Abstract
BackgroundFor cancer domains such as acute myeloid leukemia (AML), a large set of data elements is obtained from different institutions with heterogeneous data definitions within one patient course. The lack of clinical data harmonization impedes cross-institutional electronic data exchange and future meta-analyses.ObjectiveThis study aimed to identify and harmonize a semantic core of common data elements (CDEs) in clinical routine and research documentation, based on a systematic metadata analysis of existing documentation models.MethodsLists of relevant data items were collected and reviewed by hematologists from two university hospitals regarding routine documentation and several case report forms of clinical trials for AML. In addition, existing registries and international recommendations were included. Data items were coded to medical concepts via the Unified Medical Language System (UMLS) by a physician and reviewed by another physician. On the basis of the coded concepts, the data sources were analyzed for concept overlaps and identification of most frequent concepts. The most frequent concepts were then implemented as data elements in the standardized format of the Operational Data Model by the Clinical Data Interchange Standards Consortium.ResultsA total of 3265 medical concepts were identified, of which 1414 were unique. Among the 1414 unique medical concepts, the 50 most frequent ones cover 26.98% of all concept occurrences within the collected AML documentation. The top 100 concepts represent 39.48% of all concepts’ occurrences. Implementation of CDEs is available on a European research infrastructure and can be downloaded in different formats for reuse in different electronic data capture systems.ConclusionsInformation management is a complex process for research-intense disease entities as AML that is associated with a large set of lab-based diagnostics and different treatment options. Our systematic UMLS-based analysis revealed the existence of a core data set and an exemplary reusable implementation for harmonized data capture is available on an established metadata repository.
Highlights
Information management is a complex process for research-intense disease entities as acute myeloid leukemia (AML) that is associated with a large set of lab-based diagnostics and different treatment options
It extends the previous collection of key data elements for myeloid leukemia, which has undergone clinical evaluation by several hematologists [13] and focuses on specific data items for AML based on a larger dataset
The fact that most patients are treated within studies leads to further documentation arms.Different health care institutions are involved in the documentation process.The detailed analysis performed in this study could clearly show that the content of AML documentation is often quite redundant
Summary
Background Medical documentation is complex and time-consuming. In routine documentation, it accounts for approximately 25% of a physician’s workload and demands as much time as direct patient care [1] and even more in study cases [2]. All patients with acute myeloid leukemia (AML) are to be treated within studies, following expert panel recommendations [3]. The number of patients with AML is relatively low with an incidence rate of around 3.7 per 100,000 in Europe [4]. The 5-year survival rate is below 50% [4]. For cancer domains such as acute myeloid leukemia (AML), a large set of data elements is obtained from different institutions with heterogeneous data definitions within one patient course. The lack of clinical data harmonization impedes cross-institutional electronic data exchange and future meta-analyses
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