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The CIBMTR launched the Data Transformation Initiative to explore new technologies to address current and future data needs. The initial task force developed a plan to transform the CIBMTR's approach to data acquisition (collection) and data analysis.
To optimize the acquisition and utilization of entrusted data assets to accelerate breakthroughs that transform patient experiences.
To understand this vision in more detail, please view this PDF.
Historically, the CIBMTR relied on a web-based data collection model using an extensive library of forms. Staff at transplant centers manually filled out these forms at intervals across a patient's lifespan. In the first phase of the Data Transformation Initiative, the CIBMTR brought together healthcare data standards that enable research network partners to automate reporting directly from patients' electronic medical records, efficiently and securely. This new model, using a prototyped set of solutions, is both future-proof and sustainable because it meets the data where it resides and allows data to become interoperable.
The CIBMTR's new tools collect and move data from centers to the CIBMTR electronically, decreasing centers' time and effort to share data. In 2021, the CIBMTR began implementing its data acquisition suite of products with all interested centers. Most US centers who share data with the CIBMTR are either already using the suite of products - with high levels of satisfaction - or in discussions and on a path to onboard. The CIBMTR already has roadmaps to progressively increase the functionality of the new tools, including more data types that can be collected and transmitted, providing exponential value back to centers.
Abbreviations: BMT = blood and marrow transplantation; EMR = electronic medical record; FHIR = Fast Healthcare Interoperability Resources, the standard defines how healthcare information can be exchanged between different computer systems regardless of how it is stored in those systems; HLA = human leukocyte antigen; HML = a file uploader; LIMS = Laboratory Information Management System, how software curates and stores lab data; OMOP = Observational Medical Outcomes Partnership Common Data Model, systematic analysis of disparate observational databases. The concept behind this approach is to transform data contained within those databases into a common format (data model) as well as a common representation (terminologies, vocabularies, coding schemes) and then perform systematic analyses using a library of standard analytic routines that have been written based on the common format.
Not all data collection can be automated. Some data variables require a person to clinical interpret reports and other details in a patient's chart. The CIBMTR is testing new ways to make this process quicker, faster, and better for data managers. For example, the CIBMTR is exploring imputed variables from derivation logic, smart navigation across FormsNet, or parsing lengthy lab codes from a report instead of manual entry. Keep track of the latest developments by attending DTI Community Meetings
In November 2021, the CIBMTR launched Phase 2 of the Data Transformation Initiative. Within this phase, teams will design, build, and test focused areas of development that make data more actionable for research. The goal is to help researchers test a proposed hypothesis or generate a hypothesis based on data science modeling that detects data patterns for further analysis. Currently, the CIBMTR is determining whether users could create a data cohort by selecting specific criteria across different CIBMTR datasets. This would allow the user to receive aggregate data counts and metadata information to make informed decisions regarding proposed research.