## Unlocking the Secrets of Real-World Data: A Deep Dive at The Elm
Imagine a world where the stories you tell are powered not just by anecdotes and interviews, but by the raw, unfiltered data shaping our society. A world where trends emerge from the noise, and insights reveal the hidden forces driving our world.
That’s the promise of “Research with Real-World Data: Analytical Challenges and Opportunities,” the ICTR Enrichment Seminar taking place at The Elm on April 8th. This isn’t just another seminar; it’s a chance to equip yourself with the tools to navigate the complex landscape of real-world data, uncovering its potential while grappling with its ethical and logistical hurdles.
Leveraging Real-World Data for Better Research Outcomes
Real-world data (RWD) in healthcare, comprising data routinely collected in electronic health records, registries, administrative claims, and more, offer tremendous research opportunities due to their size, population coverage, and resource efficiency. However, since RWD are not collected as part of a rigorously designed epidemiologic study, research with RWD is challenging, owing to its vulnerability to multiple sources of bias.
Study 1: Overcoming Informative Observation Time Bias
In the first study, Dr. Shardell will describe a strategy to overcome informative observation time bias when estimating associations of patient-level characteristics with physical recovery using data from the Medicare Minimum Dataset, a federally mandated standardized clinical assessment tool administered by U.S. Center for Medicare & Medicaid Services to inform care management for residents in Medicare- and Medicaid-certified nursing facilities. This study aims to address the challenge of informative observation time bias in estimating associations of patient-level characteristics with physical recovery.
Study 2: Enhancing Algorithmic Fairness
In the second study, Dr. Shardell will describe multiple strategies across the artificial intelligence pipeline to enhance algorithmic fairness when predicting number of days at home, a patient-centered outcome operationalized using Medicare claims data, during the first six months after discharge from hospitalization due to hip fracture. This study aims to address the challenge of algorithmic fairness in predicting patient-centered outcomes using RWD.
Lessons Learned and Implications for Future Research
Dr. Shardell will conclude with a discussion on the lessons learned from these two studies and the implications for future research using RWD. The discussion will highlight the importance of addressing bias and ensuring algorithmic fairness in RWD research.
Resources available for RWD research, such as the UMB ICTR Biostatistics Core, will also be discussed. The Core provides support for researchers working with RWD, including data management, analysis, and interpretation. The Core’s resources and expertise can help researchers overcome the challenges associated with RWD and ensure that their research is of high quality and generates meaningful results.
Practical considerations for researchers working with RWD will also be addressed, including data management, analysis, and interpretation. The importance of collaboration and knowledge sharing in RWD research will be highlighted, with opportunities for researchers to learn from each other and share best practices.
Supporting Sustainable Research Practices
The role of sustainability in lab research is critical, with a focus on reducing waste and increasing efficiency. The Office of Sustainability will be tabling in the Bressler Research Building lobby on Thursday, March 20, from 8-10 a.m., providing information for staff, faculty, and students involved in lab research about the Green Labs checklist program, the Lab Share program, and a new digital waste sorting tool.
Initiatives and Resources for Sustainable Lab Operations
The Green Labs checklist program is a comprehensive program that helps labs reduce their environmental impact by reducing energy consumption, water usage, and waste generation. The Lab Share program is a platform that enables labs to share equipment and resources, reducing the need for duplicate purchases and minimizing waste. The digital waste sorting tool is a innovative solution that helps labs sort and manage waste efficiently.
These initiatives and resources can help labs reduce their environmental footprint and improve their sustainability. By adopting sustainable practices, labs can reduce their costs, improve their efficiency, and contribute to a more sustainable future.
The Intersection of RWD Research and Sustainable Practices
The intersection of RWD research and sustainable practices offers opportunities for innovation and collaboration. By leveraging RWD, researchers can identify areas for improvement in lab operations and develop strategies to reduce waste and increase efficiency. Conversely, sustainable lab practices can inform RWD research, with a focus on reducing waste and increasing efficiency in data collection and analysis.
The intersection of RWD research and sustainable practices is a critical area of focus, with opportunities for researchers to develop new methodologies and approaches that integrate RWD and sustainable practices. By working together, researchers can develop innovative solutions that address the challenges of RWD research and promote sustainable lab practices.
Conclusion
Conclusion: Empowering the Next Generation of Researchers with Real-World Data Insights
The recent ICTR Enrichment Seminar, “Research with Real-World Data: Analytical Challenges and Opportunities,” held at The Elm, marked a significant milestone in the pursuit of innovation and excellence in research. The seminar brought together a diverse group of students, faculty, and industry experts to discuss the intricacies of working with real-world data, a crucial aspect of modern research. Through a series of engaging presentations, panel discussions, and hands-on workshops, participants delved into the analytical challenges and opportunities that arise when leveraging real-world data in research.
The seminar’s key takeaways highlighted the importance of developing skills in data analysis, machine learning, and visualization to extract meaningful insights from complex datasets. The discussions also underscored the significance of collaboration, interdisciplinary approaches, and the responsible use of data in research. The seminar’s focus on real-world data analysis has far-reaching implications, from driving evidence-based decision-making in various fields to advancing our understanding of complex societal issues. As researchers, policymakers, and industry leaders, it is our collective responsibility to harness the power of real-world data to create a better world.
As we move forward, the seminar’s themes and takeaways will continue to shape the research landscape. The next generation of researchers will need to be equipped with the skills, knowledge, and expertise to navigate the complexities of real-world data analysis. By empowering researchers with the tools and insights to analyze and interpret complex data, we can unlock new discoveries, drive innovation, and tackle the pressing challenges of our time. As we conclude this article, we leave you with a pressing question: What will you do with the power of real-world data to create a brighter future?