Heart rate variability and subsequent psychological distress among family members of intensive care unit patients.

Objective: To determine whether heart rate variability (HRV; a physiological measure of acute stress) is associated with persistent psychological distress among family members of adult intensive care unit (ICU) patients.

Methods: This prospective study investigated family members of patients admitted to a study ICU. Participants' variability in heart rate tracings were measured by low frequency (LF)/high frequency (HF) ratio and detrended fluctuation analysis (DFA).

Depression and Change in Caregiver Burden Among Family Members of Intensive Care Unit Survivors.

Background: Family members of patients in intensive care units may experience psychological distress and substantial caregiver burden.

Objective: To evaluate whether change in caregiver burden from intensive care unit admission to 3-month follow-up is associated with caregiver depression at 3 months.

Methods: Caregiver burden was assessed at enrollment and 3 months later, and caregiver depression was assessed at 3 months.

Emergency Department Crowding Is Associated With Delayed Antibiotics for Sepsis.

Study Objective: Barriers to early antibiotic administration for sepsis remain poorly understood. We investigated the association between emergency department (ED) crowding and door-to-antibiotic time in ED sepsis.

Methods: We conducted a retrospective cohort study of ED sepsis patients presenting to 2 community hospitals, a regional referral hospital, and a tertiary teaching hospital.

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.

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.

Computerization of Mental Health Integration complexity scores at Intermountain Healthcare.

Intermountain Healthcare's Mental Health Integration (MHI) Care Process Model (CPM) contains formal scoring criteria for assessing a patient's mental health complexity as "mild," "medium," or "high" based on patient data. The complexity score attempts to assist Primary Care Physicians in assessing the mental health needs of their patients and what resources will need to be brought to bear. We describe an effort to computerize the scoring.

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.

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).

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.

The effect of computer-generated reminders on charting deficiencies in the ICU.

Objective: To examine the effect of computer-generated reminders on nurse charting deficiencies in two intensive care units.

Design: Nurses caring for a group of 60 study patients received patient-specific paper reminder reports when charting deficiencies were found at mid-day. Nurses caring for a group of 60 control patients received no reminders.