Innovation in Clinical Data Management by Lakshmi Priya Darshini Pulavarthi

India.com

29-04-2025

Innovation in Clinical Data Management by Lakshmi Priya Darshini Pulavarthi
Lakshmi Priya Darshini Pulavarthi is a skilled Clinical Data Management professional with over a decade of experience managing data from Phase I-III clinical trials. With a strong educational background including a Master of Natural Science from Southeast Missouri State University, a Master of Science in Biochemistry & Molecular Biology from Pondicherry University, and a Bachelor of Science in Biotechnology and Bioinformatics, Lakshmi combines scientific knowledge with data expertise. Her career spans multiple therapeutic areas, including Hematology, Oncology, and Cardiovascular studies.
Q 1: What drove you to become a clinical data manager?
A: I was more interested in joining this field because it allows me to blend a scientific foundation with making decisions based upon data. Now coming from a background in biochemistry and molecular biology, I found that a big part of drug development has to do with high-quality data, and it's satisfying to know that I contribute directly to helping propel the development of medical treatment that can really help millions of lives. I am also someone who thrives in the conflux of science, technology and health care.
Q 2: How do you manage data in complex therapeutic areas such as oncology?
A: For complicated therapeutic areas, I build my understanding around disease-specific modalities of endpoints and criteria of assessments like RECIST or Lugano. I devise very targeted plans for data reviewing that are made to the complexity of the data set and integrate the thorough protocol details followed with the clinical teams. Implement specialized validation checks and build visualisation dashboards using tools like TIBCO Spotfire to track response rates and progression metrics in real time. In this way, while ensuring good data quality, decision-making can take place within the trail based on insights rather than only at the end.
Q 3: Which process improvements have you executed in your career?
A: I'm extremely proud of having led several important process improvements, such as developing a comprehensive toolkit consisting of checklists and responsibility matrices for standardizing the database lock process. I also consolidated data review checks related to oncology studies, which helped eliminate redundancies and offer complete quality assurance. One of my greatest contributions was developing a data visualization approach using TIBCO Spotfire, converting static listings into interactive dashboards that both support real-time decisions by study teams.
Q 4: How do you balance meeting aggressive timelines with ensuring high-quality data?
A: I handle this particular challenge with careful study on strategy which includes breaking down the database lock process into clear-cut milestones with ample buffers set up to cater for any foreseeable issues. I work primarily with quality systems throughout data lifecycle rather than thinking of a mere quality control at the end of the process. I do my best to optimize our resource usage by executing a workload schedule and creating an opportunity via automation for repetitive tasks, which will allow for a setting of priorities for queries that need to be addressed. Setting up task teams when timing hits fever pitch for instance allows flexibility here to take decisions aligned with the best quality criteria possible. Having held this balanced approach, I have always met the timeline with high regard to quality of the data.
Q 5: How do you ensure effective communication across multiple stakeholders?
A: Communication plan is made per study specifying roles, responsibilities, and best ways to communicate as open as possible for study start up. A tiered structure of meetings cascades from technical working group meetings to cross-functional team meetings, and to steering committee meetings, each tailored to different levels of discussion. Every communication from or for each level has a role and an audience, focusing on technical details for data teams, on clinical impact for medical staff, or on risk mitigation for executive views. Visualization helps to gain agreement on study status, and future issues in communication are planned for by preparing each audience for the potential problem when a potential solution is proposed. This trust between departments fast tracks issue resolution.
Q 6: What strategies do you use for risk management in clinical data?
A: Risk management comes into play far earlier in starting up the study before exposures to foreseeable risks. Risk identification in the onset of data building is their anticipation among the study team beginning way early in development. Pre-risk mitigation policies are carved into the data collection process and embedded with a way for early detection while maintaining its reservoir of specialized surveillance reports. I argue that to be of some significance, some of the risks do come across the board in certain studies clearly identified by the target critical data points. Cross-functional risk review meetings take care of important item interconnections signaling an omniscient view of the study risks. This way, I can catch any potential risks proactively way early along, irrespective of how they impact the timelines or data quality.
Q 7: How do technology and your current updates work in the approach?
A: The pace and scope of technology is the current front runner in making more dimensions of data management possible. I adopt end-to-end clinical data ecosystems including EDCs such as Medidata Rave and Oracle Inform and leverage visualization tools such as TIBCO Spotfire to process my raw data into actionable insight. Meeting with user conferences, professional community membership with SCDM, building relationships with technology vendors, and allocating some time to just play and explore new tools on hands-on testing have been my ways of keeping current. It would be through such fluency with technology that I have been able to spearhead new things like predictive analytics for spotting potential data problems prior to becoming big issues for the study.
Q 8: Please tell us about a specific challenging situation you had to face and how you overcame it.
A: Two major challenges that I encountered when I worked on that study were:
One; there was a long turn-around-time in resolving queries because the sites were not completing the CRFs according to the guidelines. I did a root cause analysis, and I carried out some focused training on them to orient them about CCG (Clinical Completion Guidelines) expectations. I also instituted a daily query tracking system with targeted metrics that pointed knives to areas where there were problems so that it would enable the sites to better address their queries.
Two; however, there was a lot of hot reconciliation between the EDC and several other vendors. It was due to the different interpretations of specifications for data transfer relating to them. I quickly held an all-stakeholders, cross-functional meeting to align everyone with position requirements. I personally would also lead ad-hoc-focused reconciliation meetings to treat discrepancies in a structured manner.
I set up a lock readiness dashboard for real-time visibility over both. We didn't just solve the query and the reconciliation issue with this holistic approach, but we locked the database ahead of the new date. That confirmed for me the very valuable lesson on identifying proactive risks and being crystal clear in communications in clinical data management.
Q 9: What emerging trends do you see in clinical data management?
A: In fact, there are number of trends involving our field. First, though, there are decentralized clinical trials that bring new data streams from wearables and remote monitoring devices into the mix. The other trends emerging in the fields of artificial intelligence and machine learning would be based on their potential to improve data cleaning through anomaly detection as well as to infer insights out of complex datasets. The streamlining of CDISC standards would be geared towards guidance for therapeutic area-specific concepts, which makes it more consistency in terms of data. However, it would provide a more in-depth area-specific knowledge requirement. Risk-based quality management is maturing into a comprehensive framework for the whole data lifecycle. Last but not least, we are moving to a collaborative data ecosystem with new governance models and technologies in which all actors, from sponsors to CROs and sites to patients, share responsibilities for data quality.
Q 10. Any advice for those starting careers in clinical data management?
A: To start with, a strong technical foundation in EDC systems and data visualization tools should be developed. For understanding data in the medical context that gives it meaning, familiarize yourself with the therapeutic areas. Get diverse experiences across the trial lifecycle and different study phases. A full understanding of regulations like ICH GCP and how it translates into practical requirements will also be crucial. Understand the role of soft skills, such as communication and problem-solving, in becoming differentiated from good data managers. Finally, always have a thirst for knowledge through professional organizations, conferences, and networking. Remember that every data point always has the potential to affect the lives of many patients and push scientific advancement-forward; this perspective, indeed, fosters that kind of meticulousness toward exceptional clinical data management. About Lakshmi Priya Darshini Pulavarthi
Lakshmi Priya Darshini Pulavarthi is a Clinical Data Management professional with exposure to multiple therapeutic areas such as Hematology, Oncology, and Cardiovascular studies. She holds capabilities in EDC data management, study design, eCRF development, and database lock execution. She brings process improvements as well as new SOPs to achieve enhanced clinical decision-making through the application of data visualization tools. Lakshmi aims to achieve integrity in clinical data throughout the clinical trial lifecycle while maintaining regulatory compliance.