Fu, Sunyang2022-02-152022-02-152021-12https://hdl.handle.net/11299/226410University of Minnesota Ph.D. dissertation. 2021. Major: Biomedical Informatics and Computational Biology. Advisors: Hongfang Liu, Yuk Sham. 1 computer file (PDF); 151 pages.Rapid proliferation and adoption of the electronic health record (EHR) has led to seamless integration of clinical research into practice, and has facilitated healthcare decision-making through enabling accurate and timely supply of health information. Leveraging this supply of information, the Institute of Medicine envisioned the concept of continuously Learning Health Systems (LHS) in 2007, with the aim of first deriving knowledge from routine care data and then translating such knowledge into evidence-based clinical practice. To achieve such a vision, it is critical to have a robust data and informatics infrastructure with the following properties: 1) high-throughput and real-time methods for data retrieval, extraction, and analysis, 2) transparent and reproducible processes to ensure scientific rigor in clinical research, and 3) implementable and generalizable scientific findings. There are many approaches to the derivation of knowledge from care data, one of which is through the use of chart review: a common, albeit manual, approach to practice-based knowledge discovery. Traditionally, chart review is performed by manually reviewing patient medical records. As a significant portion of clinical information is represented in textual format, this manual approach can be time-consuming and costly. With the implementation of EHRs, chart review can be automated by extracting data from structured fields systematically and leveraging natural language processing (NLP) techniques to extract information from text. Rigorous development and evaluation of NLP algorithms for a specific chart review task requires, however, data abstraction and annotation (i.e., the manual creation of a gold standard clinical corpus to evaluate the developed NLP algorithm). In EHR-based settings, there is, however, a lack of standard processes or best practices for creating such a corpus due to the heterogeneity of institutional EHR systems and process variation between single and multi-site research settings. Recent advancement in healthcare AI identifies the need for detailed data provenance for data used in the training and validation of AI models. Secondary use of EHR for clinical research leveraging AI technologies such as NLP therefore requires the documentation of the provenance information relating to the process used for the retrieval and organization of the raw data used as well as the extraction and annotation of training data. We thus define this process as clinical Text Retrieval and Use towards Scientific rigor and Transparent (TRUST) process. As EHR-based research becomes increasingly integrated into clinical care, it is important to have a systematic understanding of the TRUST process, its corresponding utilization when developing informatics tools and methods, as well as its overall impact on research reproducibility. In this work, we propose a multi-phase method to develop informatics frameworks and best practices to ensure reproducible TRUST processes for single and multi-site studies. In the following chapters, we propose: 1) a definition of reproducibility in the context of the secondary use of EHRs, 2) methods to assess various levels of data heterogeneity caused by differing EHR systems and inter-institutional variations, 3) approaches to examine the implication of data heterogeneity to reproducibility, 4) steps to develop frameworks, best practices, and reporting standards conforming to the TRUST process, and 5) an application of the TRUST process in a real-world case study.enElectronic Health RecordsInformation ProvenanceInformation QualityNatural Language ProcessingReproducibilityThesis or Dissertation