With the rapidly growing adoption of patient electronic health record (EHR) systems due to Meaningful Use, and linkage of EHRs to research biorepositories, evaluating the suitability of EHR data for clinical and translational research is becoming ever more important, with ramifications for genomic and observational research, clinical trials, and comparative effectiveness studies. A key component for identifying patient cohorts in the EHR is to define inclusion and exclusion criteria that algorithmically select sets o patients based on stored clinical data. This process is commonly referred to, as """"""""EHR-driven phenotyping"""""""" is time-consuming and tedious due to the lack of a widely accepted and standards-based formal information model for defining phenotyping algorithms. To address this overall challenge, the proposed project will design, build and promote an open-access community infrastructure for standards-based development and sharing of phenotyping algorithms, as well as provide tools and resources for investigators, researchers and their informatics support staff to implement and execute the algorithms on native EHR data.
The identification of patient cohorts for clinical and genomic research is a costly and time-consuming process. This bottleneck adversely affects public health by delaying research findings, and in some cases by making research costs prohibitively high. To address this issue, leveraging electronic health records (EHRs) for identifying patient cohorts has become an increasingly attractive option. This proposal will investigate and implement standards based approaches for phenotype identification from multiple EHRs.
|Teixeira, Pedro L; Wei, Wei-Qi; Cronin, Robert M et al. (2017) Evaluating electronic health record data sources and algorithmic approaches to identify hypertensive individuals. J Am Med Inform Assoc 24:162-171|
|Robinson, Jamie R; Denny, Joshua C; Roden, Dan M et al. (2017) Genome-wide and Phenome-wide Approaches to Understand Variable Drug Actions in Electronic Health Records. Clin Transl Sci :|
|Schildcrout, Jonathan S; Denny, Joshua C; Roden, Dan M (2017) On the Potential of Preemptive Genotyping Towards Preventing Medication-Related Adverse Events: Results from the South Korean National Health Insurance Database. Drug Saf 40:1-2|
|Wei, Wei-Qi; Bastarache, Lisa A; Carroll, Robert J et al. (2017) Evaluating phecodes, clinical classification software, and ICD-9-CM codes for phenome-wide association studies in the electronic health record. PLoS One 12:e0175508|
|Kirby, Jacqueline C; Speltz, Peter; Rasmussen, Luke V et al. (2016) PheKB: a catalog and workflow for creating electronic phenotype algorithms for transportability. J Am Med Inform Assoc 23:1046-1052|
|Rasmussen, Luke V; Overby, Casey L; Connolly, John et al. (2016) Practical considerations for implementing genomic information resources. Experiences from eMERGE and CSER. Appl Clin Inform 7:870-82|
|Wei, Wei-Qi; Teixeira, Pedro L; Mo, Huan et al. (2016) Combining billing codes, clinical notes, and medications from electronic health records provides superior phenotyping performance. J Am Med Inform Assoc 23:e20-7|
|Denny, Joshua C; Bastarache, Lisa; Roden, Dan M (2016) Phenome-Wide Association Studies as a Tool to Advance Precision Medicine. Annu Rev Genomics Hum Genet 17:353-73|
|Heatherly, Raymond; Rasmussen, Luke V; Peissig, Peggy L et al. (2016) A multi-institution evaluation of clinical profile anonymization. J Am Med Inform Assoc 23:e131-7|
|Hong, Na; Pathak, Jyotishman; Chute, Christopher G et al. (2016) Developing a modular architecture for creation of rule-based clinical diagnostic criteria. BioData Min 9:33|
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