Peter Speltz

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In this study, we describe our efforts in developing a semantic framework for representing the Quality Data Model (QDM) to support phenotype authoring and execution. We discuss the modeling challenges and potentials of the framework that could not only provide a semantic meta-data repository and data elements services, but also enable a standard-based(More)
Electronic clinical quality measures (eCQMs) based on the Quality Data Model (QDM) cannot currently be executed against non-standardized electronic health record (EHR) data. To address this gap, we prototyped an implementation of a QDM-based eCQM using KNIME, an open-source platform comprising a wide array of computational workflow tools that are(More)
This study describes our efforts in developing a standards-based semantic metadata repository for supporting electronic health record (EHR)-driven phenotype authoring and execution. Our system comprises three layers: 1) a semantic data element repository layer; 2) a semantic services layer; and 3) a phenotype application layer. In a prototype(More)
BACKGROUND Electronic health records (EHRs) are increasingly used for clinical and translational research through the creation of phenotype algorithms. Currently, phenotype algorithms are most commonly represented as noncomputable descriptive documents and knowledge artifacts that detail the protocols for querying diagnoses, symptoms, procedures,(More)
OBJECTIVE To review and evaluate available software tools for electronic health record-driven phenotype authoring in order to identify gaps and needs for future development. MATERIALS AND METHODS Candidate phenotype authoring tools were identified through (1) literature search in four publication databases (PubMed, Embase, Web of Science, and Scopus) and(More)
OBJECTIVE Health care generated data have become an important source for clinical and genomic research. Often, investigators create and iteratively refine phenotype algorithms to achieve high positive predictive values (PPVs) or sensitivity, thereby identifying valid cases and controls. These algorithms achieve the greatest utility when validated and shared(More)
The objective of this pilot study is to describe a crowdsourcing effort in harmonizing high-level data elements between Quality Data Model (QDM) and HL7 Fast Healthcare Interoperability Resources (FHIR) to support electronic health records (EHR)-driven phenotype authoring and execution. In total, 194 mapping pairs between the two models were identified,(More)
OBJECTIVE Assessment of medical trainee learning through pre-defined competencies is now commonplace in schools of medicine. We describe a novel electronic advisor system using natural language processing (NLP) to identify two geriatric medicine competencies from medical student clinical notes in the electronic medical record: advance directives (AD) and(More)
Increasing interest in and experience with electronic health record (EHR)-driven phenotyping has yielded multiple challenges that are at present only partially addressed. Many solutions require the adoption of a single software platform, often with an additional cost of mapping existing patient and phenotypic data to multiple representations. We propose a(More)