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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.

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.

SummaryIntroduction: This article is part of the Focus Theme of Methods of Information in Medicine on “Managing Interoperability and Complexity in Health Systems”.Background: Data sharing and integration between the clinical research data management system and the electronic health record system remains a challenging issue. To approach the issue, there is emerging interest in utilizing the Detailed Clinical Model (DCM) approach across a variety of contexts. The Intermountain Healthcare Clinical Element Models (CEMs) have been adopted by the Office of the National Coordinator awarded Strategic Health IT Advanced Research Projects for normalization (SHARPn) project for normalizing patient data from the electronic health records (EHR).Objective: The objective of the present study is to describe our preliminary efforts toward harmonization of the SHARPn CEMs with CDISC (Clinical Data Interchange Standards Consortium) clinical study data standards.Methods: 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.Results: We have identified a set of data elements that are common to the context of both clinical study and broad secondary use of EHR data and discussed outstanding harmonization issues.Conclusions: We consider that the outcomes would be useful for defining new requirements for the DCM modeling community and ultimately facilitating the semantic interoper-ability between systems for both clinical study and broad secondary use domains.

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.

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.

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 $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.

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

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.

The Clinical Element Model (CEM) is an information model designed for representing clinical information in 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 all the detailed CEMs from XML to OWL. A panel evaluation has been conducted, and the evaluation results show that the OWL modeling can faithfully represent the CEM specification and represent patient data.

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.

Communication is one of the most important tasks performed by health care professionals. Data underlie every medical decision, and except for the personal observations made by and acted upon by physicians at the bedside, should be communicated. Often, the data are communicated through several people and via electronic strips, hand-written notes, computer displays, and computer printouts before getting to the medical decision-maker. Each step in the process, especially if it involves people and hand-written records, can result in delays and errors.

Care of the acutely ill patient requires rapid acquisition, recording and communications of data. In the modern hospital it is not unusual for a patient to be connected to several monitoring and recording devices simultaneously. Each of these devices is typically made by a different manufacturer who may specialize in one sort of measurement, for example, pulse oximetry. Most of the modern monitoring and recording devices are micro-processor based and have communication capabilities. Unfortunately, there is no operable standard communication technology available from all devices. In addition different clinical staff (physicians, nurses, or respiratory therapists) may be responsible for collecting data. As a result there is a need to develop methods, standards, and strategies for timely and automatic collection of data from these monitoring and recording devices. We report on more than 5 years of clinical experience of automated ICU data collection using a prototype of the Medical Information Bus (MIB).