Case Study: How Chart Reviews Transform Real World Data into Clinical Insights
IVRCC creates configurable electronic data collection tools for efficiently capturing retrospective data that will be submitted for subsequent analysis to answer clinical queries. Often these data interactions can reveal multifaceted relationships between certain indications, treatment factors, dosage rates, patient characteristics, and length of treatments beyond what highly controlled clinical trials may uncover.
We work very closely with our clients and partner medical experts to develop robust e-data capture tools that rely on scripting and revision of very specific and clearly articulated research questions for research analysis. Ultimately, chart reviews can detect patterns and relationships among events, symptoms, and other variables.
Close collaboration with key stakeholders (client, sponsor, site, IVRCC and statistical teams) to generate web-based tool for normalizing extraction
Modular, easily configurable building block design, allowing modifications as needed
User-friendly interfaces for all roles and permissions with easy-to-use eCRF forms
Interface utilizing a wide array of real-time interactivity and guidance tools to navigate multi-level decision trees
Accommodation of projected statistical plan requirements which can be automated to detect patterns as needed
Presentation of collected data in “Statistical Analysis Plan-ready” format
To describe the clinicopathological characteristics and treatment patterns in patients with XXXX X##X mutated NSCLC
To estimate overall survival (OS) and progression free survival (PFS) of XXXX X##X positive NSCLC patients by type of treatment, line of therapy, mutation status, and expression
- Number of academic research oncology centers: 13 (US/Canada)
- Number of patient charts collected: ~500
- Number of Questions: ~330
- Number of Unique Data Fields: ~450
- Total number of data points collected: ~95,000
- Non-small-cell lung adenocarcinoma (NSCLC) patients with XXXX X##X oncogenic mutations who were treated at academic cancer research organization centers associated with our CRO partner.
- Study Population Goal: Review up to 1200 patients’ data from de-identified electronic medical records meeting eligibility criteria, with data points spanning more than two decades. The majority of data collection occurred over approximately one year.
- Research question endpoints: Progressive-free survival (PFS), median duration of second line therapy, disease control in second line treatment setting
Our Solutions to Meet Study Challenges
- Build a collaborative IWR application, consisting of questions surrounding diagnosis and treatment of cancers; develop a process to collect important information from a patient’s medical record and transcribe that information into discrete fields or locations within IVRCC’s application.
- Templated configurable modules to optimize deployment speed, target relevant factors and minimize burden on validation activities
- Highly user-friendly web user interfaces for all roles and permissions
- Real-time interactivity with Always-On Data Review to feed into well-designed, detailed, multi-level decision trees.
- Intelligent ReSTful live-response system,
- adherence to required logic,
- specific ranges of validated entries,
- restricted options for free text,
- other data interactivity checks, within certain time windows
- Automated checks, QA flags requiring review of data for certain fields. Soft and hard stops at required entry for fields and PI review and approval of chart information
- Data points show hierarchical entities (e.g., Study Events, Forms, Item Groups, and Items), coordinating with conditionals, automated logic rules, and predefined list specifications, resulting in flat files ready to export for SAS usage and biostatistical utilization
- Extensible and flexible, designed to allow the addition of new capabilities and functionality as well as modify/add factors and logic
- We rely on scripting and revision of very specific and clearly articulated research questions, for researchers’ analysis and ultimate detection of patterns and relationships amongst events, symptoms, and variables collected
Chart Reviews with data extraction (retrospective and prospective) can contribute to better understanding the impact of products in real-world settings.
With a chart review, real-world evidence can inform early research and development.
Ultimately, analysis of these data has the potential to:
- Improve the quality and delivery of medical care;
- Fill knowledge gaps between clinical trials and clinical practice;
- Clarify how specific drugs perform within different age groups, such as the elderly, and different genders, races and ethnicities.
RWE data can better describe how a product will perform in a broader, more representative population over time.
We help transform disparate pieces of anecdotal information into organized Real World Data (RWD) that supports Real World Evidence (RWE) conclusions; this work can add critical information for better health outcomes and more inclusive populations.
Why was IVRCC the right choice for this study?
- Experienced in custom configuration of web-based applications to capture high quality data
- Generated highly configurable templated modules reflecting expected data points for Real World Data collection for this study
- Developed eCRF forms with familiar, user-friendly ePRO formats
- Framework follows guidance of Clinical Data Interchange Standards Consortium (CDISC) Operational Data Model (ODM)
- Maintained data entry in several managed successive states to ensure integrity:
- uncommitted draft status, allowing changes with no required explanations
- commitment to a “completed data entry” review condition, requiring audit input for changes
- final status commitment after PI review, which also can, under certain condtions, be un-/re-finalized with audit input.
- Notifications, instructional guidance, training manuals, modules within applications, and a review/approval mechanism provide clear navigation and guidance to mitigate errors.
Summary Results and Conclusion are in the process of peer review and publication.
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Business Development Analyst