IMPORTANCE: This manuscript will be of interest to most Clinical and Translational Science Awards (CTSA) as they retool for the increasing emphasis on translational science from translational research. This effort is an extension of the EDW4R work that most CTSAs have done to deploy infrastructure and tools for researchers to access clinical data. OBJECTIVES: The Iowa Health Data Resource (IHDR) is a strategic investment made by the University of Iowa to improve access to real-world health data. The goals of IHDR are to improve the speed of translational health research, to boost interdisciplinary collaboration, and to improve literacy about health data. The first objective toward this larger goal was to address gaps in data access, data literacy, lack of computational environments for processing Personal Health Information (PHI) and the lack of processes and expertise for creating transformative datasets. METHODS: A three-pronged approach was taken to address the objective. The approach involves integration of an intercollegiate team of non-informatics faculty and staff, a data enclave for secure patient data analyses, and novel comprehensive datasets. RESULTS: To date, all five of the health science colleges (dentistry, medicine, nursing, pharmacy, and public health) have had at least one staff and one faculty member complete the two-month experiential learning curriculum. Over the first two years of this project, nine cohorts totaling 36 data liaisons have been trained, including 18 faculty and 18 staff. IHDR data enclave eliminated the need to duplicate computational infrastructure inside the hospital firewall which reduced infrastructure, hardware and human resource costs while leveraging the existing expertise embedded in the university research computing team. The creation of a process to develop and implement transformative datasets has resulted in the creation of seven domain specific datasets to date. CONCLUSION: The combination of people, process, and technology facilitates collaboration and interdisciplinary research in a secure environment using curated data sets. While other organizations have implemented individual components to address EDW4R operational demands, the IHDR combines multiple resources into a novel, comprehensive ecosystem IHDR enables scientists to use analysis tools with electronic patient data to accelerate time to science.

OBJECTIVE: To honor the legacy of nursing informatics pioneer and visionary, Dr. Virginia Saba, the Friends of the National Library of Medicine convened a group of international experts to reflect on Dr. Saba's contributions to nursing standardized nursing terminologies. PROCESS: Experts led a day-and-a-half virtual update on nursing's sustained and rigorous efforts to develop and use valid, reliable, and computable standardized nursing terminologies over the past 5 decades. Over the course of the workshop, policymakers, industry leaders, and scholars discussed the successful use of standardized nursing terminologies, the potential for expanded use of these vetted tools to advance healthcare, and future needs and opportunities. In this article, we elaborate on this vision and key recommendations for continued and expanded adoption and use of standardized nursing terminologies across settings and systems with the goal of generating new knowledge that improves health. CONCLUSION: Much of the promise that the original creators of standardized nursing terminologies envisioned has been achieved. Secondary analysis of clinical data using these terminologies has repeatedly demonstrated the value of nursing and nursing's data. With increased and widespread adoption, these achievements can be replicated across settings and systems.

OBJECTIVES: Electronic health records (EHRs) user interfaces (UI) designed for data entry can potentially impact the quality of patient information captured in the EHRs. This review identified and synthesized the literature evidence about the relationship of UI features in EHRs on data quality (DQ). MATERIALS AND METHODS: We performed an integrative review of research studies by conducting a structured search in 5 databases completed on October 10, 2022. We applied Whittemore & Knafl's methodology to identify literature, extract, and synthesize information, iteratively. We adapted Kmet et al appraisal tool for the quality assessment of the evidence. The research protocol was registered with PROSPERO (CRD42020203998). RESULTS: Eleven studies met the inclusion criteria. The relationship between 1 or more UI features and 1 or more DQ indicators was examined. UI features were classified into 4 categories: 3 types of data capture aids, and other methods of DQ assessment at the UI. The Weiskopf et al measures were used to assess DQ: completeness (n = 10), correctness (n = 10), and currency (n = 3). UI features such as mandatory fields, templates, and contextual autocomplete improved completeness or correctness or both. Measures of currency were scarce. DISCUSSION: The paucity of studies on UI features and DQ underscored the limited knowledge in this important area. The UI features examined had both positive and negative effects on DQ. Standardization of data entry and further development of automated algorithmic aids, including adaptive UIs, have great promise for improving DQ. Further research is essential to ensure data captured in our electronic systems are high quality and valid for use in clinical decision-making and other secondary analyses.

Health Level 7®'s (HL7) Fast Healthcare Interoperability Resources® (FHIR®) is leading new efforts to make data available to healthcare clinicians, administrators, and leaders. Standardized nursing terminologies were developed to enable nursing's voice and perspective to be visible within the healthcare data ecosystem. The use of these SNTs has been shown to improve care quality and outcomes, and to provide data for knowledge discovery. The role of SNTs in describing assessments and interventions and measuring outcomes is unique in health care, and synergistic with the purpose and goals of FHIR. FHIR acknowledges nursing as a discipline of interest and yet the use of SNTs within the FHIR ecosystem is rare. The purpose of this article is to describe FHIR, SNTs, and the potential for synergy in the use of SNTs with FHIR. Toward improving understanding how FHIR works to transport and store knowledge and how SNTs work to convey meaning, we provide a framework and examples of SNTs and their coding for use within FHIR solutions. Finally, we offer recommendations for the next steps to advance FHIR-SNT collaboration. Such collaboration will advance both nursing specifically and health care in general, and most importantly, improve population health.

Journal Article Standardized nursing terminologies come of age: advancing quality of care, population health, and health equity across the care continuum Get access Karen A Monsen, PhD, RN, FAMIA, FNAP, FAAN, Karen A Monsen, PhD, RN, FAMIA, FNAP, FAAN School of Nursing, University of Minnesota, Minneapolis, MN, United States Corresponding author: Karen A. Monsen, PhD, RN, FAMIA, FNAP, FAAN, School of Nursing, University of Minnesota, 5-140 Weaver-Densford Hall, 308 Harvard Street SE, Minneapolis, MN 55455 ([email protected]) https://orcid.org/0000-0003-0196-9799 Search for other works by this author on: Oxford Academic PubMed Google Scholar Laura Heermann Langford, PhD, RN, FAMIA, FHL7, Laura Heermann Langford, PhD, RN, FAMIA, FHL7 Logica, Salt Lake City, UT, United States Search for other works by this author on: Oxford Academic PubMed Google Scholar Suzanne Bakken, PhD, MS, BSN, FAAN, FACMI, FIAHSI, Suzanne Bakken, PhD, MS, BSN, FAAN, FACMI, FIAHSI Columbia University, New York, NY, United States https://orcid.org/0000-0001-6202-6001 Search for other works by this author on: Oxford Academic PubMed Google Scholar Karen Dunn Lopez, PhD, MPH, RN, FAAN Karen Dunn Lopez, PhD, MPH, RN, FAAN College of Nursing, University of Iowa, Iowa City, IA, United States Search for other works by this author on: Oxford Academic PubMed Google Scholar Journal of the American Medical Informatics Association, Volume 30, Issue 11, November 2023, Pages 1757–1759, https://doi.org/10.1093/jamia/ocad173 Published: 19 October 2023 Article history Editorial decision: 14 August 2023 Received: 14 August 2023 Published: 19 October 2023

Current electronic health records (EHRs) are often ineffective in identifying patient priorities and care needs requiring nurses to search a large volume of text to find clinically meaningful information. Our study, part of a larger randomized controlled trial testing nursing care planning clinical decision support coded in standardized nursing languages, focuses on identifying format preferences after random assignment and interaction to 1 of 3 formats (text only, text+table, text+graph). Being assigned to the text+graph significantly increased the preference for graph (P = .02) relative to other groups. Being assigned to the text only (P = .06) and text+table (P = .35) was not significantly associated with preference for their assigned formats. Additionally, the preference for graphs was not significantly associated with understanding graph content (P = .19). Further studies are needed to enhance our understanding of how format preferences influence the use and processing of displayed information.

BACKGROUND: Improved health among older women remains elusive and may be linked to limited knowledge of and interventions targeted to population subgroups. Use of structured community nurse home visit data exploring relationships between client outcomes, phenotypes, and targeted intervention approaches may reveal new understandings of practice effectiveness. MATERIALS AND METHODS: Omaha System data of 2363 women 65 years and older with circulation problems receiving at least 2 community nurse home visits were accessed. Previously identified phenotypes (Poor circulation; Irregular heart rate; and Limited symptoms), 7 intervention approaches (High-Surveillance; High-Teaching/Guidance/Counseling; Balanced-All; Balanced-Surveillance-Teaching/Guidance/Counseling; Low-Teaching/Guidance/Counseling-Balanced Other; Low-Surveillance-Mostly-Teaching/Guidance/Couseling-TreatmentProcedure-CaseManagement; and Mostly-TreatementProcedure+CaseManagement), and client knowledge, behavior, and status outcomes were used. Client-linked intervention approach counts, proportional use per phenotypes, and associations with client outcome scores were descriptively analyzed. Associations between intervention approach proportional use by phenotype and outcome scores were analyzed using parallel coordinate graph methodology for intervention approach effectiveness. RESULTS: Percent use of intervention approach differed significantly by phenotype. The 2 most widely employed intervention approaches were characterized by either a high use of surveillance interventions or a balanced use of all intervention categories (surveillance, teaching/guidance/counseling, treatment-procedure, case-management). Mean outcome discharge and change scores significantly differed by intervention approach. Proportionally deployed intervention approach patterns by phenotype were associated with outcome small effects improvement. DISCUSSIONS AND CONCLUSIONS: The Omaha System taxonomy supported the management and exploration of large multidimensional community nursing data of older women with circulation problems. This study offers a new way to examine intervention effectiveness using phenotype- and targeted intervention approach-informed structured data.

Providing 80% of healthcare worldwide, nurses focus on physiologic and psychosocial aspects of health, which incorporate social determinants of health (SDOH). Recognizing their important role in SDOH, nurse informatics scholars included standardized measurable terms that identify and treat issues with SDOH in their classification systems, which have been readily available for over 5 decades. In this Perspective, we assert these currently underutilized nursing classifications would add value to health outcomes and healthcare, and to the goal of decreasing disparities. To illustrate this, we mapped 3 rigorously developed and linked classifications: NANDA International (NANDA-I), Nursing Interventions Classification (NIC), and Nursing Outcomes Classification (NOC) called NNN (NANDA-I, NIC, NOC), to 5 Healthy People 2030 SDOH domains/objectives, revealing the comprehensiveness, usefulness, and value of these classifications. We found that all domains/objectives were addressed and NNN terms often mapped to multiple domains/objectives. Since SDOH, corresponding interventions and measurable outcomes are easily found in standardized nursing classifications (SNCs), more incorporation of SNCs into electronic health records should be occurring, and projects addressing SDOHs should integrate SNCs like NNN into their ongoing work.

CONTEXT: Over 20% of US adults report they experience pain on most days or every day. Uncontrolled pain has led to increased healthcare utilization, hospitalization, emergency visits, and financial burden. Recognizing, assessing, understanding, and treating pain using artificial intelligence (AI) approaches may improve patient outcomes and healthcare resource utilization. A comprehensive synthesis of the current use and outcomes of AI-based interventions focused on pain assessment and management will guide the development of future research. OBJECTIVES: This review aims to investigate the state of the research on AI-based interventions designed to improve pain assessment and management for adult patients. We also ascertain the actual outcomes of Al-based interventions for adult patients. METHODS: The electronic databases searched include Web of Science, CINAHL, PsycINFO, Cochrane CENTRAL, Scopus, IEEE Xplore, and ACM Digital Library. The search initially identified 6946 studies. After screening, 30 studies met the inclusion criteria. The Critical Appraisals Skills Programme was used to assess study quality. RESULTS: This review provides evidence that machine learning, data mining, and natural language processing were used to improve efficient pain recognition and pain assessment, analyze self-reported pain data, predict pain, and help clinicians and patients to manage chronic pain more effectively. CONCLUSIONS: Findings from this review suggest that using AI-based interventions has a positive effect on pain recognition, pain prediction, and pain self-management; however, most reports are only pilot studies. More pilot studies with physiological pain measures are required before these approaches are ready for large clinical trial.

OBJECTIVE: The study sought to synthesize published literature on direct care nurses' use of workarounds related to the electronic health record. MATERIALS AND METHODS: We conducted an integrative review of qualitative and quantitative peer-reviewed research through a structured search of Academic Search Complete, EBSCO Cumulative Index of Nursing and Allied Health Literature (CINAHL), Embase, Engineering Village, Ovid Medline, Scopus, and Web of Science. We systematically applied exclusion rules at the title, abstract, and full article stages and extracted and synthesized their research methods, workaround classifications, and probable causes from articles meeting inclusion criteria. RESULTS: Our search yielded 5221 results. After removing duplicates and applying rules, 33 results met inclusion criteria. A total of 22 articles used qualitative approaches, 10 used mixed methods, and 1 used quantitative methods. While researchers may classify workarounds differently, they generally fit 1 of 3 broad categories: omission of process steps, steps performed out of sequence, and unauthorized process steps. Each study identified probable causes, which included technology, task, organizational, patient, environmental, and usability factors. CONCLUSIONS: Extensive study of nurse workarounds in acute settings highlights the gap in ambulatory care research. Despite decades of electronic health record development, poor usability remains a key concern for nurses and other members of care team. The widespread use of workarounds by the largest group of healthcare providers subverts quality health care at every level of the healthcare system. Research is needed to explore the gaps in our understanding of and identify strategies to reduce workaround behaviors.

Sabita Acharya, Barbara Di Eugenio, Andrew Boyd, Richard Cameron, Karen Dunn Lopez, Pamela Martyn-Nemeth, Debaleena Chattopadhyay, Pantea Habibi, Carolyn Dickens, Haleh Vatani, Amer Ardati. Proceedings of the 20th Annual SIGdial Meeting on Discourse and Dialogue. 2019.

OBJECTIVE: The study sought to present the findings of a systematic review of studies involving secondary analyses of data coded with standardized nursing terminologies (SNTs) retrieved from electronic health records (EHRs). MATERIALS AND METHODS: We identified studies that performed secondary analysis of SNT-coded nursing EHR data from PubMed, CINAHL, and Google Scholar. We screened 2570 unique records and identified 44 articles of interest. We extracted research questions, nursing terminologies, sample characteristics, variables, and statistical techniques used from these articles. An adapted STROBE (Strengthening The Reporting of OBservational Studies in Epidemiology) Statement checklist for observational studies was used for reproducibility assessment. RESULTS: Forty-four articles were identified. Their study foci were grouped into 3 categories: (1) potential uses of SNT-coded nursing data or challenges associated with this type of data (feasibility of standardizing nursing data), (2) analysis of SNT-coded nursing data to describe the characteristics of nursing care (characterization of nursing care), and (3) analysis of SNT-coded nursing data to understand the impact or effectiveness of nursing care (impact of nursing care). The analytical techniques varied including bivariate analysis, data mining, and predictive modeling. DISCUSSION: SNT-coded nursing data extracted from EHRs is useful in characterizing nursing practice and offers the potential for demonstrating its impact on patient outcomes. CONCLUSIONS: Our study provides evidence of the value of SNT-coded nursing data in EHRs. Future studies are needed to identify additional useful methods of analyzing SNT-coded nursing data and to combine nursing data with other data elements in EHRs to fully characterize the patient's health care experience.

Objective: To report on the state of the science of clinical decision support (CDS) for hospital bedside nurses. Materials and Methods: We performed an integrative review of qualitative and quantitative peer-reviewed original research studies using a structured search of PubMed, Embase, Cumulative Index to Nursing and Applied Health Literature (CINAHL), Scopus, Web of Science, and IEEE Xplore (Institute of Electrical and Electronics Engineers Xplore Digital Library). We included articles that reported on CDS targeting bedside nurses and excluded in stages based on rules for titles, abstracts, and full articles. We extracted research design and methods, CDS purpose, electronic health record integration, usability, and process and patient outcomes. Results: Our search yielded 3157 articles. After removing duplicates and applying exclusion rules, 28 articles met the inclusion criteria. The majority of studies were single-site, descriptive or qualitative (43%) or quasi-experimental (36%). There was only 1 randomized controlled trial. The purpose of most CDS was to support diagnostic decision-making (36%), guideline adherence (32%), medication management (29%), and situational awareness (25%). All the studies that included process outcomes (7) and usability outcomes (4) and also had analytic procedures to detect changes in outcomes demonstrated statistically significant improvements. Three of 4 studies that included patient outcomes and also had analytic procedures to detect change showed statistically significant improvements. No negative effects of CDS were found on process, usability, or patient outcomes. Discussion and Conclusions: Clinical support systems targeting bedside nurses have positive effects on outcomes and hold promise for improving care quality; however, this research is lagging behind studies of CDS targeting medical decision-making in both volume and level of evidence.

Our system generates summaries of hospital stays by combining information from two heterogenous sources: physician discharge notes and nursing plans of care.It extracts medical concepts from both sources; concepts that are identified as "complex" by our metric are explained by providing definitions obtained from three external knowledge sources.Finally, relevant concepts (with or without definition) are realized by SimpleNLG.

OBJECTIVE: To examine information flow, a vital component of a patient's care and outcomes, in a sample of multiple hospital nursing units to uncover potential sources of error and opportunities for systematic improvement. DESIGN: This was a qualitative study of a sample of eight medical-surgical nursing units from four diverse hospitals in one US state. We conducted direct work observations of nursing staff's communication patterns for entire shifts (8 or 12 h) for a total of 200 h and gathered related documentation artifacts for analyses. Data were coded using qualitative content analysis procedures and then synthesized and organized thematically to characterize current practices. RESULTS: Three major themes emerged from the analyses, which represent serious vulnerabilities in the flow of patient care information during nurse hand-offs and to the entire interdisciplinary team across time and settings. The three themes are: (1) variation in nurse documentation and communication; (2) the absence of a centralized care overview in the patient's electronic health record, ie, easily accessible by the entire care team; and (3) rarity of interdisciplinary communication. CONCLUSION: The care information flow vulnerabilities are a catalyst for multiple types of serious and undetectable clinical errors. We have two major recommendations to address the gaps: (1) to standardize the format, content, and words used to document core information, such as the plan of care, and make this easily accessible to all team members; (2) to conduct extensive usability testing to ensure that tools in the electronic health record help the disconnected interdisciplinary team members to maintain a shared understanding of the patient's plan.

OBJECTIVE: To identify factors in the nursing work domain that contribute to the problem of inpatient falls, aside from patient risk, using cognitive work analysis. DESIGN: A mix of qualitative and quantitative methods were used to identify work constraints imposed on nurses, which may underlie patient falls. MEASUREMENTS: Data collection was done on a neurology unit staffed by 27 registered nurses and utilized field observations, focus groups, time-motion studies and written surveys (AHRQ Hospital Survey on Patient Culture, NASA-TLX, and custom Nursing Knowledge of Fall Prevention Subscale). RESULTS: Four major constraints were identified that inhibit nurses' ability to prevent patient falls. All constraints relate to work processes and the physical work environment, opposed to safety culture or nursing knowledge, as currently emphasized. The constraints were: cognitive 'head data', temporal workload, inconsistencies in written and verbal transfer of patient data, and limitations in the physical environment. To deal with these constraints, the nurses tend to employ four workarounds: written and mental chunking schemas, bed alarms, informal querying of the previous care nurse, and informal video and audio surveillance. These workarounds reflect systemic design flaws and may only be minimally effective in decreasing risk to patients. CONCLUSION: Cognitive engineering techniques helped identify seemingly hidden constraints in the work domain that impact the problem of patient falls. System redesign strategies aimed at improving work processes and environmental limitations hold promise for decreasing the incidence of falls in inpatient nursing units.