August 10, 2021
Data is instrumental in every aspect of clinical research. Clinical researchers go to extra lengths to generate data from multiple sources. In the process, they generate large volumes of data that need to be analyzed and interpreted.
Clinical researchers gather variables from data sets that are relevant to the research hypotheses. For study implementation, the variables are then aggregated into data-collection forms ('Case Report Forms' or CRFs).
Data analysis is equally integral to modern clinical research. The techniques used in data analysis enable researchers to draw meaningful conclusions from the data that they have collected. This governs clinical decision-making.
Clinical research helps clinicians to make a more accurate diagnosis, and this improves patient care. From a business perspective, improved decision-making will lower the cost of operations, increase efficiency and generate higher revenues.
Data analytics optimizes clinical research.
Data that clinical researchers collect come from various sources. This includes:
Data explosion occurs when there is exponential data overload. This may result from increased data sources, Internet of Things (IoT), and faster data delivery. A study showed that clinical trials in 2015 had almost 90% more endpoints than clinical trials conducted a decade prior. Clinical researchers often face challenges that stem from data overload and explosion.
The additional data collected from modern clinical trials can give deeper insights into the operational performance of studies. However, such benefits will only be realized if the data is ingested, aggregated, and standardized in near real-time. Unfortunately, many trial sites and study teams are forced to tackle their roles in these critical, data-intensive tasks using obsolete technologies.
Clinical researchers have relatively limited cognitive processing capacity when it comes to tackling data overload. This presents several challenges that have to be dealt with. For example, they have to identify, define, and classify information from massive amounts of data. They must also make specific interpretations by combining large data sets (clinical, genomics, biomedical, omics data) from multiple sources. Eventually, they have to draw accurate conclusions to enable data-driven decision-making.
Sharing clinical research data can accelerate scientific progress and ultimately improve the safety and effectiveness of therapies for patients. Data sharing increases their knowledge about human health by potentially facilitating additional findings beyond the original, pre-specified clinical trial outcomes. Conversely, when data is not shared, opportunities for collaboration and improved clinical care are missed.
Data management helps minimize errors, prevent data loss, and redundancy and ensure the seamless flow of data among all stakeholders.
Modern clinical research generates massive amounts of data that have to be analyzed and reported to diverse audiences. How can clinical researchers prevent being overwhelmed by the sheer quantity of the data and not miss out on critical operational indicators that make running trials more efficient?
Technology is the ultimate solution for tackling data overload. IT tools can organize, analyze, and interpret massive amounts of data that clinicians can use.
Electronic Health Records (EHRs) is one such tool that facilitates clinical research and supports decision-making about the effectiveness of drugs and therapeutic strategies. EHR can help hospitals keep up with data flow through several intuitive features, such as alerts and automated tracking tools. By engaging an enterprise EHR, hospitals can optimize their operations, resulting in less stress on staff and more time for patients.
A cloud-based laboratory software for clinical research can offer a one-stop solution for data overload.
It can manage clinical research samples and the associated data effectively. It can also help standardize operations by managing workflows, tests, and reporting procedures. A laboratory software for clinical research can be integrated with EHR, patient or disease registries, and other data sources to facilitate data interoperability and centrally manage, query, and analyze large datasets.
System integrations can help to bridge the gap between two or more autonomous systems and enable reliable and efficient data exchange. This allows clinical researchers to improve their performance and productivity and scale beyond what would be possible using manual processes. Such integrations must adhere to the HL7 standard to ensure that data is transferred between systems in a standard format.
With data overload and explosion, the complexity of clinical trials is increasing. As much as researchers are trying to ensure seamless data flow among all stakeholders and make data-driven decisions, the reality is that it's getting increasingly harder to do due to the volume and complexity of data. This increasing amount of data is creating new challenges.
The solution is one: embracing technological solutions for effective data management and process automation. A cloud-based laboratory software for clinical research can support researchers to achieve their data management goals and transform raw data into meaningful data for informed decision-making.