Preventable Readmissions Research Articles

The Effects of a Stepped-Care Mental Health Program on Trauma Recidivism at a Level 1 Trauma Center.

This study aimed to analyze the effects of a stepped-care mental health program (TRRP) on trauma recidivism at a single institution. Designs: Retrospective review. single Level 1 trauma center. Trauma activation patients presenting at a single Level 1 trauma center between 2017-2021 were selected. Data on demographics, mechanism of injury, readmissions, and TRRP participation were recorded. Main outcomes included recidivism rates, risk factors for re-presentation, and determining if a change in readmission risk existed after participation in TRRP. 4,484 patients were included, with a mean age of 44.8 years. 65.9% were males and 34.1% were female. 14.2% (637/4,484) patients presented to the same institution having experienced an unrelated traumatic incident after their index injury. 75.6% patients identified by TRRP participated in at least one TRRP service. Patients who did not participate in TRRP were statistically significantly more likely to be recidivists (15.6%) compared to those who did (10.9%, p<0.001). Multivariable binary logistic regression analysis demonstrated statistically significant associations between increased trauma recidivism and being unmarried, Black/African American race, no use of seatbelts, alcohol intoxication at index injury, history of depression, alcohol use disorder, smoking, and government insurance (p≤0.024). The multivariable regression model predicted a decrease in trauma recidivism by 33% in privately insured patients (p=0.002), by 33% after participation in TRRP (p<0.001), and by 18% after participation in each additional step of the TRRP program (p<0.001). Results demonstrate reduced rates of trauma recidivism with involvement of the Trauma Resilience and Recovery Program. While programs focused solely on psychosocial interventions may be sufficient to reduce the risk of subsequent traumatic events, further development of trauma rehabilitation programs should address both the psychosocial consequences of traumatic injuries as well as re-injury and readmission prevention. III.

Enhancing care coordination through transitional care management.

232 Background: West Cancer Center and Research Institute (WCCRI) serves Memphis and the Arkansas, Mississippi, and Tennessee tri-state region as the comprehensive care leader in adult cancer care and research, amounting to over 260,000 visits across 57,000 unique patients in 2023. Cancer patients are predisposed for higher risk of hospital admissions, making uniform care coordination between the discharging hospital and their outpatient oncology care home essential to care continuity. While WCCRI attempted to mitigate readmissions, there was no formalized managed care approach for capturing and responding to unplanned patient hospital visits to prevent readmission. Auditing of claims data suggested nearly 500 patient care opportunities within a four-month period. Methods: To improve continuity of care and decrease preventable readmissions, WCCRI implemented a care transitions process based on the Centers for Medicare and Medicaid's Transitional Care Management (TCM). This required a repeatable approach of proactive outreach to patient upon discharge, a clinical assessment of patient need, and an office visit within fourteen days of discharge. To maximize impact, this project was scoped to patients with unplanned hospital admissions who are at moderate to high risk for readmission. Results: Twelve months after implementation, WCCRI has proactively identified 974 patients qualifying for TCM and successfully scheduled 91.48% for a hospital follow-up appointment with their oncologist. Proactive outreach to patients has led to a 35% increase in office visits within fourteen days of discharge, a key measure of care continuity. Conclusions: Learnings from this implementation project have been spread to formalize hospital follow-up approaches across WCCRI. By aligning policies, engaging stakeholders, and fostering a culture of collaboration and accountability, WCCRI has delivered high-quality, coordinated care, and achieved better patient outcomes.

Conference Report: Leveraging the Global Malnutrition Composite Score for Quality Improvement, Health Equity, and Better Patient Outcomes

Effective January 2024, the United States Centers for Medicare &amp; Medicaid Services (CMS) for the first time has included a nutrition-focused quality measure in the Hospital Inpatient Prospective Payment System (IPPS) Inpatient Quality Reporting (IQR) Program: the Global Malnutrition Composite Score (GMCS). The GMCS is an electronic clinical quality measure (eCQM) that hospitals can choose to report voluntarily to meet the requirements for IPPS IQR payments. The GMCS can help improve hospital malnutrition care and help advance hospital health equity goals. This conference report summarizes a panel discussion at the Academy of Nutrition and Dietetics Food and Nutrition Conference &amp; Expo in October 2023. During the panel, four health and nutrition experts described strategies for integrating the GMCS into hospital electronic health records (EHRs). They also outlined how GMCS data can be used to facilitate successful patient transitions to post-acute care through addressing food insecurity and other reasons for malnutrition. Such strategies can serve to help avoid preventable complications and readmissions, improve patient outcomes and health, and meet quality and health equity objectives, while helping reduce overall healthcare costs.

Hospital costs for unplanned re-admissions within 30 days of hospitalisations with heart failure, Australia, 2013-2017: a retrospective cohort study.

To assess the direct hospital costs for unplanned re-admissions within 30 days of hospitalisations with heart failure in Australia; to estimate the proportion of these costs attributable to potentially preventable re-admissions. Retrospective cohort study; analysis of linked admitted patient data collections data. People admitted to hospital (all public and most private hospitals in Australia) with primary diagnoses of heart failure, 1 January 2013 - 31 December 2017, who were discharged alive and re-admitted to hospital at least once (any cause) within 30 days of discharge. Estimated re-admission costs based on National Hospital Cost Data Collection, by unplanned re-admission category based on the primary re-admission diagnosis: potentially hospital-acquired condition; recurrence of heart failure; other diagnoses related to heart failure; all other diagnoses. The first two groups were deemed the most preventable. The 165 612 eligible hospitalisations of people with heart failure during 2013-2017 (mean age, 79 years [standard deviation, 12 years]; 85 964 men [51.9%]) incurred direct hospital costs of $1881.4 million (95% confidence interval [CI], $1872.5-1890.2 million), or $376.3 million per year (95% CI, $374.5-378.1 million per year) and $11 360 per patient (95% CI, $11 312-11 408 per patient). A total of 41 125 people (24.8%) experienced a total of 58 977 unplanned re-admissions within 30 days of discharge from index admissions; these re-admissions incurred direct hospital costs of $604.4 million (95% CI, $598.2-610.5 million), or 32% of total index admission costs; that is, $120.9 million per year (95% CI, $119.6-122.1 million per year), and $14 695 per patient (95% CI, $14 535-14 856 per patient). Re-admissions with potentially hospital-acquired conditions (21 641 re-admissions) accounted for 40.1% of unplanned re-admission costs, recurrence of heart failure (18 666 re-admissions) for 35.6% of re-admission costs. Unplanned re-admissions after hospitalisations with heart failure are expensive, incurring costs equivalent to 32% of those for the initial hospitalisations; a large proportion of these costs are associated with potentially preventable re-admissions. Reducing the number of unplanned re-admissions could improve outcomes for people with heart failure and reduce hospital costs.

Piroxicam and paracetamol in the prevention of early recurrent pain and emergency department readmission after renal colic: Randomized placebo-controlled trial.

Renal colic (RC) is a common urologic emergency often leading to significant pain and recurrent hospital visits. This study aimed to compare the efficacy and safety of piroxicam versus paracetamol in preventing pain recurrence and hospital readmission in patients treated for RC and discharged from the emergency department (ED). A prospective, randomized, single-blind trial was conducted in four EDs. Eligible adults with RC were randomized to receive oral piroxicam, paracetamol, or placebo for 5 days post-ED discharge. Primary outcomes included pain recurrence and ED readmission within 7 days. Secondary outcomes included time to recurrence and treatment-related side effects. Of 1383 enrolled patients, no significant differences were observed among the groups regarding baseline characteristics. Pain recurrence rates within 7 days were 29% (95% confidence interval [CI] 24.9%-33.2%) for piroxicam, 30.3% (95% CI 26.1%-34.5%) for paracetamol, and 30.8% (95% CI 26.6%-35.0%) for placebo, with no significant between-group differences (p = 0.84). Among patients experiencing recurrence, the majority encounter it within the initial 2 days following their discharge (86% in the piroxicam group, 84.1% in the paracetamol group, and 86% in the placebo group, respectively). ED readmission rates were similar across groups: 20.8% (95% CI 17.1%-24.5%) in the piroxicam group, 23.8% (95% CI 19.9%-27.7%) in the paracetamol group, and 22.9% (95% CI 19.1%-26.8%) in the placebo group (p = 0.52). The piroxicam group reported significantly higher adverse effects compared to others. Piroxicam and paracetamol did not demonstrate efficacy in preventing pain recurrence or ED readmission within the first week following RC treatment.

Dynamic Control of Service Systems with Returns: Application to Design of Postdischarge Hospital Readmission Prevention Programs

Dynamic Control of Service Systems with Returns Postdischarge interventions (e.g., follow-up phone calls, outpatient appointments) are commonly used to reduce unplanned hospital readmissions. Such interventions have been shown to be effective (to varying degrees) in reducing the probability of readmission (or return). The interventions are, however, costly, and hence, their benefits need to be carefully balanced with the costs. In “Dynamic Control of Service Systems with Returns: Application to Design of Postdischarge Hospital Readmission Prevention Programs,” Chan, Huang, and Sarhangian investigate how postservice interventions should be dynamically allocated, accounting for their costs as well as their benefits in terms of reducing returns and congestion costs. To this end, they study a transient queueing control problem and examine the structure of the optimal policy by analyzing associated fluid-control problems. The structural results motivate the design of intuitive surge protocols whereby different intensities of interventions are provided based on the congestion level of the system.

Nursing Variables Predicting Readmissions in Patients With a High Risk: A Scoping Review.

Unplanned readmission endangers patient safety and increases unnecessary healthcare expenditure. Identifying nursing variables that predict patient readmissions can aid nurses in providing timely nursing interventions that help patients avoid readmission after discharge. We aimed to provide an overview of the nursing variables predicting readmission of patients with a high risk. The authors searched five databases-PubMed, CINAHL, EMBASE, Cochrane Library, and Scopus-for publications from inception to April 2023. Search terms included "readmission" and "nursing records." Eight studies were included for review. Nursing variables were classified into three categories-specifically, nursing assessment, nursing diagnosis, and nursing intervention. The nursing assessment category comprised 75% of the nursing variables; the proportions of the nursing diagnosis (25%) and nursing intervention categories (12.5%) were relatively low. Although most variables of the nursing assessment category focused on the patients' physical aspect, emotional and social aspects were also considered. This study demonstrated how nursing care contributes to patients' adverse outcomes. The findings can assist nurses in identifying the essential nursing assessment, diagnosis, and interventions, which should be provided from the time of patients' admission. This can mitigate preventable readmissions of patients with a high risk and facilitate their safe transition from an acute care setting to the community.

Predictive Modeling of Future Trends in US Healthcare Data and Outcomes

Predictive modeling has great potential to help guide healthcare policymaking and planning through forecasting future trends in domains such as disease prevalence, resource utilization, and costs. However, past research in this area has been limited by mostly examining small, narrow datasets that only captured specific illnesses or geographic regions. This study aimed to leverage more sophisticated predictive analytics to generate informed estimations of the most consequential healthcare trends anticipated in the United States throughout the next decade. The analysis drew upon an extensive collection of over 50 million longitudinal electronic health records spanning a 5-year timeframe, comprehensive national public health statistics from the same period, and Medicare claims encompassing 72 million beneficiaries. Advanced machine learning techniques, including neural networks and Bayesian additive regression trees, were applied to identify nonlinear relationships and temporal patterns across 500 variables related to patient demographics, medical diagnoses, therapeutic procedures, reimbursement amounts, and clinical outcomes. Models were trained using data from 2010 to 2015 then utilized to project trends and forecasts for the years 2020 to 2025. Five-fold cross-validation testing was conducted to evaluate the accuracy and generalizability of the predictive models. The model projections indicate that chronic disease prevalence nationwide will rise by approximately 40% by the conclusion of 2025, primarily fueled by growing epidemics of obesity and an increasingly aging American population. Additionally, heart disease and stroke are estimated to maintain their positioning as leading causes of death, but cases of dementia and Alzheimer's disease specifically are projected to climb even more sharply at over a 50% increase. Healthcare costs on the whole are anticipated to rise on average between 4-6% annually, and costs may potentially double for elderly patients presenting with multiple morbidities. As outpatient and home-based care options expand further, inpatient hospital facility utilization may drop marginally between 10-15%. Improved management of chronic medical conditions within local community settings could reduce preventable hospital readmissions from 25-30%. Primary care, nursing, and mental healthcare roles are likely to face looming staffing shortages as well. Telehealth adoption is forecasted to surge by approximately 45% as virtual visit formats help address access obstacles. By 2025, biologics and gene therapies could account for over 25% of total drug spending pertaining to oncology and rare disease treatment. Larger Medicaid, Medicare, and ACA commercial coverage markets may motivate higher rates of health insurance enrollment over the next few years.

Hospital strategies in commercial episode-based reimbursement.

To understand hospitals' approaches to spending reduction in commercial episode-based payment programs and inform incentive design. Qualitative arm of an explanatory sequential mixed-methods study involving semistructured interviews with hospital leaders participating in a statewide quality improvement collaborative with novel episode-based incentive payments introduced by the state's largest commercial payer. We recruited 21 leaders from 8 purposively selected, diverse hospitals with both high and low performance. Video teleconference-based interviews followed a standardized protocol and addressed 4 domains: choice of clinical condition for evaluation, strategies for episode spending reduction, best practices for success in earning incentives, and barriers to achievement. Rapid qualitative analysis with purposeful data reduction was employed to generate a matrix of key themes within the study domains. Strategies were similar between high- and low-performing hospitals. When selecting conditions, some hospitals focused on areas of underperformance, aiming for improvement opportunities, whereas others chose conditions already achieving highest efficiency. Many tried to synergize with other ongoing improvement initiatives and clinical areas with established leaders and champions. Key strategies included data-driven improvement, care standardization, and protocol dissemination. Best practices for success included readmission prevention and postacute care spending containment. The findings highlighted hospitals' most common strategies and approaches, providing several insights into optimal design of commercial episode-based incentives: They must be lucrative enough to earn attention or consistent with larger federal programs; hospitals need opportunities to succeed through both improved performance and sustained excellence; and programs may incur malalignment between hospitals and credentialed physicians.

Interpretable (not just posthoc-explainable) medical claims modeling for discharge placement to reduce preventable all-cause readmissions or death

We developed an inherently interpretable multilevel Bayesian framework for representing variation in regression coefficients that mimics the piecewise linearity of ReLU-activated deep neural networks. We used the framework to formulate a survival model for using medical claims to predict hospital readmission and death that focuses on discharge placement, adjusting for confounding in estimating causal local average treatment effects. We trained the model on a 5% sample of Medicare beneficiaries from 2008 and 2011, based on their 2009–2011 inpatient episodes (approximately 1.2 million), and then tested the model on 2012 episodes (approximately 400 thousand). The model scored an out-of-sample AUROC of approximately 0.75 on predicting all-cause readmissions—defined using official Centers for Medicare and Medicaid Services (CMS) methodology—or death within 30-days of discharge, being competitive against XGBoost and a Bayesian deep neural network, demonstrating that one need-not sacrifice interpretability for accuracy. Crucially, as a regression model, it provides what blackboxes cannot—its exact gold-standard global interpretation, explicitly defining how the model performs its internal “reasoning” for mapping the input data features to predictions. In doing so, we identify relative risk factors and quantify the effect of discharge placement. We also show that the posthoc explainer SHAP provides explanations that are inconsistent with the ground truth model reasoning that our model readily admits.

Standardization of Evaluation and Management of Early Pregnancy Concerns in the Emergency Department: A Quality Improvement Initiative [ID 2683509

INTRODUCTION: This study explored the effect of a standardized protocol for gynecologic consultation on readmissions for early pregnancy concerns in the emergency department (ED). METHODS: We conducted a retrospective cohort study of pregnant patients presenting to the ED for first trimester pregnancy concerns from January 2020 to March 2023. In October 2022, ED protocols were standardized such that gynecology was consulted for all pregnant patients without intrauterine pregnancy and cardiac motion. Patients seen before implementation (January 2020 to June 2021) were compared to patients seen after (October 2022 to March 2023). Chi-squared test and Fisher's exact test were used to investigate the association between readmission and reason for readmission to intervention period. The primary outcome, preventable readmission rate, was defined as a readmission that could have been prevented if the patient received correct counseling, diagnosis, or management at the index admission. This protocol was approved for IRB exemption by the University of Texas at Austin Dell Medical School. RESULTS: We identified 383 patients in the pre-implementation cohort compared to 130 patients seen after. There was an increase in the rate of gynecologic consultation for all concerns (30% before versus 40% after; P=.06). Fewer readmissions occurred after implementation (31% before versus 26% after) and preventable readmission decreased significantly (28% before versus 0% after; P&lt;.001). Documented counseling about diagnosis also increased (74% versus 96%; P&lt;.001) post-implementation. CONCLUSION: Readmission to the ED is common in early pregnancy; standardization of gynecologic consultation for early pregnancy care in the ED can improve quality of care and reduce readmissions.

Quality of life in ICU survivors and their relatives with post-intensive care syndrome: A systematic review.

Post-intensive care syndrome (PICS) is characterized by all three adverse survivorship dimensions: physical function, cognitive function and mental health status. This review aimed to describe the quality of life (QoL) of Intensive Care Unit (ICU) survivors with PICS after discharge and of their relatives with Family Post-intensive care syndrome (PICS-F) and to report anxiety, depression and Post-Traumatic Stress Disorders (PTSD) in studies investigating PICS. A systematic review was carried out. We searched PubMed, Scopus, Web of Science and the Cumulative Index to Nursing and Allied Health Literature. This review was registered in the PROSPERO database (CRD42022382123). We included 19 studies of PICS and PICS-F in this systematic review. Fourteen observational studies report the effects of PICS on depression, 12 studies on anxiety and nine on post-traumatic stress disorder and 10 on QoL. Mobility, personal care, usual activities and pain/discomfort in QoL were the domains most affected by PICS. A significant association was demonstrated between a high level of ICU survivors' anxiety and high levels of ICU relatives' burden. Strain-related symptoms and sleep disorders were problems encountered by ICU relatives with PICS-F. PICS and PICS-F were widespread experiences among ICU survivors and their ICU relatives, respectively. The results of this review showed the adverse effects of PICS and PICS-F on QoL. PICS and PICS-F strongly impact the rehabilitation process and are measured in terms of health costs, financial stress and potentially preventable readmission.

Role of a Pharmacist in Postdischarge Care for Patients With Kidney Disease: A Scoping Review.

The objective was to explore and describe the role of pharmacists in providing postdischarge care to patients with kidney disease. PubMed, Embase (Elsevier), CINAHL (Ebscohost), Web of Science Core Collection, and Scopus were searched on January 30, 2023. Publication date limits were not included. Search terms were identified based on 3 concepts: kidney disease, pharmacy services, and patient discharge. Experimental, quasi-experimental, observational, and qualitative studies, or study protocols, describing the pharmacist's role in providing postdischarge care for patients with kidney disease, excluding kidney transplant recipients, were eligible. Six unique interventions were described in 10 studies meeting inclusion criteria. Four interventions targeted patients with acute kidney injury (AKI) during hospitalization and 2 evaluated patients with pre-existing chronic kidney disease. Pharmacists were a multidisciplinary care team (MDCT) member in 5 interventions and were the sole provider in 1. Roles commonly identified include medication review, medication reconciliation, medication action plan formation, kidney function assessment, drug dose adjustments, and disease education. Some studies showed improvements in diagnostic coding, laboratory monitoring, medication therapy problem (MTP) resolution, and patient education; prevention of hospital readmission was inconsistent. Limitations include lack of standardized reporting of kidney disease, transitions of care processes, and differences in outcomes evaluated. This review identifies potential roles of a pharmacist as part of a postdischarge MDCT for patients with varying degrees of kidney disease. The pharmacist's role in providing postdischarge care to patients with kidney disease is inconsistent. Multidisciplinary care teams including a pharmacist provided consistent identification and resolution of MTPs, improved patient education, and increased self-awareness of diagnosis.

Medication choice and psychosis Hospital readmissions: A two-year comparative study

IntroductionHospital readmissions in psychosis are a critical concern, with medication choice playing a vital role. Oral antipsychotics, though common, rely on patient adherence and can lead to relapses if not followed. Long-acting injectable atypical antipsychotics (LAIAs) provide an alternative, ensuring consistent medication release and reducing relapse risk due to missed doses. Studies indicate that LAIAs result in fewer readmissions due to improved adherence. Tailoring treatment to individual needs is essential. Medication choice significantly influences hospital readmission prevention in psychosis. LAIAs, which could offer greater adherence to treatment and symptom control, present a promising option. Individualized treatment decisions are a priority for long-term recovery.ObjectivesThis study aimed to compare the hospital readmission rates within two years post-discharge among two groups of patients diagnosed with schizophrenia and other psychotic disorders who received either oral antipsychotic treatment or LAIAs.MethodsWe collected sociodemographic and hospitalization data from 155 patients, 90 receiving oral antipsychotics and 65 receiving LAIAs, following their discharge from a psychiatric unit.ResultsThere were 90 patients in the oral treatment group, and 65 in the LAIA group, with 67.6% receiving paliperidone and 26.1% receiving aripiprazole. There were no significant differences in age or gender between the two groups. However, patients in the LAIA group had longer stays in the hospital (M=14.7; SD=10.2 vs M=11.1; SD=6.4; t(153)=2.67; p&lt;.01) and a higher number of prior admissions (M=3.2; SD=3.7 vs M=1.3; SD=3.5; t(153)=2.41; p&lt;.01) compared to the oral antipsychotic group. Additionally, a higher percentage of patients in the LAIA group were diagnosed with schizophrenia (60%) compared to the oral antipsychotic group (24%) (X2(1, N = 155)= 20.4, p&lt;.01). After two years, readmission rates were 66.6% for the oral antipsychotic group and 61.5% for the LAIA group (X2(1, N = 155)= 8.5, p &gt; .05). However, the time to readmission was shorter for patients on oral antipsychotics (M=172.4; SD=162.0) compared to those on LAIAs (M=326.2; SD=211.4; t(153)=3.05; p&lt;.01). Notably, 86.6% of patients on oral antipsychotics were readmitted within the first year, while only 52% of those on LAIAs experienced readmission during the same period (X2(1, N = 155)= 8.5, p = .001).ConclusionsLong-acting injectable antipsychotics (LAIAs) appear to reduce hospital readmissions, with a more pronounced effect in the first few months post-discharge. However, after two years, the readmission rates between LAIAs and oral antipsychotics become comparable. This data suggests that while LAIAs may reduce early readmissions, their long-term effectiveness is on par with oral antipsychotics.Disclosure of InterestNone Declared

Predicting 30-day unplanned hospital readmission after revision total knee arthroplasty: machine learning model analysis of a national patient cohort.

Revision total knee arthroplasty (TKA) is associated with a higher risk of readmission than primary TKA. Identifying individual patients predisposed to readmission can facilitate proactive optimization and increase care efficiency. This study developed machine learning (ML) models to predict unplanned readmission following revision TKA using a national-scale patient dataset. A total of 17,443 revision TKA cases (2013-2020) were acquired from the ACS NSQIP database. Four ML models (artificial neural networks, random forest, histogram-based gradient boosting, and k-nearest neighbor) were developed on relevant patient variables to predict readmission following revision TKA. The length of stay, operation time, body mass index (BMI), and laboratory test results were the strongest predictors of readmission. Histogram-based gradient boosting was the best performer in distinguishing readmission (AUC: 0.95) and estimating the readmission probability for individual patients (calibration slope: 1.13; calibration intercept: -0.00; Brier score: 0.064). All models produced higher net benefit than the default strategies of treating all or no patients, supporting the clinical utility of the models. ML demonstrated excellent performance for the prediction of readmission following revision TKA. Optimization of important predictors highlighted by our model may decrease preventable hospital readmission following surgery, thereby leading to reduced financial burden and improved patient satisfaction.

Case Management in Prevention of 30-Day Readmission in Post-Coronary Artery Bypass Graft Surgery.

Thirty-day readmission is associated with increased morbidity and mortality among postoperative coronary artery bypass graft (CABG) surgery patients. Interventions such as case management and follow-up care may reduce 30-day readmission. The purpose of this article is to report a study on modifiable factors that may have significant implications for case management in the prevention of readmission after CABG surgery. The study population included all the adult patients who underwent first-time CABG surgery from January 1, 2013, to January 1, 2016, from a Mid-South hospital. A retrospective case-control study was employed to examine 1,712 patients who underwent CABG surgery. The results revealed that patients readmitted within 30 days had a significantly shorter length of stay (LOS) (6 days vs. 10 days; p < .0001), more days in intensive care unit (6 days vs. 4 days; p = .0391), and significantly higher diabetes/renal (4% vs. 1%), infection (17% vs. 2%), and respiratory-related diagnoses (10% vs. 1%; p < .0001). Among these factors, hospital LOS is a major factor that can be addressed through case management in addition to other modifiable risk factors. Understanding modifiable factors associated with higher readmission risk is crucial for effective intervention and case management planning.

Validation of French versions of the 15-item picker patient experience questionnaire for adults, teenagers, and children inpatients.

No French validated concise scales are available for measuring the experience of inpatients in pediatrics. This study aims to adapt the adult PPE-15 to a pediatric population, and translating it in French, as well as to establish reference values for adults, teenagers, and parents of young children. Cultural adaptation involved forward and backward translations, along with pretests in all three populations. Dimensional structure and internal consistency were assessed using principal component analysis, exploratory factor analysis, and Cronbach's alpha. Construct validity was assessed by examining established associations between patient satisfaction and inpatient variables, including length of stay, and preventable readmission. A total of 25,626 adults, 293 teenagers and 1,640 parents of young children completed the French questionnaires. Factor analysis supported a single dimension (Cronbach's alpha: adults: 0.85, teenagers: 0.82, parents: 0.80). Construct validity showed the expected pattern of association, with dissatisfaction correlating with patient- and stay-related factors, notably length of stay, and readmission. The French versions of the PPE-15 for adults, teenagers and parents of pediatric patients stand as valid and reliable instruments for gauging patient satisfaction regarding their hospital stay after discharge.

Average Length of Stay, Average Preventable Readmission Rates, and Average Total Cost of Care: Is there a Relationship?

Average Length of Stay (ALOS), Average Preventable Readmission Rates (APRRs), and Average Total Cost of Care (ATCc) were examined to see if any relationship exists among the variables. Secondary data from the Texas Hospital data collection database was used for the study. Out of 379 acute care hospitals in Texas, 65 hospitals were selected for analysis using a G*Power analysis. Demographic analysis, Spearman’s Rank correlation, and Regression Analyses were conducted to explore the relationship. The results of the Spearman’s Rho correlation showed a significant negative relationship between ALOS and ATCc [ALOS – ATCc (rs = - .271, p = .016)], and for APRRs and ATCc, there was no statistically significant relationship [APRRs – ATCc (rs = .065, p = .564)]. The multiple regression analysis showed there was no statistically significant relationship between the variables [ALOS – ARRs – ATCc (F (2,62) = 1.584, p = .211)]. The results showed that ALOS and APRRs do not necessarily predict ATCc. Consequently, based on the study results ATCc is not solely determined by ALOS and APRRs. The absence of a significant relationship between ALOS, APRRs, and ATCc does not necessarily indicate inefficiency in practice or lack of effectiveness. The results showed that there are other mediating factors impacting care that were not assessed in the study that need to be examined carefully which may have implications for practice, research, and methodology. Future research should be considered to determine these moderating factors to better understand the complex relationship between ALOS, APRRs, and ATCc. An understanding of these complex dynamics may inform managerial strategies and help with critical clinical and fiscal decisions in a healthcare setting.&#x0D; Keywords: Average Length of Stay, Average Preventable Readmission Rates, Average Total Cost of care

Social Determinants of Health and 30-Day Readmissions in an Urban Community Hospital in Northwest Indiana

Background: Hospital readmission within 30 days of discharge is a quality of care indicator with implications for healthcare systems, payers, and patients. The Hospital Readmission Reduction Program enacted in 2012 aimed to reduce preventable readmissions. Yet in 2018, there were 3.8 million adult 30-day readmissions with an average rate of 14% and estimated cost of $15,200 per readmission. This study examined the influence of social determinants of health (SDOH), demographics, and health behaviors on 30-day readmissions at an urban hospital in Northwest Indiana. This is part of a Community-Based Participatory Research (CBPR) partnership between Indiana University School of Medicine-Northwest and St. Mary Medical Center (SMMC) to address SDOH.&#x0D; Methods: This retrospective study analyzed a limited dataset generated by SMMC in EPIC™ with SDOH, demographics, health behaviors, and health outcomes measures from inpatient admissions between January 2021 to March 2023. Data analysis consisted of descriptive, bivariate (Chi-Square; p&lt;0.05), and multivariate (binary logistic regression; p&lt;0.05) analyses in SPSS 29.0. This study was exempted by the Indiana University Human Research Protection Program (IRB #14040).&#x0D; Results: The sample consisted of 7445 patients, majority 65 years and above (56.5%), white (77.47%), and publicly insured (76.83%). 30-day readmissions represented 10.5% of admissions. The bivariate analysis revealed statistically significant associations between 30-dayreadmissions and age (p&lt;0.001), language (p=0.008), insurance type (p&lt;0.001), veteran status (p=0.017), and smoking (p&lt;0.001). The multivariate analysis found that age (OR=1.008; p=0.004), being a non-English speaker (OR=1.866; p=0.009), public insurance (OR=2.096; p&lt;0.001), and former smoking (OR=1.243; p=0.011) remained significantly associated with 30-day readmission.&#x0D; Conclusions: Social and behavioral factors were associated with 30-day readmissions in an urban community hospital. Incorporating SDOH and behavioral interventions into hospital readmission reduction programs may reinforce these programs. The upcoming CBPR phase will conduct advanced analysis on these findings to uncover new relationships relevant to SMMC’s objectives.

Impact of the PCMH model on post-stroke healthcare utilization

The patient-centered medical home (PCMH) model has aimed to enhance chronic care in a primary care setting where providers are expected to provide timely post-discharge follow-up and decrease potentially preventable hospital readmissions through coordinated disease management. The study examined PCMH and other (risk) factors associated with 30-day unplanned readmission as well as 7- and 28-day follow-up visit with primary care providers (PCPs). We identified 8424 New York State Medicaid patients having initial stroke discharges during 2016–2018 from the Medicaid claims database and Statewide Planning and Research Cooperative System. A patient was in the PCMH group if the attributed provider had a valid National Committee for Quality Assurance PCMH recognition upon patient’s discharge. Cox regression and competing risk analyses were used to estimate the hazard ratios of readmissions and follow-up visits between PCMH and non-PCMH groups. The PCMH group had slightly but not statistically significant lower all-cause and recurrent stroke unplanned readmission rates. While the general follow-up rates between the two groups are similar, the PCMH group had significantly higher rates of stroke-specific 7-day (HR = 1.27, P < 0.01) and 28-day follow-up visits (HR = 1.22, P < 0.01). PCPs play an essential role in post stroke care. Our findings show a significantly higher rate of stroke-specific follow-up visits and a trend towards lower readmissions among patients in the PCMH group. The intervals between stroke discharge and follow-up visit time in our study population were generally greater than the recommended timeframe. We recommend the PCMH care model to improve patients’ primary care follow-up and prevent potential readmissions.