ArticleLearning health systems2025
Multi-site research using electronic health record data: Lessons learned from a case study.
Article in Learning health systems, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Multi-site research using electronic health record data: Lessons learned from a case study.Learning health systems · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
Introduction: Multi-site research collaboration is necessary to increase generalizability, diversity, and innovation; however, there are complexities and challenges surrounding research processes, including regulatory oversight, data management, and data sharing activities. This report highlights the specific challenges of collaborative research identified in the conduct of a federally funded multi-site study and presents lessons learned to inform ways to overcome these challenges. The purpose of our current research project is to determine how interprofessional teamwork affects quality outcomes and develop tools to improve teamwork in cancer care. Methods: Our research team is comprised of 23 members across five academic institutions. Our study cohort includes approximately 20 000 adult patients with breast, colorectal, and non-small cell lung cancers diagnosed at the respective sites between January 1, 2016, and December 31, 2021. Electronic health record (EHR) access log data was extracted 12 months pre- and 24 months post-diagnosis for each patient, and outcome data of potentially preventable emergency department visits and unplanned hospitalizations was extracted from the California state All-Payer Claims database. Results: Major challenges experienced related to single institutional review board processes, establishment of contract agreements, and data management, analysis, acquisition, and transfer. Lessons learned included: (1) start research planning as early as possible, including engaging with information technology and compliance teams to identify processes and develop and share data dictionaries; (2) work closely with the institution contracting team to identify the most optimal timing and ordering for multiple data use agreement (DUA) contracts; and (3) ensure that research team members are abreast of current Health Insurance Portability and Accountability Act and institutional guidelines as they pertain to research and data practices. Conclusions: Multi-site research involving big data from the EHR requires ample planning and execution time. Adopting a single standardized DUA and developing data dictionaries that can be shared for research will improve the data acquisition phase of multi-site research studies.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.