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BACKGROUND: Each year, more than 13 million post acute referral decisions are made for Medicare recipients, yet there are no national, empirically derived decision support tools to assist in making these important decisions. OBJECTIVES: The aim of this study was to elicit expert knowledge about factors important to referral decision making and identify the characteristics of hospitalized patients who need a post acute referral. METHODS: This was a retrospective and prospective mixed-methods study of the referral decisions made by discharge planning experts for 355 hospitalized older adults. Variables included sociodemographics, living arrangement, insurance, diagnosis, comorbid conditions, adverse events, medications, home care use, hospitalization in last 30 days or 6 months, patients' perception of need for and use of assistive devices or post acute services, length of stay, cognition, self-rated health, depression, functional status, and post acute referral decision. RESULTS: The final model identified six factors associated with the need for a post acute referral. A cutpoint was derived with a sensitivity and specificity of 87.6% and 63.2%, respectively. Experts were more likely to refer patients who had no or intermittent help available (odds ratio [OR] = 3.0), major walking restrictions (OR = 6.5), less than excellent self-rated health (3.1 and 4.0 times more likely with good and fair-poor health, respectively), remained in the hospital longer (OR = 1.2), and had higher depression scores (OR = 1.1) or number of comorbidities (OR = 1.2). DISCUSSION: This study begins to identify information useful to clinicians caring for hospitalized older adults who may benefit from post acute services. By assuring the systematic, valid, and reliable collection of these items, the multidisciplinary team is alerted to patients who may benefit from post acute services. Further work is needed to increase the specificity and generalizability of the model and to test its effects on patient and clinician outcomes. [ABSTRACT FROM AUTHOR]

Dementia and delirium, the most common causes of cognitive impairment (CI) among hospitalized older adults, are associated with higher mortality rates, increased morbidity and higher health care costs. A growing body of science suggests that these older adults and their caregivers are particularly vulnerable to systems of care that either do not recognize or meet their needs. The consequences can be devastating for these older adults and add to the burden of hospital staff and caregivers, especially during the transition from hospital to home. Unfortunately, little evidence exists to guide optimal care of this patient group. Available research findings suggest that hospitalized cognitively impaired elders may benefit from interventions aimed at improving care management of both CI and co-morbid conditions but the exact nature and intensity of interventions needed are not known. This article will explore the need for improved transitional care for this vulnerable population and their caregivers. [ABSTRACT FROM AUTHOR]

OBJECTIVE: The aim of this study was to evaluate the empirical, operational, and pragmatic utility of the Omaha System to code the terms used to document nursing care in the hospital setting. DESIGN: Secondary analysis. SETTING: Hospital. POPULATION: Hospitalized patients age 70 or over.. INTERVENTIONS: The nursing progress notes, care plans, teaching plans, and advanced practice nurse logs from 30 hospitalized patients provided the units of analysis. The units of analysis were the terms within the hospital records that described patient signs and symptoms, problems, or nursing interventions. Terms that described patient signs and symptoms, problems, or nursing interventions were typed into a relational database for coding. One hundred and two signs and symptoms or problems were coded from the patient records into all four Omaha System domains. MAIN OUTCOME MEASURE(S): The Omaha System domains provided categories to code 97% of the problems in the records. Of 102 signs and symptoms or patient problems, three were coded as 'other', meaning there were no categories to label the problems. The importance of health care environment problems, fever of unknown origin, and discharge planning suggest the Omaha System was not adequate, in its present form, to code three important hospital problems. The concept match scoring quantified the degree of match between the hospital terms and the Omaha System. Thirteen out of 102 problem terms were coded as no match, meaning the Omaha System was unable to code these problems. RESULTS/CONCLUSIONS: Based on study findings, the target definitions should be reviewed and refined so there is only one place to code medications, diet, intake and output, and vital signs. Use of the Omaha System produced high reliability scores; coded 97% of the problems, all the interventions, and 99.8% of the targets; and provided an adequate description of the care of elderly hospitalized patients. The study results provide data to suggest modifications to the Omaha System to improve its utility in the hospital setting and encourage further testing with other types of hospital patients. [CINAHL abstract] [ABSTRACT FROM AUTHOR]

Background: Seriously ill older adults have high risk of mortality, symptom burden, and compromised functional status, and may benefit from community-based palliative care ("palliative care"). However, identifying potentially eligible individuals is challenging., Objectives: Identify how a palliative care team makes eligibility determinations, including the use of a mortality risk algorithm., Design: Semi-structured focus groups were conducted with palliative care providers to understand how health information is used to assess Medicare Advantage plan patients' eligibility for palliative care. Transcripts were analyzed to identify categories related to how participants used health information in making eligibility determinations., Setting/participants: All outreach and care management team members and nurse practitioners (n = 7) working for a palliative care program at a not-for-profit health care agency., Results: The palliative care team used information on symptoms, diagnoses, and acute care utilization when making eligibility determinations. The algorithm generated lists of potentially eligible patients but was deemed not useful because it provided limited information about current health status and was not well integrated into existing health record systems. Participants would like information to be current, detailed, and presented in one location., Conclusions: Palliative care team members used a variety of indicators of unmet care needs to make eligibility determinations. A mortality risk algorithm to identify potentially eligible patients was insufficiently detailed to be useful in final eligibility determinations. These findings provide insights into how health data can be presented to better integrate health information from mortality risk algorithms into existing workflows to support palliative care eligibility decision making., Competing Interests: Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

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. This study aims to (1) describe important variables associated with a higher risk of ED visits and hospitalizations in HF patients receiving HHC; (2) map data requirements of a clinical decision support (CDS) tool to the exchangeable data standard for integrating a CDS tool into the care of patients with HF; (3) outline a pipeline for developing a real-time artificial intelligence (AI)-based CDS tool., Methods: We used patient data from a large HHC organization in the Northeastern US to determine the factors that can predict ED visits and hospitalizations among patients with HF in HHC (9362 patients in 12,223 care episodes). We examined vital signs, HHC visit details (e.g., the purpose of the visit), and clinical note-derived variables. The study identified critical factors that can predict ED visits and hospitalizations and used these findings to suggest a practical CDS tool for nurses. The tool's proposed design includes a system that can analyze data quickly to offer timely advice to healthcare clinicians., Results: Our research showed that the length of time since a patient was admitted to HHC and how recently they have shown symptoms of HF were significant factors predicting an adverse event. Additionally, we found this information from the last few HHC visits before the occurrence of an ED visit or hospitalization were particularly important in the prediction. One hundred percent of clinical demographic profiles from the Outcome and Assessment Information Set variables were mapped to the exchangeable data standard, while natural language processing-driven variables couldn't be mapped due to their nature, as they are generated from unstructured data. The suggested CDS tool alerts nurses about newly emerging or rising risks, helping them make informed decisions., Conclusions: This study discusses the creation of a time-series risk prediction model and its potential CDS applications within HHC, aiming to enhance patient outcomes, streamline resource utilization, and improve the quality of care for individuals with HF., Clinical Relevance: This study provides a detailed plan for a CDS tool that uses the latest AI technology designed to aid nurses in their day-to-day HHC service. Our proposed CDS tool includes an alert system that serves as a guard rail to prevent ED visits and hospitalizations. This tool can potentially improve how nurses make decisions and improve patient outcomes by providing early warnings about ED visits and hospitalizations., (© 2024 The Author(s). Journal of Nursing Scholarship published by Wiley Periodicals LLC on behalf of Sigma Theta Tau International.)

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., Results: Interviews with thirteen HHC clinicians yielded 16 codes mapping to the CDS "Five-Rights" framework (right information, right person, right format, right channel, right time) and 11 codes for unintended consequences and training needs. Clinicians favored risk levels displayed in color-coded horizontal bars, concrete risk indicators in bullet points, and actionable instructions in the existing EHR system. They preferred non-intrusive risk alerts requiring mandatory confirmation. Clinicians anticipated risk information updates aligned with patient's condition severity and their visit pace. Additionally, they requested training to understand the CDSS's underlying logic, and raised concerns about information accuracy and data privacy., Discussion: While recognizing CDSS's value in enhancing early risk warning, clinicians highlighted concerns about increased workload, alert fatigue, and CDSS misuse. The top risk factors identified by machine learning algorithms, especially text features, can be ambiguous due to a lack of context. Future research should ensure that CDSS outputs align with clinical evidence and are explainable., Conclusion: This study identified HHC clinicians' expectations, preferences, adaptations, and unintended uses of CDSS for early risk warning. Our findings endorse operationalizing the CDS "Five-Rights" framework to optimize CDSS information delivery and integration into HHC workflows., (© The Author(s) 2024. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

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.g., standard assessment tool) and unstructured data (i.e., clinical notes). ML risk prediction models, including Light Gradient-boosting model (LightGBM) and AutoGluon, were developed using demographic information, vital signs, comorbidities, service utilization data, and the area deprivation index (ADI) associated with the patient's home address. Fairness metrics, such as Equal Opportunity, Predictive Equality, Predictive Parity, and Statistical Parity, were calculated to evaluate model performance across subpopulations., Results: Our study revealed significant disparities in model performance across diverse demographic subgroups. For example, the Hispanic, Male, High-ADI subgroup excelled in terms of Equal Opportunity with a metric value of 0.825, which was 28% higher than the lowest-performing Other, Female, Low-ADI subgroup, which scored 0.644. In Predictive Parity, the gap between the highest and lowest-performing groups was 29%, and in Statistical Parity, the gap reached 69%. In Predictive Equality, the difference was 45%., Discussion and Conclusion: The findings highlight substantial differences in fairness metrics across diverse patient subpopulations in ML risk prediction models for heart failure patients receiving home healthcare services. Ongoing monitoring and improvement of fairness metrics are essential to mitigate biases., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Study Aim: This study aims to describe the transition-in-care work process for sepsis survivors going from hospitals to home health care (HHC) and identify facilitators and barriers to enable practice change and safe care transitions using a human factors and systems engineering approach., Background: Despite high readmission risk for sepsis survivors, the transition-in-care work process from hospitals to HHC has not been described., Methods: We analyzed semi-structured needs assessment interviews with 24 stakeholders involved in transitioning sepsis survivors from two hospitals and one affiliated HHC agency participating in the parent implementation science study, I-TRANSFER. The qualitative data analysis was guided by the Systems Engineering Initiative for Patient Safety (SEIPS) framework to describe the work process and identify work system elements., Results: We identified 31 tasks characterized as decision making, patient education, communication, information, documentation, and scheduling tasks. Technological and organizational facilitators lacked in HHC compared to the hospitals. Person and organization elements in HHC had the most barriers but few facilitators. Additionally, we identified specific task barriers that could hinder sepsis information transfer from hospitals to HHC., Conclusion: This study explored the complex transition-in-care work processes for sepsis survivors going from hospitals to HHC. We identified barriers, facilitators, and critical areas for improvement to enable implementation and ensure safe care transitions. A key finding was the sepsis information transfer deficit, highlighting a critical issue for future study., Application: We recommend using the SEIPS framework to explore complex healthcare work processes before the implementation of evidence-based interventions.

Background: Some of the most promising strategies to reduce hospital readmissions in heart failure (HF) is through the timely receipt of home health care (HHC), delivered by Medicare-certified home health agencies (HHAs), and outpatient medical follow-up after hospital discharge. Yet national data show that only 12% of Medicare beneficiaries receive these evidence-based practices, representing an implementation gap. To advance the science and improve outcomes in HF, we will test the effectiveness and implementation of an intervention called Improving TRansitions ANd OutcomeS for Heart FailurE Patients in Home Health CaRe (I-TRANSFER-HF), comprised of early and intensive HHC nurse visits combined with an early outpatient medical visit post-discharge, among HF patients receiving HHC., Methods: This study will use a Hybrid Type 1, stepped wedge randomized trial design, to test the effectiveness and implementation of I-TRANSFER-HF in partnership with four geographically diverse dyads of hospitals and HHAs ("hospital-HHA" dyads) across the US. Aim 1 will test the effectiveness of I-TRANSFER-HF to reduce 30-day readmissions (primary outcome) and ED visits (secondary outcome), and increase days at home (secondary outcome) among HF patients who receive timely follow-up compared to usual care. Hospital-HHA dyads will be randomized to cross over from a baseline period of no intervention to the intervention in a randomized sequential order. Medicare claims data from each dyad and from comparison dyads selected within the national dataset will be used to ascertain outcomes. Hypotheses will be tested with generalized mixed models. Aim 2 will assess the determinants of I-TRANSFER-HF's implementation using a mixed-methods approach and is guided by the Consolidated Framework for Implementation Research 2.0 (CFIR 2.0). Qualitative interviews will be conducted with key stakeholders across the hospital-HHA dyads to assess acceptability, barriers, and facilitators of implementation; feasibility and process measures will be assessed with Medicare claims data., Discussion: As the first pragmatic trial of promoting timely HHC and outpatient follow-up in HF, this study has the potential to dramatically improve care and outcomes for HF patients and produce novel insights for the implementation of HHC nationally., Trial Registration: This trial has been registered on ClinicalTrials.Gov (#NCT06118983). Registered on 10/31/2023, https://clinicaltrials.gov/study/NCT06118983?id=NCT06118983&rank=1 ., (© 2024. The Author(s).)

Background: Organizational readiness for change, defined as the collective preparedness of organization members to enact changes, remains understudied in implementing sepsis survivor transition-in-care protocols. Effective implementation relies on collaboration between hospital and post-acute care informants, including those who are leaders and staff. Therefore, our cross-sectional study compared organizational readiness for change among hospital and post-acute care informants., Methods: We invited informants from 16 hospitals and five affiliated HHC agencies involved in implementing a sepsis survivor transition-in-care protocol to complete a pre-implementation survey, where organizational readiness for change was measured via the Organizational Readiness to Implement Change (ORIC) scale (range 12-60). We also collected their demographic and job area information. Mann-Whitney U -tests and linear regressions, adjusting for leadership status, were used to compare organizational readiness of change between hospital and post-acute care informants., Results: Eighty-four informants, 51 from hospitals and 33 from post-acute care, completed the survey. Hospital and post-acute care informants had a median ORIC score of 52 and 57 respectively. Post-acute care informants had a mean 4.39-unit higher ORIC score compared to hospital informants ( p  = 0.03)., Conclusions: Post-acute care informants had higher organizational readiness of change than hospital informants, potentially attributed to differences in health policies, expertise, organizational structure, and priorities. These findings and potential inferences may inform sepsis survivor transition-in-care protocol implementation. Future research should confirm, expand, and examine underlying factors related to these findings with a larger and more diverse sample. Additional studies may assess the predictive validity of ORIC towards implementation success., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (© 2024 Sang, Quinn, Stawnychy, Song, Hirschman, You, Pitcher, Hodgson, Garren, O'Connor, Oh and Bowles.)

Objectives: To review, evaluate, and synthesize existing literature on how health status changes were measured using the Outcome and Assessment Information Set (OASIS)., Design: Systematic review., Setting and Participants: Studies were included if they examined the health status of adult patients at 2 or more time points using OASIS, which is a comprehensive assessment tool mandated for home health (HH) patients., Methods: A systematic search was conducted in PubMed, CINAHL, Embase, and Scopus. The methodological quality of the included studies was appraised using the National Institutes of Health quality assessment tool., Results: The initial search yielded 1587 citations, resulting in 27 eligible studies published between 2005 and 2022. All included studies were retrospective cohort studies, with overall quality ranging from good to fair. These studies primarily targeted patients with heart failure, with sample sizes between 40 and 6,637,497 and mean ages of 61.2 to 82.4 years. OASIS version C was the most used version, comparing health status at admission and discharge. Patient health status outcomes encompassed functional (n = 24), physiological (n = 6), emotional/behavioral (n = 4), and cognitive (n = 2) outcomes. Various techniques were employed for scale standardization, compositing individual scores, measuring changes, and reporting outcomes. The predominant methods for assessing change included the corrected Likert scale for standardization, composite change scores for change calculation, and continuous outcomes for reporting., Conclusion and Implications: Researchers have used OASIS to assess patient health status changes in functional, physiological, emotional/behavioral, and cognitive outcomes during the HH episode. Variations in OASIS items and assessment methods across studies have been observed. Our findings underscore the need to standardize item application in research, enabling researchers to synthesize evidence effectively and enhance understanding of patient recovery and HH services. For policymakers and health care providers, these insights could inform resource allocation, care planning, and tailored interventions, ultimately improving health care quality and efficiency., Competing Interests: Disclosure The authors declare no conflicts of interest., (Copyright © 2024 AMDA – The Society for Post-Acute and Long-Term Care Medicine. Published by Elsevier Inc. All rights reserved.)

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., Subjects: There were five hundred eighty-seven participants (309 older adults, 141 informal caregivers, and 137 HH providers). Older adults, caregivers, and HH providers rated 747 unique transitions. Of these, 403 were rated by both the older adult/caregiver and their HH provider, whereas the remaining transitions were rated by either party., Measures: Construct, concurrent, and predictive validity were assessed via the overall H3TQ rating, correlation with the care transition measure (CTM), and the Medicare Outcome and Assessment Information Set (OASIS)., Results: Proportion of transitions with quality issues as identified by HH providers and older adults/caregivers, respectively; Baltimore 55%, 35%; NYC 43%, 32%. Older adults/caregivers across sites rated their transitions as higher quality than did providers (P<0.05). H3TQ summed scores showed construct validity with the CTM-3 and concurrent validity with OASIS measures. Summed H3TQ scores were not significantly correlated with 30-day ED visits or rehospitalization., Conclusions: The H3TQ identifies care transition quality issues in real-time and demonstrated construct and concurrent validity, but not predictive validity. Findings demonstrate value in collecting multiple perspectives to evaluate care transition quality. Implementing the H3TQ could help identify transition-quality intervention opportunities for HH patients., Competing Interests: The authors declare no conflict of interest., (Copyright © 2024 Wolters Kluwer Health, Inc. All rights reserved.)

In a previous study, sepsis was noted as a diagnosis on the home health record only 4% of the time for 165,000 sepsis survivors transitioning from hospital to home health care in America. If sepsis and other conditions are not clearly documented in the transitional care record this can lead to unpreparedness, missed, care, and poor patient outcomes. Our implementation science study discovered a source of this problem regarding the sepsis documentation in 16 hospitals referring to five home care agencies. Together, researchers, hospital, and home care personnel developed and implemented two information technology solutions to address this deficit in seven hospitals. The automated method was more readily adopted and effective in improving information transfer between hospital and home health care.

The use of healthcare information technology (HIT) is vital for storing and exchanging health information during patient transitions, playing a significant role in care coordination for sepsis survivors. The critical role of HIT was evident during the pre-implementation phase of a study to implement an evidence-based protocol supporting the timely transition of sepsis survivors to home health and outpatient care. Through 61 semi-structured interviews involving 91 stakeholders, over half of the 33 identified themes were related to HIT. Notably, electronic health record (EHR) alert systems led to over-capture and alarm fatigue. Efficient information transfer during HHC referral highlighted the need for improved EHR access. The study underscores HIT's importance and potential while emphasizing the need for collaborative policy and interface development to promote effective transitions in care.

Community-based palliative care is a useful, but underutilized service to support seriously ill older adults to remain safely at home and improve quality of life. Clinical decision support tools can assist palliative care need assessments if presented in a way that is useful to clinicians. This case study explores the potential for generative artificial intelligence (AI) to translate diagnostic and billing codes from administrative data into a narrative format to facilitate clinical decision-making regarding palliative care need. Informatics structures for protecting patient confidentiality are described. Generative AI has the potential to support clinical decision-making but requires additional testing, validation, and refinement.

Background: Home health care (HHC) has been increasingly used to improve care transitions and avoid poor outcomes, but there is limited data on its use and efficacy following coronary artery bypass grafting. The purpose of this study was to describe HHC use and its association with outcomes among Medicare beneficiaries undergoing coronary artery bypass grafting., Methods: Retrospective analysis of 100% of Medicare fee-for-service files identified 77 331 beneficiaries undergoing coronary artery bypass grafting and discharged to home between July 2016 and December 2018. The primary exposure of HHC use was defined as the presence of paid HHC claims within 30 days of discharge. Hierarchical logistic regression identified predictors of HHC use and the percentage of variation in HHC use attributed to the hospital. Propensity-matched logistic regression compared mortality, readmissions, emergency department visits, and cardiac rehabilitation enrollment at 30 and 90 days after discharge between HHC users and nonusers., Results: A total of 26 751 (34.6%) of beneficiaries used HHC within 30 days of discharge, which was more common among beneficiaries who were older (72.9 versus 72.5 years), male (79.4% versus 77.4%), White (90.2% versus 89.2%), and not Medicare-Medicaid dual eligible (6.7% versus 8.8%). The median hospital-level rate of HHC use was 31.0% (interquartile range, 13.7%-54.5%) and ranged from 0% to 94.2%. Nearly 30% of the interhospital variation in HHC use was attributed to the discharging hospital (intraclass correlation coefficient, 0.296 [95% CI, 0.275-0.318]). Compared with non-HHC users, those using HHC were less likely to have a readmission or emergency department visit, were more likely to enroll in cardiac rehabilitation, and had modestly higher mortality within 30 or 90 days of discharge., Conclusions: A third of Medicare beneficiaries undergoing coronary artery bypass grafting used HHC within 30 days of discharge, with wide interhospital variation in use and mixed associations with clinical outcomes and health care utilization., Competing Interests: Drs Thompson, Likosky, and Pagani received support from a contract from Blue Cross Blue Shield of Michigan. Dr Likosky serves as a consultant for the American Society of Extracorporeal Technology. Dr Pagani serves as a noncompensated scientific advisor for Medtronic, Abbott, FineHeart, and CH Biomedical.

Background: Caring for someone with heart failure takes an emotional and physical toll. Engaging in self-care may decrease stress and improve the health of informal caregivers. We conducted a randomized controlled trial testing the efficacy of a virtual health coaching intervention, compared with health information alone, on the self-care, stress, coping, and health status of heart failure caregivers., Methods: We enrolled 250 caregivers providing care at least 8 hours/week, reporting poor self-care, and able to use technology. All received a tablet device programmed with websites providing vetted information on heart failure and caregiving. Half were randomized to also receive 10 synchronous support sessions virtually with a health coach over 6 months. Data on self-care, stress, coping, and health status were collected at baseline and 3 and 6 months. Linear mixed-effects models were used to assess the interaction between time and treatment group., Results: The sample was majority female (85.2%), White (62.2%), spouses (59.8%), and aged 55±13.6 years. Many were employed full time (41.8%). They had been caring for the patient 8 hours/day for a median of 3.25 years. In the intention-to-treat analysis, caregivers who received the health coach intervention had statistically and clinically greater improvement across 6 months compared with the control group in the primary outcome of self-care maintenance (5.05±1.99; P =0.01) and stress (-4.50±1.00; P <0.0001). Self-care neglect declined significantly (-0.65±0.32; P =0.04), but the difference between the treatment arms disappeared when the results were adjusted for multiple comparisons. Mental health status improved statistically but not clinically (3.35±1.61; P =0.04). Active coping improved in both groups but not significantly more in the intervention group ( P =0.10). Physical health status was unchanged ( P =0.27)., Conclusions: This virtual health coaching intervention was effective in improving self-care and stress in heart failure caregivers., Competing Interests: None.

Background: Patients requiring skilled home health care (HH) after hospitalization are at high risk of adverse events. Human factors engineering (HFE) approaches can be useful for measure development to optimize hospital-to-home transitions., Objective: To describe the development, initial psychometric validation, and feasibility of the Hospital-to-Home-Health-Transition Quality (H3TQ) Index to identify patient safety risks., Methods: Development : A multisite, mixed-methods study at 5 HH agencies in rural and urban sites across the United States. Testing : Prospective H3TQ implementation on older adults' hospital-to-HH transitions. Populations Studied : Older adults and caregivers receiving HH services after hospital discharge, and their HH providers (nurses and rehabilitation therapists)., Results: The H3TQ is a 12-item count of hospital-to-HH transitions best practices for safety that we developed through more than 180 hours of observations and more than 80 hours of interviews. The H3TQ demonstrated feasibility of use, stability, construct validity, and concurrent validity when tested on 75 transitions. The vast majority (70%) of hospital-to-HH transitions had at least one safety issue, and HH providers identified more patient safety threats than did patients/caregivers. The most frequently identified issues were unsafe home environments (32%), medication issues (29%), incomplete information (27%), and patients' lack of general understanding of care plans (27%)., Conclusions: The H3TQ is a novel measure to assess the quality of hospital-to-HH transitions and proactively identify transitions issues. Patients, caregivers, and HH providers offered valuable perspectives and should be included in safety reporting. Study findings can guide the design of interventions to optimize quality during the high-risk hospital-to-HH transition., Competing Interests: The authors declare no conflicts of interest., (Copyright © 2024 Wolters Kluwer Health, Inc. All rights reserved.)

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. We used the Outcome and Assessment Information Set (OASIS) and service use records to examine HH utilization, patient characteristics, visit timeliness, medication issues, and 30-day emergency department (ED) visit and rehospitalization., Results: Across sites, admissions to HH declined by 23% in the pandemic's first year. Compared to the year prior, older adults receiving HH services during the first year of the pandemic were more likely to be younger, have worse mental, respiratory, and functional status in some areas, and be assessed by HH providers as having higher risk of rehospitalization. Thirty-day rehospitalization rates were lower during the first year of the pandemic. COVID-positive HH patients had lower odds of 30-day ED visit or rehospitalization. At the NYC site, extended duration between discharge and first HH visit was associated with reduced 30-day ED visit or rehospitalization., Conclusions: HH patient characteristics and utilization were distinct in Baltimore versus NYC in the initial year of the COVID-19 pandemic. Study findings suggest some older adults who needed HH may not have received it, since the decrease in HH services occurred as SNF use decreased nationally. Findings demonstrate the importance of understanding HH agency responsiveness during public health emergencies to ensure older adults' access to care., (© 2024 The American Geriatrics Society.)

Psychoeducational videoconferencing interventions bypass traditional in-person barriers to attendance and are effective in improving caregiving skills, self-care, and wellness among informal caregivers. Information on their feasibility, usability, and acceptability from the caregivers' perspective is needed to inform future designs and developments. This systematic review follows PRISMA 2020 guidelines to integrate this information. Five databases were systematically searched for relevant randomized control trials published between January 2012 and December 2022. Reference lists were cross-checked for additional studies. Relevant studies were appraised and had their data extracted. This review contains 14 randomized controlled trials. Retention rates ranged from 55.56% to 100%, and major reasons for withdrawing include deteriorating patient health, lack of interest, and technical difficulties (feasibility). Caregivers found the videoconference technology usable, although participants in one intervention experienced poor connectivity and persistent technical issues (usability). Most caregivers were satisfied with videoconferencing interventions, found their content applicable to their situation, and appreciated their structure (acceptability). Those in videoconferencing group interventions were satisfied with small caregiver group sizes (acceptability). Adding respite care to interventions and incorporating short and regular videoconferencing sessions may improve feasibility. Ensuring small group sizes in videoconferencing group interventions and using participatory design may enhance acceptability. Advocacy is needed for employees identifying as informal caregivers to receive employer support and for quality connectivity within underserved areas. This may improve the feasibility and usability of interventions, allowing caregivers to receive the support they need. In future studies, power analyses and recruiting more caregivers may better assess feasibility., (© 2023 Wiley Periodicals LLC.)

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., Objectives: To measure the added value of integrating audio-recorded home healthcare patient-nurse verbal communication into a risk identification model built on home healthcare EHR data and clinical notes., Methods: This pilot study was conducted at one of the largest not-for-profit home healthcare agencies in the United States. We audio-recorded 126 patient-nurse encounters for 47 patients, out of which 8 patients experienced ED visits and hospitalization. The risk model was developed and tested iteratively using: (1) structured data from the Outcome and Assessment Information Set, (2) clinical notes, and (3) verbal communication features. We used various natural language processing methods to model the communication between patients and nurses., Results: Using a Support Vector Machine classifier, trained on the most informative features from OASIS, clinical notes, and verbal communication, we achieved an AUC-ROC = 99.68 and an F1-score = 94.12. By integrating verbal communication into the risk models, the F-1 score improved by 26%. The analysis revealed patients at high risk tended to interact more with risk-associated cues, exhibit more "sadness" and "anxiety," and have extended periods of silence during conversation., Conclusion: This innovative study underscores the immense value of incorporating patient-nurse verbal communication in enhancing risk prediction models for hospitalizations and ED visits, suggesting the need for an evolved clinical workflow that integrates routine patient-nurse verbal communication recording into the medical record., (© The Author(s) 2023. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

A dedicated sepsis coordinator role at Penn Medicine Lancaster General Hospital led initiatives to improve sepsis core measure compliance by 40% during the course of 4 years with submission of all sepsis cases. Chart abstraction and analysis of noncompliant cases identified areas for improvement: early recognition education, order set revisions, documentation support, and the implementation of a nurse-driven 24/7 sepsis monitoring process. The cooperative work with Penn Medicine affiliates, sharing best practices, improves overall sepsis bundle compliance and transitions of care. Ongoing achievements acknowledge the value of building relationships and leading improvements through the collaborative efforts of interprofessional teams., (Copyright © 2023 Elsevier Inc. All rights reserved.)

Background: Early introduction of palliative care can improve patient-centered outcomes for older adults with complex medical conditions. However, identifying the need for and introducing palliative care with patients and caregivers is often difficult. We aim to identify how and why a multi-setting approach to palliative care discussions may improve the identification of palliative care needs and how to facilitate these conversations., Methods: Descriptive qualitative study to inform the development and future pilot testing of a model to improve recognition of, and support for, unmet palliative care needs in home health care (HHC). Thematic analysis of semi-structured interviews with providers across inpatient (n = 11), primary care (n = 17), and HHC settings (n = 10)., Results: Four key themes emerged: 1) providers across settings can identify palliative care needs using their unique perspectives of the patient's care, 2) identifying palliative care needs is challenging due to infrequent communication and lack of shared information between providers, 3) importance of identifying a clinical lead of patient care who will direct palliative care discussions (primary care provider), and 4) importance of identifying a care coordination lead (HHC) to bridge communication among multi-setting providers. These themes highlight a multi-setting approach that would improve the frequency and quality of palliative care discussions., Conclusions: A lack of structured communication across settings is a major barrier to introducing and providing palliative care. A novel model that improves communication and coordination of palliative care across HHC, inpatient and primary care providers may facilitate identifying and addressing palliative care needs in medically complex older adults., Competing Interests: Declaration of conflicting interestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. The Authors declare that there is no conflict of interest.

Objective: This study aimed to develop a natural language processing (NLP) system that identified social risk factors in home health care (HHC) clinical notes and to examine the association between social risk factors and hospitalization or an emergency department (ED) visit., Design: Retrospective cohort study., Setting and Participants: We used standardized assessments and clinical notes from one HHC agency located in the northeastern United States. This included 86,866 episodes of care for 65,593 unique patients. Patients received HHC services between 2015 and 2017., Methods: Guided by HHC experts, we created a vocabulary of social risk factors that influence hospitalization or ED visit risk in the HHC setting. We then developed an NLP system to automatically identify social risk factors documented in clinical notes. We used an adjusted logistic regression model to examine the association between the NLP-based social risk factors and hospitalization or an ED visit., Results: On the basis of expert consensus, the following social risk factors emerged: Social Environment, Physical Environment, Education and Literacy, Food Insecurity, Access to Care, and Housing and Economic Circumstances. Our NLP system performed "very good" with an F score of 0.91. Approximately 4% of clinical notes (33% episodes of care) documented a social risk factor. The most frequently documented social risk factors were Physical Environment and Social Environment. Except for Housing and Economic Circumstances, all NLP-based social risk factors were associated with higher odds of hospitalization and ED visits., Conclusions and Implications: HHC clinicians assess and document social risk factors associated with hospitalizations and ED visits in their clinical notes. Future studies can explore the social risk factors documented in HHC to improve communication across the health care system and to predict patients at risk for being hospitalized or visiting the ED., (Copyright © 2023 AMDA – The Society for Post-Acute and Long-Term Care Medicine. Published by Elsevier Inc. All rights reserved.)

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., (© The Author(s) 2023. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

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, F1, 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 F1 = 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., (© The Author(s) 2023. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

One-third of home healthcare patients are hospitalized or visit emergency departments during a 60-day episode of care. Among all risk factors, psychological, cognitive, and behavioral symptoms often remain underdiagnosed or undertreated in older adults. Little is known on subgroups of older adults receiving home healthcare services with similar psychological, cognitive, and behavioral symptom profiles and an at-risk subgroup for future hospitalization and emergency department visits. Our cross-sectional study used data from a large, urban home healthcare organization ( n  = 87,943). Latent class analysis was conducted to identify meaningful subgroups of older adults based on their distinct psychological, cognitive, and behavioral symptom profiles. Adjusted multiple logistic regression was used to understand the association between the latent subgroup and future hospitalization and emergency department visits. Descriptive and inferential statistics were conducted to describe the individual characteristics and to test for significant differences. The three-class model consisted of Class 1: "Moderate psychological symptoms without behavioral issues," Class 2: "Severe psychological symptoms with behavioral issues," and Class 3: "Mild psychological symptoms without behavioral issues." Compared to Class 3, Class 1 patients had 1.14 higher odds and Class 2 patients had 1.26 higher odds of being hospitalized or visiting emergency departments. Significant differences were found in individual characteristics such as age, gender, race/ethnicity, and insurance. Home healthcare clinicians should consider the different latent subgroups of older adults based on their psychological, cognitive, and behavioral symptoms. In addition, they should provide timely assessment and intervention especially to those at-risk for hospitalization and emergency department visits.

Introduction: A tremendous burden is placed on frontotemporal degeneration (FTD) caregivers who sacrifice their own self-care to manage the functional impairments of their loved one, contributing to high levels of stress and depression. Health coaching provides support for coping with stress while fostering self-care behaviors. We report on preliminary evidence for efficacy of a virtual health coach intervention aimed at increasing self-care., Methods: Thirty-one caregivers of persons with behavioral variant FTD (bvFTD) were assigned randomly to an intervention group, which included 10 coaching sessions over 6 months plus targeted health information or the control group receiving standard care augmented with the health information. Caregiver self-care (primary outcome), stress, depression, coping, and patient behavioral symptoms were collected at enrollment and 3 and 6 months. Change over time was evaluated between the intervention and control groups using linear mixed-effects models., Results: There was a significant group-by-time interaction for self-care monitoring ( t 58 = 2.37, p = 0.02 and self-care confidence ( t 58 = 2.32, p = 0.02) on the Self-Care Inventory, demonstrating that caregivers who received the intervention improved their self-care over time. Behavioral symptoms were reduced in bvFTD patients whose caregivers received the intervention ( t 54 = -2.15, p = 0.03)., Discussion: This randomized controlled trial (RCT) shows promise for health coaching as a way to increase support that is urgently needed to reduce poor outcomes in FTD caregivers., Competing Interests: The authors declare no conflicts of interest. Author disclosures are available in the supporting information., (© 2023 The Authors. Alzheimer's & Dementia: Translational Research & Clinical Interventions published by Wiley Periodicals LLC on behalf of Alzheimer's Association.)

Background: The Robert Wood Johnson Foundation launched the Future of Nursing Scholars program to support nurses to complete PhDs in 3 years in schools across the United States., Purpose: To explore why scholars participated in the program and to articulate challenges and facilitators to successful completion of their doctoral degrees., Method: Thirty-one scholars representing 18 different schools participated in focus groups at a convening in January 2022., Findings: Scholars identified that funding and planned length of degree completion were important factors in their choosing the accelerated program. Mentorship, networking, and support were identified as facilitators to program completion with the tight timeline of three years noted as a challenge., Discussion: Accelerated students require adequate resources including access to data, mentoring, and financing to overcome challenges presented by accelerated PhD training programs. Cohort models provide support and clarity of expectations for both students and mentors is critical., (Copyright © 2023 The Author(s). Published by Elsevier Inc. All rights reserved.)

Our objective was to detect common barriers to post-acute care (B2PAC) among hospitalized older adults using natural language processing (NLP) of clinical notes from patients discharged home when a clinical decision support system recommended post-acute care. We annotated B2PAC sentences from discharge planning notes and developed an NLP classifier to identify the highest-value B2PAC class (negative patient preferences). Thirteen machine learning models were compared with Amazon's AutoGluon deep learning model. The study included 594 acute care notes from 100 patient encounters (1156 sentences contained 11 B2PAC) in a large academic health system. The most frequent and modifiable B2PAC class was negative patient preferences (18.3%). The best supervised model was Extreme Gradient Boosting (F1: 0.859), but the deep learning model performed better (F1: 0.916). Alerting clinicians of negative patient preferences early in the hospitalization can prompt interventions such as patient education to ensure patients receive the right level of care and avoid negative outcomes., (©2022 AMIA - All rights reserved.)

Home healthcare (HHC) agencies provide care to more than 3.4 million adults per year. There is value in studying HHC narrative notes to identify patients at risk for deterioration. This study aimed to build machine learning algorithms to identify "concerning" narrative notes of HHC patients and identify emerging themes. Six algorithms were applied to narrative notes (n = 4,000) from a HHC agency to classify notes as either "concerning" or "not concerning." Topic modeling using Latent Dirichlet Allocation bag of words was conducted to identify emerging themes from the concerning notes. Gradient Boosted Trees demonstrated the best performance with a F-score = 0.74 and AUC = 0.96. Emerging themes were related to patient-clinician communication, HHC services provided, gait challenges, mobility concerns, wounds, and caregivers. Most themes have been cited by previous literature as increasing risk for adverse events. In the future, such algorithms can support early identification of patients at risk for deterioration., (©2022 AMIA - All rights reserved.)

Background: Despite providing frequent care to heart failure (HF) patients, home health care workers (HHWs) are generally considered neither part of the health care team nor the family, and their clinical observations are often overlooked. To better understand this workforce's involvement in care, we quantified HHWs' scope of interactions with clinicians, health systems, and family caregivers., Methods: Community-partnered cross-sectional survey of English- and Spanish-speaking HHWs who cared for a HF patient in the last year. The survey included 6 open-ended questions about aspects of care coordination, alongside demographic and employment characteristics. Descriptive statistics were performed., Results: Three hundred ninety-one HHWs employed by 56 unique home care agencies completed the survey. HHWs took HF patients to a median of 3 doctor appointments in the last year with 21.9% of them taking patients to ≥ 7 doctor appointments. Nearly a quarter of HHWs reported that these appointments were in ≥ 3 different health systems. A third of HHWs organized care for their HF patient with ≥ 2 family caregivers., Conclusions: HHWs' scope of health-related interactions is large, indicating that there may be novel opportunities to leverage HHWs' experiences to improve health care delivery and patient care in HF., Competing Interests: Conflict of interest: The authors have no conflicts to disclose., (© Copyright by the American Board of Family Medicine.)

Objectives: This study aimed to explore how the COVID-19 pandemic shaped the experiences of family caregivers of older adults who were hospitalized with COVID-19 and discharged to post-acute, skilled home health care (HHC) services., Methods: Thirty semi-structured interviews with family caregivers of older adults who received services from a large, not-for-profit HHC agency following hospitalization with COVID-19 infection were conducted between March-July 2021 and analyzed using thematic analysis., Results: During the pandemic, family caregivers encountered societal and institutional barriers to assisting older adults across post-acute care transitions. These barriers included hospital visitation restrictions as well as difficulties accessing community-based resources and medical equipment. Despite limitations and delays in HHC services, many family caregivers identified post-acute HHC, delivered in-person or via telehealth, as important to addressing care gaps for older adults, as well as their own needs for training and support during the pandemic., Conclusions: Policies intended to reduce the spread of COVID-19 introduced new challenges for caregivers during HHC. However, HHC agencies and their staff adapted within this context to provide a needed bridge of support., Competing Interests: Declaration of Competing Interest The authors have no competing interests to report in relationship to the research reported in this manuscript., (Copyright © 2023. Published by Elsevier B.V.)

Background: The COVID-19 pandemic increased the importance of technology for all Americans, including older adults. Although a few studies have indicated that older adults might have increased their technology use during the COVID-19 pandemic, further research is needed to confirm these findings, especially among different populations, and using validated surveys. In particular, research on changes in technology use among previously hospitalized community-dwelling older adults, especially those with physical disability, is needed because older adults with multimorbidity and hospital associated deconditioning were a population greatly impacted by COVID-19 and related distancing measures. Obtaining knowledge regarding previously hospitalized older adults' technology use, before and during the pandemic, could inform the appropriateness of technology-based interventions for vulnerable older adults., Objective: In this paper, we 1) described changes in older adult technology-based communication, technology-based phone use, and technology-based gaming during the COVID-19 pandemic, compared to before the COVID-19 pandemic and 2) tested whether technology use moderated the association between changes in in-person visits and well-being, controlling for covariates., Methods: Between December 2020 and January 2021 we conducted a telephone-based objective survey with 60 previously hospitalized older New Yorkers with physical disability. We measured technology-based communication through three questions pulled from the National Health and Aging Trends Study COVID-19 Questionnaire. We measured technology-based smart phone use and technology-based video gaming through the Media Technology Usage and Attitudes Scale. We used paired t tests and interaction models to analyze survey data., Results: This sample of previously hospitalized older adults with physical disability consisted of 60 participants, 63.3% of whom identified as female, 50.0% of whom identified as White, and 63.8% of whom reported an annual income of $25,000 or less. This sample had not had physical contact (such as friendly hug or kiss) for a median of 60 days and had not left their home for a median of 2 days. The majority of older adults from this study reported using the internet, owning smart phones, and nearly half learned a new technology during the pandemic. During the pandemic, this sample of older adults significantly increased their technology-based communication (mean difference=.74, P=.003), smart phone use (mean difference=2.9, P=.016), and technology-based gaming (mean difference=.52, P=.030). However, this technology use during the pandemic did not moderate the association between changes in in-person visits and well-being, controlling for covariates., Conclusions: These study findings suggest that previously hospitalized older adults with physical disability are open to using or learning technology, but that technology use might not be able to replace in-person social interactions. Future research might explore the specific components of in-person visits that are missing in virtual interactions, and if they could be replicated in the virtual environment, or through other means., (©Brittany F Drazich, Ji Won Lee, Kathryn H Bowles, Janiece L Taylor, Shivani Shah, Barbara Resnick, Nayeon Kim, Sarah L Szanton. Originally published in JMIR Aging (https://aging.jmir.org), 29.03.2023.)

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., (© 2022 John Wiley & Sons Ltd.)

Objective: Despite recent calls for home healthcare (HHC) to integrate informatics, the application of machine learning in HHC is relatively unknown. Thus, this study aimed to synthesize and appraise the literature describing the application of machine learning to predict adverse outcomes (e.g., hospitalization, mortality) using electronic health record (EHR) data in the HHC setting. Our secondary aim was to evaluate the comprehensiveness of predictors used in the machine learning algorithms guided by the Biopsychosocial Model., Methods: During March 2022 we conducted a literature search in four databases: PubMed, Embase, CINAHL, and Scopus. Inclusion criteria were 1) describing services provided in the HHC setting, 2) applying machine learning algorithms to predict adverse outcomes, defined as outcomes related to patient deterioration, 3) using EHR data and 4) focusing on the adult population. Predictors were mapped to the Biopsychosocial Model. A risk of bias analysis was conducted using the Prediction Model Risk Of Bias Assessment Tool., Results: The final sample included 20 studies. Eighteen studies used predictors from standardized assessments integrated in the EHR. The most common outcome of interest was hospitalization (55%), followed by mortality (25%). Psychological predictors were frequently excluded (35%). Tree based algorithms were most frequently applied (75%). Most studies demonstrated high or unclear risk of bias (75%)., Conclusion: Future studies in HHC should consider incorporating machine learning algorithms into clinical decision support systems to identify patients at risk. Based on the Biopsychosocial model, psychological and interpersonal characteristics should be used along with biological characteristics to enhance risk prediction. To facilitate the widespread adoption of machine learning, stakeholders should encourage standardization in the HHC setting., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier B.V. All rights reserved.)

Unmet needs for family caregiver assistance threaten patient outcomes during Medicare home health care (HHC). Sepsis survivors represent a growing proportion of the HHC patient population, but little is known regarding their risk for unmet caregiving needs. We describe prevalence and underlying cause of unmet caregiving needs for sepsis survivors receiving HHC, using HHC patient assessment data for 85,851 older sepsis survivors receiving post-acute HHC in 2013-2014. Unmet caregiving needs were most common for assistance with Activities of Daily Living (ADLs) (28%) and medication administration (27%). Caregivers' need for training accounted for more than three-fourths of all unmet caregiving needs. Those who experienced decline/no improvement in cognitive function were more likely to experience unmet caregiving needs. Findings highlight the potential value of expanding family caregiver training to improve HHC outcomes for sepsis survivors and indicate that caregivers of sepsis survivors with poor cognitive function may benefit most.

Objectives: This study explored the association between the timing of the first home health care nursing visits (start-of-care visit) and 30-day rehospitalization or emergency department (ED) visits among patients discharged from hospitals., Design: Our cross-sectional study used data from 1 large, urban home health care agency in the northeastern United States., Setting/participants: We analyzed data for 49,141 home health care episodes pertaining to 45,390 unique patients who were admitted to the agency following hospital discharge during 2019., Methods: We conducted multivariate logistic regression analyses to examine the association between start-of-care delays and 30-day hospitalizations and ED visits, adjusting for patients' age, race/ethnicity, gender, insurance type, and clinical and functional status. We defined delays in start-of-care as a first nursing home health care visit that occurred more than 2 full days after the hospital discharge date., Results: During the study period, we identified 16,251 start-of-care delays (34% of home health care episodes), with 14% of episodes resulting in 30-day rehospitalization and ED visits. Delayed episodes had 12% higher odds of rehospitalization or ED visit (OR 1.12; 95% CI: 1.06-1.18) compared with episodes with timely care., Conclusions and Implications: The findings suggest that timely start-of-care home health care nursing visit is associated with reduced rehospitalization and ED use among patients discharged from hospitals. With more than 6 million patients who receive home health care services across the United States, there are significant opportunities to improve timely care delivery to patients and improve clinical outcomes., (Copyright © 2022 AMDA – The Society for Post-Acute and Long-Term Care Medicine. Published by Elsevier Inc. All rights reserved.)