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). Questionnaires were completed 3 months after enrollment to ascertain outcome rates of anxiety, depression, and post-traumatic stress disorder (PTSD). RESULTS: Ninety-nine participants were enrolled (median LF/HF ratio, 0.92 [interquartile range, 0.64-1.38]). Of 92 participants who completed the 3-month follow-up, 29 (32%) had persistent anxiety. Logistic regression showed that LF/HF ratio (odds ratio [OR] 0.85, 95% confidence interval [CI] 0.43, 1.53) was not associated with 3-month outcomes. In an exploratory analysis, DFA α (OR 0.93, 95% CI 0.87, 0.99), α1 (OR 0.97, 95% CI 0.94, 0.99), and α2 (OR 0.94, 95% CI 0.88, 0.99) scaling components were associated with PTSD development. CONCLUSION: Almost one-third of family members experienced anxiety at three months after enrollment. HRV, measured by LF/HF ratio, was not a predictor of psychologic distress, however, exploratory analyses indicated that DFA may be associated with PTSD outcomes.

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. Depression was measured with the Hospital Anxiety and Depression Score. The primary analysis was the association between depression at 3 months and change in caregiver burden, controlling for a history of caregiver depression. RESULTS: One hundred one participants were enrolled; 65 participants had a surviving loved one and completed 3-month follow-up. At 3-month follow-up, 12% of participants met criteria for depression. Increased caregiver burden over time was significantly associated with depression at follow-up (Fisher exact test, P = .004), although this association was not significant after controlling for self-reported history of depression at baseline (Cochran-Mantel-Haenszel test, P = .23). CONCLUSIONS: Family members are increasingly recognized as a vulnerable population susceptible to negative psychological outcomes after a loved one's admission to the intensive care unit. In this small sample, no significant association was found between change in caregiver burden and depression at 3 months after controlling for baseline depression.

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. The primary exposure was ED occupancy rate, defined as the ratio of registered ED patients to licensed ED beds. We defined ED overcrowding as an ED occupancy rate greater than or equal to 1. We used multivariable regression to measure the adjusted association between ED crowding and door-to-antibiotic time (elapsed time from ED arrival to first antibiotic initiation). Using Markov multistate models, we also investigated the association between ED crowding and pre-antibiotic care processes. RESULTS: Among 3,572 eligible sepsis patients, 70% arrived when the ED occupancy rate was greater than or equal to 0.5 and 14% arrived to an overcrowded ED. Median door-to-antibiotic time was 158 minutes (interquartile range 109 to 216 minutes). When the ED was overcrowded, 46% of patients received antibiotics within 3 hours of ED arrival compared with 63% when it was not (difference 14.4%; 95% confidence interval 9.7% to 19.2%). After adjustment, each 10% increase in ED occupancy rate was associated with a 4.0-minute increase (95% confidence interval 2.8 to 5.2 minutes) in door-to-antibiotic time and a decrease in the odds of antibiotic initiation within 3 hours (odds ratio 0.90; 95% confidence interval 0.88 to 0.93). Increasing ED crowding was associated with slower initial patient assessment but not further delays after the initial assessment. CONCLUSION: ED crowding was associated with increased sepsis antibiotic delay. Hospitals must devise strategies to optimize sepsis antibiotic administration during periods of ED crowding.

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.

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. Based on the analysis, we developed a profile that consists of 1) FHIR resources for essential FHH data elements, 2) extensions for additional elements that were not covered by the resources, and 3) a structured definition to integrate patient and family member information in a FHIR message. We implemented the profile using an open-source based FHIR framework and validated it using patient-entered FHH data that was captured through a locally developed FHH tool.

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.

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. Informatics and MHI personnel collaboratively and iteratively refined the criteria to make them adequately explicit and reflective of MHI objectives. When tested on retrospective data of 540 patients, the clinician agreed with the computer's conclusion in 52.8% of the cases (285/540). We considered the analysis sufficiently successful to begin piloting the computerized score in prospective clinical care. So far in the pilot, clinicians have agreed with the computer in 70.6% of the cases (24/34).

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. 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.

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). 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.

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.

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. A group of 60 retrospective patients was also formed. MEASUREMENTS: The average numbers of charting deficiencies at the end of the shift in each of the three groups were compared using two planned orthogonal contrasts. RESULTS: The average in the study group patients was 1.02 deficiencies per day per patient, whereas the control group the average was 1.40 deficiencies per day per patient (p = 0.001). The average number of end-of-shift deficiencies in the pooled prospective (study/control) population was 1.21 deficiencies per day per patient, compared with the average in the retrospective group of 1.56 deficiencies per day per patient (p < 0.001). CONCLUSION: The decrease was likely due both to the appropriate response of the nurses to the reminders and to a learned attentiveness to the tasks on the part of the nurses who cared for study patients. Greater gains were hindered by incomplete "coupling" of the reminders to the end-of-shift deficiencies and by inaccuracies in the reminders.