Investigating the association between asthma and opioid use disorder with interactions of anxiety and depression among a national sample of the US population.

INTRODUCTION: Previous studies have not examined the association between asthma and opioid use disorder (OUD) in a comprehensive national sample of the U.S. population. This study aims to investigate such an association. METHODS: This is a matched retrospective cohort study, with a follow-up period of two years, utilizing longitudinal electronic medical records of a comprehensive national healthcare database in the U.S.-Cerner-Real World DataTM. Patients selected for analysis were ≥12 years old with a hospital encounter between January 2000 and June 2020. Adjusted risk ratios (aRRs) of incident OUD for those with asthma compared to those without asthma were calculated using a modified Poisson regressions with robust standard errors via the Huber-White sandwich estimator, and results were stratified by comorbid mental illnesses. RESULTS: Individuals with asthma had a greater risk of OUD compared to those without asthma (aRR = 2.12; 95% CI 2.03-2.23). When stratified by anxiety and depression status, individuals with asthma and no anxiety or depression had a greater risk of incident OUD compared to individuals with asthma and either anxiety, depression, or both. Additionally, individuals with asthma medication had 1.29 (95% CI: 1.24, 1.35) greater overall risk for incident OUD compared to those without medication. Independent of comorbid mental illnesses, individuals with asthma medication had greater risk for incident OUD compared to those without medication among individuals without severe/obstructive asthma. CONCLUSIONS: Individuals with asthma face a higher OUD risk compared to those without asthma. Comorbid mental illnesses modulate this risk. Caution is advised in opioid prescribing for asthma patients.

Using Concept Unique Identifiers to Filter Electronic Health Records for Delirium Cases.

Delirium, an acute mental status change associated with inattention, confusion, hypervigilance, or somnolence due to a medical cause, is considered a medical emergency. Unfortunately, screening and diagnosis of delirium in acute care are often inadequate. It is estimated that 60% of delirium cases are not identified, and in claims data, they are underreported. Using information technology, we investigated whether concept unique identifiers from the Unified Language Medical System Metathesaurus could be used as a method to filter electronic health records for possible delirium cases. This article provides the reader with an overview of delirium, the Unified Language Medical System Metathesaurus, and our method for retrospectively filtering electronic health records for delirium cases from our clinical research database. Using a retrospective observational approach, we randomly selected 150 electronic health records with narrative notes containing a delirium concept unique identifier. One hundred records were used for training and 50 were used for validation and interrater reliability. Our results validate electronic health record-selected concept unique identifiers and provide insights into their use. Refinement and application of this method on a larger scale can provide an initial filter for identifying patients with delirium from the electronic health record.

Clinical nurses' perspectives on discharge practice changes from participating in a translational research study.

AIM: To describe clinical nurses' experiences with practice change associated with participation in a multi-site nursing translational research study implementing new protocols for hospital discharge readiness assessment. BACKGROUND: Nurses' participation in translational research studies provides an opportunity to evaluate how implementation of new nursing interventions affects care processes within a local context. These insights can provide information that leads to successful adoption and sustainability of the intervention. METHODS: Semi-structured focus groups from 30 of 33 participating study hospitals lead by team nurse researchers. RESULTS: Nurses reported improved and earlier awareness of patients' discharge needs, changes in discharge practices, greater patient/family involvement in discharge, synergy and enhanced discharge processes, and implementation challenges. Participating nurses related the benefits of participation in nursing research. CONCLUSION: Participation in a unit-level translational research project was a successful strategy for engaging nurses in practice change to improve hospital discharge. IMPLICATIONS FOR NURSING MANAGEMENT: Leading unit-based implementation of a structured discharge readiness assessment including nurse assessment and patient self-assessment encourages earlier awareness of patients' discharge needs, improved patient assessment and greater patient/family involvement in discharge preparation. Integrating discharge readiness assessments into existing discharge care promotes communication between health team members that facilitates a timely, coordinated discharge.

Nurse Continuity at Discharge and Return to Hospital.

BACKGROUND: Promoting continuity of nurse assignment during discharge care has the potential to increase patient readiness for discharge-which has been associated with fewer readmissions and emergency department visits. The few studies that examined nurse continuity during acute care hospitalizations did not focus on discharge or postdischarge outcomes. OBJECTIVES: The aim of this research was to examine the association of continuity in nurse assignment to patients prior to hospital discharge with return to hospital (readmission and emergency department or observation visits), including exploration of the mediating pathway through patient readiness for discharge and moderating effects of unit environment and unit nurse characteristics. METHODS: In a sample of 18,203 adult, medical-surgical patients from 31 Magnet hospitals, a correlational path analysis design was used in a secondary analysis to evaluate the effect of nurse continuity on readmissions and emergency department or observation visits within 30 days after hospital discharge. The mediating pathway through discharge readiness measured by patient self-report and nurse assessments was also assessed. Moderating effects of unit environment and nursing characteristics were examined across quartiles of unit environment (nurse staffing hours per patient day) and unit nurse characteristics (education and experience). Analyses were adjusted for patient characteristics, unit fixed effects, and clustering at the unit level. RESULTS: Continuous nurse assignment on the last 2 days of hospitalization was observed in 6,441 (35.4%) patient discharges and was associated with a 0.85 absolute percentage point reduction (7.8% relative reduction) in readmissions. There was no significant association with emergency department or observation visits. Sensitivity analysis revealed a stronger effect in patients with higher Elixhauser Comorbidity Indexes. Readiness for discharge was not a mediator of the effect of continuity on return to hospital. Unit characteristics were not associated with nurse continuity. No moderation effect was evident for unit environment and nurse characteristics. DISCUSSION: Continuity of nurse assignment on the last 2 days of hospitalization can reduce readmissions. Staffing for continuity may benefit patients and healthcare systems, with greater benefits for high-comorbidity patients. Nurse continuity prior to hospital discharge should be a priority consideration in assigning acute care nurses to augment readmission reduction efforts.

Individual Nurse Productivity in Preparing Patients for Discharge Is Associated With Patient Likelihood of 30-Day Return to Hospital.

OBJECTIVE: Applied to value-based health care, the economic term "individual productivity" refers to the quality of an outcome attributable through a care process to an individual clinician. This study aimed to (1) estimate and describe the discharge preparation productivities of individual acute care nurses and (2) examine the association between the discharge preparation productivity of the discharging nurse and the patient's likelihood of a 30-day return to hospital [readmission and emergency department (ED) visits]. RESEARCH DESIGN: Secondary analysis of patient-nurse data from a cluster-randomized multisite study of patient discharge readiness and readmission. Patients reported discharge readiness scores; postdischarge outcomes and other variables were extracted from electronic health records. Using the structure-process-outcomes model, we viewed patient readiness for hospital discharge as a proximal outcome of the discharge preparation process and used it to measure nurse productivity in discharge preparation. We viewed hospital return as a distal outcome sensitive to discharge preparation care. Multilevel regression analyses used a split-sample approach and adjusted for patient characteristics. SUBJECTS: A total 522 nurses and 29,986 adult (18+ y) patients discharged to home from 31 geographically diverse medical-surgical units between June 15, 2015 and November 30, 2016. MEASURES: Patient discharge readiness was measured using the 8-item short form of Readiness for Hospital Discharge Scale (RHDS). A 30-day hospital return was a categorical variable for an inpatient readmission or an ED visit, versus no hospital return. RESULTS: Variability in individual nurse productivity explained 9.07% of variance in patient discharge readiness scores. Nurse productivity was negatively associated with the likelihood of a readmission (-0.48 absolute percentage points, P<0.001) and an ED visit (-0.29 absolute percentage points, P=0.042). CONCLUSIONS: Variability in individual clinician productivity can have implications for acute care quality patient outcomes.

Validation of the Registered Nurse Assessment of Readiness for Hospital Discharge Scale.

BACKGROUND: Statistical models for predicting readmissions have been published for high-risk patient populations but typically focus on patient characteristics; nurse judgment is rarely considered in a formalized way to supplement prediction models. OBJECTIVES: The purpose of this study was to determine psychometric properties of long and short forms of the Registered Nurse Readiness for Hospital Discharge Scale (RN-RHDS), including reliability, factor structure, and predictive validity. METHODS: Data were aggregated from two studies conducted at four hospitals in the Midwestern United States. The RN-RHDS was completed within 4 hours before hospital discharge by the discharging nurse. Data on readmissions and emergency department visits within 30 days were extracted from electronic medical records. RESULTS: The RN-RHDS, both long and short forms, demonstrate acceptable reliability (Cronbach's alphas of .90 and .73, respectively). Confirmatory factor analysis demonstrated less than adequate fit with the same four-factor structure observed in the patient version. Exploratory factor analysis identified three factors, explaining 60.2% of the variance. When nurses rate patients as less ready to go home (<7 out of 10), patients are 6.4-9.3 times more likely to return to the hospital within 30 days, in adjusted models. DISCUSSION: The RN-RHDS, long and short forms, can be used to identify medical-surgical patients at risk for potential unplanned return to hospital within 30 days, allowing nurses to use their clinical judgment to implement interventions prior to discharge. Use of the RN-RHDS could enhance current readmission risk prediction models.

Aligning Evidence-Based Practice With Translational Research: Opportunities for Clinical Practice Research.

Magnet® and other organizations investing resources in evidence-based practice (EBP) are ideal laboratories for translational nursing research. Translational research, the study of implementation of evidence into practice, provides a unique opportunity to leverage local EBP work for maximum impact. Aligning EBP projects with rigorous translational research can efficiently meet both EBP and research requirements for Magnet designation or redesignation, inform clinical practice, and place organizations at the leading edge of practice-based knowledge development for the nursing discipline.

Interprofessional Health Team Communication About Hospital Discharge: An Implementation Science Evaluation Study.

The Consolidated Framework for Implementation Research guided formative evaluation of the implementation of a redesigned interprofessional team rounding process. The purpose of the redesigned process was to improve health team communication about hospital discharge. Themes emerging from interviews of patients, nurses, and providers revealed the inherent value and positive characteristics of the new process, but also workflow, team hierarchy, and process challenges to successful implementation. The evaluation identified actionable recommendations for modifying the implementation process.

A Model for Hospital Discharge Preparation: From Case Management to Care Transition.

There has been a proliferation of initiatives to improve discharge processes and outcomes for the transition from hospital to home and community-based care. Operationalization of these processes has varied widely as hospitals have customized discharge care into innovative roles and functions. This article presents a model for conceptualizing the components of hospital discharge preparation to ensure attention to the full range of processes needed for a comprehensive strategy for hospital discharge.

Comparison of nurse staffing based on changes in unit-level workload associated with patient churn.

AIM: This analysis compares the staffing implications of three measures of nurse staffing requirements: midnight census, turnover adjustment based on length of stay, and volume of admissions, discharges and transfers. BACKGROUND: Midnight census is commonly used to determine registered nurse staffing. Unit-level workload increases with patient churn, the movement of patients in and out of the nursing unit. Failure to account for patient churn in staffing allocation impacts nurse workload and may result in adverse patient outcomes. METHOD(S): Secondary data analysis of unit-level data from 32 hospitals, where nursing units are grouped into three unit-type categories: intensive care, intermediate care, and medical surgical. RESULT: Midnight census alone did not account adequately for registered nurse workload intensity associated with patient churn. On average, units were staffed with a mixture of registered nurses and other nursing staff not always to budgeted levels. Adjusting for patient churn increases nurse staffing across all units and shifts. CONCLUSION: Use of the discharges and transfers adjustment to midnight census may be useful in adjusting RN staffing on a shift basis to account for patient churn. IMPLICATIONS FOR NURSING MANAGEMENT: Nurse managers should understand the implications to nurse workload of various methods of calculating registered nurse staff requirements.

Nurses' perceptions of sustainability of Magnet® efforts.

Magnet® designation and redesignation efforts are time and resource intensive. The results of a survey of nurses' perceptions about 4 components of sustainability indicate differences between Magnet and non-Magnet hospitals. Evaluating nurses' perception of the sustainability of Magnet efforts will help nurse executives determine where to commit financial, time, and human resources.

Exploring the Association Between Opioid Use Disorder and Alzheimer's Disease and Dementia Among a National Sample of the U.S. Population.

BACKGROUND: Past research suggests associations between heavy alcohol use and later life dementia. However, little is known about whether opioid use disorder (OUD) and dementia share this association, especially among age groups younger than 65 years old. OBJECTIVE: Examine the association between OUD and Alzheimer's disease (AD) and dementia. METHODS: Electronic health records between 2000 and 2021 for patients age 12 or older were identified in the Cerner Real-World database™. Patients with a prior diagnosis of dementia were excluded. Patients were followed for 1-10 years (grouped by one, three, five, and ten-year follow-up periods) in a matched retrospective cohort study. Cox proportional hazards regressions were used to estimate adjusted hazard ratios (aHRs) of incident AD/dementia stratified by age and follow-up group. RESULTS: A sample of 627,810 individuals with OUD were compared to 646,340 without OUD. Individuals with OUD exhibited 88% higher risk for developing AD/dementia compared to those without OUD (aHR = 1.88, 95% CI 1.74, 2.03) within 1 year follow-up and 211% (aHR = 3.11, 95% CI 2.63, 3.69) within 10 years follow-up. When stratifying by age, younger patients (age 12-44) had a greater disparity in odds of AD/dementia between OUD and non-OUD groups compared with patients older than 65 years. CONCLUSIONS: Additional research is needed to understand why an association exists between OUD and AD/dementia, especially among younger populations. The results suggest that cognitive functioning screening programs for younger people diagnosed with OUD may be useful for targeting early identification and intervention for AD/dementia in particularly high risk and marginalized populations.

Can Prehospital Data Improve Early Identification of Sepsis in Emergency Department? An Integrative Review of Machine Learning Approaches.

BACKGROUND: Sepsis is associated with high mortality, especially during the novel coronavirus disease 2019 (COVID-19) pandemic. Along with high monetary health care costs for sepsis treatment, there is a lasting impact on lives of sepsis survivors and their caregivers. Early identification is necessary to reduce the negative impact of sepsis and to improve patient outcomes. Prehospital data are among the earliest information collected by health care systems. Using these untapped sources of data in machine learning (ML)-based approaches can identify patients with sepsis earlier in emergency department (ED). OBJECTIVES: This integrative literature review aims to discuss the importance of utilizing prehospital data elements in ED, summarize their current use in developing ML-based prediction models, and specifically identify those data elements that can potentially contribute to early identification of sepsis in ED when used in ML-based approaches. METHOD: Literature search strategy includes following two separate searches: (1) use of prehospital data in ML models in ED; and (2) ML models that are developed specifically to predict/detect sepsis in ED. In total, 24 articles are used in this review. RESULTS: A summary of prehospital data used to identify time-sensitive conditions earlier in ED is provided. Literature related to use of ML models for early identification of sepsis in ED is limited and no studies were found related to ML models using prehospital data in prediction/early identification of sepsis in ED. Among those using ED data, ML models outperform traditional statistical models. In addition, the use of the free-text elements and natural language processing (NLP) methods could result in better prediction of sepsis in ED. CONCLUSION: This study reviews the use of prehospital data in early decision-making in ED and suggests that researchers utilize such data elements for prediction/early identification of sepsis in ML-based approaches.

Nonlinear association of nurse staffing and readmissions uncovered in machine learning analysis.

OBJECTIVE: Several studies of nurse staffing and patient outcomes found a curvilinear or U-shaped relationship, with benefits from additional nurse staffing diminishing or reversing at high staffing levels. This study examined potential diminishing returns to nurse staffing and the existence of a "tipping point" or the level of staffing after which higher nurse staffing no longer improves and may worsen readmissions. DATA SOURCES/STUDY SETTING: The Readiness Evaluation And Discharge Interventions (READI) study database of over 130,000 adult (18+) inpatient discharges from 62 medical, surgical, and medical-surgical (noncritical care) units from 31 United States (US) hospitals during October 2014-March 2017. STUDY DESIGN: Observational cross-sectional study using a fully nonparametric random forest machine learning method. Primary exposure was nurse hours per patient day (HPPD) broken down by registered nurses (nonovertime and overtime) and nonlicensed nursing personnel. The outcome was 30-day all-cause same-hospital readmission. Partial dependence plots were used to visualize the pattern of predicted patient readmission risk along a range of unit staffing levels, holding all other patient characteristics and hospital and unit structural variables constant. DATA COLLECTION/EXTRACTION METHODS: Secondary analysis of the READI data. Missing values were imputed using the missing forest algorithm in R. PRINCIPAL FINDINGS: Partial dependence plots were U-shaped, showing the readmission risk first declining and then rising with additional nurse staffing. The tipping points were at 6.95 and 0.21 HPPD for registered nurse staffing (nonovertime and overtime, respectively) and 2.91 HPPD of nonlicensed nursing personnel. CONCLUSIONS: The U-shaped association was consistent with diminishing returns to nurse staffing suggesting that incremental gains in readmission reduction from additional nurse staffing taper off and could reverse at high staffing levels. If confirmed in future causal analyses across multiple outcomes, accompanying investments in infrastructure and human resources may be needed to maximize nursing performance outcomes at higher levels of nurse staffing.

An integrated review of the literature on challenges confronting the acute care staff nurse in discharge planning.

AIMS AND OBJECTIVES: This integrative review presents and synthesises previous research investigating practices, perceptions and experiences of bedside staff nurses relative to hospital discharge planning. BACKGROUND: Preparation for hospital discharge should begin at or prior to admission. Forces in the acute care environment, however, often impede comprehensive discharge planning. Evidence-based redesign of discharge planning processes is a priority for nurses and health care leaders. DESIGN: An integrative review. METHOD: The review was undertaken using eleven search terms to electronically scan CINAHL, Medline, PsycINFO and Dissertations and Theses databases for the period 1990-2009. Hand searching of reference lists and author searches was also conducted. Sixty English language articles were reviewed; 38 of these 60 publications met the study inclusion criteria. RESULTS: Seven themes were identified across the studies. These themes were intra- and interdisciplinary communication; systems and structures; time; role confusion; care continuity; knowledge; and the invisibility of the staff nurse role in discharge planning. CONCLUSIONS: Acute care bedside nurses frequently encounter significant barriers while providing discharge-related care. Although these barriers have been identified consistently in numerous studies spanning nearly two decades, few published reports address interventions designed to overcome identified obstacles and inefficiencies. Additional discharge planning redesign initiatives, which include rigorous evaluation of patient outcomes, are needed. RELEVANCE TO CLINICAL PRACTICE: As the direct caregivers with the most consistent presence and knowledge of patients' postdischarge needs, acute care bedside staff nurses play a critical role in preparing patients and families for the transition from hospital to home. Nurses are uniquely positioned to identify barriers and opportunities in discharge planning processes and contribute significantly to evidence-based reform initiatives. Effective and efficient patient-centred discharge planning processes can facilitate the transition from hospital to home and begin to address the adverse events experienced by some patients in the immediate postdischarge period.

Outcomes and cost analysis of the impact of unit-level nurse staffing on post-discharg utilization.

Under a proposal from the Centers for Medicare and Medicaid Services, hospitals would no longer be reimbursed for 30-day re-admissions or emergency department (ED) visits. Increasing RN staffing to reduce post-discharge utilization is one possible solution, but one that is not financially attractive to hospitals. This study demonstrates the impact of fluctuating staffing levels on ED visits within 30 days of discharge. RN overtime and RN vacancies also affected subsequent ED visits. It is important for nurse managers, directors, and administrators to recognize the impact of RN staffing on patient outcomes. Reimbursement models will need to be realigned to benefit both hospitals and payers.

Effectiveness of using a simulation combined with online learning approach to develop discharge teaching skills.

BACKGROUND: Despite evidence of the impact of discharge teaching on patient outcomes, nursing students are poorly prepared in the pedagogical skills necessary for their role as patient and family educators in clinical practice. This study evaluated the effectiveness of simulation combined with online learning to improve nursing students' discharge teaching skills. METHODS: The module included simulations before and after an online module on patient/family teaching for hospital discharge. Evaluation measures were student and independent rater evaluations using the Quality of Discharge Teaching Scale- Evaluation form (QDTS-E). RESULTS: Students (n = 153) improved their performance on both content and delivery subscales of the QDTS-E by 20% (student self-evaluations) and 18% (independent raters). However, correlations between student and rater scores were low (r = 0.08-0.22). CONCLUSION: Use of simulation with online learning in a discharge teaching module can help students build patient education skills to improve post-discharge patient outcomes, contributing to national health priorities to reduce hospital readmissions. With further refinement and testing, the learning module and QDTS-E evaluation form may also be useful for evaluation and continuing education of clinical nursing staff.

Impact of a Procalcitonin-Based Protocol on Antibiotic Exposure and Costs in Critically Ill Patients.

To examine the impact before and after adoption of a procalcitonin-based protocol to guide sepsis management has on antibiotic use, care costs, and outcomes of critically ill patients. DESIGN: Before-after study. SETTING: ICU of an academic tertiary care center. PATIENTS: Adults over 18 years old admitted to the ICU from January 1, 2017, to January 31, 2020. INTERVENTIONS: In this before-after study, we compared the use of medications, outcomes, and overall cost before and after the introduction of a procalcitonin-based protocol for evaluation and treatment of sepsis. MEASUREMENTS AND MAIN RESULTS: The final study cohort consisted of 1,793 patients admitted to the ICU, 776 patients pre-procalcitonin and 1,017 patients in the post-procalcitonin period. Patients were not different in the pre-procalcitonin adoption period compared with post-procalcitonin adoption with regard to gender, age (62.0 vs 62.6), race, or comorbidities. Patients admitted during the post-procalcitonin adoption period were less likely to receive the examined broad-spectrum antibiotics (odds ratio, -0.58; CI, -0.99 to -0.17; p < 0.01) than patients during the pre-procalcitonin adoption period. The odds of inhospital death did not differ after procalcitonin adoption when compared with before (0.87; CI, 0.70-1.09; p = 0.234). Total charges for each admission were significantly less in the post-procalcitonin adoption period $3,834.99 compared with pre-procalcitonin adoption $4,429.47 (p < 0.05). Patients post-procalcitonin adoption incurred $1,127.18 per patient less in total charges (-1,127.18; CI, -2,014.74 to -239.62; p = 0.013) after controlling for relevant factors. CONCLUSIONS: In critically ill patients in a large U.S. tertiary care hospital, the adoption of a procalcitonin-based protocol for evaluation and treatment of sepsis may be associated with decreased antibiotic use and significant cost savings, with no change in mortality.

Cross-Cultural Adaptation and Psychometrics Evaluation of the Arabic Version of the Patient-Readiness for Hospital Discharge Scale.

BACKGROUND AND PURPOSE: The Patient Readiness for Hospital Discharge Scale (PT-RHDS) is an outcome measure of discharge care processes. The purpose of the study was to test a cross-cultural adaptation from English into Arabic. METHODS: The Rand Corporation cross-cultural adaptation method and psychometric analysis of data from 1844 adult surgical inpatients in two Saudi Arabia hospitals. RESULTS: Reliability of the Arabic version (α = .75) was adequate. Confirmatory factor analysis supported construct validity. No differences in PT-RHDS scores were detected in comparisons for marital status, sex, age, or length of stay. The Arabic PT-RHDS did not predict readmissions. CONCLUSIONS: The psychometric properties of the Arabic PT- RHDS provide preliminary evidence for its use in assessing surgical patients' perception of readiness for discharge in Arabic-speaking countries.

Effectiveness of home health care in reducing return to hospital: Evidence from a multi-hospital study in the US.

BACKGROUND: Home health care, a commonly used bridge strategy for transitioning from hospital to home-based care, is expected to contribute to readmission avoidance efforts. However, in studies using disease-specific samples, evidence about the effectiveness of home health care in reducing readmissions is mixed. OBJECTIVE: To examine the effectiveness of home health care in reducing return to hospital across a diverse sample of patients discharged home following acute care hospitalization. RESEARCH DESIGN: Secondary analysis of a multi-site dataset from a study of discharge readiness assessment and post-discharge return to hospital, comparing matched samples of patients referred and not referred for home health care at the time of hospital discharge. SETTING: Acute care, Magnet-designated hospitals in the United States PARTICIPANTS: The available sample (n = 18,555) included hospitalized patients discharged from medical-surgical units who were referred (n = 3,579) and not referred (n = 14,976) to home health care. The matched sample included 2767 pairs of home health care and non- home health care patients matched on patient and hospitalization characteristics using exact and Mahalanobis distance matching. METHODS: Unadjusted t-tests and adjusted multinomial logit regression analyses to compare the occurrence of readmissions and Emergency Department/Observation visits within 30 and 60-days post-discharge. RESULTS: No statistically significant differences in readmissions or Emergency Department /Observation visits between home health care and non-home health care patients were observed. CONCLUSIONS: Home health care referral was not associated with lower rates of return to hospital within 30 and 60 days in this US sample matched on patient and clinical condition characteristics. This result raises the question of why home health care services did not produce evidence of lower post-discharge return to hospital rates. Focused attention by home health care programs on strategies to reduce readmissions is needed.