Toward Real-World Reproducibility: Verifying Value Sets for Clinical Research.

Standardized operational definitions are an important tool to improve reproducibility of research using secondary real-world healthcare data. This approach was leveraged for studies evaluating the effectiveness of AZD7442 as COVID-19 pre-exposure prophylaxis across multiple healthcare systems. Value sets were defined, grouped, and mapped. Results of this exercise were reviewed and recorded. Value sets were updated to reflect findings.

Clinical element models in the SHARPn consortium.

OBJECTIVE: The objective of the Strategic Health IT Advanced Research Project area four (SHARPn) was to develop open-source tools that could be used for the normalization of electronic health record (EHR) data for secondary use--specifically, for high throughput phenotyping. We describe the role of Intermountain Healthcare's Clinical Element Models ([CEMs] Intermountain Healthcare Health Services, Inc, Salt Lake City, Utah) as normalization "targets" within the project. MATERIALS AND METHODS: Intermountain's CEMs were either repurposed or created for the SHARPn project. A CEM describes "valid" structure and semantics for a particular kind of clinical data. CEMs are expressed in a computable syntax that can be compiled into implementation artifacts. The modeling team and SHARPn colleagues agilely gathered requirements and developed and refined models. RESULTS: Twenty-eight "statement" models (analogous to "classes") and numerous "component" CEMs and their associated terminology were repurposed or developed to satisfy SHARPn high throughput phenotyping requirements. Model (structural) mappings and terminology (semantic) mappings were also created. Source data instances were normalized to CEM-conformant data and stored in CEM instance databases. A model browser and request site were built to facilitate the development. DISCUSSION: The modeling efforts demonstrated the need to address context differences and granularity choices and highlighted the inevitability of iso-semantic models. The need for content expertise and "intelligent" content tooling was also underscored. We discuss scalability and sustainability expectations for a CEM-based approach and describe the place of CEMs relative to other current efforts. CONCLUSIONS: The SHARPn effort demonstrated the normalization and secondary use of EHR data. CEMs proved capable of capturing data originating from a variety of sources within the normalization pipeline and serving as suitable normalization targets.

Lessons learned in detailed clinical modeling at Intermountain Healthcare.

BACKGROUND AND OBJECTIVE: Intermountain Healthcare has a long history of using coded terminology and detailed clinical models (DCMs) to govern storage of clinical data to facilitate decision support and semantic interoperability. The latest iteration of DCMs at Intermountain is called the clinical element model (CEM). We describe the lessons learned from our CEM efforts with regard to subjective decisions a modeler frequently needs to make in creating a CEM. We present insights and guidelines, but also describe situations in which use cases conflict with the guidelines. We propose strategies that can help reconcile the conflicts. The hope is that these lessons will be helpful to others who are developing and maintaining DCMs in order to promote sharing and interoperability. METHODS: We have used the Clinical Element Modeling Language (CEML) to author approximately 5000 CEMs. RESULTS: Based on our experience, we have formulated guidelines to lead our modelers through the subjective decisions they need to make when authoring models. Reported here are guidelines regarding precoordination/postcoordination, dividing content between the model and the terminology, modeling logical attributes, and creating iso-semantic models. We place our lessons in context, exploring the potential benefits of an implementation layer, an iso-semantic modeling framework, and ontologic technologies. CONCLUSIONS: We assert that detailed clinical models can advance interoperability and sharing, and that our guidelines, an implementation layer, and an iso-semantic framework will support our progress toward that goal.

Building a robust, scalable and standards-driven infrastructure for secondary use of EHR data: the SHARPn project.

The Strategic Health IT Advanced Research Projects (SHARP) Program, established by the Office of the National Coordinator for Health Information Technology in 2010 supports research findings that remove barriers for increased adoption of health IT. The improvements envisioned by the SHARP Area 4 Consortium (SHARPn) will enable the use of the electronic health record (EHR) for secondary purposes, such as care process and outcomes improvement, biomedical research and epidemiologic monitoring of the nation's health. One of the primary informatics problem areas in this endeavor is the standardization of disparate health data from the nation's many health care organizations and providers. The SHARPn team is developing open source services and components to support the ubiquitous exchange, sharing and reuse or 'liquidity' of operational clinical data stored in electronic health records. One year into the design and development of the SHARPn framework, we demonstrated end to end data flow and a prototype SHARPn platform, using thousands of patient electronic records sourced from two large healthcare organizations: Mayo Clinic and Intermountain Healthcare. The platform was deployed to (1) receive source EHR data in several formats, (2) generate structured data from EHR narrative text, and (3) normalize the EHR data using common detailed clinical models and Consolidated Health Informatics standard terminologies, which were (4) accessed by a phenotyping service using normalized data specifications. The architecture of this prototype SHARPn platform is presented. The EHR data throughput demonstration showed success in normalizing native EHR data, both structured and narrative, from two independent organizations and EHR systems. Based on the demonstration, observed challenges for standardization of EHR data for interoperable secondary use are discussed.

An OWL meta-ontology for representing the Clinical Element Model.

The Clinical Element Model (CEM) is a strategy designed to represent logical models for clinical data elements to ensure unambiguous data representation, interpretation, and exchange within and across heterogeneous sources and applications. The current representations of CEMs have limitations on expressing semantics and formal definitions of the structure and the semantics. Here we introduce our initial efforts on representing the CEM in OWL, so that the enrichment with OWL semantics and further semantic processing can be achieved in CEM. The focus of this paper is the CEM meta-ontology where the basic structures, the properties and their relationships, and the constraints are defined. These OWL representation specifications have been reviewed by CEM experts to ensure they capture the intended meaning of the model faithfully.

Detailed clinical models for sharable, executable guidelines.

The goal of shareable, executable clinical guidelines is both worthwhile and challenging. One of the largest hurdles is that of representing the necessary clinical information in a precise and shareable manner. Standard terminologies and common information models, such as the HL7 RIM, are necessary, they are not sufficient. In addition, common detailed clinical models are needed to give precise semantics and to make the task of mapping between models manageable. We discuss the experience of the SAGE project related to detailed clinical models.

Generating medical logic modules for clinical trial eligibility criteria.

Clinical trials are an important part of modern medical research, however the effort required to find candidates for participation in such trial is significant. With the increasing prevalence of electronic medical records, automated or semi-automated solutions become feasible. We present an semi-automated approach for determining clinical trial eligibility based on information available in an electronic medical record.