December 16, 2021
Clinical researchers gather information from sources such as medical records, administrative databases, laboratory tests, and others. The collected data is then processed, analyzed, and interpreted. Proper data management and analysis are essential for informed decision-making. Data-driven decisions enhance efficiency, accelerate the drug discovery process, and decrease operating costs. Data management has become a major challenge for clinical research laboratories due to data explosion in the past couple of decades. It becomes difficult to extract important and relevant data from the huge volumes of collected data without proper data management.
Data explosion is an exponential increase in the amount of data generated and stored digitally. The data deluge from various sources has left laboratories struggling with large and complicated volumes of information. Clinical research laboratories now study more diseases, traits, and conditions than ever before. The increasing number of clinical trials and studies has led to the continuous generation of increased amounts of data. Data can now be stored and shared at unprecedented levels, thus leading to data explosion. Rapid advances in technology such as the Internet of Things (IoT) also aggravate data overload. However, it is important to properly aggregate, identify, classify, and organize the data to get the most out of it.
Clinical research laboratories need to manage diverse data such as sample data, patient data, data related to the clinical studies, and much more. Clinical researchers also need to combine large data sets, including clinical, genomics, biomedical, and omics data from multiple sources to make specific interpretations. They need to manage data in a way that they can draw accurate conclusions for improved decision-making. It becomes challenging for laboratories to manually manage and process such high volumes of data.
Data sharing in clinical research enhances collaboration and enables researchers to share their insights with each other, paving the way for more discoveries. Data sharing also brings transparency in the clinical research process, increasing public trust in clinical trials. Data sharing also improves the safety and effectiveness of therapies for patients and accelerates biomedical research. However, data sharing also brings in a few challenges. Clinical research laboratories need to ensure the protection of confidential clinical data and securely manage potentially large and heterogeneous datasets.
Clinical data supports drug discovery and helps identify novel biomarkers. It also helps provide personalized health care to patients. Clinical research laboratories need to manage all data without compromising its quality and security. Tools such as Electronic Health Records (EHRs) facilitate clinical research and support decision-making about the effectiveness of drugs and therapeutic strategies. Furthermore, it enables laboratories to optimize their operations, prevent data loss and redundancy, and ensure the seamless flow of data among all stakeholders.
Technology contributes a lot in seamlessly tackling data overload. The data is electronically stored, thus minimizing manual errors and enhancing data quality. It prevents clinical researchers from getting overwhelmed by the sheer quantity of the data. A Laboratory Information Management System (LIMS) can help clinical researchers overcome their day-to-day data overload and data management challenges. A laboratory software for clinical research, also known as a Clinical LIMS, can help in the following ways:
Data overload interrupts seamless data flow among all stakeholders, thus slowing down the decision-making process. The increasing volume and complexity of data are posing challenges for clinical researchers as they tend to miss out on important information. Clinical research laboratories must have effective data management and automation solutions to overcome these challenges. A laboratory software for clinical research can help laboratories seamlessly manage, track, and securely share data with stakeholders, automate processes, and prevent data loss.