Data analytics is tremendously vital in healthcare. The most significant advantages of data analytics in healthcare include better patient outcomes, improved medical research, obtaining operational insights from healthcare provider data, and improved staffing.
Data analytics in medicine has numerous uses and can be applied to any aspect of healthcare, from patient care to operations management. They can also optimize clinical trials, such as those for diabetes.
How can data analytics help optimize diabetes clinical trials? This article aims to answer that question and show how data management can aid diabetes-related research.
Importance of Diabetes Clinical Trials
Diabetes clinical trials offer the opportunity to evaluate the safety and efficacy of new medications or devices before they are approved for use by the general public.
According to the CDC, 37.3 million Americans (one in 10 people) have diabetes, and one in five people do not even know they have this chronic condition. At the same time, 96 million American adults (one in three people) have prediabetes, and over eight in 10 people are not aware of it.
On a global level, the prevalence of diabetes jumped from 108 million in 1980 to 422 million in 2014, according to the WHO.
The number of people worldwide with diabetes is growing rapidly, making clinical trials essential to develop new medications or treatment approaches. Researchers have the opportunity to track metabolism, compare and contrast blood glucose levels, and closely observe organ functionality associated with the use of specific medications. The information they obtain during clinical trials allows for the adjustment of formulas to improve the effectiveness of the treatment.
Data Analytics in Diabetes Clinical Trials
Simply put, data analytics is the process of evaluating data sets to identify trends and reach conclusions based on the information they contain. It increases efficiency and productivity and is used in all industries, from healthcare to banking. Diabetes clinical trials can greatly benefit from data analytics.
Data analysis and management in clinical trials are complex processes that require faster and more reliable solutions than traditional methods. Properly managing a high volume of complex data in a manual manner is hardly possible. A study from the Journal of Healthcare Engineering confirms that the use of information technologies in diabetes research is inevitable. Data analytics software can facilitate and improve the process of generating and managing case reports or collecting data in clinical trials focusing on diabetes.
Data analytics is inevitable in diabetes clinical trials (and other clinical trials in healthcare) because traditional data management has limited capabilities. Data analytics deals with a greater volume or size of data and has a greater velocity (the rate at which systems receive and process data) and variety (how many different types of data are included in datasets).
Data Analytics Improves Productivity in Clinical Trials and Drug Discovery
Data analytics can optimize diabetes clinical trials through predictive modeling. This allows researchers to streamline the processes by predicting results. Nowadays, technology can predict drug efficacy, side effects, and medical interactions before moving to the next phase of the clinical trial.
One of the most significant advantages of data analytics in clinical trials is that it can improve patient outcomes. The algorithms and cutting-edge software solutions can predict potential issues with devices or medications before the start of human trials. As a result, it becomes possible to prevent dangerous side effects in human subjects.
At the same time, data analytics in clinical trials improves productivity by automating research and administrative tasks. This allows researchers to do more and make the most of their time or expenses; the system they use completes tasks that would be time-consuming, allowing the researchers to focus on more important processes.
Data Analytics Saves Time
Clinical trials require much information or data, which is typically collected manually or in paper-based form. In their qualitative study, A. Nourani et al. revealed that collecting paper-based data is time-consuming, particularly in multicenter diabetes clinical trials. This method wastes time, compromises data security, and makes it difficult to track data. The old-fashioned approach to collecting and handling data makes the conduct of clinical trials complicated. Paper-based case report forms decrease the response rate, emphasizing the need for more advanced methods, such as the use of technology.
Indeed, in their Frontiers in Public Health study, X. Kong et al. focused on the architecture of diabetes-intelligent digital platforms and their role in data analysis. The platform-building technology relied on the Hadoop system and data processing analysis methods based on machine learning. Results showed that the platform runs smoothly and successfully while handling a massive amount of data in real time. As a result, the analysis and processing of data for medical research becomes convenient and fast.
Improved Data Protection
Clinical trials involve a substantial amount of data. Data protection is necessary to prevent the potential misuse of health information. As briefly mentioned above, A. Nourani et al. reported that paper-based data collection and analysis compromise data security. For example, data is stored in file cabinets and locked in rooms with a key that is only available to a principal investigator. In these circumstances, data isn’t protected from fire or flood.
Data analytics software can protect the security and confidentiality of subjects’ data in several ways, including restricting access, database backups, and data encryption. Other methods to provide data protection in clinical trials are event log files and role-based access control. Technology makes it possible to save and protect data against various factors, whether it’s the prevention of misuse or unfortunate scenarios such as floods and fires.
Reduced Costs of Clinical Trials and Improved Efficiency of Data Management
Manual data management is both time-consuming and costly. The costs cover printing case report forms, distributing those forms to researchers in different geographical areas, hiring human resources to check data, returning the forms to research centers in case of data errors, and retrieving data from paper to enter them into specific software. Data analytics software optimizes diabetes clinical trials by significantly reducing costs.
The use of technology and software for data analytics makes it easy to handle data without having to distribute or print it. According to a study from BMC Medical Informatics and Decision Making, the computer-based system lowers the cost of clinical data management.
The same paper concluded that a data analytics system can meet users’ expectations and, when tailored to their specific needs, allow for more systematic future trials. As a result, data analytics software can improve the efficiency and effectiveness of clinical data management.
