Developing a Tool to Support Decisions on Patient Prioritization at Admission to Home Health Care

Background and aims: Millions of Americans are discharged from hospitals to home health every year and about third of them return to hospitals. A significant number of rehospitalizations (up to 60%) happen within the first two weeks of services. Early targeted allocation of services for patients who need them the most, have the potential to decrease readmissions. Unfortunately, there is only fragmented evidence on factors that should be used to identify high-risk patients in home health. This dissertation study aimed to (1) identify factors associated with priority for the first home health nursing visit and (2) to construct and validate a decision support tool for patient prioritization. I recruited a geographically diverse convenience sample of nurses with expertise in care transitions and care coordination to identify factors supporting home health care prioritization. Methods: This was a predictive study of home health visit priority decisions made by 20 nurses for 519 older adults referred to home health. Variables included sociodemographics, diagnosis, comorbid conditions, adverse events, medications, hospitalization in last 6 months, length of stay, learning ability, self-rated health, depression, functional status, living arrangement, caregiver availability and ability and first home health visit priority decision. A combination of data mining and logistic regression models was used to construct and validate the final model. Results: The final model identified five factors associated with first home health visit priority. A cutpoint for decisions on low/medium versus high priority was derived with a sensitivity of 80% and specificity of 57.9%, area under receiver operator curve (ROC) 75.9%. Nurses were more likely to prioritize patients who had wounds (odds ratio [OR]=1.88), comorbid condition of depression (OR=1.73), limitation in current toileting status (OR= 2.02), higher numbers of medications (increase in OR for each medication =1.04) and comorbid conditions (increase in OR for each condition =1.04). Discussion: This dissertation study developed one of the first clinical decision support tools for home health, the PREVENT - Priority for Home Health Visit Tool. Further work is needed to increase the specificity and generalizability of the tool and to test its effects on patient outcomes

Qualitative Analysis of Naturalistic Decision Making in Adults with Chronic Heart Failure

Background: Self-care of heart failure has been described as a naturalistic decision-making process, but the data available to defend this description are anecdotal. Objectives: The aim of this study was to explore the process used by adults with chronic heart failure to make decisions about their symptoms. Methods: This was a secondary analysis of data obtained from four mixed methods studies. The full data set held qualitative data on 120 adults over the age of 18 years. For this analysis, maximum variation sampling was used to purposively select a subset of 36 of the qualitative interviews to reanalyze. Results: In this sample, equally distributed by gender, 56% Caucasian, between 40 and 98 years, the overarching theme was that decisions about self-care reflect a naturalistic decision-making process with components of situation awareness with mental simulation of a plausible course of action and an evaluation of the outcome of the action. In addition to situation awareness and mental simulation, three key factors were identified as influencing self-care decision making: (a) experience; (b) decision characteristics such as uncertainty, ambiguity, high stakes, urgency, illness, and involvement of others in the decision-making process; and (c) personal goals. Discussion: These results support naturalistic decision making as the process used by this sample of adults with heart failure to make decisions about self-care

Using Growth Mixture Modeling to Identify Classes of Sodium Adherence in Adults with Heart Failure

BACKGROUND: The prevention of fluid retention is important to reduce hospitalizations in patients with heart failure (HF). Following a low-sodium diet helps to reduce fluid retention. OBJECTIVE: The primary objective of this study was to use growth mixture modeling to identify distinct classes of sodium adherence-characterized by shared growth trajectories of objectively measured dietary sodium. The secondary objective was to identify patient-level determinants of the nonadherent trajectory. METHODS: This was a secondary analysis of data collected from a prospective longitudinal study of 279 community-dwelling adults with previously or currently symptomatic HF. Growth mixture modeling was used to identify distinct trajectories of change in 24-hour urinary sodium excretion measured at 3 time points over 6 months. Logistic modeling was used to predict membership in observed trajectories. RESULTS: The sample was predominantly male (64%), had a mean age of 62 years, was functionally compromised (59% New York Heart Association class III), and had nonischemic HF etiology. Two distinct trajectories of sodium intake were identified and labeled adherent (66%) and nonadherent (34%) to low-sodium diet recommendations. Three predictors of the nonadherent trajectory were identified, confirming our previous mixed-effect analysis. Compared with being normal weight (body mass index/m2), being overweight and obese was associated with a 4-fold incremental increase in the likelihood of being in the nonadherent trajectory (odds ratio [OR], 4.63; 95% confidence interval [CI], 1.66-12.91; P \u3c .002). Being younger than 65 years (OR, 4.66; 95% CI, 1.04-20.81; P = .044) or having diabetes (OR, 4.15; 95% CI, 1.29-13.40; P = .016) were both associated with more than 4 times the odds of being in the nonadherent urine sodium trajectory compared with being older than 65 years or not having diabetes, respectively. CONCLUSIONS: Two distinct trajectories of sodium intake were identified in patients with HF. The nonadherent trajectory was characterized by an elevated pattern of dietary sodium intake shown by others to be associated with adverse outcomes in HF. Predictors of the nonadherent trajectory included higher body mass index, younger age, and diabetes

Identifying Predictors of High Sodium Excretion in Patients with Heart Failure: A Mixed Effect Analysis of Longitudinal Data

BACKGROUND: A low-sodium diet is a core component of heart failure self-care but patients have difficulty following the diet. AIM: The aim of this study was to identify predictors of higher than recommended sodium excretion among patients with heart failure. METHODS: The World Health Organization Five Dimensions of Adherence model was used to guide analysis of existing data collected from a prospective, longitudinal study of 280 community-dwelling adults with previously or currently symptomatic heart failure. Sodium excretion was measured objectively using 24-hour urine sodium measured at three time points over six months. A mixed effect logistic model identified predictors of higher than recommended sodium excretion. RESULTS: The adjusted odds of higher sodium excretion were 2.90, (95% confidence interval (CI): 1.15-4.25, pp=0.007) for patients with diabetes; and 2.22 (95% CI: 1.09-4.53, p=0.028) for patients who were cognitively intact. CONCLUSION: Three factors were associated with excess sodium excretion and two factors, obesity and diabetes, are modifiable by changing dietary food patterns

Nursing informatics competencies for emerging professionals: International leaders panel

© 2016 IMIA and IOS Press. To achieve a cursory review of the competencies necessary for acquire a successful career in a competitive job market, the panel will bring together leaders from renowned academic, successful health corporations, and international leaders in nursing informatics to the table for discussion, dialogue, and make recommendations. Panelists will reflect on their experiences within the different types of informatics organizations and present some of the current challenges when educating skillful professionals. The panel will provide personal experiences, thoughts, and advice on the competencies development in nursing informatics from their lens

Forecasting Informatics Competencies for Nurses in the Future of Connected Health

The IMIA-NIstudents’ and emerging professionals’ working group conducted a large international survey in 2015 regarding research trends in nursing informatics. The survey was translated into half-a-dozen languages and distributed through 18 international research collaborators’ professional connections. The survey focused on the perspectives of nurse informaticians. A total of 272 participants responded to an open ended question concerning recommendations to advance nursing informatics. Five key areas for action were identified through our thematic content analysis: education, research, practice, visibility and collaboration. This chapter discusses these results with implications for nursing competency development. We propose how components of various competency lists might support the key areas for action. We also identify room to further develop existing competency guidelines to support in-service education for practicing nurses, promote nursing informatics visibility, or improve and facilitate collaboration and integration with other professions. </p

Nursing Informatics 2018

The curriculum associated with nursing informatics (NI) education is not standardized, therefore the perspectives of new and emerging nurse informaticians is important. How these curricula differences affect career opportunities of new nursing informaticians, and in turn influenced current career choices will be explored. Synthesizing opinions with themes extracted from a 2014 international study—Advancing nursi informatics in the next decade: Recommendations from an international survey will be summarized.</p

Nurses and Midwives in the Digital Age

Technological development has enabled Artificial Intelligence (AI) to better support health care delivery and nursing. The need for nurses to be involved and steer the development and implementation of AI in health care is recognized. A 60-minute scientific debate is organized to explore if AI will replace nursing

Nursing Informatics 2018

In nursing, a community of practice have been recognized as an important construct to build capacity and support knowledge dissemination activities. The purpose of this poster is to use a community of practice framework to describe the collaborative work of an international nursing informatics, graduate student and emerging professional group.</p