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Background: Patients hospitalized with COVID-19 can clinically deteriorate after a period of initial stability, making optimal timing of discharge a clinical and operational challenge. Objective: To determine risks for post-discharge readmission and death among patients hospitalized with COVID-19. Design: Multicenter retrospective observational cohort study, 2020–2021, with 30-day follow-up. Participants: Adults admitted for care of COVID-19 respiratory disease between March 2, 2020, and February 11, 2021, to one of 180 US hospitals affiliated with the HCA Healthcare system. Main Measures: Readmission to or death at an HCA hospital within 30 days of discharge was assessed. The area under the receiver operating characteristic curve (AUC) was calculated using an internal validation set (33% of the HCA cohort), and external validation was performed using similar data from six academic centers associated with a hospital medicine research network (HOMERuN). Key Results: The final HCA cohort included 62,195 patients (mean age 61.9 years, 51.9% male), of whom 4704 (7.6%) were readmitted or died within 30 days of discharge. Independent risk factors for death or readmission included fever within 72 h of discharge; tachypnea, tachycardia, or lack of improvement in oxygen requirement in the last 24 h; lymphopenia or thrombocytopenia at the time of discharge; being ≤ 7 days since first positive test for SARS-CoV-2; HOSPITAL readmission risk score ≥ 5; and several comorbidities. Inpatient treatment with remdesivir or anticoagulation were associated with lower odds. The model's AUC for the internal validation set was 0.73 (95% CI 0.71–0.74) and 0.66 (95% CI 0.64 to 0.67) for the external validation set. Conclusions: This large retrospective study identified several factors associated with post-discharge readmission or death in models which performed with good discrimination. Patients 7 or fewer days since test positivity and who demonstrate potentially reversible risk factors may benefit from delaying discharge until those risk factors resolve. [ABSTRACT FROM AUTHOR]

Background: Patient-physician sex discordance (when patient sex does not match physician sex) has been associated with reduced clinical rapport and adverse outcomes including post-operative mortality and unplanned hospital readmission. It remains unknown whether patient-physician sex discordance is associated with "before medically advised" hospital discharge (BMA discharge; commonly known as discharge "against medical advice"). Objective: To evaluate whether patient-physician sex discordance is associated with BMA discharge. Design: Retrospective cohort study using 15 years (2002–2017) of linked population-based administrative health data for all non-elective, non-obstetrical acute care hospitalizations from British Columbia, Canada. Participants: All individuals with eligible hospitalizations during study interval. Main Measures: Exposure: patient-physician sex discordance. Outcomes: BMA discharge (primary), 30-day hospital readmission or death (secondary). Results: We identified 1,926,118 eligible index hospitalizations, 2.6% of which ended in BMA discharge. Among male patients, sex discordance was associated with BMA discharge (crude rate, 4.0% vs 2.9%; adjusted odds ratio [aOR] 1.08; 95%CI 1.03–1.14; p = 0.003). Among female patients, sex discordance was not associated with BMA discharge (crude rate, 2.0% vs 2.3%; aOR 1.02; 95%CI 0.96–1.08; p = 0.557). Compared to patient-physician sex discordance, younger patient age, prior substance use, and prior BMA discharge all had stronger associations with BMA discharge. Conclusions: Patient-physician sex discordance was associated with a small increase in BMA discharge among male patients. This finding may reflect communication gaps, differences in the care provided by male and female physicians, discriminatory attitudes among male patients, or residual confounding. Improved communication and better treatment of pain and opioid withdrawal may reduce BMA discharge. [ABSTRACT FROM AUTHOR]

Objective: Over 25% of the 27 million uninsured individuals in the United States are eligible for Medicaid. Many hospitals have insurance linkage programs that assist eligible patients with enrollment, but little is known about the impact of these programs on care utilization. This research assessed health care utilization and health outcomes among patients enrolled in Medicaid via a hospital-based insurance linkage program. Methods: This retrospective cohort study included adults aged 18–64 admitted to the hospital from 2016 to 2021. Those who obtained insurance retroactively via insurance linkage (RI) were compared with those who presented with Medicaid (MI) or remained uninsured (UI). The primary outcome was the presence of at least one visit with a primary care provider (PCP) in the 12 months following index admission. Secondary outcomes included having an assigned PCP, ED revisits, and hospital readmissions. For patients with diabetes and hypertension, 12-month hemoglobin A1c (HbA1c) and blood pressure (BP) readings were tracked. Results: Of 3882 patients admitted with no insurance, 2905 (74.8%) were enrolled in insurance (RI). In multivariable analysis, RI patients were 14% more likely (OR 1.14, p = 0.020) to have completed at least one PCP visit by 12 months after index admission compared to those with preexisting Medicaid (MI), and uninsured patients were 29% less likely (OR 0.71, p = 0.003). MI and RI patients also had more ED revisits (p < 0.001) and greater 12-month reductions in blood pressure (p < 0.001) compared with uninsured patients. Conclusion: Hospital-based insurance linkage reached three-quarters of uninsured patients and was associated with increased utilization of acute and outpatient health care services. An acute care encounter represents an opportunity to connect patients to insurance, a key step toward improving their health outcomes. [ABSTRACT FROM AUTHOR]

Background: No clinical tools currently exist to stratify patients' risks of patient-directed discharge (PDD). Objective: This study aims to identify trends and factors associated with PDD, representation, and readmission. Design: This was an IRB-approved, single-centered, retrospective study. Participants: Patients aged > 18, admitted to medicine service, were included from January 1st through December 31st, 2019. Patients admitted to ICU or surgical services were excluded. Main Measures: Demographics, insurance information, medical history, social history, rates of events occurrences, and discharge disposition were obtained. Key Results: Of the 16,889 encounters, there were 776 (4.6%) PDDs, 4312 (25.5%) representations, and 2924 (17.3%) readmissions. Of those who completed PDDs, 42.1% represented and 26.4% were readmitted. Male sex, age ≤ 45, insurance type, homelessness, and substance use disorders had higher rates of PDD (OR = 2.0; 4.2; 4.5; 6.2; 5.2; p < 0.0001, respectively). Patients with homelessness, substance use disorders, mental health disorders, or prior history of PDD were more likely to represent (OR = 3.6; 2.0; 2.0; 1.5; p < 0.0001, respectively) and be readmitted (OR = 2.2; 1.6; 1.9; 1.5; p < 0.0001, respectively). Patients aged 30–35 had the highest PDD rate at 16%, but this was not associated with representations or readmissions. Between July and September, the PDD rate peaked at 5.5% and similarly representation and readmission rates followed. The rates of subsequent readmissions after PDDs were nearly two-fold compared to non-PDD patients in later half of the year. 51% of all subsequent readmissions occur within 7 days of PDD, compared to 34% in the non-PDD group (OR = 2.0; p < 0.0001). Patients with primary diagnosis of abscess had 16% PDDs. Conclusions: Factors associated with PDD include male, younger age, insurance type, substance use, homelessness, and primary diagnosis of abscess. Factors associated with representation and readmission are homelessness, substance use disorders, mental health disorders, and prior history of PDD. Further research is needed to develop a risk stratification tool to identify at-risk patients. [ABSTRACT FROM AUTHOR]

Background: In patients with new-onset heart failure (HF), coronary artery disease (CAD) testing remains underutilized. Whether widespread CAD testing in patients with new-onset HF leads to improved outcomes remains to be determined. Objective: We sought to examine whether CAD testing, and its timing, among patients hospitalized with new-onset HF with reduced ejection fraction (HFrEF), is associated with improved outcomes. Design: Retrospective cohort study. Participants: Adult (≥ 18 years) non-pregnant patients with new-onset HFrEF hospitalized within one of 15 Kaiser Permanente Southern California medical centers between 2016 and 2021. Key exclusion criteria included history of heart transplant, hospice, and a do-not-resuscitate order. Main Measures: Primary outcome was a composite of HF readmission or all-cause mortality through end of follow-up on 12/31/2022. Key Results: Among 2729 patients hospitalized with new-onset HFrEF, 1487 (54.5%) received CAD testing. The median age was 66 (56–76) years old, 1722 (63.1%) were male, and 1074 (39.4%) were White. After a median of 1.8 (0.6–3.4) years, the testing group had a reduced risk of HF readmission or all-cause mortality (aHR [95%CI], 0.71 [0.63–0.79]). These results were consistent across subgroups by history of atrial fibrillation, diabetes, renal disease, myocardial infarction, and elevated troponin during hospitalization. In a secondary analysis where CAD testing was further divided to early (received testing before discharge) and late testing (up to 90 days after discharge), there was no difference in late vs early testing (0.97 [0.81–1.16]). Conclusions: In a contemporary and diverse cohort of patients hospitalized with new-onset HFrEF, CAD testing within 90 days of hospitalization was associated with a lower risk of HF readmission or all-cause mortality. Testing within 90 days after discharge was not associated with worse outcomes. [ABSTRACT FROM AUTHOR]

Background: The simplified HOSPITAL score is an easy-to-use prediction model to identify patients at high risk of 30-day readmission before hospital discharge. An earlier stratification of this risk would allow more preparation time for transitional care interventions. Objective: To assess whether the simplified HOSPITAL score would perform similarly by using hemoglobin and sodium level at the time of admission instead of discharge. Design: Prospective national multicentric cohort study. Participants: In total, 934 consecutively discharged medical inpatients from internal general services. Main Measures: We measured the composite of the first unplanned readmission or death within 30 days after discharge of index admission and compared the performance of the simplified score with lab at discharge (simplified HOSPITAL score) and lab at admission (early HOSPITAL score) according to their discriminatory power (Area Under the Receiver Operating characteristic Curve (AUROC)) and the Net Reclassification Improvement (NRI). Key Results: During the study period, a total of 3239 patients were screened and 934 included. In total, 122 (13.2%) of them had a 30-day unplanned readmission or death. The simplified and the early versions of the HOSPITAL score both showed very good accuracy (Brier score 0.11, 95%CI 0.10–0.13). Their AUROC were 0.66 (95%CI 0.60–0.71), and 0.66 (95%CI 0.61–0.71), respectively, without a statistical difference (p value 0.79). Compared with the model at discharge, the model with lab at admission showed improvement in classification based on the continuous NRI (0.28; 95%CI 0.08 to 0.48; p value 0.004). Conclusion: The early HOSPITAL score performs, at least similarly, in identifying patients at high risk for 30-day unplanned readmission and allows a readmission risk stratification early during the hospital stay. Therefore, this new version offers a timely preparation of transition care interventions to the patients who may benefit the most. [ABSTRACT FROM AUTHOR]

Background: The Medicare Bundled Payments for Care Improvement (BPCI) program reimburses 90-day care episodes post-hospitalization. COPD is a leading cause of early readmissions making it a target for value-based payment reform. Objective: Evaluate the financial impact of a COPD BPCI program. Design, Participants, Interventions: A single-site retrospective observational study evaluated the impact of an evidence-based transitions of care program on episode costs and readmission rates, comparing patients hospitalized for COPD exacerbations who received versus those who did not receive the intervention. Main Measures: Mean episode costs and readmissions. Key Results: Between October 2015 and September 2018, 132 received and 161 did not receive the program, respectively. Mean episode costs were below target for six out of eleven quarters for the intervention group, as opposed to only one out of twelve quarters for the control group. Overall, there were non-significant mean savings of $2551 (95% CI: − $811 to $5795) in episode costs relative to target costs for the intervention group, though results varied by index admission diagnosis-related group (DRG); there were additional costs of $4184 per episode for the least-complicated cohort (DRG 192), but savings of $1897 and $1753 for the most complicated index admissions (DRGs 191 and 190, respectively). A significant mean decrease of 0.24 readmissions per episode was observed in 90-day readmission rates for intervention relative to control. Readmissions and hospital discharges to skilled nursing facilities were factors of higher costs (mean increases of $9098 and $17,095 per episode respectively). Conclusions: Our COPD BPCI program had a non-significant cost-saving effect, although sample size limited study power. The differential impact of the intervention by DRG suggests that targeting interventions to more clinically complex patients could increase the financial impact of the program. Further evaluations are needed to determine if our BPCI program decreased care variation and improved quality of care. Primary Source of Funding: This research was supported by NIH NIA grant #5T35AG029795-12. [ABSTRACT FROM AUTHOR]

Background: Early hospital discharge planning can help to reduce the length of stay and unplanned readmission in high-risk patients. Therefore, it is important to select patients who can benefit from a personalized discharge planning based on validated tools. The modified Blaylock Risk Assessment Screening Score (BRASS) is routinely used in the Molinette Hospital (Turin, Italy) to screen patients at high risk for discharge, but the effectiveness of the discharge planning is uncertain in intermediate-risk patients. Objective: To evaluate the best strategy for discharge planning by the Continuity of Care Hospital Unit (CCHU) in intermediate-risk patients according to modified BRASS. Design: Cluster-randomized, multiple crossover trial. Participants: Adult patients admitted in the Medicine and Neurology departments of the Molinette Hospital in Turin, Italy, between June 2018 and May 2019 with a BRASS intermediate risk. Interventions: A routine discharge planning strategy (RDP, Routine Discharge Plan), which involved the management of all intermediate-risk patients, was compared to an on-demand discharge planning strategy (DDP, on-Demand Discharge Planning), which involved only selected patients referred to the CCHU by ward staff. Main Measures: The primary outcome was the 90-day hospital readmission for any cause (HR90). Secondary outcomes included the prolonged length of stay (pLOS). Key Results: Eight hundred two patients (median age 79 years) were included (414 RDP and 388 DDP). Comparing RDP vs. DDP periods, HR90 was 27.6% and 27.3% (OR 1.01, 90%CI 0.76–1.33, p = 0.485); and pLOS was 47 (11.4%) and 40 (10.3%) (OR 1.24, 95%CI 0.72–2.13, p = 0.447), respectively. Conclusions: This is one of the largest randomized study conducted to compare the effectiveness of two different hospital discharge planning strategies. In patients with intermediate risk of hospital discharge, a RDP offers no advantage over a DDP and results in an unnecessary increase in staff workload. Trial Registration: Clinicaltrials.gov: NCT03436940 [ABSTRACT FROM AUTHOR]

Background: Heart failure is common and is associated with high rates of hospitalization. Home care support clinics/hospitals (HCSCs) and enhanced HCSCs were introduced in Japan in 2006 and 2012, respectively. Objective: This study aimed to examine the effect of post-discharge care by conventional or enhanced HCSCs on readmission, compared with general clinics. Design: Retrospective cohort study using the Japanese nationwide health insurance claims database. Participants: Participants were ≥65 years of age, admitted for heart failure and discharged between July 2014 and August 2015 and received a home visit within a month following the discharge (n=12,393). Main Measures: The exposure was the type of medical facility that provides post-discharge home healthcare: general clinics, conventional HCSCs, and enhanced HCSCs. The primary outcome was all-cause readmission for 6 months after the first visit; the incidence of emergency house calls was a secondary outcome. We used a competing risk regression using the Fine and Gray method, in which death was regarded as a competing event. Key Results: At 6 months, readmissions were lower in conventional (38%) or enhanced HCSCs (38%) than general clinics (43%). The adjusted subdistribution hazard ratio (sHR) of readmission was 0.87 (95% CI: 0.78–0.96) for conventional and 0.86 (0.78–0.96) for enhanced HCSCs. Emergency house calls increased with conventional (sHR: 1.77, 95% CI:1.57–2.00) and enhanced HCSCs (sHR: 1.93, 95% CI: 1.71-2.17). Conclusions: Older Japanese patients with heart failure receiving post-discharge home healthcare by conventional or enhanced HCSCs had lower readmission rates, possibly due to compensation with more emergency house calls. Conventional and enhanced HCSCs may be effective in reducing the risk of rehospitalization. Further studies are necessary to confirm the medical functions performed by HCSCs. [ABSTRACT FROM AUTHOR]

Background: Hospitalizations for infective endocarditis (IE) associated with opioid use disorder (O-IE) have increased in the USA and have been linked to high rates of discharge against medical advice (DAMA). DAMA represents a truncation of care for a severe infection, yet patient outcomes after DAMA are unknown. Objective: This study aimed to assess readmissions following O-IE and quantify the impact of DAMA on outcomes. Design: A retrospective study of a nationally representative dataset of persons' inpatient discharges in the USA in 2016 Participants: A total of 6018 weighted persons were discharged for O-IE, stratified by DAMA vs. other discharge statuses. Of these, 1331 (22%) were DAMA. Main Measures: The primary outcome of interest was 30-day readmission rates, stratified by discharge type. We also examined the total number of hospitalizations during the year and estimated the effect of DAMA on readmission. Key Results: Compared with non-DAMA, those experiencing DAMA were more commonly female, resided in metropolitan areas, lower income, and uninsured. Crude 30-day readmission following DAMA was 50%, compared with 21% for other discharge types. DAMA was strongly associated with readmission in an adjusted logistic regression model (OR 3.72, CI 3.02–4.60). Persons experiencing DAMA more commonly had ≥2 more hospitalizations during the period (31% vs. 18%, p<0.01), and were less frequently readmitted at the same hospital (49% vs 64%, p<0.01). Conclusions: DAMA occurs in nearly a quarter of patients hospitalized for O-IE and is strongly associated with short-term readmission. Interventions to address the root causes of premature discharges will enhance O-IE care, reduce hospitalizations and improve outcomes. [ABSTRACT FROM AUTHOR]

Background: Reducing hospital readmissions is a federal policy priority, and predictive models of hospital readmissions have proliferated in recent years; however, most such models tend to focus on the 30-day readmission time horizon and do not consider readmission over shorter (or longer) windows. Objectives: To evaluate the performance of a predictive model of hospital readmissions over three different readmission timeframes in a commercially insured population. Design: Retrospective multivariate logistic regression with an 80/20 train/test split. Participants: A total of 2,213,832 commercially insured inpatient admissions from 2016 to 2017 comprising 782,768 unique patients from the Health Care Cost Institute. Main Measures: Outcomes are readmission within 14 days, 15–30 days, and 31–60 days from discharge. Predictor variables span six different domains: index admission, condition history, demographic, utilization history, pharmacy, and environmental controls. Key Results: Our model generates C-statistics for holdout samples ranging from 0.618 to 0.915. The model's discriminative power declines with readmission time horizon: discrimination for readmission predictions within 14 days following discharge is higher than for readmissions 15–30 days following discharge, which in turn is higher than predictions 31–60 days following discharge. Additionally, the model's predictive power increases nonlinearly with the inclusion of successive risk factor domains: patient-level measures of utilization and condition history add substantially to the discriminative power of the model, while demographic information, pharmacy utilization, and environmental risk factors add relatively little. Conclusion: It is more difficult to predict distant readmissions than proximal readmissions, and the more information the model uses, the better the predictions. Inclusion of utilization-based risk factors add substantially to the discriminative ability of the model, much more than any other included risk factor domain. Our best-performing models perform well relative to other published readmission prediction models. It is possible that these predictions could have operational utility in targeting readmission prevention interventions among high-risk individuals. [ABSTRACT FROM AUTHOR]

Background: Interdisciplinary teams (IDTs) have been implemented to improve collaboration in hospital care, but their impact on patient outcomes, including readmissions, has been mixed. These mixed results might be rooted in differences in organization of IDT meetings between hospitals, as well as variation in IDT characteristics and function. We hypothesize that relationships between IDT members are an important team characteristic, influencing IDT function in terms of how members make sense of what is happening with patients, a process called sensemaking Objective: (1) To describe how IDT meetings are organized in practice, (2) assess differences in IDT member relationships and sensemaking during patient discussions, and (3) explore their potential association with risk-stratified readmission rates (RSRRs). Design: Observational, explanatory convergent mixed-methods case-comparison study of IDT meetings in 10 Veterans Affairs hospitals. Participants: Clinicians participating in IDTs and facility leadership. Approach: Three-person teams observed and recorded IDT meetings during week-long visits. We used observational data to characterize relationships and sensemaking during IDT patient discussions. To assess sensemaking, we used 2 frameworks that reflected sensemaking around each patient's situation generally, and around care transitions specifically. We examined the association between IDT relationships and sensemaking, and RSRRs. Key Results: We observed variability in IDT organization, characteristics, and function across 10 hospitals. This variability was greater between hospitals than between teams at the same hospital. Relationship characteristics and both types of sensemaking were all significantly, positively correlated. General sensemaking regarding each patient was significantly negatively associated with RSRR (− 0.65, p = 0.044). Conclusions: IDTs vary not only in how they are organized, but also in team relationships and sensemaking. Though our design does not allow for inferences of causation, these differences may be associated with hospital readmission rates. [ABSTRACT FROM AUTHOR]

The article "Can Timely Outpatient Visits Reduce Readmissions and Mortality Among Heart Failure Patients?" discusses the impact of timely outpatient follow-ups on readmission and mortality rates among heart failure patients. The study, conducted in Singapore, addresses the challenges faced by physicians in Tampa, FL, such as language barriers, lack of resources, and inadequate patient education. The article also explores the use of telehealth as an alternative for in-person visits, highlighting its advantages and disadvantages in post-discharge care. Additionally, the importance of interdisciplinary discharge planning and follow-up coordination is emphasized to prevent readmissions and adverse events. [Extracted from the article]

Background: Heart failure (HF) hospitalizations are characterized by vulnerability in functioning and frequent post-discharge healthcare utilization in both acute and post-acute settings.To determine, in patients hospitalized for decompensated HF, the association of vulnerability with (1) detailed forms of post-discharge healthcare utilization, and (2) days spent away from home after initial hospital discharge.Secondary analysis of a prospective longitudinal cohort study from a single-center academic institution in the USA.Adults admitted with acute decompensated HF who were discharged alive.The Vulnerable Elders Survey 13 (VES-13) measured functional vulnerability at baseline. The primary outcome was the Highest Healthcare Utilization (HHU) 90 days post-discharge, from the following ordered categories: at home, emergency room visit, skilled nursing facility stay, hospital readmission, or death. The secondary outcome was the proportion of days not at home (DNAH) within the first 90 days. Analyses were performed using a partial proportional odds model with adjustment for demographics and health characteristics.A total of 806 patients were included with median age 65, interquartile range [IQR] 55–73 years. Fewer than half (N = 345 [43%]) of patients remained alive and at home during 90-day follow-up. There were 286 [35%] hospital readmissions and 70 [8.7%] participants died. The median DNAH was 3 [IQR 0–16]. Increased vulnerability was associated with (1) HHU, (2) higher odds of utilizing healthcare or dying versus being at home alive 90 days post-discharge (OR 1.81 [95% CI, 1.35, 2.42]), and (3) higher odds of DNAH in the first 90 days (OR 1.55 [95% CI, 1.27, 1.89]).In this cohort of patients hospitalized for decompensated HF, vulnerability predicted higher levels of healthcare utilization, as well as total days not at home in the 90 days following hospitalization. Vulnerability may have clinical applications to identify patients at greatest need for comprehensive, patient-centered discharge planning.Objective: Heart failure (HF) hospitalizations are characterized by vulnerability in functioning and frequent post-discharge healthcare utilization in both acute and post-acute settings.To determine, in patients hospitalized for decompensated HF, the association of vulnerability with (1) detailed forms of post-discharge healthcare utilization, and (2) days spent away from home after initial hospital discharge.Secondary analysis of a prospective longitudinal cohort study from a single-center academic institution in the USA.Adults admitted with acute decompensated HF who were discharged alive.The Vulnerable Elders Survey 13 (VES-13) measured functional vulnerability at baseline. The primary outcome was the Highest Healthcare Utilization (HHU) 90 days post-discharge, from the following ordered categories: at home, emergency room visit, skilled nursing facility stay, hospital readmission, or death. The secondary outcome was the proportion of days not at home (DNAH) within the first 90 days. Analyses were performed using a partial proportional odds model with adjustment for demographics and health characteristics.A total of 806 patients were included with median age 65, interquartile range [IQR] 55–73 years. Fewer than half (N = 345 [43%]) of patients remained alive and at home during 90-day follow-up. There were 286 [35%] hospital readmissions and 70 [8.7%] participants died. The median DNAH was 3 [IQR 0–16]. Increased vulnerability was associated with (1) HHU, (2) higher odds of utilizing healthcare or dying versus being at home alive 90 days post-discharge (OR 1.81 [95% CI, 1.35, 2.42]), and (3) higher odds of DNAH in the first 90 days (OR 1.55 [95% CI, 1.27, 1.89]).In this cohort of patients hospitalized for decompensated HF, vulnerability predicted higher levels of healthcare utilization, as well as total days not at home in the 90 days following hospitalization. Vulnerability may have clinical applications to identify patients at greatest need for comprehensive, patient-centered discharge planning.Design: Heart failure (HF) hospitalizations are characterized by vulnerability in functioning and frequent post-discharge healthcare utilization in both acute and post-acute settings.To determine, in patients hospitalized for decompensated HF, the association of vulnerability with (1) detailed forms of post-discharge healthcare utilization, and (2) days spent away from home after initial hospital discharge.Secondary analysis of a prospective longitudinal cohort study from a single-center academic institution in the USA.Adults admitted with acute decompensated HF who were discharged alive.The Vulnerable Elders Survey 13 (VES-13) measured functional vulnerability at baseline. The primary outcome was the Highest Healthcare Utilization (HHU) 90 days post-discharge, from the following ordered categories: at home, emergency room visit, skilled nursing facility stay, hospital readmission, or death. The secondary outcome was the proportion of days not at home (DNAH) within the first 90 days. Analyses were performed using a partial proportional odds model with adjustment for demographics and health characteristics.A total of 806 patients were included with median age 65, interquartile range [IQR] 55–73 years. Fewer than half (N = 345 [43%]) of patients remained alive and at home during 90-day follow-up. There were 286 [35%] hospital readmissions and 70 [8.7%] participants died. The median DNAH was 3 [IQR 0–16]. Increased vulnerability was associated with (1) HHU, (2) higher odds of utilizing healthcare or dying versus being at home alive 90 days post-discharge (OR 1.81 [95% CI, 1.35, 2.42]), and (3) higher odds of DNAH in the first 90 days (OR 1.55 [95% CI, 1.27, 1.89]).In this cohort of patients hospitalized for decompensated HF, vulnerability predicted higher levels of healthcare utilization, as well as total days not at home in the 90 days following hospitalization. Vulnerability may have clinical applications to identify patients at greatest need for comprehensive, patient-centered discharge planning.Participants: Heart failure (HF) hospitalizations are characterized by vulnerability in functioning and frequent post-discharge healthcare utilization in both acute and post-acute settings.To determine, in patients hospitalized for decompensated HF, the association of vulnerability with (1) detailed forms of post-discharge healthcare utilization, and (2) days spent away from home after initial hospital discharge.Secondary analysis of a prospective longitudinal cohort study from a single-center academic institution in the USA.Adults admitted with acute decompensated HF who were discharged alive.The Vulnerable Elders Survey 13 (VES-13) measured functional vulnerability at baseline. The primary outcome was the Highest Healthcare Utilization (HHU) 90 days post-discharge, from the following ordered categories: at home, emergency room visit, skilled nursing facility stay, hospital readmission, or death. The secondary outcome was the proportion of days not at home (DNAH) within the first 90 days. Analyses were performed using a partial proportional odds model with adjustment for demographics and health characteristics.A total of 806 patients were included with median age 65, interquartile range [IQR] 55–73 years. Fewer than half (N = 345 [43%]) of patients remained alive and at home during 90-day follow-up. There were 286 [35%] hospital readmissions and 70 [8.7%] participants died. The median DNAH was 3 [IQR 0–16]. Increased vulnerability was associated with (1) HHU, (2) higher odds of utilizing healthcare or dying versus being at home alive 90 days post-discharge (OR 1.81 [95% CI, 1.35, 2.42]), and (3) higher odds of DNAH in the first 90 days (OR 1.55 [95% CI, 1.27, 1.89]).In this cohort of patients hospitalized for decompensated HF, vulnerability predicted higher levels of healthcare utilization, as well as total days not at home in the 90 days following hospitalization. Vulnerability may have clinical applications to identify patients at greatest need for comprehensive, patient-centered discharge planning.Main Measures: Heart failure (HF) hospitalizations are characterized by vulnerability in functioning and frequent post-discharge healthcare utilization in both acute and post-acute settings.To determine, in patients hospitalized for decompensated HF, the association of vulnerability with (1) detailed forms of post-discharge healthcare utilization, and (2) days spent away from home after initial hospital discharge.Secondary analysis of a prospective longitudinal cohort study from a single-center academic institution in the USA.Adults admitted with acute decompensated HF who were discharged alive.The Vulnerable Elders Survey 13 (VES-13) measured functional vulnerability at baseline. The primary outcome was the Highest Healthcare Utilization (HHU) 90 days post-discharge, from the following ordered categories: at home, emergency room visit, skilled nursing facility stay, hospital readmission, or death. The secondary outcome was the proportion of days not at home (DNAH) within the first 90 days. Analyses were performed using a partial proportional odds model with adjustment for demographics and health characteristics.A total of 806 patients were included with median age 65, interquartile range [IQR] 55–73 years. Fewer than half (N = 345 [43%]) of patients remained alive and at home during 90-day follow-up. There were 286 [35%] hospital readmissions and 70 [8.7%] participants died. The median DNAH was 3 [IQR 0–16]. Increased vulnerability was associated with (1) HHU, (2) higher odds of utilizing healthcare or dying versus being at home alive 90 days post-discharge (OR 1.81 [95% CI, 1.35, 2.42]), and (3) higher odds of DNAH in the first 90 days (OR 1.55 [95% CI, 1.27, 1.89]).In this cohort of patients hospitalized for decompensated HF, vulnerability predicted higher levels of healthcare utilization, as well as total days not at home in the 90 days following hospitalization. Vulnerability may have clinical applications to identify patients at greatest need for comprehensive, patient-centered discharge planning.Key Results: Heart failure (HF) hospitalizations are characterized by vulnerability in functioning and frequent post-discharge healthcare utilization in both acute and post-acute settings.To determine, in patients hospitalized for decompensated HF, the association of vulnerability with (1) detailed forms of post-discharge healthcare utilization, and (2) days spent away from home after initial hospital discharge.Secondary analysis of a prospective longitudinal cohort study from a single-center academic institution in the USA.Adults admitted with acute decompensated HF who were discharged alive.The Vulnerable Elders Survey 13 (VES-13) measured functional vulnerability at baseline. The primary outcome was the Highest Healthcare Utilization (HHU) 90 days post-discharge, from the following ordered categories: at home, emergency room visit, skilled nursing facility stay, hospital readmission, or death. The secondary outcome was the proportion of days not at home (DNAH) within the first 90 days. Analyses were performed using a partial proportional odds model with adjustment for demographics and health characteristics.A total of 806 patients were included with median age 65, interquartile range [IQR] 55–73 years. Fewer than half (N = 345 [43%]) of patients remained alive and at home during 90-day follow-up. There were 286 [35%] hospital readmissions and 70 [8.7%] participants died. The median DNAH was 3 [IQR 0–16]. Increased vulnerability was associated with (1) HHU, (2) higher odds of utilizing healthcare or dying versus being at home alive 90 days post-discharge (OR 1.81 [95% CI, 1.35, 2.42]), and (3) higher odds of DNAH in the first 90 days (OR 1.55 [95% CI, 1.27, 1.89]).In this cohort of patients hospitalized for decompensated HF, vulnerability predicted higher levels of healthcare utilization, as well as total days not at home in the 90 days following hospitalization. Vulnerability may have clinical applications to identify patients at greatest need for comprehensive, patient-centered discharge planning.Conclusions: Heart failure (HF) hospitalizations are characterized by vulnerability in functioning and frequent post-discharge healthcare utilization in both acute and post-acute settings.To determine, in patients hospitalized for decompensated HF, the association of vulnerability with (1) detailed forms of post-discharge healthcare utilization, and (2) days spent away from home after initial hospital discharge.Secondary analysis of a prospective longitudinal cohort study from a single-center academic institution in the USA.Adults admitted with acute decompensated HF who were discharged alive.The Vulnerable Elders Survey 13 (VES-13) measured functional vulnerability at baseline. The primary outcome was the Highest Healthcare Utilization (HHU) 90 days post-discharge, from the following ordered categories: at home, emergency room visit, skilled nursing facility stay, hospital readmission, or death. The secondary outcome was the proportion of days not at home (DNAH) within the first 90 days. Analyses were performed using a partial proportional odds model with adjustment for demographics and health characteristics.A total of 806 patients were included with median age 65, interquartile range [IQR] 55–73 years. Fewer than half (N = 345 [43%]) of patients remained alive and at home during 90-day follow-up. There were 286 [35%] hospital readmissions and 70 [8.7%] participants died. The median DNAH was 3 [IQR 0–16]. Increased vulnerability was associated with (1) HHU, (2) higher odds of utilizing healthcare or dying versus being at home alive 90 days post-discharge (OR 1.81 [95% CI, 1.35, 2.42]), and (3) higher odds of DNAH in the first 90 days (OR 1.55 [95% CI, 1.27, 1.89]).In this cohort of patients hospitalized for decompensated HF, vulnerability predicted higher levels of healthcare utilization, as well as total days not at home in the 90 days following hospitalization. Vulnerability may have clinical applications to identify patients at greatest need for comprehensive, patient-centered discharge planning. [ABSTRACT FROM AUTHOR]

Background: Health care systems are increasingly partnering with community-based organizations to address social determinants of health (SDH). We established a program that educates and connects patients with SDH needs at a primary care clinic to community services and facilitated referrals.To evaluate the effect of addressing SDH soon after discharge on hospital readmission in a clinic population.Pre/post, quasi-experimental design with longitudinal data analysis for quality improvement.Clinic patients (n = 754) having at least one hospital discharge between June 1, 2020, and October 31, 2021, were included. Of these, 145 patients received the intervention and 609 served as comparison.A primary care liaison was employed to assess and educate recently discharged clinic patients for SDH needs and refer them for needed community services from June 1, 2020, to October 31, 2021.Hospital readmissions within 30, 60, and 90 days of discharge were tracked at 6-month intervals. Covariates included patient age, sex, race/ethnicity, insurance status, income, Hierarchical Condition Category risk scores, and Clinical Classification Software diagnosis groups. Data for all hospital discharges during the intervention period were used for the main analysis and data for the year before the intervention were extracted for comparison.Overall, patients in the intervention group were older, sicker, and more likely to have public insurance. The reductions in 30-, 60-, and 90-day readmissions during the intervention period were 14.39%, 13.28%, and 12.04% respectively in the intervention group, while no significant change was observed in the comparison group. The group difference in reduction over time was statistically significant for 30-day (Diff = 12.54%; p = 0.032), 60-day (Diff = 14.40%; p = 0.012), and 90-day readmissions (Diff = 14.71%; p = 0.036).Our findings suggest that screening clinic patients for SDH, and educating and connecting them to community services during post-hospital care may be associated with reductions in hospital readmissions.Objective: Health care systems are increasingly partnering with community-based organizations to address social determinants of health (SDH). We established a program that educates and connects patients with SDH needs at a primary care clinic to community services and facilitated referrals.To evaluate the effect of addressing SDH soon after discharge on hospital readmission in a clinic population.Pre/post, quasi-experimental design with longitudinal data analysis for quality improvement.Clinic patients (n = 754) having at least one hospital discharge between June 1, 2020, and October 31, 2021, were included. Of these, 145 patients received the intervention and 609 served as comparison.A primary care liaison was employed to assess and educate recently discharged clinic patients for SDH needs and refer them for needed community services from June 1, 2020, to October 31, 2021.Hospital readmissions within 30, 60, and 90 days of discharge were tracked at 6-month intervals. Covariates included patient age, sex, race/ethnicity, insurance status, income, Hierarchical Condition Category risk scores, and Clinical Classification Software diagnosis groups. Data for all hospital discharges during the intervention period were used for the main analysis and data for the year before the intervention were extracted for comparison.Overall, patients in the intervention group were older, sicker, and more likely to have public insurance. The reductions in 30-, 60-, and 90-day readmissions during the intervention period were 14.39%, 13.28%, and 12.04% respectively in the intervention group, while no significant change was observed in the comparison group. The group difference in reduction over time was statistically significant for 30-day (Diff = 12.54%; p = 0.032), 60-day (Diff = 14.40%; p = 0.012), and 90-day readmissions (Diff = 14.71%; p = 0.036).Our findings suggest that screening clinic patients for SDH, and educating and connecting them to community services during post-hospital care may be associated with reductions in hospital readmissions.Design: Health care systems are increasingly partnering with community-based organizations to address social determinants of health (SDH). We established a program that educates and connects patients with SDH needs at a primary care clinic to community services and facilitated referrals.To evaluate the effect of addressing SDH soon after discharge on hospital readmission in a clinic population.Pre/post, quasi-experimental design with longitudinal data analysis for quality improvement.Clinic patients (n = 754) having at least one hospital discharge between June 1, 2020, and October 31, 2021, were included. Of these, 145 patients received the intervention and 609 served as comparison.A primary care liaison was employed to assess and educate recently discharged clinic patients for SDH needs and refer them for needed community services from June 1, 2020, to October 31, 2021.Hospital readmissions within 30, 60, and 90 days of discharge were tracked at 6-month intervals. Covariates included patient age, sex, race/ethnicity, insurance status, income, Hierarchical Condition Category risk scores, and Clinical Classification Software diagnosis groups. Data for all hospital discharges during the intervention period were used for the main analysis and data for the year before the intervention were extracted for comparison.Overall, patients in the intervention group were older, sicker, and more likely to have public insurance. The reductions in 30-, 60-, and 90-day readmissions during the intervention period were 14.39%, 13.28%, and 12.04% respectively in the intervention group, while no significant change was observed in the comparison group. The group difference in reduction over time was statistically significant for 30-day (Diff = 12.54%; p = 0.032), 60-day (Diff = 14.40%; p = 0.012), and 90-day readmissions (Diff = 14.71%; p = 0.036).Our findings suggest that screening clinic patients for SDH, and educating and connecting them to community services during post-hospital care may be associated with reductions in hospital readmissions.Participants: Health care systems are increasingly partnering with community-based organizations to address social determinants of health (SDH). We established a program that educates and connects patients with SDH needs at a primary care clinic to community services and facilitated referrals.To evaluate the effect of addressing SDH soon after discharge on hospital readmission in a clinic population.Pre/post, quasi-experimental design with longitudinal data analysis for quality improvement.Clinic patients (n = 754) having at least one hospital discharge between June 1, 2020, and October 31, 2021, were included. Of these, 145 patients received the intervention and 609 served as comparison.A primary care liaison was employed to assess and educate recently discharged clinic patients for SDH needs and refer them for needed community services from June 1, 2020, to October 31, 2021.Hospital readmissions within 30, 60, and 90 days of discharge were tracked at 6-month intervals. Covariates included patient age, sex, race/ethnicity, insurance status, income, Hierarchical Condition Category risk scores, and Clinical Classification Software diagnosis groups. Data for all hospital discharges during the intervention period were used for the main analysis and data for the year before the intervention were extracted for comparison.Overall, patients in the intervention group were older, sicker, and more likely to have public insurance. The reductions in 30-, 60-, and 90-day readmissions during the intervention period were 14.39%, 13.28%, and 12.04% respectively in the intervention group, while no significant change was observed in the comparison group. The group difference in reduction over time was statistically significant for 30-day (Diff = 12.54%; p = 0.032), 60-day (Diff = 14.40%; p = 0.012), and 90-day readmissions (Diff = 14.71%; p = 0.036).Our findings suggest that screening clinic patients for SDH, and educating and connecting them to community services during post-hospital care may be associated with reductions in hospital readmissions.Interventions: Health care systems are increasingly partnering with community-based organizations to address social determinants of health (SDH). We established a program that educates and connects patients with SDH needs at a primary care clinic to community services and facilitated referrals.To evaluate the effect of addressing SDH soon after discharge on hospital readmission in a clinic population.Pre/post, quasi-experimental design with longitudinal data analysis for quality improvement.Clinic patients (n = 754) having at least one hospital discharge between June 1, 2020, and October 31, 2021, were included. Of these, 145 patients received the intervention and 609 served as comparison.A primary care liaison was employed to assess and educate recently discharged clinic patients for SDH needs and refer them for needed community services from June 1, 2020, to October 31, 2021.Hospital readmissions within 30, 60, and 90 days of discharge were tracked at 6-month intervals. Covariates included patient age, sex, race/ethnicity, insurance status, income, Hierarchical Condition Category risk scores, and Clinical Classification Software diagnosis groups. Data for all hospital discharges during the intervention period were used for the main analysis and data for the year before the intervention were extracted for comparison.Overall, patients in the intervention group were older, sicker, and more likely to have public insurance. The reductions in 30-, 60-, and 90-day readmissions during the intervention period were 14.39%, 13.28%, and 12.04% respectively in the intervention group, while no significant change was observed in the comparison group. The group difference in reduction over time was statistically significant for 30-day (Diff = 12.54%; p = 0.032), 60-day (Diff = 14.40%; p = 0.012), and 90-day readmissions (Diff = 14.71%; p = 0.036).Our findings suggest that screening clinic patients for SDH, and educating and connecting them to community services during post-hospital care may be associated with reductions in hospital readmissions.Main Measures: Health care systems are increasingly partnering with community-based organizations to address social determinants of health (SDH). We established a program that educates and connects patients with SDH needs at a primary care clinic to community services and facilitated referrals.To evaluate the effect of addressing SDH soon after discharge on hospital readmission in a clinic population.Pre/post, quasi-experimental design with longitudinal data analysis for quality improvement.Clinic patients (n = 754) having at least one hospital discharge between June 1, 2020, and October 31, 2021, were included. Of these, 145 patients received the intervention and 609 served as comparison.A primary care liaison was employed to assess and educate recently discharged clinic patients for SDH needs and refer them for needed community services from June 1, 2020, to October 31, 2021.Hospital readmissions within 30, 60, and 90 days of discharge were tracked at 6-month intervals. Covariates included patient age, sex, race/ethnicity, insurance status, income, Hierarchical Condition Category risk scores, and Clinical Classification Software diagnosis groups. Data for all hospital discharges during the intervention period were used for the main analysis and data for the year before the intervention were extracted for comparison.Overall, patients in the intervention group were older, sicker, and more likely to have public insurance. The reductions in 30-, 60-, and 90-day readmissions during the intervention period were 14.39%, 13.28%, and 12.04% respectively in the intervention group, while no significant change was observed in the comparison group. The group difference in reduction over time was statistically significant for 30-day (Diff = 12.54%; p = 0.032), 60-day (Diff = 14.40%; p = 0.012), and 90-day readmissions (Diff = 14.71%; p = 0.036).Our findings suggest that screening clinic patients for SDH, and educating and connecting them to community services during post-hospital care may be associated with reductions in hospital readmissions.Key Results: Health care systems are increasingly partnering with community-based organizations to address social determinants of health (SDH). We established a program that educates and connects patients with SDH needs at a primary care clinic to community services and facilitated referrals.To evaluate the effect of addressing SDH soon after discharge on hospital readmission in a clinic population.Pre/post, quasi-experimental design with longitudinal data analysis for quality improvement.Clinic patients (n = 754) having at least one hospital discharge between June 1, 2020, and October 31, 2021, were included. Of these, 145 patients received the intervention and 609 served as comparison.A primary care liaison was employed to assess and educate recently discharged clinic patients for SDH needs and refer them for needed community services from June 1, 2020, to October 31, 2021.Hospital readmissions within 30, 60, and 90 days of discharge were tracked at 6-month intervals. Covariates included patient age, sex, race/ethnicity, insurance status, income, Hierarchical Condition Category risk scores, and Clinical Classification Software diagnosis groups. Data for all hospital discharges during the intervention period were used for the main analysis and data for the year before the intervention were extracted for comparison.Overall, patients in the intervention group were older, sicker, and more likely to have public insurance. The reductions in 30-, 60-, and 90-day readmissions during the intervention period were 14.39%, 13.28%, and 12.04% respectively in the intervention group, while no significant change was observed in the comparison group. The group difference in reduction over time was statistically significant for 30-day (Diff = 12.54%; p = 0.032), 60-day (Diff = 14.40%; p = 0.012), and 90-day readmissions (Diff = 14.71%; p = 0.036).Our findings suggest that screening clinic patients for SDH, and educating and connecting them to community services during post-hospital care may be associated with reductions in hospital readmissions.Conclusion: Health care systems are increasingly partnering with community-based organizations to address social determinants of health (SDH). We established a program that educates and connects patients with SDH needs at a primary care clinic to community services and facilitated referrals.To evaluate the effect of addressing SDH soon after discharge on hospital readmission in a clinic population.Pre/post, quasi-experimental design with longitudinal data analysis for quality improvement.Clinic patients (n = 754) having at least one hospital discharge between June 1, 2020, and October 31, 2021, were included. Of these, 145 patients received the intervention and 609 served as comparison.A primary care liaison was employed to assess and educate recently discharged clinic patients for SDH needs and refer them for needed community services from June 1, 2020, to October 31, 2021.Hospital readmissions within 30, 60, and 90 days of discharge were tracked at 6-month intervals. Covariates included patient age, sex, race/ethnicity, insurance status, income, Hierarchical Condition Category risk scores, and Clinical Classification Software diagnosis groups. Data for all hospital discharges during the intervention period were used for the main analysis and data for the year before the intervention were extracted for comparison.Overall, patients in the intervention group were older, sicker, and more likely to have public insurance. The reductions in 30-, 60-, and 90-day readmissions during the intervention period were 14.39%, 13.28%, and 12.04% respectively in the intervention group, while no significant change was observed in the comparison group. The group difference in reduction over time was statistically significant for 30-day (Diff = 12.54%; p = 0.032), 60-day (Diff = 14.40%; p = 0.012), and 90-day readmissions (Diff = 14.71%; p = 0.036).Our findings suggest that screening clinic patients for SDH, and educating and connecting them to community services during post-hospital care may be associated with reductions in hospital readmissions.Graphical Abstract: Health care systems are increasingly partnering with community-based organizations to address social determinants of health (SDH). We established a program that educates and connects patients with SDH needs at a primary care clinic to community services and facilitated referrals.To evaluate the effect of addressing SDH soon after discharge on hospital readmission in a clinic population.Pre/post, quasi-experimental design with longitudinal data analysis for quality improvement.Clinic patients (n = 754) having at least one hospital discharge between June 1, 2020, and October 31, 2021, were included. Of these, 145 patients received the intervention and 609 served as comparison.A primary care liaison was employed to assess and educate recently discharged clinic patients for SDH needs and refer them for needed community services from June 1, 2020, to October 31, 2021.Hospital readmissions within 30, 60, and 90 days of discharge were tracked at 6-month intervals. Covariates included patient age, sex, race/ethnicity, insurance status, income, Hierarchical Condition Category risk scores, and Clinical Classification Software diagnosis groups. Data for all hospital discharges during the intervention period were used for the main analysis and data for the year before the intervention were extracted for comparison.Overall, patients in the intervention group were older, sicker, and more likely to have public insurance. The reductions in 30-, 60-, and 90-day readmissions during the intervention period were 14.39%, 13.28%, and 12.04% respectively in the intervention group, while no significant change was observed in the comparison group. The group difference in reduction over time was statistically significant for 30-day (Diff = 12.54%; p = 0.032), 60-day (Diff = 14.40%; p = 0.012), and 90-day readmissions (Diff = 14.71%; p = 0.036).Our findings suggest that screening clinic patients for SDH, and educating and connecting them to community services during post-hospital care may be associated with reductions in hospital readmissions. [ABSTRACT FROM AUTHOR]

Estimating 30-day readmission risk allows healthcare facilities to focus potentially expensive mitigating interventions on their highest-risk patients. We assessed additive value via likelihood ratio chi-square testing.[3] For our secondary outcomes, we assessed the individual additive prognostic values and c-statistic differentials for each of the coordinators in the study cohort. The primary outcome was the added prognostic value of CC risk re-assessments to our system's risk score. [Extracted from the article]

Background: Previous studies have identified disparities in readmissions among Medicare beneficiaries hospitalized for the Hospital Readmissions Reduction Program's (HRRP's) priority conditions. Evidence suggests timely follow-up is associated with reduced risk of readmission, but it is unknown whether timely follow-up reduces disparities in readmission.Objective: To assess whether follow-up within 7 days after discharge from a hospitalization reduces risk of readmission and mitigates identified readmission disparities.Design: A retrospective cohort study using Cox proportional hazards models to estimate the associations between sociodemographic characteristics (race and ethnicity, dual-eligibility status, rurality, and area social deprivation), follow-up, and readmission. Mediation analysis was used to examine if disparities in readmission were mitigated by follow-up.Participants: We analyzed data from 749,402 Medicare fee-for-service beneficiaries hospitalized for acute myocardial infarction, chronic obstructive pulmonary disease, heart failure, or pneumonia, and discharged home between January 1 and December 1, 2018.Main Measure: All-cause unplanned readmission within 30 days after discharge.Key Results: Post-discharge follow-up within 7 days of discharge was associated with a substantially lower risk of readmission (HR: 0.52, 95% CI: 0.52-0.53). Across all four HRRP conditions, beneficiaries with dual eligibility and beneficiaries living in areas with high social deprivation had a higher risk of readmission. Non-Hispanic Black beneficiaries had higher risk of readmission after hospitalization for pneumonia relative to non-Hispanic Whites. Mediation analysis suggested that 7-day follow-up mediated 21.2% of the disparity in the risk of readmission between dually and non-dually eligible beneficiaries and 50.7% of the disparity in the risk of readmission between beneficiaries living in areas with the highest and lowest social deprivation. Analysis suggested that after hospitalization for pneumonia, 7-day follow-up mediated nearly all (97.5%) of the increased risk of readmission between non-Hispanic Black and non-Hispanic White beneficiaries.Conclusions: Improving rates of follow-up could be a strategy to reduce readmissions for all beneficiaries and reduce disparities in readmission based on sociodemographic characteristics. [ABSTRACT FROM AUTHOR]

Background: Driven by quality outcomes and economic incentives, predicting 30-day hospital readmissions remains important for healthcare systems. The Cleveland Clinic Health System (CCHS) implemented an internally validated readmission risk score in the electronic medical record (EMR). Objective: We evaluated the predictive accuracy of the readmission risk score across CCHS hospitals, across primary discharge diagnosis categories, between surgical/medical specialties, and by race and ethnicity. Design: Retrospective cohort study. Participants: Adult patients discharged from a CCHS hospital April 2017–September 2020. Main Measures: Data was obtained from the CCHS EMR and billing databases. All patients discharged from a CCHS hospital were included except those from Oncology and Labor/Delivery, patients with hospice orders, or patients who died during admission. Discharges were categorized as surgical if from a surgical department or surgery was performed. Primary discharge diagnoses were classified per Agency for Healthcare Research and Quality Clinical Classifications Software Level 1 categories. Discrimination performance predicting 30-day readmission is reported using the c-statistic. Results: The final cohort included 600,872 discharges from 11 Northeast Ohio and Florida CCHS hospitals. The readmission risk score for the cohort had a c-statistic of 0.6875 with consistent yearly performance. The c-statistic for hospital sites ranged from 0.6762, CI [0.6634, 0.6876], to 0.7023, CI [0.6903, 0.7132]. Medical and surgical discharges showed consistent performance with c-statistics of 0.6923, CI [0.6807, 0.7045], and 0.6802, CI [0.6681, 0.6925], respectively. Primary discharge diagnosis showed variation, with lower performance for congenital anomalies and neoplasms. COVID-19 had a c-statistic of 0.6387. Subgroup analyses showed c-statistics of > 0.65 across race and ethnicity categories. Conclusions: The CCHS readmission risk score showed good performance across diverse hospitals, across diagnosis categories, between surgical/medical specialties, and by patient race and ethnicity categories for 3 years after implementation, including during COVID-19. Evaluating clinical decision-making tools post-implementation is crucial to determine their continued relevance, identify opportunities to improve performance, and guide their appropriate use. [ABSTRACT FROM AUTHOR]

Background: A great deal of research has focused on how hospitals influence readmission rates. While hospitals play a vital role in reducing readmissions, a significant portion of the work also falls to primary care practices. Despite this critical role of primary care, little empirical evidence has shown what primary care characteristics or activities are associated with reductions in hospital admissions. Objective: To examine the relationship between practices' readmission reduction activities and their readmission rates. Design, Setting, and Participants: A retrospective study of 1,788 practices who responded to the National Survey of Healthcare Organizations and Systems (fielded 2017–2018) and 415,663 hospital admissions for Medicare beneficiaries attributed to those practices from 2016 100% Medicare claims data. We constructed mixed-effects logistic regression models to estimate practice-level readmission rates and a linear regression model to evaluate the association between practices' readmission rates with their number of readmission reduction activities. Interventions: Standardized composite score, ranging from 0 to 1, representing the number of a practice's readmission reduction capabilities. The composite score was composed of 12 unique capabilities identified in the literature as being significantly associated with lower readmission rates (e.g., presence of care manager, medication reconciliation, shared-decision making, etc.). Main Outcomes and Measures: Practices' readmission rates for attributed Medicare beneficiaries. Key Results: Routinely engaging in more readmission reduction activities was significantly associated (P <.05) with lower readmission rates. On average, practices experienced a 0.05 percentage point decrease in readmission rates for each additional activity. Average risk-standardized readmission rates for practices performing 10 or more of the 12 activities in our composite measure were a full percentage point lower than risk-standardized readmission rates for practices engaging in none of the activities. Conclusions: Primary care practices that engaged in more readmission reduction activities had lower readmission rates. These findings add to the growing body of evidence suggesting that engaging in multiple activities, rather than any single activity, is associated with decreased readmissions. [ABSTRACT FROM AUTHOR]

Background: For patients with substance use disorder (SUD), a peer recovery coach (PRC) intervention increases engagement in recovery services; effective support services interventions have occasionally demonstrated cost savings through decreased acute care utilization. Objective: Examine effect of PRCs on acute care utilization. Design: Combined results of 2 parallel 1:1 randomized controlled trials. Participants: Inpatient adults with substance use disorder Interventions: Inpatient PRC linkage and follow-up contact for 6 months vs usual care (providing contact information for SUD resources and PRCs) Main Measures: Acute care encounters (emergency and inpatient) 6 months before and after enrollment; encounter type by primary diagnosis code category (mental/behavioral vs medical); 30-day readmissions with Lace+ readmission risk scores. Key Results: A total of 193 patients were randomized: 95 PRC; 98 control. In the PRC intervention, 66 patients had a pre-enrollment acute care encounter and 56 had an encounter post-enrollment, compared to the control group with 59 pre- and 62 post-enrollment (odds ratio [OR] = −0.79, P = 0.11); there was no significant effect for sub-groups by encounter location (emergency vs inpatient). There was a significant decrease in mental/behavioral ED visits (PRC: pre-enrollment 17 vs post-enrollment 10; control: pre-enrollment 13 vs post-enrollment 16 (OR = −2.62, P = 0.02)) but not mental/behavioral inpatient encounters or medical emergency or inpatient encounters. There was no significant difference in 30-day readmissions corrected for Lace+ scores (15.8% PRC vs 17.3% control, OR = 0.19, P = 0.65). Conclusions: PRCs did not decrease overall acute care utilization but may decrease emergency encounters related to substance use. Trial Registration: ClinicalTrials.gov (NCT04098601, NCT04098614) [ABSTRACT FROM AUTHOR]

Background: The Centers for Medicare and Medicaid Services (CMS) penalizes hospitals for higher than expected 30-day mortality rates using methods without accounting for condition severity risk adjustment. For patients with stroke, CMS claims did not quantify stroke severity until recently, when the National Institutes of Health Stroke Scale (NIHSS) reporting began. Objective: Examine the predictive ability of claim-based NIHSS to predict 30-day mortality and 30-day hospital readmission in patients with ischemic stroke. Design: Retrospective cohort study of Medicare claims data. Patients: Medicare beneficiaries with ischemic stroke (N=43,241) acute hospitalization between October 2016 and November 2017. Measurements: All-cause 30-day mortality and 30-day hospital readmission. NIHSS score was derived from ICD-10 codes and stratified into the following: minor to moderate, moderate, moderate to severe, and severe categories. Results: Among 43,241 patients with ischemic stroke with NIHSS from 2,659 US hospitals, 64.6% had minor to moderate stroke, 14.3% had moderate, 12.7% had moderate to severe, and 8.5% had a severe stroke,10.1% died within 30 days, 12.1% were readmitted within 30 days. The NIHSS exhibited stronger discriminant property (C-statistic 0.83, 95% CI: 0.82–0.84) for 30-day mortality compared to Elixhauser (0.74, 95% CI: 0.73–0.75). A monotonic increase in the adjusted 30-day mortality risk occurred relative to minor to moderate stroke category: hazard ratio [HR]=2.92 (95% CI=2.59–3.29) for moderate stroke, HR=5.49 (95% CI=4.90–6.15) for moderate to severe stroke, and HR=7.82 (95% CI=6.95–8.80) for severe stroke. After accounting for competing risk of mortality, there was a significantly higher readmission risk in the moderate stroke (HR=1.11, 95% CI=1.03–1.20), but significantly lower readmission risk in the severe stroke (HR=0.84, 95% CI=0.74–0.95) categories. Limitation: Timing of NIHSS reporting during hospitalization is unknown. Conclusions: Medicare claim–based NIHSS is significantly associated with 30-day mortality in Medicare patients with ischemic stroke and significantly improves discriminant property relative to the Elixhauser comorbidity index. [ABSTRACT FROM AUTHOR]

Background: In 2012, the Ministry of Health in British Columbia, Canada, introduced a $75 incentive payment that could be claimed by hospital physicians each time they produced a written post-discharge care plan for a complex patient at the time of hospital discharge. Objective: To examine whether physician financial payments incentivizing enhanced discharge planning reduce subsequent unplanned hospital readmissions. Design: Interrupted time series analysis of population-based hospitalization data. Participants: Individuals with one or more eligible hospitalizations occurring in British Columbia between 2007 and 2017. Main Measures: The proportion of index hospital discharges with subsequent unplanned hospital readmission within 30 days, as measured each month of the 11-year study interval. We used interrupted time series analysis to determine if readmission risk changed after introduction of the incentive payment policy. Key Results: A total of 40,588 unplanned hospital readmissions occurred among 409,289 eligible index hospitalizations (crude 30-day readmission risk, 9.92%). Policy introduction was not associated with a significant step change (0.393%; 95CI, − 0.190 to 0.975%; p = 0.182) or change-in-trend (p = 0.317) in monthly readmission risk. Policy introduction was associated with significantly fewer prescription fills for potentially inappropriate medications among older patients, but no improvement in prescription fills for beta-blockers after cardiovascular hospitalization and no change in 30-day mortality. Incentive payment uptake was incomplete, rising from 6.4 to 23.5% of eligible hospitalizations between the first and last year of the post-policy interval. Conclusion: The introduction of a physician incentive payment was not associated with meaningful changes in hospital readmission rate, perhaps in part because of incomplete uptake by physicians. Policymakers should consider these results when designing similar interventions elsewhere. Trial Registration: ClinicalTrials.gov ID, NCT03256734 [ABSTRACT FROM AUTHOR]

Background: Home telemonitoring has been used with discharged patients in an attempt to reduce 30-day readmissions with mixed results. Objective: To assess whether home 30-day telemonitoring after discharge for patients at high risk of readmission would reduce readmissions or mortality. Design: Prospective, randomized controlled trial. Patients: We compared 30-day readmission rates and mortality for patients at high risk for readmission who received home telemonitoring versus standard care between November 1, 2014, and November 30, 2018, in 2 tertiary care hospitals. Interventions: The intervention group received home-installed equipment to measure blood pressure, heart rate, pulse oximetry, weight if heart failure was present, and glucose if diabetes was present. Results were transmitted daily and reviewed by a nurse. Both groups received standard care. Main Measures: The primary outcome was a composite end point of hospital readmission or death within 30 days after discharge. The secondary outcome was an emergency department visit within 30 days after discharge. Key Results: A total of 1380 participants (mean [SD] age, 66 [14] years; 722 [52.3%] men and 658 [47.7%] women) participated in this study. Using a modified intention-to-treat analysis, the risk of readmission or death within 30 days among patients at high readmission risk was 23.7% (137/578) in the control group and 18.2% (87/477) in the telemonitoring group (absolute risk difference, − 5.5% [95% CI, − 10.4 to − 0.6%]; relative risk, 0.77 [95% CI, 0.61 to 0.98]; P =.03). Emergency department visits occurred within 30 days after discharge in 14.2% (81/570) of patients in the control group and 8.6% (40/464) of patients in the telemonitoring group (absolute risk difference, − 5.6% [95% CI, − 9.4 to − 1.8%]; relative risk, 0.61 [95% CI, 0.42 to 0.87]; P =.005). Conclusions: Thirty days of postdischarge telemonitoring may reduce readmissions of high-risk patients. Trial Registration: ClinicalTrials.gov identifier: NCT02136186. [ABSTRACT FROM AUTHOR]

Background: Trauma of hospitalization is characterized by patient-reported disturbances in sleep, mobility, nutrition, and/or mood and one study suggested it was associated with more 30-day readmissions. Objective: To define the trauma of hospitalization in medical inpatients and determine whether higher rates of disturbance correlate with adverse post-discharge outcomes. Design: A prospective cohort study was conducted between June 2018 and August 2019 with patients reporting disturbances in sleep, mobility, nutrition, and/or mood. High trauma of hospitalization was defined as disturbance in 3 or 4 domains. Participants: General medicine inpatients at an academic hospital in Edmonton, Canada. Main Measures: 7-day, 30-day, and 90-day rates of death, unplanned hospital readmission, or emergency department (ED) visit. Key Results: Of 299 patients (mean age 65.9 years, 47.8% female, mean Charlson score 3.6, and mean length of stay 8.2 days), 260 (87.0%) reported disturbance in at least one domain (most commonly nutrition or mobility) during their hospitalization, 179 (59.9%) reported disturbances in multiple domains, and 87 (29.1%) met the criteria for high trauma of hospitalization. Patients who reported a high trauma of hospitalization did not differ from those reporting less hospitalization disturbances in terms of demographics, burden of comorbidities, or length of stay, but did report higher rates of pre-hospital disturbances in sleep (32.3% vs. 14.4%, p = 0.03), nutrition (77.4% vs. 54.4%, p = 0.02), and mood (41.9% vs. 13.3%, p = 0.0007). High trauma of hospitalization was not significantly associated with death, readmission, or ED visit at 7 days (12.6% vs. 11.3%, aOR 1.13 [95% CI 0.52–2.46]), 30 days (31.0% vs. 32.1%, aOR 1.03 [95% CI 0.59–1.79]), or 90 days (52.9% vs. 50.9%, aOR 1.16 [95% CI 0.69–1.94]) after discharge. Conclusions: In-hospital disturbances in sleep, mobility, nutrition, and mood are common in medical inpatients but were not associated with post-discharge outcomes. [ABSTRACT FROM AUTHOR]

Background: Electronic health record (EHR)-based readmission risk prediction models can be automated in real-time but have modest discrimination and may be missing important readmission risk factors. Clinician predictions of readmissions may incorporate information unavailable in the EHR, but the comparative usefulness is unknown. We sought to compare clinicians versus a validated EHR-based prediction model in predicting 30-day hospital readmissions. Methods: We conducted a prospective survey of internal medicine clinicians in an urban safety-net hospital. Clinicians prospectively predicted patients' 30-day readmission risk on 5-point Likert scales, subsequently dichotomized into low- vs. high-risk. We compared human with machine predictions using discrimination, net reclassification, and diagnostic test characteristics. Observed readmissions were ascertained from a regional hospitalization database. We also developed and assessed a "human-plus-machine" logistic regression model incorporating both human and machine predictions. Results: We included 1183 hospitalizations from 106 clinicians, with a readmission rate of 20.8%. Both clinicians and the EHR model had similar discrimination (C-statistic 0.66 vs. 0.66, p = 0.91). Clinicians had higher specificity (79.0% vs. 48.9%, p < 0.001) but lower sensitivity (43.9 vs. 75.2%, p < 0.001) than EHR model predictions. Compared with machine, human was better at reclassifying non-readmissions (non-event NRI + 30.1%) but worse at reclassifying readmissions (event NRI − 31.3%). A human-plus-machine approach best optimized discrimination (C-statistic 0.70, 95% CI 0.67–0.74), sensitivity (65.5%), and specificity (66.7%). Conclusion: Clinicians had similar discrimination but higher specificity and lower sensitivity than EHR model predictions. Human-plus-machine was better than either alone. Readmission risk prediction strategies should incorporate clinician assessments to optimize the accuracy of readmission predictions. [ABSTRACT FROM AUTHOR]

Background: The demographics of heart failure are changing. The rate of growth of the "older" heart failure population, specifically those ≥ 75, has outpaced that of any other age group. These older patients were underrepresented in the early beta-blocker trials. There are several reasons, including a decreased potential for mortality benefit and increased risk of side effects, why the risk/benefit tradeoff may be different in this population. Objective: We aimed to determine the association between receipt of a beta-blocker after heart failure discharge and early mortality and readmission rates among patients with heart failure and reduced ejection fraction (HFrEF), specifically patients aged 75+. Design and Participants: We used 100% Medicare Parts A and B and a random 40% sample of Part D to create a cohort of beneficiaries with ≥ 1 hospitalization for HFrEF between 2008 and 2016 to run an instrumental variable analysis. Main Measure: The primary measure was 90-day, all-cause mortality; the secondary measure was 90-day, all-cause readmission. Key Results: Using the two-stage least squared methodology, among all HFrEF patients, receipt of a beta-blocker within 30-day of discharge was associated with a − 4.35% (95% CI − 6.27 to − 2.42%, p < 0.001) decrease in 90-day mortality and a − 4.66% (95% CI − 7.40 to − 1.91%, p = 0.001) decrease in 90-day readmission rates. Even among patients ≥ 75 years old, receipt of a beta-blocker at discharge was also associated with a significant decrease in 90-day mortality, − 4.78% (95% CI − 7.19 to − 2.40%, p < 0.001) and 90-day readmissions, − 4.67% (95% CI − 7.89 to − 1.45%, p < 0.001). Conclusion: Patients aged ≥ 75 years who receive a beta-blocker after HFrEF hospitalization have significantly lower 90-day mortality and readmission rates. The magnitude of benefit does not appear to wane with age. Absent a strong contraindication, all patients with HFrEF should attempt beta-blocker therapy at/after hospital discharge, regardless of age. [ABSTRACT FROM AUTHOR]

Background: Although early follow-up after discharge from an index admission (IA) has been postulated to reduce 30-day readmission, some researchers have questioned its efficacy, which may depend upon the likelihood of readmission at a given time and the health conditions contributing to readmissions.Objective: To investigate the relationship between post-discharge services utilization of different types and at different timepoints and unplanned 30-day readmission, length of stay (LOS), and inpatient costs.Design, Setting, and Participants: The study sample included 583,199 all-cause IAs among 2014 Medicare fee-for-service beneficiaries that met IA inclusion criteria.Main Measures: The outcomes were probability of 30-day readmission, average readmission LOS per IA discharge, and average readmission inpatient cost per IA discharge. The primary independent variables were 7 post-discharge health services (institutional outpatient, primary care physician, specialist, non-physician provider, emergency department (ED), home health care, skilled nursing facility) utilized within 7 days, 14 days, and 30 days of IA discharge. To examine the association with post-discharge services utilization, we employed multivariable logistic regressions for 30-day readmissions and two-part models for LOS and inpatient costs.Key Results: Among all IA discharges, the probability of unplanned 30-day readmission was 0.1176, the average readmission LOS per discharge was 0.67 days, and the average inpatient cost per discharge was $5648. Institutional outpatient, home health care, and primary care physician visits at all timepoints were associated with decreased readmission and resource utilization. Conversely, 7-day and 14-day specialist visits were positively associated with all three outcomes, while 30-day visits were negatively associated. ED visits were strongly associated with increases in all three outcomes at all timepoints.Conclusion: Post-discharge services of different types and at different timepoints have varying impacts on 30-day readmission, LOS, and costs. These impacts should be considered when coordinating post-discharge follow-up, and their drivers should be further explored to reduce readmission throughout the health care system. [ABSTRACT FROM AUTHOR]

Background: People who are homeless have a higher burden of illness and higher rates of hospital admission and readmission compared to the general population. Identifying the factors associated with hospital readmission could help healthcare providers and policymakers improve post-discharge care for homeless patients. Objective: To identify factors associated with hospital readmission within 90 days of discharge from a general internal medicine unit among patients experiencing homelessness. Design: This prospective observational study was conducted at an urban academic teaching hospital in Toronto, Canada. Interviewer-administered questionnaires and chart reviews were completed to assess medical, social, processes of care, and hospitalization data. Multivariable logistic regression with backward selection was used to identify factors associated with a subsequent readmission and estimate odds ratios and 95% confidence intervals. Participants: Adults (N = 129) who were admitted to the general internal medicine service between November 2017 and November 2018 and who were homeless at the time of admission. Main Measures: Unplanned all-cause readmission to the study hospital within 90 days of discharge. Key Results: Thirty-five of 129 participants (27.1%) were readmitted within 90 days of discharge. Factors associated with lower odds of readmission included having an active case manager (adjusted odds ratios [aOR]: 0.31, 95% CI, 0.13–0.76), having informal support such as friends and family (aOR: 0.25, 95% CI, 0.08–0.78), and sending a copy of the patient's discharge plan to a primary care physician who had cared for the patient within the last year (aOR: 0.44, 95% CI, 0.17–1.16). A higher number of medications prescribed at discharge was associated with higher odds of readmission (aOR: 1.12, 95% CI, 1.02–1.23). Conclusion: Interventions to reduce hospital readmission for people who are homeless should evaluate tailored discharge planning and dedicated resources to support implementation of these plans in the community. [ABSTRACT FROM AUTHOR]

Background: While health-related social needs (HRSN) are known to compromise health, work to date has not clearly demonstrated the relationship between clinically acknowledged social needs, via ICD-10 Z-codes, and readmission.Objective: Assess the rate of 30-, 60-, and 90-day readmission by the level of ICD-10-identified social need. In addition, we examined the associations between demographics, social need, hospital characteristics, and comorbidities on 30-day readmission.Design: Retrospective study using the 2017 Nationwide Readmission Database PARTICIPANTS: We identified 5 domains of HRSN from ICD-10 diagnosis codes including employment, family, housing, psychosocial, and socioeconomic status (SES) and identified how many and which an individual was coded with during the year.Main Measures: The proportion of patients with 30-, 60-, and 90-day readmission stratified by the number of HRSN domains with a multivariable logistic regression to examine the relationship between the number/type of and readmission adjusting for sex, age, payer, hospital characteristics, functional limitations, and comorbidities.Key Results: From 13,217,506 patients, only 2.4% had at least one HRSN diagnosis. Among patients without HRSN, 11.5% had a 30-day readmission, compared to 27.0% of those with 1 domain, increasing to 63.5% for patients with codes in 5 domains. Similar trends were observed for 60- and 90-day readmission; 78.7% of patients with documented HRSN in all 5 domains were hospitalized again within 90 days. The adjusted odds ratio for readmission for individuals with all 5 domains was 12.55 (95% CI: 9.04, 17.43). Housing and employment emerged as two of the most commonly documented HRSN, as well as having the largest adjusted odds ratio.Conclusions: There is a dose-response relationship between the number of HRSN diagnoses and hospital readmission. This work calls attention to the need to develop interventions to reduce readmissions for those at social risk and demonstrates the significance of ICD-10 Z-codes in health outcomes studies. [ABSTRACT FROM AUTHOR]

Background: Post-hospital discharge follow-up appointments are intended to evaluate patients' recovery following a hospitalization, but it is unclear how appointment statuses are associated with readmissions. Objective: To examine the association between post-discharge ambulatory follow-up status, (1) having a scheduled appointment and (2) arriving to said appointment, and 30-day readmission. Design and Setting: A retrospective cohort study of patients hospitalized at 12 hospitals in an Integrated Delivery Network and their ambulatory appointments in that same network. Patients and Main Measures: We included 50,772 patients who had an ambulatory appointment within 18 months of an inpatient admission in 2018. Primary outcome was readmission within 30 days post-discharge. Key Results: There were 32,108 (63.2%) patients with scheduled follow-up appointments and 18,664 (36.8%) patients with no follow-up; 28,313 (88.2%) patients arrived, 3149 (9.8%) missed, and 646 (2.0%) were readmitted prior to their scheduled appointments. Overall 30-day readmission rate was 7.3%; 6.0% [5.75–6.31] for those who arrived, 8.8% [8.44–9.25] for those without follow-up, and 10.3% [9.28–11.40] for those who missed a scheduled appointment (p < 0.001). After adjusting for covariates, patients who arrived at their appointment in the first week following discharge were significantly less likely to be readmitted than those not having any follow-up scheduled (medical adjusted hazard ratio (aHR) 0.57 [0.47–0.69], p < 0.001; surgical aHR 0.58 [0.44–0.75], p < 0.001) There was an increased risk at weeks 3 and 4 for medical patients who arrived at a follow-up compared to those with no follow-up scheduled (week 3 aHR 1.29 [1.10–1.51], p = 0.001; week 4 aHR 1.46 [1.26–1.70], p < 0.001). Conclusions: The benefit of patients arriving to their post-discharge appointments compared with patients who missed their follow-up visits or had no follow-up scheduled, is only significant during first week post-discharge, suggesting that coordination within 1 week of discharge is critical in reducing 30-day readmissions. [ABSTRACT FROM AUTHOR]

Background: Discharge against medical advice may be associated with more readmissions. Objective: To evaluate DAMA in patients with acute ischemic stroke (AIS) and identify the relationship between DAMA and 30-day unplanned readmissions. Design: A retrospective cohort study. Participants: The National Readmission Database was used to identify inpatients with a primary diagnosis of AIS who were either discharged home or DAMA between 2010 and 2017 in the USA. Measures: Demographic features, hospital type, comorbidities, stroke risk factors, severity indices, and treatments were compared between patients discharged routinely and DAMA. Multivariable logistic regression was used to evaluate predictors of DAMA, and a double robust inverse probability of treatment weighting method was used to assess the association between DAMA and 30-day unplanned readmissions. Key Results: Overall, 1,335,484 patients with AIS were included, of whom 2.09% (n = 27,892) were DAMA. The prevalence of DAMA in AIS patients increased from 1.65 in 2010 to 2.57% in 2017. The rates of 30-day unplanned readmissions for DAMA and non-DAMA patients were 16.81% and 7.78%, respectively. Patients with drug abuse, alcohol abuse, smoking, prior stroke, psychoses, and intravenous thrombolysis had greater odds of DAMA. DAMA was associated with all-cause readmissions (OR, 2.04; 95% CI, 2.01–2.07) and remained a strong predictor for transient ischemic attack/stroke-specific and cardiac-specific causes of readmissions. Conclusions: Although the DAMA rate is low in AIS patients, DAMA is a risk factor for all-cause and recurrent stroke-specific readmissions. Future studies are needed to address issues around compliance and engagement with health care to reduce DAMA. [ABSTRACT FROM AUTHOR]

Background: The surge of coronavirus 2019 (COVID-19) hospitalizations in New York City required rapid discharges to maintain hospital capacity. Objective: To determine whether lenient provisional discharge guidelines with remote monitoring after discharge resulted in safe discharges home for patients hospitalized with COVID-19 illness. Design: Retrospective case series Setting: Tertiary care medical center Patients: Consecutive adult patients hospitalized with COVID-19 illness between March 26, 2020, and April 8, 2020, with a subset discharged home Interventions: COVID-19 Discharge Care Program consisting of lenient provisional inpatient discharge criteria and option for daily telephone monitoring for up to 14 days after discharge Measurements: Fourteen-day emergency department (ED) visits and hospital readmissions Results: Among 812 patients with COVID-19 illness hospitalized during the study time period, 15.5% died prior to discharge, 24.1% remained hospitalized, 10.0% were discharged to another facility, and 50.4% were discharged home. Characteristics of the 409 patients discharged home were mean (SD) age 57.3 (16.6) years; 245 (59.9%) male; 27 (6.6%) with temperature ≥ 100.4 °F; and 154 (37.7%) with oxygen saturation < 95% on day of discharge. Over 14 days of follow-up, 45 patients (11.0%) returned to the ED, of whom 31 patients (7.6%) were readmitted. Compared to patients not referred, patients referred for remote monitoring had fewer ED visits (8.3% vs 14.1%; OR 0.60, 95% CI 0.31–1.15, p = 0.12) and readmissions (6.9% vs 8.3%; OR 1.15, 95% CI 0.52–2.52, p = 0.73). Limitations: Single-center study; assignment to remote monitoring was not randomized. Conclusions: During the COVID-19 surge in New York City, lenient discharge criteria in conjunction with remote monitoring after discharge were associated with a rate of early readmissions after COVID-related hospitalizations that was comparable to the rate of readmissions after other reasons for hospitalization before the COVID pandemic. [ABSTRACT FROM AUTHOR]

Background: To address concerns that the Hospital Readmissions Reduction Program (HRRP) unfairly penalized safety net hospitals treating patients with high social and functional risks, Medicare recently modified HRRP to compare hospitals with similar proportions of high-risk, dual-eligible patients ("peer group hospitals"). Whether the change fully accounts for patients' social and functional risks is unknown. Objective: Examine risk-standardized readmission rates (RSRRs) and hospital penalties after adding patient-level social and functional and community-level risk factors. Design: Using 2000-2014 Medicare hospital discharge, Health and Retirement Study, and community-level data, latent factors for patient social and functional factors and community factors were identified. We estimated RSRRs for peer groups and by safety net status using four hierarchical logistic regression models: "base" (HRRP model); "patient" (base plus patient factors); "community" (base plus community factors); and "full" (all factors). The proportion of hospitals penalized was calculated by safety net status. Patients: 20,255 fee-for-service Medicare beneficiaries (65+) with eligible index hospitalizations Main Measures: RSRRs Key Results: Half of safety net hospitals are in peer group 5. Compared with other hospitals, peer group 5 hospitals (most dual-eligibles) treated sicker, more functionally limited patients from socially disadvantaged groups. RSRRs decreased by 0.7% for peer groups 2 and 4 and 1.3% for peer group 5 under the patient and full (versus base) models. Measured performance improved after adjusting for patient risk factors for hospitals in peer group 4 and 5 hospitals, but worsened for those in peer groups 1, 2, and 3. Under the patient (versus base) model, fewer safety net hospitals (48.7% versus 51.3%) but more non-safety net hospitals (50.0% versus 49.1%) were penalized. Conclusions: Patient-level risk adjustment decreased RSRRs for hospitals serving more at-risk patients and proportion of safety net hospitals penalized, while modestly increasing RSRRs and proportion of non-safety net hospitals penalized. Results suggest HRRP modifications may not fully account for hospital variation in patient-level risk. [ABSTRACT FROM AUTHOR]

Background: Hospital readmission rates decreased for myocardial infarction (AMI), heart failure (CHF), and pneumonia with implementation of the first phase of the Hospital Readmissions Reduction Program (HRRP). It is not established whether readmissions fell for chronic obstructive pulmonary disease (COPD), an HRRP condition added in 2014. Objective: We sought to determine whether HRRP penalties influenced COPD readmissions among Medicare, Medicaid, or privately insured patients. Design: We analyzed a retrospective cohort, evaluating readmissions across implementation periods for HRRP penalties ("pre-HRRP" January 2010–April 2011, "implementation" May 2011–September 2012, "partial penalty" October 2012–September 2014, and "full penalty" October 2014–December 2016). Patients: We assessed discharged patients ≥ 40 years old with COPD versus those with HRRP Phase 1 conditions (AMI, CHF, and pneumonia) or non-HRRP residual diagnoses in the Nationwide Readmissions Database. Interventions: HRRP was announced and implemented during this period, forming a natural experiment. Measurements: We calculated differences-in-differences (DID) for 30-day COPD versus HRRP Phase 1 and non-HRRP readmissions. Key Results: COPD discharges for 1.2 million Medicare enrollees were compared with 22 million non-HRRP and 3.4 million HRRP Phase 1 discharges. COPD readmissions decreased from 19 to 17% over the study. This reduction was significantly greater than non-HRRP conditions (DID − 0.41%), but not HRRP Phase 1 (DID + 0.02%). A parallel trend was observed in the privately insured, with significant reduction compared with non-HRRP (DID − 0.83%), but not HRRP Phase 1 conditions (DID − 0.45%). Non-significant reductions occurred in Medicaid (DID − 0.52% vs. non-HRRP and − 0.21% vs. Phase 1 conditions). Conclusions: In Medicare, HRRP implementation was associated with reductions in COPD readmissions compared with non-HRRP controls but not versus other HRRP conditions. Parallel findings were observed in commercial insurance, but not in Medicaid. Condition-specific penalties may not reduce readmissions further than existing HRRP trends. [ABSTRACT FROM AUTHOR]

Barnes Jewish Hospital (BJH) has developed intervention programs for patients 65 and older diagnosed with acute myocardial infarction (AMI), congestive heart failure (CHF), chronic obstructive pulmonary disease (COPD), and pneumonia (PNA). Additionally, a model was developed using the TDA Mapper algorithm which divides the patients into sub-groups, and then trains independent models for each group.[6] Model parameters are selected via cross-validation, and SMOTE/ROSE sampling was tested to address the low readmission rate. Eligible patients are high-risk inpatients identified using the LACE Index Scoring Tool,[1] which is the most widely used method to quantify the risk of readmission, but only achieves a C-statistic of 0.59 in our population, compared to range 0.63-0.70 in other systems.[2] Yu et al. reported that "the institution specific readmission risk prediction framework is more flexible and more effective than the one-size-fit-all models like the LACE",[3] and several groups have used machine learning models to improve readmissions predictions.[4],[5] Therefore, in this proof of concept study, we use available variables to better identify at-risk patients (compared to the LACE score) at BJH in order to enroll them into the current readmission reduction programs early in their hospital course. [Extracted from the article]

Background: Limitations in instrumental activities of daily living (IADL) hinder a person's ability to live independently in the community and self-manage their conditions, but its impact on hospital readmission has not been firmly established. Objective: To test the importance of IADL dependency as a predictor of 30-day readmissions and quantify its impact relative to other morbidities. Design: A retrospective cohort study of the population-based Health and Retirement Study linked to Medicare claims data. Random forest was used to rank each predictor variable in terms of its ability to predict readmission. Classification and regression tree (CART) was used to identify complex multimorbidity combinations associated with high or low risk of readmission. Generalized linear regression was used to estimate the adjusted relative risk of readmission for IADL limitations. Subjects: Hospitalizations of adults age 65 and older (n = 20,007), from 6617 unique subjects. Main Measures: The main outcome was 30-day all-cause unplanned readmission. The main predictor of interest was self-reported IADL limitation. Other key predictors were self-reported complex multimorbidity including chronic diseases, geriatric syndromes, and activities of daily living (ADL) limitations, along with demographic, socioeconomic, and behavioral factors. Key Results: The overall 30-day readmission rate in the study was 16.4%. Random forest analysis ranked ADLs and IADL limitations as the two most important predictors of 30-day readmission. CART identified hospitalizations of patients with IADL limitations and diabetes as a subgroup at the highest risk of readmission (26% readmitted). Multivariable regression analyses showed that ADL limitations were associated with 1.17 (1.06–1.29) times higher risk of readmission even after adjusting for other patient covariates. Risk prediction was modest though for even the best model (AUC = 0.612). Conclusions: IADL limitations are key predictors of 30-day readmission as demonstrated using several machine learning methods. Routine assessment of functional abilities in hospital settings could help identify those most at risk. [ABSTRACT FROM AUTHOR]

Background: As the U.S. homeless population grows, so has the challenge of providing effective care to homeless individuals. Understanding hospitals that achieve better outcomes after hospital discharge for homeless patients has important implications for making our health system more sustainable and equitable. Objective: To determine whether homeless patients experience higher rates of readmissions and emergency department (ED) visits after hospital discharge than non-homeless patients, and whether the homeless patients exhibit lower rates of readmissions and ED visits after hospital discharge when they were admitted to hospitals experienced with the treatment of the homeless patients ("homeless-serving" hospitals—defined as hospitals in the top decile of the proportion of homeless patients). Design: A population-based longitudinal study, using the data including all hospital admissions and ED visits in FL, MA, MD, and NY in 2014. Participants: Participants were 3,527,383 patients (median age [IQR]: 63 [49–77] years; 1,876,466 [53%] women; 134,755 [4%] homeless patients) discharged from 474 hospitals. Main Measures: Risk-adjusted rates of 30-day all-cause readmissions and ED visits after hospital discharge. Key Results: After adjusting for potential confounders, homeless patients had higher rates of readmissions (adjusted rate, 27.3% vs. 17.5%; adjusted odds ratio [aOR], 1.93; 95% CI, 1.69–2.21; p < 0.001) and ED visits after hospital discharge (37.1% vs. 23.6%; aOR, 1.98; 95% CI, 1.74–2.25; p < 0.001) compared with non-homeless patients. Homeless patients treated at homeless-serving hospitals exhibited lower rates of readmissions (23.9% vs. 33.4%; p < 0.001) and ED visits (31.4% vs. 45.4%; p < 0.001) after hospital discharge than homeless patients treated at non-homeless-serving hospitals. Conclusions: Homeless patients were more likely to be readmitted or return to ED within 30 days after hospital discharge, especially when they were treated at hospitals that treat a small proportion of homeless patients. These findings suggest that homeless patients may receive better discharge planning and care coordination when treated at hospitals experienced with caring for homeless people. [ABSTRACT FROM AUTHOR]

Background: Individuals experiencing homelessness have higher hospitalization and mortality rates compared with the housed. Whether they also experience higher readmission rates, and if readmissions vary by region or cause of hospitalization is unknown. Objective: Evaluate the association of homelessness with readmission rates across multiple US states. Design: Retrospective analysis of administrative claims Patients: All inpatient hospitalizations in Florida, Massachusetts, and New York from January 2010 to October 2015 Main Measures: Thirty- and 90-day readmission rates Key Results: Out of a total of 23,103,125 index hospitalizations, 515,737 were for patients who were identified as homeless at the time of discharge. After adjusting for cause of index hospitalization, state, demographics, and clinical comorbidities, 30-day and 90-day readmission rates were higher for index hospitalizations in the homeless compared with those in the housed group. The difference in 30-day readmission rates between homeless and housed groups was the largest in Florida (30.4% vs. 19.3%; p < 0.001), followed by Massachusetts (23.5% vs. 15.2%; p < 0.001) and New York (15.7% vs. 13.4%; p < 0.001) (combined 17.3% vs. 14.0%; p < 0.001). Among the most common causes of hospitalization, 30-day readmission rates were 4.1 percentage points higher for the homeless group for mental illness, 4.9 percentage points higher for diseases of the circulatory system, and 2.4 percentage points higher for diseases of the digestive system. Conclusions: After adjusting for demographic and clinical characteristics, homelessness is associated with significantly higher 30- and 90-day readmission rates, with a significant variation across the three states. Interventions to reduce the burden of readmissions among individuals experiencing homelessness are urgently needed. Differences across states point to the potential of certain public policies to impact health outcomes for individuals experiencing homelessness. [ABSTRACT FROM AUTHOR]

Background: Predictive models based on electronic health records (EHRs) are used to identify patients at high risk for 30-day hospital readmission. However, these models' ability to accurately detect who could benefit from inclusion in prevention interventions, also termed "perceived impactibility", has yet to be realized.Objective: We aimed to explore healthcare providers' perspectives of patient characteristics associated with decisions about which patients should be referred to readmission prevention programs (RPPs) beyond the EHR preadmission readmission detection model (PREADM).Design: This cross-sectional study employed a multi-source mixed-method design, combining EHR data with nurses' and physicians' self-reported surveys from 15 internal medicine units in three general hospitals in Israel between May 2016 and June 2017, using a mini-Delphi approach.Participants: Nurses and physicians were asked to provide information about patients 65 years or older who were hospitalized at least one night.Main Measures: We performed a decision-tree analysis to identify characteristics for consideration when deciding whether a patient should be included in an RPP.Key Results: We collected 817 questionnaires on 435 patients. PREADM score and RPP inclusion were congruent in 65% of patients, whereas 19% had a high PREADM score but were not referred to an RPP, and 16% had a low-medium PREADM score but were referred to an RPP. The decision-tree analysis identified five patient characteristics that were statistically associated with RPP referral: high PREADM score, eligibility for a nursing home, having a condition not under control, need for social-services support, and need for special equipment at home.Conclusions: Our study provides empirical evidence for the partial congruence between classifications of a high PREADM score and perceived impactibility. Findings emphasize the need for additional research to understand the extent to which combining EHR data with provider insights leads to better selection of patients for RPP inclusion. [ABSTRACT FROM AUTHOR]

Introduction: Interhospital fragmentation of care occurs when patients are admitted to different, disconnected hospitals. It has been hypothesized that this type of care fragmentation decreases the quality of care received and increases hospital costs and healthcare utilization. This systematic review aims to synthesize the existing literature exploring the association between interhospital fragmentation of care and patient outcomes.Methods: MEDLINE, the Cochrane Library, EMBASE, and the Science Citation Index were systematically searched for studies published up to April 30, 2018 reporting the association between interhospital fragmentation of care and patient outcomes. We included peer-reviewed observational studies conducted in adults that reported measures of association between interhospital care fragmentation and one or more of the following patient outcomes: mortality, hospital length of stay, cost, and subsequent hospital readmission.Results: Seventy-nine full texts were reviewed and 22 met inclusion criteria. Nearly all studies defined fragmentation of care as a readmission to a different hospital than the patient was previously discharged from. The strongest association reported was that between a fragmented readmission and in-hospital or short-term mortality (adjusted odds ratio range 0.95-3.62). Over half of the studies reporting length-of-stay showed longer length of stay in fragmented readmissions. All three studies that investigated healthcare utilization suggested an association between fragmented care and odds of subsequent readmission. The study populations and exposures were too heterogenous to perform a meta-analysis.Discussion: Our review suggests that fragmented hospital readmissions contribute to increased mortality, longer length-of-stay, and increased risk of readmission to the hospital. [ABSTRACT FROM AUTHOR]

Background: Little is known about the frequency, patterns, and determinants of readmissions among patients initially hospitalized for an ambulatory care-sensitive condition (ACSC). The degree to which hospitalizations in close temporal proximity cluster has also not been studied. Readmission patterns involving clustering likely reflect different underlying determinants than the same number of readmissions more evenly spaced.Objective: To characterize readmission rates, patterns, and predictors among patients initially hospitalized with an ACSC.Design: Retrospective analysis of the 2010-2014 Nationwide Readmissions Database.Participants: Non-pregnant patients aged 18-64 years old during initial ACSC hospitalization and who were discharged alive (N = 5,007,820).Main Measures: Frequency and pattern of 30-day all-cause readmissions, grouped as 0, 1, 2+ non-clustered, and 2+ clustered readmissions.Key Results: Approximately 14% of patients had 1 readmission, 2.4% had 2+ non-clustered readmissions, and 3.3% patients had 2+ clustered readmissions during the 270-day follow-up. A higher Elixhauser Comorbidity Index was associated with increased risk for all readmission groups, namely with adjusted odds ratios (AORs) ranging from 1.12 to 3.34. Compared to patients aged 80 years and older, those in younger age groups had increased risk of 2+ non-clustered and 2+ clustered readmissions (AOR range 1.27-2.49). Patients with chronic versus acute ACSCs had an increased odds ratio of all readmission groups compared to those with 0 readmissions (AOR range 1.37-2.69).Conclusions: Among patients with 2+ 30-day readmissions, factors were differentially distributed between clustered and non-clustered readmissions. Identifying factors that could predict future readmission patterns can inform primary care in the prevention of readmissions following ACSC-related hospitalizations. [ABSTRACT FROM AUTHOR]

Background: Transitions from hospital to home in older patients are a high-risk period for adverse outcomes in a population that may have more challenges navigating the healthcare system. There is little information about the association of patient-reported quality of hospital discharge processes with clinical outcomes.Objectives: We evaluated whether patient-reported quality of hospital discharge processes was associated with emergency department utilization and rehospitalization within 30 days of discharge after hospitalization for acute myocardial infarction (AMI) in older adults.Design: Multi-center, prospective cohort study.Patients: The ComprehenSIVe Evaluation of Risk Factors in Older Patients with Acute Myocardial Infarction (SILVER-AMI) study was a longitudinal study of 3006 adults age 75 and older hospitalized with AMI recruited from 94 academic and community hospitals from across the USA.Intervention: N/A MAIN MEASURES: Patients answered a subset of questions from the Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) survey. Readmissions and emergency department utilization within 30 days of discharge were ascertained through medical record review.Key Results: A total of 2132 patients were included in the study. Patients' median age was 81 years and the response rate to the survey of discharge quality was 87%. Patients who reported being asked about having the help they needed at home were significantly less likely to have emergency room utilization within 30 days of discharge in both the unadjusted (0.65, 95% CI 0.43-0.99) and adjusted (0.65, 95% CI 0.42-0.997) models, though there was no significant association with readmission.Conclusion: Report of an assessment of help needed at home during hospitalization was associated with lower post-discharge emergency department utilization. Efforts to improve outcomes after hospital discharge in older patients may benefit from greater focus on assessing need of help at home. [ABSTRACT FROM AUTHOR]

Background: Early morning patient discharge from the hospital is increasingly being recognized as a key dimension of quality of care. At our institution, there is a significantly lower early discharge rate on the teaching hospitalist teams in comparison with the non-teaching teams.Objective: To implement a resident-driven intervention in the teaching medical services to increase overall discharge order rate before 11 am (DOB-11) and assess the effect of this intervention on hospital length of stay (LOS), 30-day readmission rates (RR), and resident perception.Design: Interrupted time series as well as controlled before-after designs.Participants: All inpatients discharged from general medicine units.Interventions: We implemented an educational didactic in conjunction with resident-attending daily walk rounds followed by resident-led multidisciplinary discharge huddles to identify next-day discharges.Main Measures: The primary outcome was DOB-11 rates 18 months pre- and 12 months post-intervention.Secondary Outcomes: LOS and RR. Additionally, we assessed residents' perception of the early discharge protocol.Key Results: The DOB-11 rate increased from 12 to 29% (p < 0.001), LOS increased by 1.47 days (P < 0.001), and RR increased by 0.32% (P = 0.84), respectively, on the teaching teams. Compared with the non-teaching (control) teams, the teaching teams registered a greater increase in DOB-11 rate (by 17%, p < 0.001; ratio of adjusted ORs 2.16; 95% CI, 1.65, 2.85; p value < 0.001), small increase in LOS (by 0.74 day, p = 0.39; ratio of adjusted post-/pre-intervention ratio [teaching] and post-/pre- intervention ratio [non-teaching] = 1.05, 95% CI, 0.97, 1.14, p = 0.23), and relative increase in RR (by 3.98%, p = 0.07, and ratio of ORs = 1.35, 95% CI, 1.03, 1.8), p = 0.03). Approximately 55% (16/29) of the residents agreed that the early discharge initiative helped in understanding the importance of prioritizing patients for early discharge. Additionally, 55% (20/36) of the residents "agreed" that the early discharge initiative compromised their learning during teaching rounds.Conclusion: Our study demonstrates that DOB-11 is an achievable goal, not only for non-teaching teams but also for resident-run teaching teams. [ABSTRACT FROM AUTHOR]

Background: Hospitals are increasingly at risk for post-acute care outcomes and spending, such as those in skilled nursing facilities (SNFs). While hospitalists are thought to improve patient outcomes of acute care, whether these effects extend to the post-acute setting in SNFs is unknown.Objective: To compare longer term outcomes of patients discharged to SNFs who were treated by hospitalists vs. non-hospitalists during their hospitalization.Design: This was a retrospective cohort study.Participants: Participants are Medicare fee-for-service beneficiaries over 66 years of age who were hospitalized and discharged to a SNF in 2012-2014 (N = 2,839,779).Main Measures: We estimated the effect of being treated by a hospitalist on 30-day rehospitalization and mortality, 60-day episode Medicare payments (Parts A and B), and successful discharge to community. Patients discharged to the community within 100 days of SNF admission who remained alive and not readmitted to a hospital or SNF for at least 30 days were considered successfully discharged. All outcomes were adjusted for demographics and clinical characteristics. To account for heterogeneity across facilities, we included hospital fixed effects.Key Results: The 30-day rehospitalization rate was 17.59% for hospitalists' vs. 17.31% for non-hospitalists' patients (adjusted difference, 0.28%; 95% CI, 0.13 to 0.44). Sixty-day payments were $26,301 for hospitalists' vs. $25,996 for non-hospitalists' patients (adjusted difference, $305; 95% CI, $243 to $367). There was a non-significant trend toward lower successful discharge to the community rate (adjusted difference, - 0.26%; 95% CI, - 0.48 to - 0.04) and lower mortality for patients of hospitalists (adjusted difference, - 0.12%; 95% CI, - 0.22 to - 0.02).Conclusions: Among hospitalized Medicare beneficiaries who were discharged to SNFs, readmissions and Medicare costs were slightly higher for stays under the care of hospitalists compared with those of non-hospitalist generalist physicians, but there was a non-significant trend toward lower mortality. [ABSTRACT FROM AUTHOR]

Background: There are many interventions to facilitate seamless continuity of care for patients in transition from hospital back to primary care; however, disparities remain in readmission rates for vulnerable populations, especially African-Americans.Objectives: We set out to investigate the association of race and ethnicity with 30-day readmission in our urban academic setting and to identify factors that could be leveraged in primary care to address disparities in hospitalizations.Methods/approach: Using data originally collected for quality improvement purposes, we evaluated 30-day readmission rates for our primary care patients (January 1, 2013-September 30, 2014) by race and ethnicity, adjusting for demographic and clinical characteristics. Then, using inductive and deductive methods, we coded semi-structured interviews with 24 African-American primary care patients who were discharged from the Medicine or Cardiology service at our tertiary care hospital during the study period.Key Results: African-Americans had the highest readmission rate (21.7%) and a higher adjusted odds of readmission (1.37; 95% CI 1.04-1.81) compared to Whites. Five major themes emerged as having potential to be leveraged in primary care to help prevent multiple hospitalizations: (1) dependable patient-physician relationships, (2) healthcare coordination across settings, (3) continuity with one primary care provider (PCP), (4) disease self-management, and (5) trust in resident physicians. Participants also made several recommendations to keep patients like themselves from returning to the hospital: increased time to tell their story during their primary care visit, more direct patient-physician communication during the visit, and improved access between visits.Conclusions: While African-American patients in our practice experience higher rates of hospital readmissions than their White counterparts, they emphasize the significance of their PCP relationship and communication to enhance disease management and prevent hospitalizations. Ongoing efforts are needed to establish and implement best practice communication trainings for patients at increased risk of hospitalization, particularly for resident physicians. [ABSTRACT FROM AUTHOR]

To date, efforts to reduce hospital readmissions have centered largely on hospitals. In a recently published environmental scan, we examined the literature focusing on primary care-based efforts to reduce readmissions. While rigorous studies on interventions arising from primary care are limited, we found that multi-component care transitions programs that are initiated early in the hospitalization and are part of broader primary care practice transformation appear most promising. However, policy changes are necessary to spur innovation and support effective primary care-led transitions interventions. Though more rigorous research is needed, our findings suggest that primary care can and should lead future efforts for reducing hospital readmissions. [ABSTRACT FROM AUTHOR]

Background: Post-stroke care delivery may be affected by provider participation in Medicare Shared Savings Program (MSSP) Accountable Care Organizations (ACOs) through systematic changes to discharge planning, care coordination, and transitional care.Objective: To evaluate the association of MSSP with patient outcomes in the year following hospitalization for ischemic stroke.Design: Retrospective cohort SETTING: Get With The Guidelines (GWTG)-Stroke (2010-2014) PARTICIPANTS: Hospitalizations for mild to moderate incident ischemic stroke were linked with Medicare claims for fee-for-service beneficiaries ≥ 65 years (N = 251,605).Main Measures: Outcomes included discharge to home, 30-day all-cause readmission, length of index hospital stay, days in the community (home-time) at 1 year, and 1-year recurrent stroke and mortality. A difference-in-differences design was used to compare outcomes before and after hospital MSSP implementation for patients (1) discharged from hospitals that chose to participate versus not participate in MSSP or (2) assigned to an MSSP ACO versus not or both. Unique estimates for 2013 and 2014 ACOs were generated.Key Results: For hospitals joining MSSP in 2013 or 2014, the probability of discharge to home decreased by 2.57 (95% confidence intervals (CI) = - 4.43, - 0.71) percentage points (pp) and 1.84 pp (CI = - 3.31, - 0.37), respectively, among beneficiaries not assigned to an MSSP ACO. Among discharges from hospitals joining MSSP in 2013, beneficiary ACO alignment versus not was associated with increased home discharge, reduced length of stay, and increased home-time. For patients discharged from hospitals joining MSSP in 2014, ACO alignment was not associated with changes in utilization. No association between MSSP and recurrent stroke or mortality was observed.Conclusions: Among patients with mild to moderate ischemic stroke, meaningful reductions in acute care utilization were observed only for ACO-aligned beneficiaries who were also discharged from a hospital initiating MSSP in 2013. Only 1 year of data was available for the 2014 MSSP cohort, and these early results suggest further study is warranted.Registration: None. [ABSTRACT FROM AUTHOR]

This suggests that the benefits of engagement with ACT may persist; however, ongoing ACT involvement for patients who frequently touch the healthcare system may be beneficial. For patients ever seen by ACT during a 5-year period (October 2014-2019), we calculated the frequencies of consults, diagnosis codes, and 30-day readmission rates. METHODS In 2014, Massachusetts General Hospital (MGH) started an inpatient addiction consult team (ACT) consisting of a rotating group of addiction medicine and psychiatry physicians, advanced practice nurses, social workers, recovery coaches, and trainees. [Extracted from the article]

Frustratingly, multiple well-run RCTs like this one looking to improve outcomes on such cohorts have been negative.[2] This, despite knowing that patients at risk for high healthcare utilization can be readily identified.[3] If we know we can identify such patients and yet cannot intervene, then we must ask the question: why are we failing? Prior Presentations: None The article by Henschen et al. highlights a cohort of patients that is increasingly the focus of interventions to decrease healthcare utilization.[1] This frequently hospitalized cohort of patients is medically diverse with regard to both acute and chronic disease conditions, often has a high burden of social and behavioral needs, and disproportionately utilizes healthcare resources. [Extracted from the article]

Background: Efforts to reduce hospital readmissions include post-discharge interventions related to the illness treated during the index hospitalization (IH). These efforts may be inadequate because readmissions are precipitated by a wide range of health conditions unrelated to the primary diagnosis of the IH.Objective: To investigate the relationship between post-discharge health services utilization for the same or a different diagnosis than the IH and unplanned 30-day readmission.Design and Participants: The study sample included 583,199 all-cause IHs among 2014 Medicare fee-for-service beneficiaries. For all-cause IH, as well as individually for heart failure, myocardial infarction, and pneumonia IH, we used multivariable logistic regressions to investigate the association between post-discharge services utilization and readmission.Main Measures: The outcome was unplanned 30-day readmission. Primary independent variables were post-discharge services utilization, including institutional outpatient, office-based primary care, office-based specialist, office-based non-physician practitioner, emergency department, home health care, and skilled nursing facility providers.Key Results: Among all-cause IH, 11.7% resulted in unplanned 30-day readmissions, and only 18.1% of readmissions occurred for the same primary diagnosis as IH. A substantial majority of post-discharge health services were utilized for a primary diagnosis differing from IH. Compared with no visit, institutional outpatient visits for the same primary diagnosis as IH (odds ratio [OR], 0.33; 95% confidence interval [CI], 0.31-0.34) and for a different primary diagnosis than IH (OR, 0.36; 95% CI, 0.35-0.37) were similarly strongly associated with decreased unplanned 30-day readmission. Primary care physician, specialist, non-physician practitioner, and home health care showed similar patterns. IH for heart failure, myocardial infarction, and pneumonia manifested similar patterns to all-cause IH both in terms of post-discharge services utilization and in terms of its impact on readmission.Conclusions: To reduce unplanned 30-day readmission more effectively, discharge planning should include post-discharge services to address health conditions beyond the primary cause of the IH. [ABSTRACT FROM AUTHOR]