Bonnie L. Westra

Medication regimens in older home care patients.

Medication regimens in older patients have been strongly associated with adverse events leading to hospitalization in ambulatory care settings. Despite a 29% hospitalization rate, to date, no research regarding medication regimens and readmission to the hospital has been completed in the home care setting. As part of a larger study evaluating predictors of readmission to the hospital from home care, descriptive analyses, chi-square tests, and t tests for independent samples were used in this secondary analysis to evaluate the Outcome and Assessment Information Set and medication records from 911 older patients admitted from the hospital to 15 home care agencies. Patients readmitted back to the hospital were older, sicker, and more cognitively impaired, and had complex medication regimens that included significant polypharmacy and inappropriate medication use. Nurses working with older adults need to be especially vigilant in monitoring medication regimens of patients to reduce opportunities for adverse drug events and subsequent hospitalization.

The feasibility of integrating the Omaha system data across home care agencies and vendors.

Federal and state initiatives are aligning around the goal that by 2014 all Americans will have electronic health records to support access to their health information any time and anywhere. As a key healthcare provider, nursing data must be included to enhance patient safety, effectiveness, and efficiency of care that is patient-centric. The purpose of this study was to test the feasibility of abstracting, integrating, and comparing the effective use of a standardized terminology, the Omaha System, across software vendors and 15 home care agencies. Results showed that the 2900 patients in this study had an average of four problems on care plans, with interventions most frequently addressing surveillance (39%) and teaching (30%). Findings in this study support the feasibility of integrating data across software vendors and agencies as well as the usefulness for describing care provided in home care. However, before exchanging data across systems, data quality issues found in this study need attention. There were missing data for 10.8% of patients as well as concerns about the validity of using the problem rating scale for outcomes. Strategies for effective use of standardized nursing terminologies are recommended.

Evidence-based Standardized Care Plans for Use Internationally to Improve Home Care Practice and Population Health

OBJECTIVES To develop evidence-based standardized care plans (EB-SCP) for use internationally to improve home care practice and population health. METHODS A clinical-expert and scholarly method consisting of clinical experts recruitment, identification of health concerns, literature reviews, development of EB-SCPs using the Omaha System, a public comment period, revisions and consensus. RESULTS Clinical experts from Canada, the Netherlands, New Zealand, and the United States participated in the project, together with University of Minnesota School of Nursing graduate students and faculty researchers. Twelve Omaha System problems were selected by the participating agencies as a basic home care assessment that should be used for all elderly and disabled patients. Interventions based on the literature and clinical expertise were compiled into EB-SCPs, and reviewed by the group. The EB-SCPs were revised and posted on-line for public comment; revised again, then approved in a public meeting by the participants. The EB-SCPs are posted on-line for international dissemination. CONCLUSIONS Home care EB-SCPs were successfully developed and published on-line. They provide a shared standard for use in practice and future home care research. This process is an exemplar for development of evidence-based practice standards to be used for assessment and documentation to support global population health and research.

Validation of Concept Mapping Between PNDS and SNOMED CT

The perioperative nursing data set (PNDS) is a structured vocabulary developed by AORN to help document perioperative nursing practices. The PNDS has been mapped to the Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT) reference terminology model to support the electronic exchange of nursing data. This study validates the concept mapping between the PNDS and SNOMED CT, supporting an equivalent meaning of concepts between the two terminology systems.

Interpretable Predictive Models for Knowledge Discovery from Home-Care Electronic Health Records

The purpose of this methodological study was to compare methods of developing predictive rules that are parsimonious and clinically interpretable from electronic health record (EHR) home visit data, contrasting logistic regression with three data mining classification models. We address three problems commonly encountered in EHRs: the value of including clinically important variables with little variance, handling imbalanced datasets, and ease of interpretation of the resulting predictive models. Logistic regression and three classification models using Ripper, decision trees, and Support Vector Machines were applied to a case study for one outcome of improvement in oral medication management. Predictive rules for logistic regression, Ripper, and decision trees are reported and results compared using F-measures for data mining models and area under the receiver-operating characteristic curve for all models. The rules generated by the three classification models provide potentially novel insights into mining EHRs beyond those provided by standard logistic regression, and suggest steps for further study.

Developing a personal health record for community-dwelling older adults and clinicians: technology and content.

To empower older consumers and improve health outcomes, a consumer-friendly personal health record (PHR) is needed. The purpose of this article was to evaluate PHR technology and content for older community-dwelling consumers. Specific aims were to: (a) develop a secure, web-based application for a PHR to enable interoperable exchanges of data between consumers and clinicians; (b) develop structured, evidence-based shared care plan content for the PHR using an interface terminology standard; and (c) validate the shared care plans with consumers. An interoperable web-based form was developed. The standardized PHR content was developed by expert panel consensus using the Omaha System problem list and care plans, and validated by consumer interviews. Evidence-based shared care plans for 21 problems common among community-dwelling older adults were developed and encoded with Omaha System terms for data capture in the PHR. An additional problem, Neighborhood-workplace safety, was identified by consumers and will be added to the care plans.

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. The purpose of this study was to align a minimum set of physiological nursing assessment data elements with national standardized coding systems. Six institutions shared their 100 most common electronic health record nursing assessment data elements. From these, a set of distinct elements was mapped to nationally recognized Logical Observations Identifiers Names and Codes (LOINC®) and Systematized Nomenclature of Medicine–Clinical Terms (SNOMED CT®) standards. We identified 137 observation names (55% new to LOINC), and 348 observation values (20% new to SNOMED CT) organized into 16 panels (72% new LOINC). This reference set can support the exchange of nursing information, facilitate multi-site research, and provide a framework for nursing data analysis.

Forensic Style Analysis with Survival Trajectories

Electronic Health Records (EHRs) consists of patient information such as demographics, medications, laboratory test results, diagnosis codes and procedures. Mining EHRs could lead to improvement in patient healthcare management as EHRs contain detailed information related to disease prognosis for large patient populations. We hypothesize that a patients condition does not deteriorate at random, the trajectories, sequences in which diseases appear in a patient, are determined by a finite number of underlying disease mechanisms. In this work, we exploit this idea by predicting a patients risk of mortality in the context of the metabolic syndrome by assessing which of many available trajectories a patient is following and progression along this trajectory. Implementing this idea required innovative enhancements both for the study design and also for the fitting algorithm. We propose a forensic-style study design, which aligns patients on last follow-up and measures time backwards. We modify the time-dependent covariate Cox proportional hazards model to better capture coefficients of covariate that follow a particular temporal sequence, such as trajectories. Knowledge extracted from such analysis can lead to personalized treatments, thereby forming the basis for future trajectory-centered guidelines.

Omaha System data: Methods for research and program evaluation

Researchers are developing methods to evaluate health care quality and effectiveness using health informatics data sets. Standardized taxonomies such as the Omaha System are being used in computerized documentation systems to generate data on client assessments and health care services. Questions such as prevalence of client problems, care utilization, differential intervention effectiveness, and problem-specific client outcomes can be investigated. Approaches for analyzing Omaha System data are emerging in the literature and in practice settings, and will be described in this poster.

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. There were 650 journal articles identified in 17 key nursing informatics, general biomedical informatics, and nursing research journals in the Web of Science database. After screening for inclusion and exclusion criteria, 17 studies published in 18 articles were identified as big data nursing research applied to practice. DISCUSSION Nurses clearly are beginning to conduct big data research applied to practice. These studies represent multiple data sources and settings. Although numerous analytic methods were used, the fundamental issue remains to define the types of analyses consistent with big data analytic methods. CONCLUSION There are needs to increase the visibility of big data and data science research conducted by nurse scientists, further examine the use of state of the science in data analytics, and continue to expand the availability and use of a variety of scientific, governmental, and industry data resources. A major implication of this literature review is whether nursing faculty and preparation of future scientists (PhD programs) are prepared for big data and data science.