Venous Access: National Guideline and Registry Development (VANGUARD): Advancing Patient-Centered Venous Access Care Through the Development of a National Coordinated Registry Network.

There are over 8 million central venous access devices inserted each year, many in patients with chronic conditions who rely on central access for life-preserving therapies. Central venous access device-related complications can be life-threatening and add tens of billions of dollars to health care costs, while their incidence is most likely grossly mis- or underreported by medical institutions. In this communication, we review the challenges that impair retention, exchange, and analysis of data necessary for a meaningful understanding of critical events and outcomes in this clinical domain.

Logical Observation Identifiers Names and Codes (LOINC) Applied to Microbiology: A National Laboratory Mapping Experience in Taiwan.

Background And Objective: Logical Observation Identifiers Names and Codes (LOINC) is a universal standard for identifying laboratory tests and clinical observations. It facilitates a smooth information exchange between hospitals, locally and internationally. Although it offers immense benefits for patient care, LOINC coding is complex, resource-intensive, and requires substantial domain expertise.

Logical Observation Identifiers Names and Codes for Laboratorians.

Context.—: The Logical Observation Identifiers Names and Codes (LOINC) system is supposed to facilitate interoperability, and it is the federally required code for exchanging laboratory data.

Objective.

Lessons Learned in Creating Interoperable Fast Healthcare Interoperability Resources Profiles for Large-Scale Public Health Programs.

Objective: This article describes lessons learned from the collaborative creation of logical models and standard Health Level Seven (HL7) Fast Healthcare Interoperability Resources (FHIR) profiles for family planning and reproductive health. The National Health Service delivery program will use the FHIR profiles to improve federal reporting, program monitoring, and quality improvement efforts.

Materials And Methods: Organizational frameworks, work processes, and artifact testing to create FHIR profiles are described.

The Pluripotent Rendering of Clinical Data for Precision Medicine.

Health care and biomedical research are awash in data. Traditional data warehouse methodologies do not scale to this challenge; nor do their schema match the variety of analytic use cases. An alternative model, which shreds data into well-formed constituent data elements, conformant with the emerging CIMI-FHIR standards and stored together with the complete, raw, source data using modern and scalable data utilities such as Hadoop and its derivatives, affords the creation of pluripotent data repositories.

Profiling Fast Healthcare Interoperability Resources (FHIR) of Family Health History based on the Clinical Element Models.

In this study we developed a Fast Healthcare Interoperability Resources (FHIR) profile to support exchanging a full pedigree based family health history (FHH) information across multiple systems and applications used by clinicians, patients, and researchers. We used previously developed clinical element models (CEMs) that are capable of representing the FHH information, and derived essential data elements including attributes, constraints, and value sets. We analyzed gaps between the FHH CEM elements and existing FHIR resources.

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.

Completing Death Certificates from an EMR: Analysis of a Novel Public-Private Partnership.

With the objective of increasing electronic death registration, Intermountain Healthcare and the Utah Office of Vital Records and Statistics have developed a system enabling death certification from within Intermountain's electronic medical record (EMR), consisting of an EMR module and an HL7 interface. Comparison of post-intervention death certification at Intermountain Healthcare against a baseline study found a slight increase in the percentage of deaths certified electronically (73% pre vs. 77% post).

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.

Developing a section labeler for clinical documents.

Natural language processing (NLP) technologies provide an opportunity to extract key patient data from free text documents within the electronic health record (EHR). We are developing a series of components from which to construct NLP pipelines. These pipelines typically begin with a component whose goal is to label sections within medical documents with codes indicating the anticipated semantics of their content.

Normalization and standardization of electronic health records for high-throughput phenotyping: the SHARPn consortium.

Research Objective: To develop scalable informatics infrastructure for normalization of both structured and unstructured electronic health record (EHR) data into a unified, concept-based model for high-throughput phenotype extraction.

Materials And Methods: Software tools and applications were developed to extract information from EHRs. Representative and convenience samples of both structured and unstructured data from two EHR systems-Mayo Clinic and Intermountain Healthcare-were used for development and validation.

Towards a semantic lexicon for clinical natural language processing.

A semantic lexicon which associates words and phrases in text to concepts is critical for extracting and encoding clinical information in free text and therefore achieving semantic interoperability between structured and unstructured data in Electronic Health Records (EHRs). Directly using existing standard terminologies may have limited coverage with respect to concepts and their corresponding mentions in text. In this paper, we analyze how tokens and phrases in a large corpus distribute and how well the UMLS captures the semantics.

Modeling and executing electronic health records driven phenotyping algorithms using the NQF Quality Data Model and JBoss® Drools Engine.

With increasing adoption of electronic health records (EHRs), the need for formal representations for EHR-driven phenotyping algorithms has been recognized for some time. The recently proposed Quality Data Model from the National Quality Forum (NQF) provides an information model and a grammar that is intended to represent data collected during routine clinical care in EHRs as well as the basic logic required to represent the algorithmic criteria for phenotype definitions. The QDM is further aligned with Meaningful Use standards to ensure that the clinical data and algorithmic criteria are represented in a consistent, unambiguous and reproducible manner.

A semantic-web oriented representation of the clinical element model for secondary use of electronic health records data.

The clinical element model (CEM) is an information model designed for representing clinical information in electronic health records (EHR) systems across organizations. The current representation of CEMs does not support formal semantic definitions and therefore it is not possible to perform reasoning and consistency checking on derived models. This paper introduces our efforts to represent the CEM specification using the Web Ontology Language (OWL).

There are important reasons for delaying implementation of the new ICD-10 coding system.

Federal authorities have recently signaled that they would consider delaying some aspects of implementation of the newest version of the International Classification of Diseases, known as ICD-10-CM, a coding system used to define health care charges and diagnoses. Some industry groups have reacted with dismay, and many providers with relief. We are concerned that adopting this new classification system for reimbursement will be disruptive and costly and will offer no material improvement over the current system.

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.

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.

Investigating the semantic interoperability of laboratory data exchanged using LOINC codes in three large institutions.

LOINC codes are seeing increased use in many organizations. In this study, we examined the barriers to semantic interoperability that still exist in electronic data exchange of laboratory results even when LOINC codes are being used as the observation identifiers. We analyzed semantic interoperability of laboratory data exchanged using LOINC codes in three large institutions.

The SHARPn project on secondary use of Electronic Medical Record data: progress, plans, and possibilities.

SHARPn is a collaboration among 16 academic and industry partners committed to the production and distribution of high-quality software artifacts that support the secondary use of EMR data. Areas of emphasis are data normalization, natural language processing, high-throughput phenotyping, and data quality metrics. Our work avails the industrial scalability afforded by the Unstructured Information Management Architecture (UIMA) from IBM Watson Research labs, the same framework which underpins the Watson Jeopardy demonstration.