Interprofessional Research in the CPCCRN

Lecture presentations hosted by the College of Nursing featuring faculty, graduate students, nursing professionals, and allied health experts. Each lunchtime lecture is devoted to a timely topic examining the training, research and scholarly needs of the nursing community.Lecture

PhD

dissertationDetailed research protocols standardize the interventions for clinical studies, but the protocols can be complex. Clinical decision support systems (CDS) can interpret detailed protocols for care providers. Nurses are expected to comply with research protocols; however, nurses sometimes reject the CDS recommendations. This study evaluated patterns of noncompliance with recommendations from a computerized insulin-titration CDS (eProtocol-insulin). The primary research question was: Are there patterns to noncompliance with recommendations from eProtocol-insulin? The study used a descriptive/exploratory design; with data from a nurse questionnaire supplementing data from a retrospective patient cohort reflecting 2 years of software use. A content analysis of text entered by nurses into the software described reasons for noncompliance. Patient data, such as trends over an extended time, were the reason for almost half (49.2%) of declines. The nurse disagreed with knowledge base rules for one quarter (27.3%) of declines, and nurses experienced barriers to compliance for 12.3% of declined recommendations. Organizational factors, usability, and attitudes were not reasons for any declined recommendations in this analysis. A statistical analysis evaluated the relationship between patient variables and noncompliance with recommendations. Demographic characteristics were not associated with declined recommendations. Associations were found between specific patient conditions and declines. However, no clear pattern of predictors emerged. It is likely that more complex models would be needed to reliably predict noncompliance. Nurses described factors that they perceived as important for compliance. The nurses understood the overall protocol but wanted explanations available on demand. Other factors perceived as important included help files accessible through the software interface, maintaining the ability to decline recommendations, and having trust in the protocol development process. Noncompliance was a relatively uncommon event, occurring less than 6% of the time. However, patterns of noncompliance revealed areas where the CDS could be improved. Patients experienced events not accommodated by protocol rules, which the nurses appeared to perceive as safety issues. Even with high levels of overall compliance, evaluation of declined recommendations can lead to improved decision support systems and better patient safety

The Utah PRISMS Infrastructure for Generating Air Quality Exposomes

Understanding the effects of the modern environment on pediatric asthma requires generation of a complete picture of the contributing environmental exposures and socio-economic factors. Such an exposome requires integration of data from wearable and stationary sensors, environmental monitors, physiology, medication use and other clinical data. In addition, such an integration would need to have a high spatial-temporal resolution for correlating times and location of exposures to occurrences of conditions and their severities. This would require filling any gaps in the measured data with modeled data along with characterization of any uncertainties. The Utah PRISMS Federated Integration Architecture is a comprehensive, standards-based, open-source informatics platform that allows sensor data and biomedical data to be integrated in a meaningful manner. We are developing the architecture, data models, processes, hardware and software to acquire, manage, process, and communicate high-resolution clinically relevant exposome information from environmental, physiological, and behavioral sensors and computational models. We envision this infrastructure to support different types of environmental biomedical studies of pediatric asthma and other chronic conditions, and potentially other research. In this presentation, we discuss two main components of the Utah PRISMS infrastructure: Sensor Common Data Model: Federating or integrating increasingly large, complex and multi-dimensional sensor data requires a thorough human understanding of them. These metadata specifications are designed to support the conduct of research utilizing personalized and environmental sensors. These includes sensors ranging from nano-sensors up to satellites. Sensor measurements may include physical, chemical, and biological species. In addition, these sensors include those that instantaneously (or with a transient storage) measure these species or those that collect physical samples with material transfer for later analysis. Sensors may be deployed in various environments, including personal (i.e. implanted & mobile), immediate (i.e. indoor), and general environment (i.e. external environmental protection agency monitors). The purpose of the data model is to establish a library of instruments; describe and document deployments of sensors; assess quality of data collected by different instruments within its deployment environments; support harmonization and integration of data collected from various sensors; and guide for structuring and storing sensor output data. Central Big Data Federation/Integration Platform: This is a standards-based, open-access infrastructure that integrates sensor data and mathematically modeled data with biomedical information along with characterizing uncertainties associated with using these data. The platform consists of components that: Discover, characterize, store and version metadata of different sensor data sources Standardize semantics across different sensor data sources using ontology services Store data in a temporal event based model to support different research uses-cases Methods to transform and present data for different use-cases of exposomic studies. In this symposium, we present initial lessons from the Utah PRISMS Platform that summarizes the interconnected work by diverse expertise including electrical, computer, chemical and industrial engineers, atmospheric and computer scientists, informaticists and pediatric researchers in developing an infrastructure for generating exposomes

130 Developing a Conceptual Data Model for Nursing Workload

OBJECTIVES/GOALS: Nurses are leaving the profession at an alarming rate due to increased workload and burnout.#_msocom_1 Computational models that are reliable and reproducible are needed to quantitatively examine nursing workload and estimate potential effect of interventions. This project developed a logical data model to represent nursing EHR interactions. METHODS/STUDY POPULATION: With nursing EHR interactions as a starting point, we expand upon literature that examined the EHR workload of physicians. We conducted an exploratory analysis of nursing EHR audit log data at a large academic medical center, and explored components of nursing workload that can be extracted from other health system data. Using concepts derived from the studying temporal biomedical data patterns, we formulated a data structure that describes nurse EHR interactions, nurse intrinsic and situational characteristics, and nurse outcomes of interest in a scalable and extensible manner. RESULTS/ANTICIPATED RESULTS: Temporal machine learning models are grounded in the concept of vectors. We developed a logical data model that describes tasks performed by nurses (NTask), nurse types (NType), and nursing outcomes (NOutcome). For each nurse (k), we define a function , i=1 to N as a vector of dimension N, where N is the number of time periods in the study. The i component corresponds to the activity that the nurse is doing. The model will allow the quantitative classification of activity patterns for any finite number of nurses for an arbitrary set of tasks and for time at any specified resolution. The expected outcome is a set of vectors that can then be utilized to quantitatively model nurse activity trajectories and other patterns of nurse EHR interactions. DISCUSSION/SIGNIFICANCE: By instantiating the logical data model, we will demonstrate how nurse EHR interactions can be studied using temporal unsupervised learning and state-of-the-art artificial intelligence methods. We plan to simulate the potential impact of workload interventions and predict risk for nurse burnout

Evaluating the relevance and clarity of the heart failure eMeasure implementation toolkit by using a webbased survey instrument

Background: The impact on workfl ow is an important component in determining whether an HIT implementation will be successful. Workfl ow is, unfortunately, a concept that is often ignored when implementing HIT and the literature about workfl ow in domains of quality improvement, system implementation, and process improvement has not been adequate. HIT is not always designed to fi t the workfl ow of a given organization, making it diffi cult to truly assess HIT impact on outcomes or processes.</p

Case Study

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