot of money. After a drug has been developed, it is tested in various phases of clinical trials until final approval. Lengthy processes that often take many years - years that may count for patients waiting for medication.

The digitalization of these processes and new innovations not only optimize the development and conduct of clinical trials, but also offer new opportunities for patients.

 

More efficiency through digital clinical trials

 

With the help of high-quality data sets, clinical studies will be conducted virtually in the future. Initially, however, analog studies will not be replaced, but supplemented by new, digital possibilities. 

 

Big Data solutions in clinical trials

 

The simulation of a clinical trial puts the effectiveness of drugs or therapies to the test. This virtual implementation reveals initial administrative errors or potential side effects, for example, which can then be remedied at an early stage. This helps to optimize processes before therapies are administered to humans in clinical trials.

 

Virtual patients in clinical trials

 

How is a study conducted even though there are not enough patients available? One approach is to develop a virtual patient. By virtually recreating the human anatomy, additional study participants are created. Tests are conducted on these from the development phase to long-term monitoring. A simulation that will make clinical trials faster and more efficient in the future.

 

Wearables for data collection

 

Various wearables offer opportunities for more efficient and faster data generation and analysis in clinical trials. Small devices such as smartwatches or fitness trackers measure patients' vital signs directly on their bodies and forward them to study centers via a cloud. This enables a significantly higher and faster collection of data. Patients' comfort is also increased: the separate measurement of values with external devices as well as the entry of data is often eliminated. In addition, patients develop a sensitivity to their own values and react more quickly to changes in their health.

 

More flexibility in clinical trials through telemedicine

 

So-called telemedicine is another digitalization approach that is increasingly being used, especially due to the Corona pandemic. For example, doctors hold conversations in the context of clinical trials via video communication platforms instead of in the doctor's office.

This is not only an advantage for the doctor and the patient, but also for the clinical trial. This decentralization, away from the study center, means that participants can be recruited for study projects regardless of their location.

 

Artificial intelligence in clinical trials

 

The use of artificial intelligence in clinical trials is versatile. Among other things, AI is used to evaluate collected information more quickly, flexibly and reliably with the help of a data management system. Currently, data is mainly evaluated at the end of a study. With artificial intelligence, this already takes place during the course of the clinical trial, so errors caused by manual evaluation are noticed earlier and are reduced.

 

Artificial intelligence and machine learning have become increasingly important in business, science and society in recent years. Read here about the opportunities this technology offers.

 

Challenges on the way to digital clinical trials

 

Innovations are often linked to hurdles and challenges. Especially in a sensitive sector such as the health industry, new approaches to digitalization should be sufficiently tested before implementation. These are, among others, challenges on the way to digitalization:

• Large, high-quality data sets needed to simulate clinical trials using AI are currently rather rare.

• Centralization and standardization of data still pose difficulties for the industry. Only if the collected information is available in a uniform format can it be further used for analyses and evaluations. However, study data is often collected via a wide variety of technical devices. 

• The number of validation studies is currently extremely low. These studies test whether a drug or a therapeutic approach improves the patient's health under the conditions of the respective clinical trial. Before the results of Big Data studies have been compared with sufficient validation studies, they should therefore only be analyzed and used with extreme caution.

 

^{Text: Alcedis - Editorial Team}