Developing and Testing a Two-Part Intervention: Enhancing Dementia Instruction and Tool in Home Hospice Care.

Family care partners (FCP) of persons living with dementia provide extensive care and experience significant stress. Hospice nurses and social workers seldom receive training to help FCP. This article describes the development and pilot testing of Enhancing Dementia Instruction and Tool in Home Hospice Care (EDITH-HC).

Building a Time-Series Model to Predict Hospitalization Risks in Home Health Care: Insights Into Development, Accuracy, and Fairness.

Objectives: Home health care (HHC) serves more than 5 million older adults annually in the United States, aiming to prevent unnecessary hospitalizations and emergency department (ED) visits. Despite efforts, up to 25% of patients in HHC experience these adverse events. The underutilization of clinical notes, aggregated data approaches, and potential demographic biases have limited previous HHC risk prediction models.

Decoding disparities: evaluating automatic speech recognition system performance in transcribing Black and White patient verbal communication with nurses in home healthcare.

Objectives: As artificial intelligence evolves, integrating speech processing into home healthcare (HHC) workflows is increasingly feasible. Audio-recorded communications enhance risk identification models, with automatic speech recognition (ASR) systems as a key component. This study evaluates the transcription accuracy and equity of 4 ASR systems-Amazon Web Services (AWS) General, AWS Medical, Whisper, and Wave2Vec-in transcribing patient-nurse communication in US HHC, focusing on their ability in accurate transcription of speech from Black and White English-speaking patients.

Developing a clinical decision support framework for integrating predictive models into routine nursing practices in home health care for patients with heart failure.

Background: The healthcare industry increasingly values high-quality and personalized care. Patients with heart failure (HF) receiving home health care (HHC) often experience hospitalizations due to worsening symptoms and comorbidities. Therefore, close symptom monitoring and timely intervention based on risk prediction could help HHC clinicians prevent emergency department (ED) visits and hospitalizations.

Randomized Trial of Self-Guided, Internet-Based, Cognitive Behavior Therapies for Migraine.

Background: Self-guided Internet-based cognitive behavior therapy (iCBT) for migraine interventions could improve access to care, but there is poor evidence of their efficacy.

Methods: A three-arm randomized controlled trial compared: iCBT focused on psychoeducation, self-monitoring and skills training (SPHERE), iCBT focused on identifying and managing personal headache triggers (PRISM) and a waitlist control. The primary treatment outcome was a ≥ 50% reduction in monthly headache days at 4 months post-randomization.

Exploring home healthcare clinicians' needs for using clinical decision support systems for early risk warning.

Objectives: To explore home healthcare (HHC) clinicians' needs for Clinical Decision Support Systems (CDSS) information delivery for early risk warning within HHC workflows.

Methods: Guided by the CDS "Five-Rights" framework, we conducted semi-structured interviews with multidisciplinary HHC clinicians from April 2023 to August 2023. We used deductive and inductive content analysis to investigate informants' responses regarding CDSS information delivery.

Towards a Crisis Management Playbook: Hospice and Palliative Team Members' Views Amid COVID-19.

Context: The critical role of hospice and palliative care in response to the COVID-19 pandemic is well recognized, but there is limited evidence to guide healthcare leadership through future crises.

Objectives: Our goal was to support future organizational resilience by exploring hospice and palliative team members' perspectives on crisis leadership during the COVID-19 pandemic in New York City (NYC).

Methods: This qualitative descriptive study used individual, semi-structured interviews of purposively sampled interdisciplinary team members.

Navigating a "Good Death" During COVID-19: Understanding Real-Time End-of-Life Care Structures, Processes, and Outcomes Through Clinical Notes.

Background And Objectives: The coronavirus disease 2019 (COVID-19) pandemic severely disrupted hospice care, yet there is little research regarding how widespread disruptions affected clinician and family decision-making. We aimed to understand how the pandemic affected structures, processes, and outcomes of end-of-life care.

Research Design And Methods: Retrospective narrative chart review of electronic health records of 61 patients referred and admitted to hospice from 3 New York City geriatrics practices who died between March 1, 2020, and March 31, 2021.

Fairness gaps in Machine learning models for hospitalization and emergency department visit risk prediction in home healthcare patients with heart failure.

Objectives: This study aims to evaluate the fairness performance metrics of Machine Learning (ML) models to predict hospitalization and emergency department (ED) visits in heart failure patients receiving home healthcare. We analyze biases, assess performance disparities, and propose solutions to improve model performance in diverse subpopulations.

Methods: The study used a dataset of 12,189 episodes of home healthcare collected between 2015 and 2017, including structured (e.

Hospital-to-Home-Health Transition Quality (H3TQ) Index: Further Evidence on its Validity and Recommendations for Implementation.

Background: We developed the Hospital-to-Home-Health Transition Quality (H3TQ) Index for skilled home healthcare (HH) agencies to identify threats to safe, high-quality care transitions in real time.

Objective: Assess the validity of H3TQ in a large sample across diverse communities.

Research Design: A survey of recently hospitalized older adults referred for skilled HH services and their HH provider at two large HH agencies in Baltimore, MD, and New York, NY.

Natural Language Processing to Identify Home Health Care Patients at Risk for Becoming Incapacitated With No Evident Advance Directives or Surrogates.

Objectives: Home health care patients who are at risk for becoming Incapacitated with No Evident Advance Directives or Surrogates (INEADS) may benefit from timely intervention to assist them with advance care planning. This study aimed to develop natural language processing algorithms for identifying home care patients who do not have advance directives, family members, or close social contacts who can serve as surrogate decision-makers in the event that they lose decisional capacity.

Design: Cross-sectional study of electronic health records.

Leveraging home health aides to improve outcomes in heart failure: A pilot study protocol.

Heart failure (HF) affects six million people in the U.S., is associated with high morbidity, mortality, and healthcare utilization.

Characterizing changes to older adults' care transition patterns from hospital to home care in the initial year of COVID-19.

Background: Skilled home healthcare (HH) provided in-person care to older adults during the COVID-19 pandemic, yet little is known about the pandemic's impact on HH care transition patterns. We investigated pandemic impact on (1) HH service volume; (2) population characteristics; and (3) care transition patterns for older adults receiving HH services after hospital or skilled nursing facility (SNF) discharge.

Methods: Retrospective, cohort, comparative study of recently hospitalized older adults (≥ 65 years) receiving HH services after hospital or SNF discharge at two large HH agencies in Baltimore and New York City (NYC) 1-year pre- and 1-year post-pandemic onset.

Home Care Clinicians' Perspectives on Advance Care Planning for Patients at Risk for Becoming Incapacitated With No Evident Advance Directives or Surrogates.

Advance care planning is important and timely for patients receiving home health services; however, opportunities to facilitate awareness and engagement in this setting are often missed. This qualitative descriptive study elicited perspectives of home health nurses and social workers regarding barriers and facilitators to creating advance care plans in home health settings, with particular attention to patients with few familial or social contacts who can serve as surrogate decision-makers. We interviewed 15 clinicians employed in a large New York City-based home care agency in 2021-2022.

Utilizing patient-nurse verbal communication in building risk identification models: the missing critical data stream in home healthcare.

Background: In the United States, over 12 000 home healthcare agencies annually serve 6+ million patients, mostly aged 65+ years with chronic conditions. One in three of these patients end up visiting emergency department (ED) or being hospitalized. Existing risk identification models based on electronic health record (EHR) data have suboptimal performance in detecting these high-risk patients.

Having a Say Matters: The Association Between Home Health Aides' Voice and Job Satisfaction.

Purpose: Despite a rapidly growing need for home health aides (HHAs), turnover rates are high. While this is driven in large part by the demanding nature of their work and low wages, another factor may be that HHAs are often not considered part of the medical team which can leave them feeling unheard by other healthcare professionals. We sought to determine whether this concept, or HHAs' perceived voice, was associated with job satisfaction.

Covid-19: Home Health Aides' Perceived Preparedness and Self-Reported Availability for Work: Six Month Survey Results.

Throughout the COVID-19 pandemic New York City home health aides continuously provided care, including to patients actively infected or recovering from COVID-19. Analyzing survey data from 1316 aides, we examined factors associated with perceptions of how well their employer prepared them for COVID-19 and their self-reported availability for work (did they "call out" more than usual). Organizational work environment and COVID-19-related supports were predominant predictors of self-reported perceptions of preparedness.

Continuity of Care Versus Language Concordance as an Intervention to Reduce Hospital Readmissions From Home Health Care.

Background: Language concordance between health care practitioners and patients have recently been shown to lower the risk of adverse health events. Continuity of care also been shown to have the same impact.

Objective: The purpose of this paper is to examine the relative effectiveness of both continuity of care and language concordance as alternative or complementary interventions to improve health outcomes of people with limited English proficiency.

Community practices as coping mechanisms for mental health in Kashmir.

With a steady rise in mental health issues such as depression, anxiety, and post-traumatic stress disorder (PTSD) in Kashmir, the need to address mental health in this region becomes important. This paper reports on traditional and current community mental health coping mechanisms among residents in Kashmir. Six focus group discussions (FGD) were facilitated with key informants.

Predicting emergency department visits and hospitalizations for patients with heart failure in home healthcare using a time series risk model.

Objectives: Little is known about proactive risk assessment concerning emergency department (ED) visits and hospitalizations in patients with heart failure (HF) who receive home healthcare (HHC) services. This study developed a time series risk model for predicting ED visits and hospitalizations in patients with HF using longitudinal electronic health record data. We also explored which data sources yield the best-performing models over various time windows.

Uncovering hidden trends: identifying time trajectories in risk factors documented in clinical notes and predicting hospitalizations and emergency department visits during home health care.

Objective: This study aimed to identify temporal risk factor patterns documented in home health care (HHC) clinical notes and examine their association with hospitalizations or emergency department (ED) visits.

Materials And Methods: Data for 73 350 episodes of care from one large HHC organization were analyzed using dynamic time warping and hierarchical clustering analysis to identify the temporal patterns of risk factors documented in clinical notes. The Omaha System nursing terminology represented risk factors.