This paper introduces a conceptual framework for investigating individual ability to navigate healthcare in the contexts of the built environment, social environment, and healthcare infrastructure in which a person is embedded. Given the complexity of healthcare delivery in the United States, consumers are expected to have an increasingly sophisticated set of skills in order to effectively navigate and benefit from the healthcare resources available to them. Addressing barriers to navigation in vulnerable populations may be essential to reducing health disparities. This paper builds on previous conceptual developments in the areas of healthcare use, navigation, and ecological perspectives on health in order to present a behavioral-ecological framework for examining healthcare navigation and access. The model posits that healthcare navigation is an ecologically informed process not only because of the spatial distribution of health services, but because of the spatial distribution of individual and environmental factors that influence decision-making and behavior with respect to service use. The paper discusses areas for further research on healthcare navigation, challenges for research, and implications for reducing health disparities.
Variations in patients' self-management knowledge, skills, and confidence as measured by the Patient Activation Measure (PAM) have been linked to variations in health behavior and outcomes. In a randomized trial, we tested two blood pressure (BP) control interventions, one grounded in activation principles. Study participants were Black home care patients (N = 587) with uncontrolled hypertension. This article examines intervention impacts on 12-month PAM score change, other predictors of PAM change, and associations between PAM change and BP outcomes. In multivariate models, the interventions did not significantly affect PAM change. Baseline characteristics associated with increased PAM were lower PAM score, higher income, higher health literacy, younger age, lower systolic BP, diabetes, and fewer medications. PAM increase was associated with a modest reduction in diastolic BP but not with improved systolic BP or BP control. Although studies suggest that increasing activation may lead to improved patient outcomes, this study did not find it to be so.
Keywords:Sepsis transitional care hospital discharge home healthcare readmission a b s t r a c t Objective: To profile the characteristics of growing numbers of sepsis survivors receiving home healthcare (HHC) by type of sepsis before, during, and after a sepsis hospitalization and identify characteristics significantly associated with 7-day readmission. Design: Cross-sectional descriptive study. Data sources included the Outcome and Assessment Information Set (OASIS) and Medicare administrative and claims data. Setting and Participants: National sample of Medicare beneficiaries hospitalized for sepsis who were discharged to HHC between July 1, 2013 and June 30, 2014 (N ¼ 165,228). Methods: We used an indicator distinguishing among 3 types of sepsis: explicitly coded sepsis diagnosis without organ dysfunction; severe sepsis with organ dysfunction; and septic shock. We compared these subgroups' demographic, clinical and functional characteristics, comorbidities, risk factors for rehospitalization, characteristics of the index hospital stay, and predicted 7-day hospital readmission. Results: The majority (80.7%) had severe sepsis, 5.7% had septic shock, and 13.6% had sepsis without acute organ system dysfunction. The medical diagnoses recorded at HHC admission identified sepsis or blood infection only 7% of the time, potentially creating difficulty identifying the sepsis survivor in HHC. Among sepsis types, septic shock survivors had the greatest illness burden profile. This study describes 12 key variables, each of which individually raises the relative 7-day readmission risk by as much as 60%. Increased risk of 7-day rehospitalization was found among those with septic shock, 3 or more previous inpatient stays, index hospital length of stay of >8 days, dyspnea, >6 functional dependencies, and other risk factors. Conclusions and Implications: Implications for practice include using our findings to identify sepsis survivors who are at risk for early readmission. Assessment for these factors may profile the at-risk patient, thereby triggering the call for additional acute care intervention such as delayed discharge, or post-acute intervention such as early home visit and outpatient follow-up.Ó 2019 AMDA e The Society for Post-Acute and Long-Term Care Medicine.Hospitals in the United States discharge over 1 million sepsis survivors annually. 1 Sepsis survivors are a population that experience substantial morbidity and mortality, with readmission rates rivaling or exceeding those for heart failure, pneumonia, and myocardial infarction. 2 Sepsis survivors are twice as likely to be readmitted by 30 days as nonsepsis patients, 3 with 32% of readmissions occurring within 7 days.
Background: About 30% of home health care patients are hospitalized or visit an emergency department (ED) during a home health care (HHC) episode. Novel data science methods are increasingly used to improve identification of patients at risk for negative outcomes. Objectives:To identify patients at heightened risk hospitalization or ED visits using HHC narrative data (clinical notes).Methods: This study used a large database of HHC visit notes (n = 727,676) documented for 112,237 HHC episodes (89,459 unique patients) by clinicians of the largest nonprofit home health care agency in the United States. Text mining and machine learning algorithms (Naïve Bayes, decision tree, random forest) were implemented to predict patient hospitalization or ED visits using the content of clinical notes. Risk factors associated with hospitalization or ED visits were identified using a feature selection technique (gain ratio attribute evaluation). Results:Best performing text mining method (random forest) achieved good predictive performance. Seven risk factors categories were identified, with clinical factors, coordination/ communication, and service use being the most frequent categories.Discussion: This study was the first to explore the potential contribution of HHC clinical notes to identifying patients at risk for hospitalization or an ED visit. Our results suggest that HHC visit notes are highly informative and can contribute significantly to identification of patients at risk. Further studies are needed to explore ways to improve risk prediction by adding more data elements from additional data sources. Keywordshome health care; natural language processing; nursing informatics; risk prediction; text mining Every year, more than 11,000 home health care (HHC) agencies across the United States provide care to more than 5 million older adults (MedPac, 2014). Currently, about one in three HHC patients are hospitalized or visit an emergency department (ED) during the 30-
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.