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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]

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]

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]

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]