Public sector and not-for-profit research is informing critical guidelines and regulations that influence every aspect of human health services. It is a dynamic and continuous process that aims to address emerging health challenges and improve patient quality of care. Systematic literature reviews (SLR) are a critical tool in helping policymakers make informed decisions based on the best available evidence. However, the rapid expansion of published evidence combined with a shortage of qualified and available research professionals is creating a massive challenge in generating SLRs and in keeping them up-to-date. Furthermore, most systematic reviews must be updated regularly in order to remain relevant.
Many research teams continue to use simple, non-collaborative tools, or even Excel, to manage their SLRs and track their data. This approach, however, does not scale. It is simply too time-consuming, cost heavy, labor-intensive, and error prone to deliver on what society needs from our limited research resources. To keep pace with the dramatic increase in available research something must change.
Many researchers are turning to automation platforms powered by artificial intelligence (AI) to address these challenges and streamline their SLR process. This blog post will explore why not-for-profit researchers should consider automating their SLR process.
Increased Efficiency
Manual SLRs take between 6 months and 2 years to complete, with an average development time of 15 months1, requiring considerable resources and time. By automating the SLR process, researchers are increasing efficiency, reducing error rates and eliminating time spent on tedious and repetitive tasks that do not contribute to the research itself. Tools such as text mining, machine learning, and natural language processing (NLP) are helping researchers identify relevant studies, extract data, and synthesize findings more quickly and accurately than manual methods. For example, AI can learn from your reference screening pattern and continuously re-sort your references in the background as you review them, bringing the most promising references to the top of the pile.
Improved Accuracy
Manual processes are prone to error. SLRs are no exception and inconsistencies and errors, particularly in large-scale studies that involve multiple reviewers, will always creep in. Automation platforms can reduce errors and improve the accuracy of the SLR process. For instance, machine learning algorithms can act as secondary reviewers to check for inaccuracies in the data entered by human researchers. Accidentally excluded references can be flagged and can be validated against the machine model.
Cost Savings
Systematic literature reviews cost on average, US$100,0002 to produce. Automation can dramatically reduce the cost of SLRs by eliminating many processes that were once manual. One example is creating a PRISMA 2020 diagram with a single click while thereby freeing our human experts to focus on the science, rather than the process.
Scalability
Automation can enable not-for-profit researchers to cover more critical research areas and to keep their research up to date. Software provides us with the ability to process large volumes of data quickly and efficiently, enabling researchers to expand their study scope and depth. By automating the SLR process, researchers can free up time and resources to focus on activities that actually require human expertise, such as data analysis and interpretation.
In conclusion, the benefits of automating systematic literature review processes for researchers are significant. Automation can improve efficiency, accuracy, cost-effectiveness, and scalability, enabling researchers to conduct high-quality SLRs more efficiently and effectively. By embracing AI-powered automation, not-for-profit researchers can unlock the full potential of their research acumen and drive urgently needed advancements in human health evidence.
Learn More
- DistillerSR for Public Sector, NFPs and NGOs.
- Health Agency used AI to reduce COVID-19 citation screening and data extraction by 50%.
1 The Dark Side of Rapid Reviews: A Retreat From Systematic Approaches and the Need for Clear Expectations and Reporting
Zachary Munn, Danielle Pollock, Timothy Hugh Barker, Jennifer Stone, Cindy Stern, Edoardo Aromataris, Alan Pearson, Sharon Straus, Hanan Khalil, Reem A Mustafa, Andrea C Tricco, Holger J Schünemann
2 The significant cost of systematic reviews and meta-analyses: A call for greater involvement of machine learning to assess the promise of clinical trials
Matthew Michelsona,∗, Katja Reuterb,c a Evid Science, 2361 Rosecrans Ave #348, El Segundo, 90245, Los Angeles, CA, United States b Institute for Health Promotion and Disease Prevention Research, Department of Preventive Medicine, Keck School of Medicine, University of Southern California, 2001 N Soto St, Los Angeles, CA, 90032, United States c Southern California Clinical and Translational Science Institute, Keck School of Medicine, University of Southern California, 2250 Alcazar, Los Angeles, CA, 90089, United States
