Clinical trials are an important component of the overall healthcare landscape, leading to new treatments and revealing novel techniques for disease detection and diagnosis. They have a significant impact on standardizing patient care.
Conducting trials is a complex task involving diverse stakeholders—investigators, patients, funding organizations, regulatory bodies, and more. A great deal of coordination between these disparate parties is required for a successful clinical trial, not to mention time, money, institutional energy, and management and analysis of enormous amounts of data.
Advances in artificial intelligence technology are currently being harnessed to aid the effectiveness and efficiency of clinical trials, giving us a glimpse into the future of clinical trials as augmented by machine learning.
Data Automation Made Easy
Any successful clinical trial depends upon the accurate and timely collection of data. Clinical data management professionals are responsible for everything from the collection of data to the statistical analysis of data to the preparation of data for regulatory assessment. Credibility, reliability, and repeatability are at a premium when it comes to clinical trials, and data management technologies and practices are at the heart of those values.
The amount of trial-relevant data is so vast that healthcare data management professionals have recently advocated new, technologically-minded perspectives on data management practices. Healthcare is at an inflection point in which technology, and machine learning in particular, must begin to play a more significant role.
Patient data from electronic health records (EHRs) is grist for the mill of machine learning algorithms, and that patient data is a crucial aspect of clinical trials. Machine learning algorithms can effectively identify which patients are eligible for clinical trials. They can also subsequently track, collect, manage, transform, and interpret clinical trial data in ways that greatly amplify the abilities of individual researchers, leading to better outcomes for patients.
Text Mining Capabilities
Adequate healthcare requires not only the existence of information but the ability to find information that is necessary in particular cases. Even healthcare specialists in narrow fields struggle to keep up with the latest trends and developments in technology, treatment, drugs, and diagnostic techniques.
While the ability of machine learning algorithms to analyze and categorize textual sources has been well known for decades, the utility of machine learning algorithms for clinical trials has just taken hold in the quickly-evolving world of healthcare and clinical research.
Text mining and analysis can provide a significant boost in both time and labor to clinical researchers by giving them the ability to quickly identify important information in peer-reviewed journals, regulatory documents, and other sources. Being able to find relevant data, evidence, and research trends enables researchers to more quickly and effectively design and execute clinical trials. Machine learning keeps clinical trials on track when it comes to regulatory requirements and policy-guided decision-making.
A Shift to Decentralizing
Decentralized clinical trials have become more common in recent years, a trend that will likely continue. Decentralized clinical trials, sometimes called remote, virtual, or siteless trials, are designed so researchers can gather data while also minimizing physical contact with patients. Specific data-collection technologies are required to conduct such trials, including wearable sensors, telemedicine options, and EHRs.
The benefits to both patients and clinicians are many. Decentralized trials are patient-centric, allow for better patient recruitment and retention, and give researchers a chance to collect continuous data different in quantity, quality, and scope than more traditional clinical trials.
Machine learning algorithms have different applications in decentralized trials. One is to enhance the quality and overall diversity of patients. The net results are that patient recruitment is more efficient and that trial results are more generalizable, both outcomes that augur well for the future of decentralized clinical trials.
Decentralized trials have a penchant for creating a large volume of data that isn’t harmonized across devices and formats, creating an opening for another application of machine learning. Machine learning algorithms excel at being able to coordinate and analyze data from diverse sources and can thus be used to enhance data quality for researchers tasked with analyzing and understanding decentralized clinical trials. New data sources and analysis leads to better trials which, in turn, leads to better patient outcomes.
Staying Ahead of the Curve
Artificial intelligence and machine learning technologies are now widespread in many industries, and are continuing to grow and expand. At this stage, it is widely agreed that machine learning models are particularly valuable for the healthcare sector writ large, and for clinical trials in particular.
The benefits of machine learning are undeniable from a business perspective, saving healthcare companies time and money. The result is an opportunity for healthcare organizations to spend more time focusing on what is important: caring for patients. Nevertheless, understanding and implementing machine learning in a business requires expertise, and that’s why it’s a good time to invest in healthcare technology consulting.
illumisoft offers targeted, patient-oriented custom software solutions for your healthcare business, including machine learning capabilities. The illumisoft team works exclusively with health professionals, using their expertise to create custom solutions for complex technology issues in the healthcare space. With illumisoft as a partner you’ll be part of the future of healthcare technology innovations.
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