Clinical Knowledge Model for the Prevention of Healthcare-Associated Venous Thromboembolism.

Knowledge models inform organizational behavior through the logical association of documentation processes, definitions, data elements, and value sets. The development of a well-designed knowledge model allows for the reuse of electronic health record data to promote efficiency in practice, data interoperability, and the extensibility of data to new capabilities or functionality such as clinical decision support, quality improvement, and research. The purpose of this article is to describe the development and validation of a knowledge model for healthcare-associated venous thromboembolism prevention.

Information Models Offer Value to Standardize Electronic Health Record Flowsheet Data: A Fall Prevention Exemplar.

Purpose: The rapid implementation of electronic health records (EHRs) resulted in a lack of data standardization and created considerable difficulty for secondary use of EHR documentation data within and between organizations. While EHRs contain documentation data (input), nurses and healthcare organizations rarely have useable documentation data (output). The purpose of this article is to describe a method of standardizing EHR flowsheet documentation data using information models (IMs) to support exchange, quality improvement, and big data research.

A refined methodology for validation of information models derived from flowsheet data and applied to a genitourinary case.

Use of electronic health record data is expanding to support quality improvement and research; however, this requires standardization of the data and validation within and across organizations. Information models (IMs) are created to standardize data elements into a logical organization that includes data elements, definitions, data types, values, and relationships. To be generalizable, these models need to be validated across organizations.

Machine Learned Mapping of Local EHR Flowsheet Data to Standard Information Models using Topic Model Filtering.

Electronic health record (EHR) data must be mapped to standard information models for interoperability and to support research across organizations. New information models are being developed and validated for data important to nursing, but a significant problem remains for how to correctly map the information models to an organization's specific flowsheet data implementation. This paper describes an approach for automating the mapping process by using stacked machine learning models.

Knowledge Discovery With Machine Learning for Hospital-Acquired Catheter-Associated Urinary Tract Infections.

Massive generation of health-related data has been key in enabling the big data science initiative to gain new insights in healthcare. Nursing can benefit from this era of big data science, as there is a growing need for new discoveries from large quantities of nursing data to provide evidence-based care. However, there are few nursing studies using big data analytics.

A Framework for Visualizing Data Quality for Predictive Models and Clinical Quality Measures.

The ability to assess data quality is essential for secondary use of EHR data and an automated Healthcare Data Quality Framework (HDQF) can be used as a tool to support a healthcare organization's data quality initiatives. Use of a general purpose HDQF provides a method to assess and visualize data quality to quickly identify areas for improvement. The value of the approach is illustrated for two analytics use cases: 1) predictive models and 2) clinical quality measures.

Multiple Factors Drive Opioid Prescribing at the Time of Discharge.

Multiple factors potentially influence pain intensity or frequency, and consequently the need for an opioid prescription. This study aims to identify factors associated with being discharged with an outpatient opioid prescription. We constructed a database containing clinical, non-clinical, and organizational variables from the EHR that are potentially relevant for ordering an opioid at discharge.

Mortality Risk in Homebound Older Adults Predicted From Routinely Collected Nursing Data.

Background: Newer analytic approaches for developing predictive models provide a method of creating decision support to translate findings into practice.

Objectives: The aim of this study was to develop and validate a clinically interpretable predictive model for 12-month mortality risk among community-dwelling older adults. This is done by using routinely collected nursing assessment data to aide homecare nurses in identifying older adults who are at risk for decline, providing an opportunity to develop care plans that support patient and family goals for care.

Assessment of Personal Health Care Management and Chronic Disease Prevalence: Comparative Analysis of Demographic, Socioeconomic, and Health-Related Variables.

Background: The use of personal health care management (PHM) is increasing rapidly within the United States because of implementation of health technology across the health care continuum and increased regulatory requirements for health care providers and organizations promoting the use of PHM, particularly the use of text messaging (short message service), Web-based scheduling, and Web-based requests for prescription renewals. Limited research has been conducted comparing PHM use across groups based on chronic conditions.

Objective: This study aimed to describe the overall utilization of PHM and compare individual characteristics associated with PHM in groups with no reported chronic conditions, with 1 chronic condition, and with 2 or more such conditions.

A Holistic Clustering Methodology for Liver Transplantation Survival.

Background: Liver transplants account for a high number of procedures with major investments from all stakeholders involved; however, limited studies address liver transplant population heterogeneity pretransplant predictive of posttransplant survival.

Objective: The aim of the study was to identify novel and meaningful patient clusters predictive of mortality that explains the heterogeneity of liver transplant population, taking a holistic approach.

Methods: A retrospective cohort study of 344 adult patients who underwent liver transplantation between 2008 through 2014.

Validation and Refinement of a Pain Information Model from EHR Flowsheet Data.

Background: Secondary use of electronic health record (EHR) data can reduce costs of research and quality reporting. However, EHR data must be consistent within and across organizations. Flowsheet data provide a rich source of interprofessional data and represents a high volume of documentation; however, content is not standardized.

Factors Associated With Healthcare-Acquired Catheter-Associated Urinary Tract Infections: Analysis Using Multiple Data Sources and Data Mining Techniques.

Purpose: The purpose of this study was to identify factors associated with healthcare-acquired catheter-associated urinary tract infections (HA-CAUTIs) using multiple data sources and data mining techniques.

Subjects And Setting: Three data sets were integrated for analysis: electronic health record data from a university hospital in the Midwestern United States was combined with staffing and environmental data from the hospital's National Database of Nursing Quality Indicators and a list of patients with HA-CAUTIs.

Methods: Three data mining techniques were used for identification of factors associated with HA-CAUTI: decision trees, logistic regression, and support vector machines.

Delay Within the 3-Hour Surviving Sepsis Campaign Guideline on Mortality for Patients With Severe Sepsis and Septic Shock.

Objectives: To specify when delays of specific 3-hour bundle Surviving Sepsis Campaign guideline recommendations applied to severe sepsis or septic shock become harmful and impact mortality.

Design: Retrospective cohort study.

Setting: One health system composed of six hospitals and 45 clinics in a Midwest state from January 01, 2011, to July 31, 2015.

Quantifying the Effect of Data Quality on the Validity of an eMeasure.

Objective The objective of this study was to demonstrate the utility of a healthcare data quality framework by using it to measure the impact of synthetic data quality issues on the validity of an eMeasure (CMS178—urinary catheter removal after surgery). Methods Data quality issues were artificially created by systematically degrading the underlying quality of EHR data using two methods: independent and correlated degradation. A linear model that describes the change in the events included in the eMeasure quantifies the impact of each data quality issue.

Modeling Flowsheet Data to Support Secondary Use.

The purpose of this study was to create information models from flowsheet data using a data-driven consensus-based method. Electronic health records contain a large volume of data about patient assessments and interventions captured in flowsheets that measure the same "thing," but the names of these observations often differ, according to who performs documentation or the location of the service (eg, pulse rate in an intensive care, the emergency department, or a surgical unit documented by a nurse or therapist or captured by automated monitoring). Flowsheet data are challenging for secondary use because of the existence of multiple semantically equivalent measures representing the same concepts.

Secondary Analysis of an Electronic Surveillance System Combined with Multi-focal Interventions for Early Detection of Sepsis.

Unlabelled: To conduct an independent secondary analysis of a multi-focal intervention for early detection of sepsis that included implementation of change management strategies, electronic surveillance for sepsis, and evidence based point of care alerting using the POC AdvisorTM application.

Methods: Propensity score matching was used to select subsets of the cohorts with balanced covariates. Bootstrapping was performed to build distributions of the measured difference in rates/means.

Big data science: A literature review of nursing research exemplars.

Background: Big data and cutting-edge analytic methods in nursing research challenge nurse scientists to extend the data sources and analytic methods used for discovering and translating knowledge.

Purpose: The purpose of this study was to identify, analyze, and synthesize exemplars of big data nursing research applied to practice and disseminated in key nursing informatics, general biomedical informatics, and nursing research journals.

Methods: A literature review of studies published between 2009 and 2015.

Predictors of Liver Transplant Patient Survival.

Objective: Liver transplantation is a costly and risky procedure, representing 25 050 procedures worldwide in 2013, with 6729 procedures performed in the United States in 2014. Considering the scarcity of organs and uncertainty regarding prognosis, limited studies address the variety of risk factors before transplantation that might contribute to predicting patient's survival and therefore developing better models that address a holistic view of transplant patients. This critical review aimed to identify predictors of liver transplant patient survival included in large-scale studies and assess the gap in risk factors from a holistic approach using the Wellbeing Model and the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement.

Big Data and Perioperative Nursing.

Big data are large volumes of digital data that can be collected from disparate sources and are challenging to analyze. These data are often described with the five "Vs": volume, velocity, variety, veracity, and value. Perioperative nurses contribute to big data through documentation in the electronic health record during routine surgical care, and these data have implications for clinical decision making, administrative decisions, quality improvement, and big data science.

Expanding Interprofessional EHR Data in i2b2.

Emerging issues of team-based care, precision medicine, and big data science underscore the need for health information technology (HIT) tools for integrating complex data in consistent ways to achieve the triple aims of improving patient outcomes, patient experience, and cost reductions. The purpose of this study was to demonstrate the feasibility of creating a hierarchical flowsheet ontology in i2b2 using data-derived information models and determine the underlying informatics and technical issues. This study is the first of its kind to use information models that aggregate team-based care across time, disciplines, and settings into 14 information models that were integrated into i2b2 in a hierarchical model.

A Data Mining Approach to Determine Sepsis Guideline Impact on Inpatient Mortality and Complications.

Sepsis incidents have doubled from 2000 through 2008, and hospitalizations for these diagnoses have increased by 70%. The use of the Surviving Sepsis Campaign (SSC) guidelines can lead to earlier diagnosis and treatment; however, the effectiveness of the SSC guidelines in preventing complications for this population is unclear. The overall purpose of this study was to apply SSC guideline recommendations to EHR data for patients with severe sepsis or septic shock and determine guideline compliance as well as its impact on inpatient mortality and sepsis complications.

Sinusitis Treatment Guideline Adherence in the E-Visit Setting: A Performance Improvement Project.

Background: Studies demonstrate poor guideline adherence by health care providers for the treatment of upper respiratory infections, particularly acute bacterial rhinosinusitis (ABRS), in the appropriate prescribing of antibiotic medications.

Objective: The purpose of this quality improvement project was to evaluate the effect of implementing interventions for improving adherence to a clinical practice guideline for the management of ABRS for patients treated in the e-visit setting. Interventions included: providing a report to providers of their adherence to the ABRS clinical guideline prior to the intervention, providing updated education on the ABRS guideline, and implementing a clinical decision support system reminder.

Standardizing Physiologic Assessment Data to Enable Big Data Analytics.

Disparate data must be represented in a common format to enable comparison across multiple institutions and facilitate Big Data science. Nursing assessments represent a rich source of information. However, a lack of agreement regarding essential concepts and standardized terminology prevent their use for Big Data science in the current state.