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

BACKGROUND: We developed the Hospital-to-Home-Health Transition Quality (H3TQ) Index for skilled home healthcare (HH) agencies to identify threats to safe, high-quality care transitions in real time. OBJECTIVE: Assess the validity of H3TQ in a large sample across diverse communities. RESEARCH DESIGN: A survey of recently hospitalized older adults referred for skilled HH services and their HH provider at two large HH agencies in Baltimore, MD, and New York, NY. 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.

Feasibility, usability, and acceptability of psychoeducational videoconferencing interventions for informal caregivers: A systematic review of randomized controlled trials.

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.

Application of a Human Factors and Systems Engineering Approach to Explore Care Transitions of Sepsis Survivors From Hospital to Home Health Care.

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.

The identification of clusters of risk factors and their association with hospitalizations or emergency department visits in home health care.

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.

RWJF Future of Nursing Scholars experience and recommendations: Focus group results at final convening.

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.

Clinical notes: An untapped opportunity for improving risk prediction for hospitalization and emergency department visit during home health care.

BACKGROUND/OBJECTIVE: Between 10 and 25% patients are hospitalized or visit emergency department (ED) during home healthcare (HHC). Given that up to 40% of these negative clinical outcomes are preventable, early and accurate prediction of hospitalization risk can be one strategy to prevent them. In recent years, machine learning-based predictive modeling has become widely used for building risk models. This study aimed to compare the predictive performance of four risk models built with various data sources for hospitalization and ED visits in HHC. METHODS: Four risk models were built using different variables from two data sources: structured data (i.e., Outcome and Assessment Information Set (OASIS) and other assessment items from the electronic health record (EHR)) and unstructured narrative-free text clinical notes for patients who received HHC services from the largest non-profit HHC organization in New York between 2015 and 2017. Then, five machine learning algorithms (logistic regression, Random Forest, Bayesian network, support vector machine (SVM), and Naïve Bayes) were used on each risk model. Risk model performance was evaluated using the F-score and Precision-Recall Curve (PRC) area metrics. RESULTS: During the study period, 8373/86,823 (9.6%) HHC episodes resulted in hospitalization or ED visits. Among five machine learning algorithms on each model, the SVM showed the highest F-score (0.82), while the Random Forest showed the highest PRC area (0.864). Adding information extracted from clinical notes significantly improved the risk prediction ability by up to 16.6% in F-score and 17.8% in PRC. CONCLUSION: All models showed relatively good hospitalization or ED visit risk predictive performance in HHC. Information from clinical notes integrated with the structured data improved the ability to identify patients at risk for these emergent care events.

Nurse perspectives on the psychosocial care of patients with urinary incontinence in home hospice: A qualitative study.

BACKGROUND: Urinary incontinence is prevalent among patients receiving home hospice and presents multiple care management challenges for nurses and family caregivers. AIM: This study sought to understand how urinary incontinence influences the psychosocial care of patients receiving home hospice and the strategies that nurses employ to maximize patient and family comfort. DESIGN: Qualitative descriptive study using semi-structured interviews. SETTING/PARTICIPANTS: Nurses employed at a large not-for-profit hospice agency in New York City. RESULTS: Analyses of 32 interviews revealed three primary themes. First, nurses considered urinary incontinence to be associated with multiple psychosocial issues including embarrassment for patients and caregiver burden. Second, nurses described urinary incontinence as a threat to patient dignity and took steps to preserve their continence function. Third, nurses assisted patients and their families to cope with urinary incontinence through normalization, reframing incontinence as part of the disease process, mobilizing caregiving assistance, and encouraging use of continence supplies such as diapers and liners. CONCLUSION: Urinary incontinence influences the psychosocial care of patients receiving home hospice and nurses employ strategies to maximize patient and family comfort. Additional research is needed to examine the psychosocial benefits of facilitated discussions with patients and family members about incontinence, provision of caregiving support, and distribution of comprehensive incontinence supplies to patients with fewer resources.

Detecting Language Associated With Home Healthcare Patient's Risk for Hospitalization and Emergency Department Visit.

BACKGROUND: About one in five patients receiving home healthcare (HHC) services are hospitalized or visit an emergency department (ED) during a home care episode. Early identification of at-risk patients can prevent these negative outcomes. However, risk indicators, including language in clinical notes that indicate a concern about a patient, are often hidden in narrative documentation throughout their HHC episode. OBJECTIVE: The aim of the study was to develop an automated natural language processing (NLP) algorithm to identify concerning language indicative of HHC patients' risk of hospitalizations or ED visits. METHODS: This study used the Omaha System-a standardized nursing terminology that describes problems/signs/symptoms that can occur in the community setting. First, five HHC experts iteratively reviewed the Omaha System and identified concerning concepts indicative of HHC patients' risk of hospitalizations or ED visits. Next, we developed and tested an NLP algorithm to identify these concerning concepts in HHC clinical notes automatically. The resulting NLP algorithm was applied on a large subset of narrative notes (2.3 million notes) documented for 66,317 unique patients ( n = 87,966 HHC episodes) admitted to one large HHC agency in the Northeast United States between 2015 and 2017. RESULTS: A total of 160 Omaha System signs/symptoms were identified as concerning concepts for hospitalizations or ED visits in HHC. These signs/symptoms belong to 31 of the 42 available Omaha System problems. Overall, the NLP algorithm showed good performance in identifying concerning concepts in clinical notes. More than 18% of clinical notes were detected as having at least one concerning concept, and more than 90% of HHC episodes included at least one Omaha System problem. The most frequently documented concerning concepts were pain, followed by issues related to neuromusculoskeletal function, circulation, mental health, and communicable/infectious conditions. CONCLUSION: Our findings suggest that concerning problems or symptoms that could increase the risk of hospitalization or ED visit were frequently documented in narrative clinical notes. NLP can automatically extract information from narrative clinical notes to improve our understanding of care needs in HHC. Next steps are to evaluate which concerning concepts identified in clinical notes predict hospitalization or ED visit.

Surviving COVID-19 After Hospital Discharge: Symptom, Functional, and Adverse Outcomes of Home Health Recipients.

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.

Improving transitions and outcomes of sepsis survivors (I-TRANSFER): a type 1 hybrid protocol.

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.

Cognitive impairment associated with greater care intensity during home health care.

BACKGROUND: In Medicare-funded home health care (HHC), one in three patients has cognitive impairment (CI), but little is known about the care intensity they receive in this setting. Recent HHC reimbursement changes fail to adjust for patient CI, potentially creating a financial disincentive to caring for these individuals. METHODS: This cohort study included a nationally representative sample of 1214 Medicare HHC patients between 2011 and 2016. Multivariable logistic and negative binomial regressions modelled the relationship between patient CI and care intensity-measured as the number and type of visits received during HHC and likelihood of receiving multiple successive HHC episodes. RESULTS: Patients with CI had 45% (P < .05) greater odds of receiving multiple successive HHC episodes and received an additional 2.82 total (P < .001), 1.39 nursing (P = .003), 0.72 physical therapy (P = .03), and 0.60 occupational therapy visits (P = .01) during the index HHC episode. DISCUSSION: Recent HHC reimbursement changes do not reflect the more intensive care needs of patients with CI.

Electronic Health Record Usability: Associations With Nurse and Patient Outcomes in Hospitals.

BACKGROUND: Electronic health record (EHR) usability issues represent an emerging threat to the wellbeing of nurses and patients; however, few large studies have examined these relationships. OBJECTIVE: To examine associations between EHR usability and nurse job (burnout, job dissatisfaction, and intention to leave) and surgical patient (inpatient mortality and 30-day readmission) outcomes. METHODS: A cross-sectional analysis of linked American Hospital Association, state patient discharge, and nurse survey data was conducted. The sample included 343 hospitals, 1,281,848 surgical patients, and 12,004 nurses. Logistic regression models were used to assess relationships between EHR usability and outcomes, before and after accounting for EHR adoption level (comprehensive vs. basic or less) and other confounders. RESULTS: In fully adjusted models, nurses who worked in hospitals with poorer EHR usability had significantly higher odds of burnout [odds ratio (OR), 1.41; 95% confidence interval (CI), 1.21-1.64], job dissatisfaction (OR, 1.61; 95% CI, 1.37-1.90) and intention to leave (OR, 1.31; 95% CI, 1.09-1.58) compared with nurses working in hospitals with better usability. Surgical patients treated in hospitals with poorer EHR usability had significantly higher odds of inpatient mortality (OR, 1.21; 95% CI, 1.09-1.35) and 30-day readmission (OR, 1.06; 95% CI, 1.01-1.12) compared with patients in hospitals with better usability. Comprehensive EHR adoption was associated with higher odds of nurse burnout (OR, 1.14; 95% CI, 1.01-1.28). CONCLUSION: Employing EHR systems with suboptimal usability was associated with higher odds of adverse nurse job outcomes and surgical patient mortality and readmission. EHR usability may be more important to nurse job and patient outcomes than comprehensive EHR adoption.

Lessons learned from the implementation of a video health coaching technology intervention to improve self-care of family caregivers of adults with heart failure.

Individuals with heart failure (HF) typically live in the community and are cared for at home by family caregivers. These caregivers often lack supportive services and the time to access those services when available. Technology can play a role in conveniently bringing needed support to these caregivers. The purpose of this article is to describe the implementation of a virtual health coaching intervention with caregivers of HF patients ("Virtual Caregiver Coach for You"-ViCCY). A randomized controlled trial is currently in progress to test the efficacy of the intervention to improve self-care. In this trial, 250 caregivers will be randomly assigned to receive health information via a tablet computer (hereafter, tablet) plus 10 live health coaching sessions delivered virtually (intervention group; n = 125) or health information via a tablet only (control group; n = 125). Each tablet has specific health information websites preloaded. To inform others embarking on similar technology projects, here we highlight the technology challenges encountered with the first 15 caregivers who received the ViCCY intervention and the solutions used to overcome those challenges. Several adaptations to the implementation of ViCCY were needed to address hardware, software, and network connectivity challenges. Even with a well-designed research implementation plan, it is important to re-examine strategies at every step to solve implementation barriers and maximize fidelity to the intervention. Researcher and interventionist flexibility in adapting to new strategies is essential when implementing a technology-based virtual health coaching intervention.

Patients' and caregivers' perspectives in determining discharge readiness from home health.

There are no national, empirically derived clinical decision support tools to assist the interprofessional home health team in determining readiness for discharge from skilled home health. Eliciting patient and family caregiver perspectives around readiness for home health discharge is integral to developing tools that address their needs in this decision-making process. The purpose of this study was to describe the factors home health patients and their family caregivers perceive as critical when determining readiness for discharge from services. A qualitative descriptive study was conducted among skilled home health recipients and their family caregivers who were either recently discharged or recertified for additional care from two different Medicare-certified skilled home health agencies. Nine themes emerged: self-care ability, functional status, status of condition(s) and symptoms, presence of a caregiver, support for the caregiver, connection to community resources/support, safety needs of the home environment addressed, adherence to the prescribed regimen, and care coordination.

Planning the Episode: Home Care Admission Nurse Decision-Making Regarding the Patient Visit Pattern.

During home health care (HHC) admissions, nurses provide input into decisions regarding the skilled nursing visit frequency and episode duration. This important clinical decision can impact patient outcomes including hospitalization. Episode duration has recently gained greater importance due to the Centers for Medicare and Medicaid Services (CMS) decrease in reimbursable episode length from 60 to 30 days. We examined admissions nurses' visit pattern decision-making and whether it is influenced by documentation available before and during the first home visit, agency standards, other disciplines being scheduled, and electronic health record (EHR) use. This observational mixed-methods study included admission document analysis, structured interviews, and a think-aloud protocol with 18 nurses from 3 diverse HHC agencies (6 at each) admitting 2 patients each (36 patients). Findings show that prior to entering the home, nurses had an information deficit; they either did not predict the patient's visit frequency and episode duration or stated them based on experience with similar patients. Following patient interaction in the home, nurses were able to make this decision. Completion of documentation using the EHR did not appear to influence visit pattern decisions. Patient condition and insurance restrictions were influential on both frequency and duration. Given the information deficit at admission, and the delay in visit pattern decision making, we offer health information technology recommendations on electronic communication of structured information, and EHR documentation and decision support.

Live Discharge From Hospice Due to Acute Hospitalization: The Role of Neighborhood Socioeconomic Characteristics and Race/Ethnicity.

BACKGROUND: Acute hospitalization is a frequent reason for live discharge from hospice. Although risk factors for live discharge among hospice patients have been well documented, prior research has not examined the role of neighborhood socioeconomic characteristics, or how these characteristics relate to racial/ethnic disparities in hospice outcomes. OBJECTIVE: To examine associations between neighborhood socioeconomic characteristics and risk for live discharge from hospice because of acute hospitalization. The authors also explore the moderating role of race/ethnicity in any observed relationship. RESEARCH DESIGN: Retrospective cohort study using electronic medical records of hospice patients (N=17,290) linked with neighborhood-level socioeconomic data (N=55 neighborhoods). Multilevel models were used to identify the independent significance of patient and neighborhood-level characteristics for risk of live discharge because of acute hospitalization. RESULTS: Compared with the patients in the most well-educated and affluent sections of New York City [quartile (Q)4], the odds of live discharge from hospice because of acute hospitalization were greater among patients who resided in neighborhoods where lower proportions of residents held college degrees [Q1 adjusted odds ratio (AOR), 1.36; 95% confidence interval (CI), 1.06-1.75; Q2 AOR, 1.41; 95% CI, 1.07-1.84] and median household incomes were lower (Q1 AOR, 1.42; 95% CI, 1.10-1.85; Q2 AOR, 1.43; 95% CI, 1.10-1.85; Q3 AOR, 1.39; 95% CI, 1.07-1.80). However, these observed relationships were not equally distributed by patient race/ethnicity; the association of neighborhood socioeconomic disadvantage and risk for live discharge was significantly lower among Hispanic compared with white patients. CONCLUSIONS: Findings demonstrate neighborhood socioeconomic disadvantage poses a significant risk for live discharge from hospice. Additional research is needed to clarify the social mechanisms underlying this association, including greater attention to the experiences of hospice patients from under-represented racial/ethnic groups.

Factors Affecting Patient Prioritization Decisions at Admission to Home Healthcare: A Predictive Study to Develop a Risk Screening Tool.

There is a lack of evidence on how to identify high-risk patients admitted to home healthcare. This study aimed (1) to identify which disease characteristics, medications, patient needs, social support characteristics, and other factors are associated with patient priority for the first home health nursing visit; and (2) to construct and validate a predictive model of patient priority for the first home health nursing visit. This was a predictive study of home health visit priority decisions made by 20 nurses for 519 older adults. The study found that nurses were more likely to prioritize patients who had wounds (odds ratio = 1.88), comorbid condition of depression (odds ratio = 1.73), limitation in current toileting status (odds ratio = 2.02), higher number of medications (increase in odds ratio for each medication = 1.04), and comorbid conditions (increase in odds ratio for each condition = 1.04). This study developed one of the first clinical decision support tools for home healthcare called "PREVENT". (PRiority home health Visit Tool). Further work is needed to increase the specificity and generalizability of the tool and to test its effects on patient outcomes.

Does Early Follow-Up Improve the Outcomes of Sepsis Survivors Discharged to Home Health Care?

BACKGROUND: There is little evidence to guide the care of over a million sepsis survivors following hospital discharge despite high rates of hospital readmission. OBJECTIVE: We examined whether early home health nursing (first visit within 2 days of hospital discharge and at least 1 additional visit in the first posthospital week) and early physician follow-up (an outpatient visit in the first posthospital week) reduce 30-day readmissions among Medicare sepsis survivors. DESIGN: A pragmatic, comparative effectiveness analysis of Medicare data from 2013 to 2014 using nonlinear instrumental variable analysis. SUBJECTS: Medicare beneficiaries in the 50 states and District of Columbia discharged alive after a sepsis hospitalization and received home health care. MEASURES: The outcomes, protocol parameters, and control variables were from Medicare administrative and claim files and the home health Outcome and Assessment Information Set (OASIS). The primary outcome was 30-day all-cause hospital readmission. RESULTS: Our sample consisted of 170,571 mostly non-Hispanic white (82.3%), female (57.5%), older adults (mean age, 76 y) with severe sepsis (86.9%) and a multitude of comorbid conditions and functional limitations. Among them, 44.7% received only the nursing protocol, 11.0% only the medical doctor protocol, 28.1% both protocols, and 16.2% neither. Although neither protocol by itself had a statistically significant effect on readmission, both together reduced the probability of 30-day all-cause readmission by 7 percentage points (P=0.006; 95% confidence interval=2, 12). CONCLUSIONS: Our findings suggest that, together, early postdischarge care by home health and medical providers can reduce hospital readmissions for sepsis survivors.

Mining fall-related information in clinical notes: Comparison of rule-based and novel word embedding-based machine learning approaches.

BACKGROUND: Natural language processing (NLP) of health-related data is still an expertise demanding, and resource expensive process. We created a novel, open source rapid clinical text mining system called NimbleMiner. NimbleMiner combines several machine learning techniques (word embedding models and positive only labels learning) to facilitate the process in which a human rapidly performs text mining of clinical narratives, while being aided by the machine learning components. OBJECTIVE: This manuscript describes the general system architecture and user Interface and presents results of a case study aimed at classifying fall-related information (including fall history, fall prevention interventions, and fall risk) in homecare visit notes. METHODS: We extracted a corpus of homecare visit notes (n = 1,149,586) for 89,459 patients from a large US-based homecare agency. We used a gold standard testing dataset of 750 notes annotated by two human reviewers to compare the NimbleMiner's ability to classify documents regarding whether they contain fall-related information with a previously developed rule-based NLP system. RESULTS: NimbleMiner outperformed the rule-based system in almost all domains. The overall F- score was 85.8% compared to 81% by the rule based-system with the best performance for identifying general fall history (F = 89% vs. F = 85.1% rule-based), followed by fall risk (F = 87% vs. F = 78.7% rule-based), fall prevention interventions (F = 88.1% vs. F = 78.2% rule-based) and fall within 2 days of the note date (F = 83.1% vs. F = 80.6% rule-based). The rule-based system achieved slightly better performance for fall within 2 weeks of the note date (F = 81.9% vs. F = 84% rule-based). DISCUSSION & CONCLUSIONS: NimbleMiner outperformed other systems aimed at fall information classification, including our previously developed rule-based approach. These promising results indicate that clinical text mining can be implemented without the need for large labeled datasets necessary for other types of machine learning. This is critical for domains with little NLP developments, like nursing or allied health professions.

Machine Learning Methods for Identifying Critical Data Elements in Nursing Documentation.

BACKGROUND: Public health nurses (PHNs) engage in home visiting services and documentation of care services for at-risk clients. To increase efficiency and decrease documentation burden, it would be useful for PHNs to identify critical data elements most associated with patient care priorities and outcomes. Machine learning techniques can aid in retrospective identification of critical data elements. OBJECTIVE: We used two different machine learning feature selection techniques of minimum redundancy-maximum relevance (mRMR) and LASSO (least absolute shrinkage and selection operator) and elastic net regularized generalized linear model (glmnet in R). METHODS: We demonstrated application of these techniques on the Omaha System database of 205 data elements (features) with a cohort of 756 family home visiting clients who received at least one visit from PHNs in a local Midwest public health agency. A dichotomous maternal risk index served as the outcome for feature selection. APPLICATION: Using mRMR as a feature selection technique, out of 206 features, 50 features were selected with scores greater than zero, and generalized linear model applied on the 50 features achieved highest accuracy of 86.2% on a held-out test set. Using glmnet as a feature selection technique and obtaining feature importance, 63 features had importance scores greater than zero, and generalized linear model applied on them achieved the highest accuracy of 95.5% on a held-out test set. DISCUSSION: Feature selection techniques show promise toward reducing public health nursing documentation burden by identifying the most critical data elements needed to predict risk status. Further studies to refine the process of feature selection can aid in informing PHNs' focus on client-specific and targeted interventions in the delivery of care.