Thomas A. Oniki

Results of a collaborative quality improvement program on outcomes and costs in a tertiary critical care unit

OBJECTIVE To demonstrate that by using the knowledge and skills of the primary care provider and by applying statistical and scientific principles of quality improvement, outcomes can be improved and costs significantly reduced. DESIGN A before and after quasi-experimentally designed trial using historical controls plus an analysis of costs in areas not influenced by intensive care unit (ICU) practice to control for possible secular changes. SETTING A tertiary ICU. PATIENTS All patients admitted to the above-mentioned ICU from January 1, 1991, through December 31, 1995. INTERVENTIONS a) A focused program that applied statistical and scientific quality improvement processes to the practice of intensive care. b) An organized effort to modify the culture, thinking, and behavior of the personnel who practice in the ICU. MEASUREMENTS Severity of illness, ICU and hospital lengths of stay, ICU and hospital mortality rates, total hospital costs as analyzed by the cost center, and measures of improvement in specific areas of care. MAIN RESULTS Significant improvement in glucose control, use of enteral feeding, antibiotic use, adult respiratory distress syndrome survival, laboratory use, blood gases use, radiograph use, and appropriate use of sedation. A severity adjusted total hospital cost reduction of

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

2,580,981 in 1991 dollars when comparing 1995 with the control year of 1991, with 87% of the reduction in those cost centers directly influenced by the intervention. CONCLUSIONS A focused quality improvement program in the ICU can have a beneficial impact on care and simultaneously reduce costs.

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

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. Extracted information was standardized and normalized to meaningful use (MU) conformant terminology and value set standards using Clinical Element Models (CEMs). These resources were used to demonstrate semi-automatic execution of MU clinical-quality measures modeled using the Quality Data Model (QDM) and an open-source rules engine. RESULTS Using CEMs and open-source natural language processing and terminology services engines-namely, Apache clinical Text Analysis and Knowledge Extraction System (cTAKES) and Common Terminology Services (CTS2)-we developed a data-normalization platform that ensures data security, end-to-end connectivity, and reliable data flow within and across institutions. We demonstrated the applicability of this platform by executing a QDM-based MU quality measure that determines the percentage of patients between 18 and 75 years with diabetes whose most recent low-density lipoprotein cholesterol test result during the measurement year was <100 mg/dL on a randomly selected cohort of 273 Mayo Clinic patients. The platform identified 21 and 18 patients for the denominator and numerator of the quality measure, respectively. Validation results indicate that all identified patients meet the QDM-based criteria. CONCLUSIONS End-to-end automated systems for extracting clinical information from diverse EHR systems require extensive use of standardized vocabularies and terminologies, as well as robust information models for storing, discovering, and processing that information. This study demonstrates the application of modular and open-source resources for enabling secondary use of EHR data through normalization into standards-based, comparable, and consistent format for high-throughput phenotyping to identify patient cohorts.

Lessons learned in detailed clinical modeling at Intermountain Healthcare

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). The CEM-OWL representation connects the CEM content with the Semantic Web environment, which provides authoring, reasoning, and querying tools. This work may also facilitate the harmonization of the CEMs with domain knowledge represented in terminology models as well as other clinical information models such as the openEHR archetype model. We have created the CEM-OWL meta ontology based on the CEM specification. A convertor has been implemented in Java to automatically translate detailed CEMs from XML to OWL. A panel evaluation has been conducted, and the results show that the OWL modeling can faithfully represent the CEM specification and represent patient data.

Chapter 17 – Ontologies, Vocabularies and Data Models

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.

Harmonization of detailed clinical models with clinical study data standard

The purpose of this chapter is to describe current vocabulary and terminology issues and challenges related specifically to the successful implementation of clinical decision support (CDS) systems. The chapter discusses: why standard coded data are essential for accurate and reliable execution of decision logic; how to unambiguously reference data in the electronic health record (EHR) from CDS expressions; alternatives for pre- and post-coordinated representations of data; representation of patient data as name-value pairs; the relationship between terms and information/data models which provide the context of use; terminology in the life cycle of CDS; the next steps that are needed in standardizing models and terminology for use in CDS.

A Semantic-Web Oriented Representation of Clinical Element Model for Secondary Use of Electronic Healthcare Data

Data sharing and integration between clinical research data management system (CDMS) and electronic health record (EHR) system remains a challenging issue. To deal with the challenge, there is emerging interest in utilizing the Detailed Clinical Modeling (DCM) approach across a variety of contexts. The Intermountain Healthcare Clinical Element Models (CEMs) have been adopted by the Office of the National Coordinator (ONC) awarded SHARPn project for normalizing patient data from the electronic medical records (EMRs). The objective of the present study is to describe our preliminary efforts on harmonization of the SHARPn CEMs with CDISC (Clinical Data Interchange Standards Consortium) clinical study data standards. We were focused on three generic domains: Demographics, Lab Tests and Medications. We performed a panel review on each data element extracted from the CDISC templates and SHARPn CEMs. We have identified a set of data elements that are common to the context of both clinical research and secondary use and discussed outstanding harmonization issues. We consider that the outcomes would be useful for defining new requirements for the DCM modeling community and ultimately facilitating the semantic interoperability between systems for both clinical research and secondary use.

The Effect of Computer-generated Reminders on Charting Deficiencies in the ICU

Healthcare system interoperability is one of the most important goals for Meaningful Use of the Electronic Health Records (EHR). It is essential to facilitate IT support for workflow management, decision support systems, and evidence-based healthcare, as well as secondary use of EHR across healthcare organizations. The Clinical Element Model (CEM) was designed to provide a consistent architecture for representing clinical information in EHR systems. The CEM has been adopted in the Strategic Health IT Advanced Research Project, secondary use of EHR (SHARPn) as the common unified information model for unambiguous data representation, interpretation, and exchange within and across heterogeneous sources and applications.