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. The team used FloMap, an Internet-based survey resource, to compare metadata from six healthcare organizations to an initial draft model. The team used consensus decision-making over time to compare survey results. The resulting model included seven panels, 41 questions, and 231 values. A second validation step included completion of an Internet-based survey with 26 staff nurse respondents representing 15 healthcare organizations, two electronic health record vendors, and one academic institution. The final knowledge model contained nine Logical Observation Identifiers Names and Codes panels, 32 concepts, and 195 values representing an additional six panels (groupings), 15 concepts (questions), and the specification of 195 values (answers). The final model is useful for consistent documentation to demonstrate the contribution of nursing practice to the prevention of venous thromboembolism.

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. As an exemplar, EHR flowsheet metadata (input) from multiple organizations was used to validate a fall prevention IM. DESIGN: A consensus-based, qualitative, descriptive approach was used to identify a minimum set of essential fall prevention data concepts documented by staff nurses in acute care. The goal was to increase generalizable and comparable nurse-sensitive data on the prevention of falls across organizations for big data research. METHODS: The research team conducted a retrospective, observational study using an iterative, consensus-based approach to map, analyze, and evaluate nursing flowsheet metadata contributed by eight health systems. The team used FloMap software to aggregate flowsheet data across organizations for mapping and comparison of data to a reference IM. The FloMap analysis was refined with input from staff nurse subject matter experts, review of published evidence, current documentation standards, Magnet Recognition nursing standards, and informal fall prevention nursing use cases. FINDINGS: Flowsheet metadata analyzed from the EHR systems represented 6.6 million patients, 27 million encounters, and 683 million observations. Compared to the original reference IM, five new IM classes were added, concepts were reduced by 14 (from 57 to 43), and 157 value set items were added. The final fall prevention IM incorporated 11 condition or age-specific fall risk screening tools and a fall event details class with 14 concepts. CONCLUSION: The iterative, consensus-based refinement and validation of the fall prevention IM from actual EHR fall prevention flowsheet documentation contributes to the ability to semantically exchange and compare fall prevention data across multiple health systems and organizations. This method and approach provides a process for standardizing flowsheet data as coded data for information exchange and use in big data research. CLINICAL RELEVANCE: Opportunities exist to work with EHR vendors and the Office of the National Coordinator for Health Information Technology to implement standardized IMs within EHRs to expand interoperability of nurse-sensitive data.

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. The purpose of this case report is to describe a refined methodology for validation of flowsheet IMs and apply the revised process to a genitourinary IM created in one organization. The refined IM process, adding evidence and input from experts, produced a clinically relevant and evidence-based model of genitourinary care. The refined IM process provides a foundation for optimizing electronic health records with comparable nurse sensitive data that can add to common data models for continuity of care and ongoing use for quality improvement and research.

Electronic Method for Measuring Nursing: Workload Action Measures Method (WAMM).

The Workload Action Measures Method (WAMM) is a predictive model of care workload for individual patients. DESIGN: The Clinical Care Classification Information Model quantifies nursing workload combining two aspects of nursing practice by measuring a patient's specific nursing service time using relative value units and a patient's disease condition using the intensity of four Healthcare Patterns/Care Components. RESULTS: WAMM provides reliable workload calculations for disease conditions combined with Nursing Interventions Actions.

Point of Care Solutions for Electronic Documentation of Nursing Practice.

The panel focuses on Point-of-Care (POC) solutions for the documentation of nursing practice in electronic health record (EHR) and/or healthcare information technology (HIT) systems using the Clinical Care Classification (CCC) System. The CCC System was developed by Dr. Saba and Colleagues for the electronic documentation of patient care by nurses and allied health professionals and has been approved by American Nurses Association and U.S. Department of Health and Human Services as an interoperable, standardized nursing terminology The unique POC solutions will be described by different nursing informaticians, designers, and implementers who will describe how they document patient care using the CCC System and their impact on care outcomes. Also the Nursing Informatics (NI) Experts will discuss the effects of POC solutions on care quality and safety, as well as highlight how the data analytics are used to measure and predict workload, staffing, and cost. They will also describe how the information is used to support evidence-based practice and advance nursing knowledge. LEARNING OBJECTIVES: 1) Understand POC solutions using a standardized, coded, nursing terminology based on its Information Model for the e-documentation of nursing practice; 2) Describe the CCC System impacts on care quality, safety, and outcomes as well as measure workload, staffing, and cost. 3) Highlight how POC solutions'data analytics support evidenced-based practice and advance nursing science.

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. Health care organizations design and implement customized content for different care areas creating duplicative data that is noncomparable. In a prior study, 10 information models (IMs) were derived from an EHR that included 2.4 million patients. There was a need to evaluate the generalizability of the models across organizations. The pain IM was selected for evaluation and refinement because pain is a commonly occurring problem associated with high costs for pain management. OBJECTIVE: The purpose of our study was to validate and further refine a pain IM from EHR flowsheet data that standardizes pain concepts, definitions, and associated value sets for assessments, goals, interventions, and outcomes. METHODS: A retrospective observational study was conducted using an iterative consensus-based approach to map, analyze, and evaluate data from 10 organizations. RESULTS: The aggregated metadata from the EHRs of 8 large health care organizations and the design build in 2 additional organizations represented flowsheet data from 6.6 million patients, 27 million encounters, and 683 million observations. The final pain IM has 30 concepts, 4 panels (classes), and 396 value set items. Results are built on Logical Observation Identifiers Names and Codes (LOINC) pain assessment terms and extend the need for additional terms to support interoperability. CONCLUSION: The resulting pain IM is a consensus model based on actual EHR documentation in the participating health systems. The IM captures the most important concepts related to pain.

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.

Electronic Nursing Documentation: Patient Care Continuity Using the Clinical Care Classification System (CCC).

An innovative nursing documentation project conducted at Bumrungrad International Hospital in Bangkok, Thailand demonstrated patient care continuity between nursing patient assessments and nursing Plans of Care using the Clinical Care Classification System (CCC). The project developed a new generation of interactive nursing Plans of Care using the six steps of the American Nurses Association (ANA) Nursing process and the MEDCIN® clinical knowledgebase to present CCC coded concepts as a natural by-product of a nurse's documentation process. The MEDCIN® clinical knowledgebase is a standardized point-of-care terminology intended for use in electronic health record systems. The CCC is an ANA recognized nursing terminology.

Clinical Knowledgebase Integration of Nursing Terminology.

Medicomp Point of Care Clinical Knowledgebase, MEDCIN® is designed to support integrated care documentation and the care planning functions of nurses and allied health professional. In electronic medical records (EMRs) starting with one or more clinical diagnoses and/or patient signs and symptoms, the capabilities of the MEDCIN® knowledgebase are used to prompt nursing terminology concepts following the American Nurses Association (ANA) nursing process [1]. The MEDCIN® diagnostic index dynamically constructs Interactive Plans of Care (PoC) using the six standards of the nursing process and allows aggregated data analysis. MEDCIN® is widely used in the US by EMR clients and health information technology systems around the world.

A pilot study to explore the feasibility of using theClinical Care Classification System for developing a reliable costing method for nursing services.

While nursing activities represent a significant proportion of inpatient care, there are no reliable methods for determining nursing costs based on the actual services provided by the nursing staff. Capture of data to support accurate measurement and reporting on the cost of nursing services is fundamental to effective resource utilization. Adopting standard terminologies that support tracking both the quality and the cost of care could reduce the data entry burden on direct care providers. This pilot study evaluated the feasibility of using a standardized nursing terminology, the Clinical Care Classification System (CCC), for developing a reliable costing method for nursing services. Two different approaches are explored; the Relative Value Unit RVU and the simple cost-to-time methods. We found that the simple cost-to-time method was more accurate and more transparent in its derivation than the RVU method and may support a more consistent and reliable approach for costing nursing services.

Workflow viewpoints: Analysis of nursing workflow documentation in the electronic health record.

This article amplifies the emphasis on organizational workflow reignited by the Institute of Medicine reports on healthcare quality. The analysis of nursing workflow is central to understanding the power of technology to modify the fundamental constructs of nursing practice. The aim is to understand the evolution of nursing workflow and the concept of workflow from the management and computer science perspectives used in electronic health records and computerized provider order entry. The understanding of the workflow models within health information disciplines may improve the model of nursing workflow underlying the implementation of electronic health record systems. The article follows the Walker and Avant evolutionary method of concept analysis.

Nursing terminology: documentation of quality outcomes.

Recently, government and healthcare providers have recognized the tremendous value and importance of advancing the use of electronic healthcare information systems. Without question, the issue of quality has become a focus of concern for consumers, politicians and healthcare providers and a priority for healthcare organizations in the 21st century. The nursing profession through the American Nurses Association has long recognized the need for quantitative evidence to measure the relationship of documentation to the impact on patient care. The following article discusses the ability of electronic healthcare information systems to collect nursing data using nursing terminology for care quality and outcome management. The purpose is to inform informatics professionals of an immediate need to connect electronic healthcare information systems with a standard, concept-based, atomic-level, coded nursing terminology readily available in the public domain for the longitudinal analyses of the continuity of care and of the nurses' contribution to healthcare quality.

Nursing point of care documentation for the evaluation of human quality.

Nurses must have a point of care documentation method to share health information and transmit nursing data. Health information exchange using an online metadata registry allows nurses, health professionals, hospital administrators, and diverse information systems to make significant strides towards improving health outcomes and human quality of care. Health information exchange using point of care documentation supports research methods to determine the cost of nursing service as well as supports the professional assessment of our significant contribution to human quality and care outcomes in electronic health record systems.

Use of metadata registry for nursing: A customer-centered electronic health record.

An online metadata registry promotes cross-system and cross-organization descriptions of common units of health data. Nurses must have a method to share health information and transmit nursing data. Health information sharing through an online metadata registry allows nurses, health administrators, and hospital systems to make significant strides towards improving customer-centered electronic health records, patient safety, reducing errors, lowering healthcare costs, and strengthening public health and disaster preparedness initiatives.