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Establish linkages between components of the Self- and Family Management Framework and outcomes of the Nursing Outcomes Classification to evaluate the comprehensiveness of outcomes addressing self- and family management in the Nursing Outcomes Classification.Descriptive study.Experts conducted a six-step process to establish linkages: (1) preliminary mapping of all relevant nursing outcomes to the framework; (2) development of checklists for team members serving as 'identifiers' and 'reviewers'; (3) mapping all relevant nursing outcomes to the framework; (4) final agreement on mapped outcomes; (5) establishment of inter-rater reliability; and (6) discussion of findings with authors of the Self- and Family Management Framework.Three hundred and sixty-three nursing outcomes were identified as related to the management of chronic disease across all components of the framework: outcomes related to patient self-management (n = 336), family functioning (n = 16) and family caregivers (n = 11).The Nursing Outcomes Classification outcomes comprehensively address self-management, and, less so, family functioning, and caregivers.Established linkages can be used by nurses to track and support patient and family management outcomes across the care continuum.Linking standardized nursing outcomes to the Self- and Family Management Framework can assist in goal setting and measurement of nursing care during chronic disease management. This work can help describe to funders, policy makers and others invested in health care reform the specific contributions of nurses to self- and family management of chronic disease.This paper demonstrates the linkages between components of the Self- and Family Management Framework and Nursing Outcomes Classification outcomes. The results of this study offer the opportunity to quantify the impact of nursing care and enhance nursing practice for patients with chronic conditions as well as contribute to developing Nursing Outcomes Classification outcomes that consider self-management processes.

AIMS: To identify clusters of risk factors in home health care and determine if the clusters are associated with hospitalizations or emergency department visits. DESIGN: A retrospective cohort study. METHODS: This study included 61,454 patients pertaining to 79,079 episodes receiving home health care between 2015 and 2017 from one of the largest home health care organizations in the United States. Potential risk factors were extracted from structured data and unstructured clinical notes analysed by natural language processing. A K-means cluster analysis was conducted. Kaplan–Meier analysis was conducted to identify the association between clusters and hospitalizations or emergency department visits during home health care. RESULTS: A total of 11.6% of home health episodes resulted in hospitalizations or emergency department visits. Risk factors formed three clusters. Cluster 1 is characterized by a combination of risk factors related to “impaired physical comfort with pain,” defined as situations where patients may experience increased pain. Cluster 2 is characterized by “high comorbidity burden” defined as multiple comorbidities or other risks for hospitalization (e.g., prior falls). Cluster 3 is characterized by “impaired cognitive/psychological and skin integrity” including dementia or skin ulcer. Compared to Cluster 1, the risk of hospitalizations or emergency department visits increased by 1.95 times for Cluster 2 and by 2.12 times for Cluster 3 (all p < .001). CONCLUSION: Risk factors were clustered into three types describing distinct characteristics for hospitalizations or emergency department visits. Different combinations of risk factors affected the likelihood of these negative outcomes. IMPACT: Cluster-based risk prediction models could be integrated into early warning systems to identify patients at risk for hospitalizations or emergency department visits leading to more timely, patient-centred care, ultimately preventing these events. PATIENT OR PUBLIC CONTRIBUTION: There was no involvement of patients in developing the research question, determining the outcome measures, or implementing the study.

Patients with heart failure (HF) who actively engage in their own self-management have better outcomes. Extracting data through natural language processing (NLP) holds great promise for identifying patients with or at risk of poor self-management.To identify home health care (HHC) patients with HF who have poor self-management using NLP of narrative notes, and to examine patient factors associated with poor self-management.An NLP algorithm was applied to extract poor self-management documentation using 353,718 HHC narrative notes of 9,710 patients with HF. Sociodemographic and structured clinical data were incorporated into multivariate logistic regression models to identify factors associated with poor self-management.There were 758 (7.8%) patients in this sample identified as having notes with language describing poor HF self-management. Younger age (OR 0.982, 95% CI 0.976-0.987, p .001), longer length of stay in HHC (OR 1.036, 95% CI 1.029- 1.043, p .001), diagnosis of diabetes (OR 1.47, 95% CI 1.3-1.67, p .001) and depression (OR 1.36, 95% CI 1.09-1.68, p .01), impaired decision-making (OR 1.64, 95% CI 1.37-1.95, p .001), smoking (OR 1.7, 95% CI 1.4-2.04, p .001), and shortness of breath with exertion (OR 1.25, 95% CI 1.1-1.42, p .01) were associated with poor self-management.Patients with HF who have poor self-management can be identified from the narrative notes in HHC using novel NLP methods. Meaningful information about the self-management of patients with HF can support HHC clinicians in developing individualized care plans to improve self-management and clinical outcomes.

BACKGROUND: About one in five patients receiving home health care (HHC) services are hospitalized or visit an emergency department (ED) during a home care episode. Early identification of patients at risk can prevent these negative outcomes. However, risk indicators, including language in clinical notes that indicate a concern about a patient, are often hidden in narrative documentation throughout their HHC episode. OBJECTIVE: To develop an automated natural language processing (NLP) algorithm to identify concerning language indicative of HHC patient risk of hospitalizations or ED visits. METHODS: This study used the Omaha System—a standardized nursing terminology that describes problems/signs/symptoms that can occur in the community setting. First, five HHC experts iteratively reviewed the Omaha System and identified concerning concepts indicative of HHC patient risk of hospitalizations or ED visits. Next, we developed and tested an NLP algorithm to identify these concerning concepts in HHC clinical notes automatically. The resulting NLP algorithm was applied on a large subset of narrative notes (2.3 million notes) documented for 66,317 unique patients (n = 87,966 HHC episodes) admitted to one large HHC agency in the Northeast United States between 2015 and 2017. RESULTS: A total of 160 Omaha System signs/symptoms were identified as concerning concepts for hospitalizations or ED visits in HHC. These signs/symptoms belong to 31 of the 42 available Omaha System problems. Overall, the NLP algorithm showed good performance in identifying concerning concepts in clinical notes. More than 18% of clinical notes were detected as having at least one concerning concept, and more than 90% of HHC episodes included at least one Omaha System problem. The most frequently documented concerning concepts were pain, followed by issues related to neuro-musculoskeletal function, circulation, mental health, and communicable/infectious conditions. CONCLUSION: Our findings suggest that concerning problems or symptoms that could increase the risk of hospitalization or ED visit were frequently documented in narrative clinical notes. NLP can automatically extract information from narrative clinical notes to improve our understanding of care needs in HHC. Next steps are to evaluate which concerning concepts identified in clinical notes predict hospitalization or ED visit.

The purpose of this integrative review is to identify literature describing (a) subgrouping patients with cancer based on symptom experiences and their patterns of symptom changes over time and (b) methodologies of subgrouping patients with cancer based on symptom experiences.PubMed®, CINAHL®, and PsycINFO® were searched through January 2019.Studies were appraised for patterns of symptom change over time and methodologic approach using the QualSyst quality rating scale.11 studies met inclusion criteria. Clinical variables that influence symptom patterns were diverse. The most common clustering method was latent variable analysis (73%), and all studies collected symptom data prospectively using survey analysis to assess symptoms.The majority of studies (91%) observed that the symptom experience within the group of patients with cancer changed over time. Identifying groups of patients with similar symptom experiences is useful to determine which patients need more intensive symptom management over the trajectory of cancer treatment, which is essential to improve symptoms and quality of life.

Palliative and end-of-life care has been pushed to the forefront of medical care during the pandemic caused by the coronavirus-2019 (COVID-19). Palliative care organizations have responded to the growing demand for the rapid dissemination of research, clinical guidance, and instructions for care to clinicians, patients with COVID-19, and their caregivers by creating COVID-19 resource Web pages. Here, end users can access resources that can be updated in real time. These Web pages, however, can be variable in what resources they offer and for whom they are designed for (clinicians, patients, caregivers). Therefore, this project was conducted to consolidate these resources via summary tables of specific contents available through each Web page grouped by palliative care domains (eg, care discussion and planning, communication, symptom management, care access) and to identify the target audience. This environmental scan was conducted by compiling a comprehensive list of COVID-19 resource Web pages of palliative care organizations generated by reviewing previously published research studies and consulting with palliative care research experts. Snowballing techniques were used to identify resource Web pages not captured in the initial scan. Two reviewers independently evaluated eligible Web pages for content via a form developed for the study, and Cohen κ statistic was calculated to ensure interrater reliability. The final κ statistic was 0.76. Of the 24 websites screened, 15 websites met our eligibility criteria. Among the eligible resource Web pages, most (n = 12, 80%) had specific target audiences and care settings, whereas the rest presented information targeted to all audiences. Although 11 Web pages offered resources that addressed all 4 domains, only 1 Web page conveyed all 12 subdomains. We recommend the use of this guide to all frontline clinicians who require guidance in clinically managing patients with COVID-19 receiving palliative care and/or end-of-life care.

To maintain their quality of life and avoid hospitalization and early mortality, patients with heart failure must recognize and respond to symptoms of exacerbation. A promising method for engaging patients in their self-care is through mobile health applications (mHealth apps). However, for mHealth to have its greatest chance for improving patient outcomes, the app content must be readable, provide useful functions and be based in evidence. The study aimed to determine: (1) readability, (2) types of functions, and (3) linkage to authoritative sources of evidence for self-care focused mHealth apps targeting heart failure patients that are available in the Apple and Google Play Stores. We systematically searched for mHealth apps targeting patients with heart failure in the Apple and Google Play Stores and applied selection criteria. Readability of randomly selected informational paragraphs were determined using Flesch-Kincaid grade level test tool in Microsoft Word. Ten mHealth apps met our criteria. Only one had a reading grade level at or below the recommended 6th grade reading level (average 9.35). The most common functions were tracking, clinical data feedback, and non-data-based reminders and alerts. Only three had statements that clearly linked the mHealth app content to trustworthy, evidence-based sources. Only two had interoperability with the electronic health record and only one had a communication feature with clinicians. Future mHealth designs that are tailored to patients' literacy level and have advanced functions may hold greater potential for improving patient outcomes.

The goal of this natural language processing (NLP) study was to identify patients in home healthcare with heart failure symptoms and poor self-management (SM). The preliminary lists of symptoms and poor SM status were identified, NLP algorithms were used to refine the lists, and NLP performance was evaluated using 2.3 million home healthcare clinical notes. The overall precision to identify patients with heart failure symptoms and poor SM status was 0.86. The feasibility of methods was demonstrated to identify patients with heart failure symptoms and poor SM documented in home healthcare notes. This study facilitates utilizing key symptom information and patients' SM status from unstructured data in electronic health records. The results of this study can be applied to better individualize symptom management to support heart failure patients' quality-of-life.

BACKGROUND/OBJECTIVE: Between 10 and 25% patients are hospitalized or visit emergency department (ED) during home healthcare (HHC). Given that up to 40% of these negative clinical outcomes are preventable, early and accurate prediction of hospitalization risk can be one strategy to prevent them. In recent years, machine learning-based predictive modeling has become widely used for building risk models. This study aimed to compare the predictive performance of four risk models built with various data sources for hospitalization and ED visits in HHC. METHODS: Four risk models were built using different variables from two data sources: structured data (i.e., Outcome and Assessment Information Set (OASIS) and other assessment items from the electronic health record (EHR)) and unstructured narrative-free text clinical notes for patients who received HHC services from the largest non-profit HHC organization in New York between 2015 and 2017. Then, five machine learning algorithms (logistic regression, Random Forest, Bayesian network, support vector machine (SVM), and Naïve Bayes) were used on each risk model. Risk model performance was evaluated using the F-score and Precision-Recall Curve (PRC) area metrics. RESULTS: During the study period, 8373/86,823 (9.6%) HHC episodes resulted in hospitalization or ED visits. Among five machine learning algorithms on each model, the SVM showed the highest F-score (0.82), while the Random Forest showed the highest PRC area (0.864). Adding information extracted from clinical notes significantly improved the risk prediction ability by up to 16.6% in F-score and 17.8% in PRC. CONCLUSION: All models showed relatively good hospitalization or ED visit risk predictive performance in HHC. Information from clinical notes integrated with the structured data improved the ability to identify patients at risk for these emergent care events.

Background: Physician Orders for Life-Sustaining Treatments (POLST) is an advance care planning (ACP) tool that is designed to facilitate End-of-Life (EoL) care discussions between a medical provider and a terminally ill patient. It is often used as a tool to translate care wishes into a medical order, which can be honored across healthcare settings. With an increased utilization of the POLST paradigm in various healthcare settings along with continued dissemination across the nation, it is critical to examine whether documented wishes on POLST are concordant with subsequent care delivered. Purpose of this article was to examine concordance rate between POLST and subsequent care delivered in any care settings and communities. Design: Systematic review. Results: Of 1,406 articles identified, 10 articles met inclusion criteria. Together, included studies represent 5,688 POLST forms reviewed from individuals residing in a total of 126 nursing care facilities, 9 elderly care centers, 4 community settings, and 2 hospitals. Preference for cardiopulmonary resuscitation and actual delivery/ withholding of resuscitation was the most observed intervention in study of concordance (n = 8). It is also where highest concordance rate (97.5%) was reported. Seven studies compared care provided during EoL and the level of medical intervention requested on POLST forms (91.17% concordance). Preference to use artificial nutrition/ hydration, and actual delivery was 93.0% (n = 4 studies), and antibiotics use preference and delivery was 96.5% (reported in 4 studies). Conclusion: Published literature evidence suggests that EoL care wishes documented on POLST forms were largely concordant with subsequent care delivered. Additional research is needed to evaluate concordance between POLST documentation and care received among POLST users, who experienced multiple care transitions across healthcare settings, or across state during EoL care journey.

Objective: Knowledge of one’s gene status for adult onset conditions provides opportunity to make advance end-of-life (EOL) plans. The purposes of these analyses were to (1) determine the prevalence of EOL plans, including advance directives (ADs) among persons across 3 stages of Huntington disease (HD) and (2) examine factors associated with having ADs in this sample. Methods: Data are from 503 participants in the HD Quality of Life study. Participants completed an online health-related quality-of-life survey that included questions regarding EOL planning and self-reported HD symptoms. Frequencies were calculated for EOL planning by the HD stage. Bivariate analysis and logistic regression were used to identify variables associated with having ADs. Results: A total of 38.2% of participants stated they had ADs and fewer than half had other EOL plans. Being older, increased HD stage, more years of education, lower anxiety, more swallowing symptoms, and higher meaning and purpose were associated with having ADs. Conclusion: The prevalence of ADs in our sample is comparable to the general US population, but surprisingly low, considering the severity and long disease course of HD. Practice Implications: Health-care providers should develop specific interventions early in the disease process to increase ADs in this population.

The purpose of this integrative review is to synthesize recent literature that used NANDA International diagnoses, Nursing Interventions Classification (NIC), and Nursing Outcomes Classification (NOC) to determine the effectiveness of nursing interventions and cost-analysis and to identify the direction for future effectiveness research using standardized nursing terminologies (SNTs). A search was performed using the Cumulative Index to Nursing and Allied Health Literature, Scopus, and KoreaMed, covering the period from 2003 to 2018. A total 267 articles were identified, and 24 articles were analyzed for this review. Eighteen studies evaluated the effectiveness of nursing interventions based on outcomes, and of those 18 studies, four examined the effectiveness based on the development of NNN linkages. Six studies analyzed the cost of nursing interventions. Integrating SNTs into electronic health records (EHRs), developing NNN linkages, and further effectiveness studies using SNTs are required to determine the value of nursing care to improve patient outcomes.

130 Background: Cancer-related symptoms are associated with decreased quality of life, increased health care utilization, and shorter life expectancy. There is limited understanding of how multiple chronic conditions (MCC) contribute to variability in symptoms experienced in the context of cancer. Data mining the EHR will allow us to use real clinical data to identify multimorbidity phenotypes based on the clinical similarity of patients. Purpose of this study is to identify distinct subgroups of patients based on the MCC and cancer diagnoses and describe differences across these subgroups. Methods: EHR data was extracted from adult patients (n=2977) newly diagnosed with cancer in 2017 at one academic medical center. The SEER cancer site/histology list was used to group cancer diagnosis. MCC present for >6 months on the problem list or ICD-10 billing data were used. K-Means and K-Modes clustering procedures, with K equaling 7, were used to cluster patients based on MCC. Results: The sample consisted of 58% women, 93% white, with mean age of 62.4 (16.1) years. The most frequent cancers were GI (17%), gynecological (14%), and pulmonary (10%). The most frequent MCC were hypertension (33%), anemia (24%), and metabolic diseases (21%). Seven clusters correspond to following primary cancer sites: GI, pulmonary, urinary, gynecological, breast, endocrine, and skin. The MCC rates varied significantly across different primary sites with hypertension being present in call clusters, but anemia was present only in GI and urinary system cancers clusters. Conclusions: K-Means and K-Modes clustering procedures, with K equaling 7, produced similar clusters of cancer primary sites and MCCs, indicating our findings are stable and replicable. Our data extraction methods and clustering techniques worked well and can be expanded upon. Our next step is to repeat the data extraction and clustering analysis with the full data from the data warehouse (>30,000 records). The identified multimorbidity phenotypes will be used as inclusion criteria for prospective research with patients to explore the relationships among MCCs and symptoms in the context of cancer.

Purpose : To determine whether warming of irrigation fluids (32°C-40°C) compared with using room-temperature irrigation fluids (20°C-22°C) decreases the risk of perioperative hypothermia (

Purpose: This was a correlational study to identify effects of perception of clinical ladder system on job satisfaction and intention to leave in perioperative nurses. Methods: Participants were 154 of perioperative nurses from larger general hospitals in Seoul. Data were collected from April, 16 to 22, 2013 using self-report questionnaires which included items on perception of clinical ladder system, job satisfaction, and intention to leave. Data were analyzed