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BackgroundHomecare settings across the United States provide care to more than 5 million patients every year. About one in five homecare patients are rehospitalized during the homecare episode, with up to two-thirds of these rehospitalizations occurring within the first 2 weeks of services. Timely allocation of homecare services might prevent a significant portion of these rehospitalizations. The first homecare nursing visit is one of the most critical steps of the homecare episode. This visit includes an assessment of the patient’s capacity for self-care, medication reconciliation, an examination of the home environment, and a discussion regarding whether a caregiver is present. Hence, appropriate timing of the first visit is crucial, especially for patients with urgent health care needs. However, nurses often have limited and inaccurate information about incoming patients, and patient priority decisions vary significantly between nurses. We developed an innovative decision support tool called Priority for the First Nursing Visit Tool (PREVENT) to assist nurses in prioritizing patients in need of immediate first homecare nursing visits. ObjectiveThis study aims to evaluate the effectiveness of the PREVENT tool on process and patient outcomes and to examine the reach, adoption, and implementation of PREVENT. MethodsEmploying a pre-post design, survival analysis, and logistic regression with propensity score matching analysis, we will test the following hypotheses: compared with not using the tool in the preintervention phase, when homecare clinicians use the PREVENT tool, high-risk patients in the intervention phase will (1) receive more timely first homecare visits and (2) have decreased incidence of rehospitalization and have decreased emergency department use within 60 days. Reach, adoption, and implementation will be assessed using mixed methods including homecare admission staff interviews, think-aloud observations, and analysis of staffing and other relevant data. ResultsThe study research protocol was approved by the institutional review board in October 2019. PREVENT is currently being integrated into the electronic health records at the participating study sites. Data collection is planned to start in early 2021. ConclusionsMixed methods will enable us to gain an in-depth understanding of the complex socio-technological aspects of the hospital to homecare transition. The results have the potential to (1) influence the standardization and individualization of nurse decision making through the use of cutting-edge technology and (2) improve patient outcomes in the understudied homecare setting. Trial RegistrationClinicalTrials.gov NCT04136951; https://clinicaltrials.gov/ct2/show/NCT04136951 International Registered Report Identifier (IRRID)PRR1-10.2196/20184

This study aimed to: (1) validate a natural language processing (NLP) system developed for the home health care setting to identify signs and symptoms of Alzheimer's disease and related dementias (ADRD) documented in clinicians' free-text notes; (2) determine whether signs and symptoms detected via NLP help to identify patients at risk of a new ADRD diagnosis within four years after admission. This study applied NLP to a longitudinal dataset including medical record and Medicare claims data for 56,652 home health care patients and Cox proportional hazard models to the subset of 24,874 patients admitted without an ADRD diagnosis. Selected ADRD signs and symptoms were associated with increased risk of a new ADRD diagnosis during follow-up, including: motor issues; hoarding/cluttering; uncooperative behavior; delusions or hallucinations; mention of ADRD disease names; and caregiver stress. NLP can help to identify patients in need of ADRD-related evaluation and support services. [ABSTRACT FROM AUTHOR]

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. Worker characteristics and COVID-19-related stressors were predominant predictors of self-reported availability. Mental distress, satisfaction with employer communications, and satisfaction with supervisor instructions were significantly associated with both outcomes. The study uniquely describes self-reported perceptions of preparedness and availability as two separate worker outcomes potentially modifiable by different interventions. Better public health emergency training and adequate protective equipment may increase aides' perceived preparedness; more household supports could facilitate their availability. More effective employer communications and mental health initiatives could potentially improve both outcomes. Industry collaboration and systemic changes in federal, state, and local policies should enhance intervention impacts. [ABSTRACT FROM AUTHOR]

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. First, clinical characteristics were compared between clusters. Next, multivariate logistic regression was used to examine the association between clusters and risk for hospitalizations or ED visits. Omaha System domains corresponding to risk factors were analyzed and described in each cluster. Results Six temporal clusters emerged, showing different patterns in how risk factors were documented over time. Patients with a steep increase in documented risk factors over time had a 3 times higher likelihood of hospitalization or ED visit than patients with no documented risk factors. Most risk factors belonged to the physiological domain, and only a few were in the environmental domain. Discussion An analysis of risk factor trajectories reflects a patient's evolving health status during a HHC episode. Using standardized nursing terminology, this study provided new insights into the complex temporal dynamics of HHC, which may lead to improved patient outcomes through better treatment and management plans. Conclusion Incorporating temporal patterns in documented risk factors and their clusters into early warning systems may activate interventions to prevent hospitalizations or ED visits in HHC. [ABSTRACT FROM AUTHOR]

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. Materials and Methods We used data collected from 9362 patients from a large HHC agency. We iteratively developed risk models using both structured (eg, standard assessment tools, vital signs, visit characteristics) and unstructured data (eg, clinical notes). Seven specific sets of variables included: (1) the Outcome and Assessment Information Set, (2) vital signs, (3) visit characteristics, (4) rule-based natural language processing-derived variables, (5) term frequency-inverse document frequency variables, (6) Bio-Clinical Bidirectional Encoder Representations from Transformers variables, and (7) topic modeling. Risk models were developed for 18 time windows (1–15, 30, 45, and 60 days) before an ED visit or hospitalization. Risk prediction performances were compared using recall, precision, accuracy, F 1, and area under the receiver operating curve (AUC). Results The best-performing model was built using a combination of all 7 sets of variables and the time window of 4 days before an ED visit or hospitalization (AUC = 0.89 and F 1 = 0.69). Discussion and Conclusion This prediction model suggests that HHC clinicians can identify patients with HF at risk for visiting the ED or hospitalization within 4 days before the event, allowing for earlier targeted interventions. [ABSTRACT FROM AUTHOR]

INTRODUCTION: Home health (HH) may be an important source of care for those with early‐stage/undiagnosed Alzheimer's Disease and Related Dementias (ADRD), but little is known regarding prevalence or predictors of incident ADRD diagnosis following HH. METHODS: Using 2010–2012 linked Master Beneficiary Summary File (MBSF) and HH assessment data for 40,596 Medicare HH patients, we model incident ADRD diagnosis within 1 year of HH via multivariable logistic regression. RESULTS: Among HH patients without diagnosed ADRD, 10% received an incident diagnosis within 1 year. In adjusted models, patients were three times more likely to receive an incident ADRD diagnosis if they had HH clinician‐reported impaired overall cognition (compared to patients without reported impairment) and twice as likely if they were community‐referred (compared to hospital‐referred patients). DISCUSSION: There is a pressing need to develop tailored HH clinical pathways and protect access to community‐referred HH to support community‐living older adults with early‐stage/undiagnosed ADRD. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Background: This protocol is based on home health care (HHC) best practice evidence showing the value of coupling timely post-acute care visits by registered nurses and early outpatient provider follow-up for sepsis survivors. We found that 30-day rehospitalization rates were 7 percentage points lower (a 41% relative reduction) when sepsis survivors received a HHC nursing visit within 2 days of hospital discharge, at least 1 more nursing visit the first week, and an outpatient provider follow-up visit within 7 days compared to those without timely follow-up. However, nationwide, only 28% of sepsis survivors who transitioned to HHC received this timely visit protocol. The opportunity exists for many more sepsis survivors to benefit from timely home care and outpatient services. This protocol aims to achieve this goal. Methods: Guided by the Consolidated Framework for Implementation Research, this Type 1 hybrid pragmatic study will test the effectiveness of the Improving Transitions and Outcomes of Sepsis Survivors (I-TRANSFER) intervention compared to usual care on 30-day rehospitalization and emergency department use among sepsis survivors receiving HHC. The study design includes a baseline period with no intervention, a six-month start-up period followed by a one-year intervention period in partnership with five dyads of acute and HHC sites. In addition to the usual care/control periods from the dyad sites, additional survivors from national data will serve as control observations for comparison, weighted to produce covariate balance. The hypotheses will be tested using generalized mixed models with covariates guided by the Andersen Behavioral Model of Health Services. We will produce insights and generalizable knowledge regarding the context, processes, strategies, and determinants of I-TRANSFER implementation. Discussion: As the largest HHC study of its kind and the first to transform this novel evidence through implementation science, this study has the potential to produce new knowledge about the impact of timely attention in HHC to alleviate symptoms and support sepsis survivor's recovery at home. If effective, the impact of this intervention could be widespread, improving the quality of life and health outcomes for a growing, vulnerable population of sepsis survivors. A national advisory group will assist with widespread results dissemination. [ABSTRACT FROM AUTHOR]

Objective: To identify the risk factors home healthcare (HHC) clinicians associate with patient deterioration and understand how clinicians respond to and document these risk factors.Methods: We interviewed multidisciplinary HHC clinicians from January to March of 2021. Risk factors were mapped to standardized terminologies (eg, Omaha System). We used directed content analysis to identify risk factors for deterioration. We used inductive thematic analysis to understand HHC clinicians' response to risk factors and documentation of risk factors.Results: Fifteen HHC clinicians identified a total of 79 risk factors that were mapped to standardized terminologies. HHC clinicians most frequently responded to risk factors by communicating with the prescribing provider (86.7% of clinicians) or following up with patients and caregivers (86.7%). HHC clinicians stated that a majority of risk factors can be found in clinical notes (ie, care coordination (53.3%) or visit (46.7%)).Discussion: Clinicians acknowledged that social factors play a role in deterioration risk; but these factors are infrequently studied in HHC. While a majority of risk factors were represented in the Omaha System, additional terminologies are needed to comprehensively capture risk. Since most risk factors are documented in clinical notes, methods such as natural language processing are needed to extract them.Conclusion: This study engaged clinicians to understand risk for deterioration during HHC. The results of our study support the development of an early warning system by providing a comprehensive list of risk factors grounded in clinician expertize and mapped to standardized terminologies. [ABSTRACT FROM AUTHOR]

Home health care (HHC) clinicians serving individuals with Alzheimer's disease and related dementias (ADRD) do not always have information about the person's ADRD diagnosis, which may be used to improve the HHC plan of care. This retrospective cohort study examined characteristics of 56,652 HHC patients with varied documentation of ADRD diagnoses. Data included clinical assessments and Medicare claims for a 6-month look-back period and 4-year follow-up. Nearly half the sample had an ADRD diagnosis observed in the claims either prior to or following the HHC admission. Among those with a prior diagnosis, 63% did not have it documented on the HHC assessment; the diagnosis may not have been known to the HHC team or incorporated into the care plan. Patients with ADRD had heightened risk for adverse outcomes (e.g., urinary tract infection and aspiration pneumonia). Interoperable data across health care settings should include ADRD-specific elements about diagnoses, symptoms, and risk factors. [ABSTRACT FROM AUTHOR]

Background: Despite improvements in hypertension treatment in the United States, Black and Hispanic individuals experience poor blood pressure control and have worse hypertension-related outcomes compared to Whites. Objective: The aim of the study was to determine the effect on hospitalization of supplementing usual home care (UHC) with two hypertension-focused transitional care interventions--one deploying nurse practitioners (NPs) and the other NPs plus health coaches. Methods: We examined post hoc the effect of two hypertension-focused NP interventions on hospitalizations in the Community Transitions Intervention trial--a three-arm, randomized controlled trial comparing the effectiveness of (a) UHC with (b) UHC plus a 30-day NP transitional care intervention or (c) UHC plus NP plus 60-day health coach intervention. Results: The study comprised 495 participants: mean age = 66 years; 57% female; 70% Black, non-Hispanic; 30% Hispanic. At the 3- and 12-month follow-up, all three groups showed a significant decrease in the average number of hospitalizations compared to baseline. The interventions were not significantly different from UHC. Conclusion: The results of this post hoc analysis show that, during the study period, decreases in hospitalizations in the intervention groups were comparable to those in UHC, and deploying NPs provided no detectable value added. Future research should focus on testing ways to optimize UHC services. [ABSTRACT FROM AUTHOR]

Background: Little is known about recovery from coronavirus disease 2019 (COVID-19) after hospital discharge.Objective: To describe the home health recovery of patients with COVID-19 and risk factors associated with rehospitalization or death.Design: Retrospective observational cohort.Setting: New York City.Participants: 1409 patients with COVID-19 admitted to home health care (HHC) between 1 April and 15 June 2020 after hospitalization.Measurements: Covariates and outcomes were obtained from the mandated OASIS (Outcome and Assessment Information Set). Cox proportional hazards models were used to estimate the hazard ratio (HR) of risk factors associated with rehospitalization or death.Results: After an average of 32 days in HHC, 94% of patients were discharged and most achieved statistically significant improvements in symptoms and function. Activity-of-daily-living dependencies decreased from an average of 6 (95% CI, 5.9 to 6.1) to 1.2 (CI, 1.1 to 1.3). Risk for rehospitalization or death was higher for male patients (HR, 1.45 [CI, 1.04 to 2.03]); White patients (HR, 1.74 [CI, 1.22 to 2.47]); and patients with heart failure (HR, 2.12 [CI, 1.41 to 3.19]), diabetes with complications (HR, 1.71 [CI, 1.17 to 2.52]), 2 or more emergency department visits in the past 6 months (HR, 1.78 [CI, 1.21 to 2.62]), pain daily or all the time (HR, 1.46 [CI, 1.05 to 2.05]), cognitive impairment (HR, 1.49 [CI, 1.04 to 2.13]), or functional dependencies (HR, 1.09 [CI, 1.00 to 1.20]). Eleven patients (1%) died, 137 (10%) were rehospitalized, and 23 (2%) remain on service.Limitations: Care was provided by 1 home health agency. Information on rehospitalization and death after HHC discharge is not available.Conclusion: Symptom burden and functional dependence were common at the time of HHC admission but improved for most patients. Comorbid conditions of heart failure and diabetes, as well as characteristics present at admission, identified patients at greatest risk for an adverse event.Primary Funding Source: No direct funding. [ABSTRACT FROM AUTHOR]