Rethinking Thirty-Day Hospital Readmissions: Shorter Intervals Might Be Better Indicators Of Quality Of Care

Hospital-Level Variation in COVID-19 Treatment Among Hospitalized Adults in the United States: A Retrospective Cohort Study

STUDY DESIGN

Retrospective cohort study.

OBJECTIVE

To characterize variation in dexamethasone and remdesivir use over time among hospitals.

BACKGROUND

Little is known about hospital-level variation in COVID-19 drug treatments in a large and diverse network in the United States.

METHODS

We selected individuals hospitalized with COVID-19 across 163 hospitals between February 23, 2020 and October 31, 2021 from using the HCA CHARGE, an electronic health record repository from a network of community health care facilities in the United States. We quantified receipt of dexamethasone, remdesivir, and combined use of dexamethasone and remdesivir during the hospital stay. We used 2-level logistic regression models to determine the intraclass correlation coefficient (ICC) at the hospital level, adjusting for patient and hospital characteristics. The ICC shows the proportion of total variation in drug use accounted for by hospitals.

RESULTS

Among 161,667 individuals hospitalized with COVID-19, 73.0% were treated with dexamethasone, 49.1% with remdesivir, and 45.0% with both dexamethasone and remdesivir. The proportion of variation in dexamethasone use was 12.7% (adjusted ICC: 0.127), 8.5% for remdesivir, and 11.3% for combined drug use, indicating low interhospital variation. In the fully adjusted models, between-facility variation in dexamethasone use declined from 34.1% in February-March 2020 to 11.3% in January-March 2021 and then increased to 17.3% in July-October 2021. The variation in remdesivir use remained relatively stable during the study period.

CONCLUSIONS

During the first 2 years of the pandemic, there was relatively consistent use of dexamethasone and remdesivir across the hospitals examined. Consistent adoption and implementation of treatment guidelines across the hospitals examined may have led to a decrease in variation in drug usage over time.

Associations of Internal Medicine Residency Milestone Ratings and Certification Examination Scores With Patient Outcomes

Importance

Despite its importance to medical education and competency assessment for internal medicine trainees, evidence about the relationship between physicians' milestone residency ratings or the American Board of Internal Medicine's initial certification examination and their hospitalized patients' outcomes is sparse.

Objective

To examine the association between physicians' milestone ratings and certification examination scores and hospital outcomes for their patients.

Design, Setting, and Participants

Retrospective cohort analyses of 6898 hospitalists completing training in 2016 to 2018 and caring for Medicare fee-for-service beneficiaries during hospitalizations in 2017 to 2019 at US hospitals.

Main Outcomes and Measures

Primary outcome measures included 7-day mortality and readmission rates. Thirty-day mortality and readmission rates, length of stay, and subspecialist consultation frequency were also assessed. Analyses accounted for hospital fixed effects and adjusted for patient characteristics, physician years of experience, and year.

Exposures

Certification examination score quartile and milestone ratings, including an overall core competency rating measure equaling the mean of the end of residency milestone subcompetency ratings categorized as low, medium, or high, and a knowledge core competency measure categorized similarly.

Results

Among 455 120 hospitalizations, median patient age was 79 years (IQR, 73-86 years), 56.5% of patients were female, 1.9% were Asian, 9.8% were Black, 4.6% were Hispanic, and 81.9% were White. The 7-day mortality and readmission rates were 3.5% (95% CI, 3.4%-3.6%) and 5.6% (95% CI, 5.5%-5.6%), respectively, and were 8.8% (95% CI, 8.7%-8.9%) and 16.6% (95% CI, 16.5%-16.7%) for mortality and readmission at 30 days. Mean length of stay and number of specialty consultations were 3.6 days (95% CI, 3.6-3.6 days) and 1.01 (95% CI, 1.00-1.03), respectively. A high vs low overall or knowledge milestone core competency rating was associated with none of the outcome measures assessed. For example, a high vs low overall core competency rating was associated with a nonsignificant 2.7% increase in 7-day mortality rates (95% CI, -5.2% to 10.6%; P = .51). In contrast, top vs bottom examination score quartile was associated with a significant 8.0% reduction in 7-day mortality rates (95% CI, -13.0% to -3.1%; P = .002) and a 9.3% reduction in 7-day readmission rates (95% CI, -13.0% to -5.7%; P < .001). For 30-day mortality, this association was -3.5% (95% CI, -6.7% to -0.4%; P = .03). Top vs bottom examination score quartile was associated with 2.4% more consultations (95% CI, 0.8%-3.9%; P < .003) but was not associated with length of stay or 30-day readmission rates.

Conclusions and Relevance

Among newly trained hospitalists, certification examination score, but not residency milestone ratings, was associated with improved outcomes among hospitalized Medicare beneficiaries.

Readmission Rates Among Acute Reperfusion Treatment Modalities for Patients With Ischemic Stroke

Background and purpose

Understanding various aspects associated with readmission after acute ischemic stroke (AIS) is an important priority. Our study aims to examine whether 60-day readmission rates differed among patients with AIS who were treated with different acute reperfusion treatment modalities along with associated clinical factors.

Methods

This is a retrospective analysis of a continuous cohort of patient with AIS, who received either intravenous recombinant tissue plasminogen activator (IV rtPA), endovascular treatment (EVT) or both, and were discharged alive. Patients readmitted within 60 days were identified as the readmission group. Multivariable logistic regression was used to identify all-cause readmission post-stroke between treatment groups.

Results

The final cohort comprised of 358 patients with AIS receiving IV rtPA only (N = 160), EVT only (N = 106), or both (N = 92). Fifty-six patients were readmitted to the hospital within 60-day follow-up period. The adjusted logistic regression model indicated that compared to patients who received IV tPA only, patients receiving both IV rtPA and EVT had significantly lower odds (OR = .27; 95% CI = .10, .75)) of getting readmitted within 60-day post-discharge from stroke admission.

Conclusion

In this sample of AIS hospitalizations, treatment-type was positively associated with 60-day readmission. Future studies are necessary to understand whether treatment-related adverse events, and readmission are avoidable.

Inpatient midwifery staffing levels and postpartum readmissions: a retrospective multicentre longitudinal study

BACKGROUND

Preventing readmission to hospital after giving birth is a key priority, as rates have been rising along with associated costs. There are many contributing factors to readmission, and some are thought to be preventable. Nurse and midwife understaffing has been linked to deficits in care quality. This study explores the relationship between staffing levels and readmission rates in maternity settings.

METHODS

We conducted a retrospective longitudinal study using routinely collected individual patient data in three maternity services in England from 2015 to 2020. Data on admissions, discharges and case-mix were extracted from hospital administration systems. Staffing and workload were calculated in Hours Per Patient day per shift in the first two 12-hour shifts of the index (birth) admission. Postpartum readmissions and staffing exposures for all birthing admissions were entered into a hierarchical multivariable logistic regression model to estimate the odds of readmission when staffing was below the mean level for the maternity service.

RESULTS

64 250 maternal admissions resulted in birth and 2903 mothers were readmitted within 30 days of discharge (4.5%). Absolute levels of staffing ranged between 2.3 and 4.1 individuals per midwife in the three services. Below average midwifery staffing was associated with higher rates of postpartum readmissions within 7 days of discharge (adjusted OR (aOR) 1.108, 95% CI 1.003 to 1.223). The effect was smaller and not statistically significant for readmissions within 30 days of discharge (aOR 1.080, 95% CI 0.994 to 1.174). Below average maternity assistant staffing was associated with lower rates of postpartum readmissions (7 days, aOR 0.957, 95% CI 0.867 to 1.057; 30 days aOR 0.965, 95% CI 0.887 to 1.049, both not statistically significant).

CONCLUSION

We found evidence that lower than expected midwifery staffing levels is associated with more postpartum readmissions. The nature of the relationship requires further investigation including examining potential mediating factors and reasons for readmission in maternity populations.

Racial disparities in law enforcement/court-ordered psychiatric inpatient admissions after the 2008 recession: a test of the frustration-aggression-displacement hypothesis

BACKGROUND

Societies under duress may selectively increase the reporting of disordered persons from vulnerable communities to law enforcement. Mentally ill African American males reportedly are perceived as more threatening relative to females and other race/ethnicities. We examine whether law enforcement/court order-requested involuntary psychiatric hospitalizations increased among African American males shortly after ambient economic decline-a widely characterized population stressor.

METHODS

We identified psychiatric inpatient admissions requested by law enforcement/court orders from 2006 to 2011 across four US states (Arizona, California, New York, North Carolina). Our analytic sample comprises 13.1 million psychiatric inpatient admissions across 95 counties over 72 months. We operationalized exposure to economic downturns as percent change in monthly employment in a metropolitan statistical area (MSA). We used zero inflated negative binomial and linear fixed effects regression analyses to examine psychiatric inpatient admissions requested by law enforcement/court orders following regional employment decline over a time period that includes the Great Recession of 2008.

FINDINGS

Declines in monthly employment precede by one month a 6% increase in psychiatric hospitalizations requested by law enforcement/court order among African American males (p < 0.05), but not among other race/sex groups. Estimates amount to an excess of 2554 involuntary admissions among African American males statistically attributable to aggregate-level employment decline.

CONCLUSIONS

Economic downturns may increase involuntary psychiatric commitments among African American males. Our findings underscore the unique vulnerability of racial/ethnic minorities during economic contractions.

Predicting the risk of hospital readmissions using a machine learning approach: a case study on patients undergoing skin procedures

Introduction

Even with modern advancements in medical care, one of the persistent challenges hospitals face is the frequent readmission of patients. These recurrent admissions not only escalate healthcare expenses but also amplify mental and emotional strain on patients.

Methods

This research delved into two primary areas: unraveling the pivotal factors causing the readmissions, specifically targeting patients who underwent dermatological treatments, and determining the optimal machine learning algorithms that can foresee potential readmissions with higher accuracy.

Results

Among the multitude of algorithms tested, including logistic regression (LR), support vector machine (SVM), random forest (RF), Naïve Bayesian (NB), artificial neural network (ANN), xgboost (XG), and k-nearest neighbor (KNN), it was noted that two models-XG and RF-stood out in their prediction prowess. A closer inspection of the data brought to light certain patterns. For instance, male patients and those between the ages of 21 and 40 had a propensity to be readmitted more frequently. Moreover, the months of March and April witnessed a spike in these readmissions, with ~6% of the patients returning within just a month after their first admission.

Discussion

Upon further analysis, specific determinants such as the patient's age and the specific hospital where they were treated emerged as key indicators influencing the likelihood of their readmission.

Improving patient activation with a tailored nursing discharge teaching intervention for multimorbid inpatients: A quasi-experimental study

OBJECTIVE

Preliminary effectiveness test of a novel structured personalized discharge teaching intervention for multimorbid inpatients.

METHODS

Using a 2-group sequential pre/post-intervention design, the sample comprised 68 pre-intervention control group and 70 post- intervention group participants. The discharge teaching intervention by trained clinical nurses used structured tools to engage patients and individualize discharge teaching. Outcomes measures included Patient Activation Measure, Readiness for Hospital Discharge Scale, Discharge Care Experiences Survey, and readmission with 10 days post-discharge.

RESULTS

The intervention had a statistically significant positive effect on improving patient activation (M=4.8; p = 0.05) from admission to post-discharge. The participation subscale of the Discharge Care Experiences Survey was higher in the intervention (M=4.1, SD=0.7) than the control group (M=3.8, SD=0.7; t (127)= -2.79, p = .01, effect size= .34). There were no significant between-group differences in Readiness for Hospital Discharge Scale and readmission.

CONCLUSIONS

Our results suggest that a structured personalized discharge teaching intervention can improve patient activation and participation in discharge care. Further refinement of the intervention is needed to evaluate and improve specific components of the intervention.

PRACTICE IMPLICATIONS

Structured personalized discharge teaching should include patient engagement strategies in the teaching-learning process.

Effect of the Nurse Work Environment on Older Hispanic Surgical Patient Readmissions

BACKGROUND

Readmissions following hospitalization for common surgical procedures are prevalent among older adults and are disproportionally experienced by Hispanic patients. One potential explanation for these disparities is that Hispanic patients may receive care in hospitals with lower-quality nursing care.

OBJECTIVES

The objective of this study was to evaluate the relationship between the hospital-level work environment of nurses and hospital readmissions among older Hispanic patients.

METHODS

Using linked data sources from 2014 to 2016, we conducted a cross-sectional analysis of 522 hospitals and 732,035 general, orthopedic, and vascular surgical patients (80,978 Hispanic patients and 651,057 non-Hispanic White patients) in four states. Multivariable logistic regression models were employed to determine the relationship between the work environment and older Hispanic patient readmissions at multiple time periods (7, 30, and 90 days).

RESULTS

In final adjusted models that included an interaction between work environment and ethnicity, an increase in the quality of the work environment resulted in a decrease in the odds of readmission that was greater for older Hispanic surgical patients at all time periods. Specifically, an increase in three of the five work environment subscales (Nurse Participation in Hospital Affairs, Nursing Foundations for Quality of Care, and Staffing and Resource Adequacy) was associated with a reduction in the odds of readmission that was greater for Hispanic patients than their non-Hispanic White counterparts.

DISCUSSION

System-level investments in the work environment may reduce Hispanic patient readmission disparities. This study's findings may be used to inform the development of targeted interventions to prevent hospital readmissions for Hispanic patients.

The role of targeted temperature management in 30-day hospital readmissions in cardiac arrest survivors: A national population-based study

Background

Targeted temperature management (TTM) implementation following resuscitation from cardiac arrest is controversial. Although prior studies have shown that TTM improves neurological outcomes and mortality, less is known about the rates or causes of readmission in cardiac arrest survivors within 30 days. We aimed to determine whether the implementation of TTM improves all-cause 30-day unplanned readmission rates in cardiac arrest survivors.

Methods

Using the Nationwide Readmissions Database, we identified 353,379 adult cardiac arrest index hospitalizations and discharges using the International Classification of Diseases, 9th and 10th codes. The primary outcome was 30-day all-cause unplanned readmissions following cardiac arrest discharge. Secondary outcomes included 30-day readmission rates and reasons, including impacts on other organ systems.

Results

Of 353,379 discharges for cardiac arrest with 30-day readmission, 9,898 (2.80%) received TTM during index hospitalization. TTM implementation was associated with lower 30-day all-cause unplanned readmission rates versus non-recipients (6.30% vs. 9.30%, p < 0.001). During index hospitalization, receiving TTM was also associated with higher rates of AKI (41.12% vs. 37.62%, p < 0.001) and AHF (20.13% vs. 17.30%, p < 0.001). We identified an association between lower rates of 30-day readmission for AKI (18.34% vs. 27.48%, p < 0.05) and trend toward lower AHF readmissions (11.32% vs. 17.97%, p = 0.05) among TTM recipients.

Conclusions

Our study highlights a possible negative association between TTM and unplanned 30-day readmission in cardiac arrest survivors, thereby potentially reducing the impact and burden of increased short-term readmission in these patients. Future randomized studies are warranted to optimize TTM use during post-arrest care.

Predicting Hospital Readmissions in a Commercially Insured Population over Varying Time Horizons

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.

Does prospective payment influence quality of care? A systematic review of the literature

In the light of rising health expenditures, the cost-efficient provision of high-quality inpatient care is on the agenda of policy-makers worldwide. In the last decades, prospective payment systems (PPS) for inpatient care were used as an instrument to contain costs and increase transparency of provided services. It is well documented in the literature that prospective payment has an impact on structure and processes of inpatient care. However, less is known about its effect on key outcome indicators of quality of care. In this systematic review, we synthesize evidence from studies investigating how financial incentives induced by PPS affect indicators of outcome quality domains of care, i.e. health status and user evaluation outcomes. We conduct a review of evidence published in English, German, French, Portuguese and Spanish language produced since 1983 and synthesize results of the studies narratively by comparing direction of effects and statistical significance of different PPS interventions. We included 64 studies, where 10 are of high, 18 of moderate and 36 of low quality. The most commonly observed PPS intervention is the introduction of per-case payment with prospectively set reimbursement rates. Abstracting evidence on mortality, readmission, complications, discharge disposition and discharge destination, we find the evidence to be inconclusive. Thus, claims that PPS either cause great harm or significantly improve the quality of care are not supported by our findings. Further, the results suggest that reductions of length of stay and shifting treatment to post-acute care facilities may occur in the course of PPS implementations. Accordingly, decision-makers should avoid low capacity in this area.

Association between inpatient palliative care encounter and 30-day all-cause readmissions after index hospitalization for chronic obstructive pulmonary disease

BACKGROUND

Studies exist on the association between inpatient Palliative Care Encounter (iPCE) and 30-day rehospitalization among cancer and several non-cancer conditions but limited in persons with Chronic Obstructive Pulmonary Disease (COPD).

OBJECTIVE

To assess the association between an iPCE with the risk of 30-day rehospitalization after an index hospitalization for COPD.

METHODS

We conducted a cross-sectional analysis of the Nationwide Readmissions Database (2010-2014). Index hospitalizations were defined as persons ≥ 18 years of age, discharge destinations of either Home/Routine, Home with Home Care, or a Facility, and an index hospitalization with Diagnosis Related Group of COPD. The International Classification of Diseases, 9th revision codes were used to extract comorbidities and a Palliative Care Encounter (V66.7).

RESULTS

There were 3,163,889 index hospitalizations and iPCE occurred in 21,330 (0.67%). There were 558,059 (17.63%) with a 30-day rehospitalization. An iPCE was associated with a significantly lower adjusted odds of 30-day readmission (Odds Ratio [OR], 0.50; 95% Confidence Interval [CI], 0.46 to 0.54).  By discharge destination, the odds of 30-day rehospitalization were for a discharged to a facility (OR, 0.37; 95% CI, 0.32 to 0.42), to home with home health (OR, 0.42; 95% CI, 0.37 to 0.47), and to home (OR, 0.98; 95% CI, 0.85 to 1.12) for those with relative to without iPCE.

CONCLUSION

Inpatient PCE was associated with a 50% lower relative odds of 30-day rehospitalization after an index hospitalization for COPD. This association varied by discharge destination being statistically significant among those with a discharge destination of a facility (63%) and home with home care (58%).

Association of palliative care and hospital outcomes among solid tumour oncology inpatients

OBJECTIVES

We aimed to explore the association between receiving an inpatient palliative care consultation and hospital outcomes, including in-hospital death, intensive care unit (ICU) use, discharge to hospice, 30-day readmissions and 30-day emergency department (ED) visits.

METHODS

We conducted a retrospective chart review of Yale New Haven Hospital medical oncology admissions from January 2018 through December 2021, with and without inpatient palliative care consultations. Hospital outcome data were extracted from medical records and operationalised as binary. Multivariable logistic regression was used to estimate ORs for the association between number of inpatient palliative care consultations and hospital outcomes.

RESULTS

Our sample included 19 422 patients. Age, Rothman Index, site of malignancy, length of stay, discharge to hospice, ICU admissions, hospital death and readmissions within 30 days differed significantly between patients who received versus did not receive a palliative care consultation. On multivariable analysis, receiving one additional palliative care consultation was significantly associated with higher odds of hospital death (adjusted OR=1.15, 95% CI 1.12 to 1.17) and discharge to hospice (adjusted OR = 1.23, 95% CI 1.20 to 1.26), and lower odds of ICU admission (adjusted OR=0.94, 95% CI 0.92 to 0.97). There was no significant association between palliative care consultations and readmission within 30 days or with ED visits within 30 days.

CONCLUSION

Inpatients receiving palliative care had increased likelihood of hospital death. However, when controlling for significant differences in patient presentation, patients had nearly 25% greater odds of discharge to hospice and less odds to transition to ICU level of care.

Nurse Education and Hospital Readmissions for Children With and Without a Mental Health Condition

OBJECTIVES

In adults, receiving care in a hospital with more baccalaureate-prepared nurses improves outcomes. This relationship is magnified in adults with serious mental illness or cognitive impairment. Whether the same is true in children with and without a mental health condition is unknown. The study purposes were to determine 1) whether the proportion of baccalaureate-prepared nurses affected the odds of readmission in children; and 2) whether this relationship differed for children with a mental health condition.

PATIENTS AND METHODS

We linked cross-sectional data from the 2016 Healthcare Cost and Utilization Project State Inpatient Databases, the RN4CAST-US nurse survey in Florida, and the American Hospital Association. Inclusion criteria were ages 3 to 21 years. Mental health conditions were defined as psychiatric or developmental/behavioral diagnoses. These were identified using the Child and Adolescent Mental Health Disorders Classification System. We used multivariable, hierarchical logistic regression models to assess the relationship between nurse training and readmissions.

RESULTS

In 35 081 patients admitted to 122 hospitals with 4440 nurses, 21.0% of patients had a mental health condition and 4.2% had a 7-day readmission. For individuals without a mental health condition, each 10% increase in the proportion of baccalaureate-prepared nurses was associated with 8.0% lower odds of readmission (odds ratio = 0.92, 95% confidence interval = 0.87-0.97). For those with a mental health condition, each 10% increase in the proportion of baccalaureate-prepared nurses was associated with 16.0% lower odds of readmission (odds ratio = 0.84, 95% confidence interval = 0.78-0.91).

CONCLUSIONS

A higher proportion of baccalaureate-educated nurses is associated with lower odds of readmission for pediatric patients. This association has a larger magnitude in patients with a mental health condition.

Early prediction of 30- and 14-day all-cause unplanned readmissions

BACKGROUND

An unplanned readmission is a dual metric for both the cost and quality of medical care.

METHODS

We employed the random forest (RF) method to build a prediction model using a large dataset from patients' electronic health records (EHRs) from a medical center in Taiwan. The discrimination abilities between the RF and regression-based models were compared using the areas under the ROC curves (AUROC).

RESULTS

When compared with standardized risk prediction tools, the RF constructed using data readily available at admission had a marginally yet significantly better ability to identify high-risk readmissions within 30 and 14 days without compromising sensitivity and specificity. The most important predictor for 30-day readmissions was directly related to the representing factors of index hospitalization, whereas for 14-day readmissions the most important predictor was associated with a higher chronic illness burden.

CONCLUSIONS

Identifying dominant risk factors based on index admission and different readmission time intervals is crucial for healthcare planning.

Readmission Within the First Day of Discharge Is Painful: Experience From an Australian General Surgical Service

Background Unplanned readmission to the hospital after discharge is a costly issue for healthcare systems and patients. It is a delicate balance between the resolution of the surgical problem and the length of hospital stay. Most studies have focused on readmissions within 28 or 30 days after discharge, despite data showing that many occur early in this period. This study examined the reasons for unplanned readmission within the first day after discharge.  Methods A retrospective cohort analysis of readmissions between 1st May 2016 and 1st May 2021 was undertaken by chart review. Readmissions on the "day of" and the "day after" discharge and their respective index admissions were identified via the hospital's patient administration database, webPAS (DXC Technology, USA). Results There were 126 readmissions (0.5%) across 25,119 admissions. Common reasons for readmission were pain (28%, n=35), readmission for the same diagnosis (21%, n=26), surgical site infection (SSI) (11%, n=14), bleeding (11%, n=14) and ileus (6%, n=7). Analysis of index admissions showed that 18/35 readmissions for pain had inadequate pain management based on pain scores, analgesic use and discharge medications and 7/14 readmissions for SSI did not have appropriate treatment of a recognised SSI or did not have antibiotic prophylaxis guidelines adhered to. Fourteen of 26 readmissions for the same diagnosis received just continuation of treatment initiated at index admission.  Conclusion Pain is the most common reason for readmission within the first day after discharge in surgical patients. Better pain management, following antibiotic prophylaxis guidelines, and involving patients in discharge planning could prevent many readmissions.

An explanatory machine learning framework for studying pandemics: The case of COVID-19 emergency department readmissions

One of the major challenges that confront medical experts during a pandemic is the time required to identify and validate the risk factors of the novel disease and to develop an effective treatment protocol. Traditionally, this process involves numerous clinical trials that may take up to several years, during which strict preventive measures must be in place to control the outbreak and reduce the deaths. Advanced data analytics techniques, however, can be leveraged to guide and speed up this process. In this study, we combine evolutionary search algorithms, deep learning, and advanced model interpretation methods to develop a holistic exploratory-predictive-explanatory machine learning framework that can assist clinical decision-makers in reacting to the challenges of a pandemic in a timely manner. The proposed framework is showcased in studying emergency department (ED) readmissions of COVID-19 patients using ED visits from a real-world electronic health records database. After an exploratory feature selection phase using genetic algorithm, we develop and train a deep artificial neural network to predict early (i.e., 7-day) readmissions (AUC = 0.883). Lastly, a SHAP model is formulated to estimate additive Shapley values (i.e., importance scores) of the features and to interpret the magnitude and direction of their effects. The findings are mostly in line with those reported by lengthy and expensive clinical trial studies.

Predicting and Validating 30-day Hospital Readmission in Adults With Diabetes Whose Index Admission Is Diabetes-related

OBJECTIVE

The primary objective is to develop a prediction model of 30-day hospital readmission among adults with diabetes mellitus (DM) whose index admission was DM-related. The secondary aims are to internally and externally validate the prediction model and compare its performance with 2 existing models.

RESEARCH DESIGN AND SETTING

Data of inpatients aged ≥ 18 years from 2008 to 2015 were extracted from the electronic medical record system of the National University Hospital, Singapore. Unplanned readmission within 30 days was calculated from the discharge date of the index hospitalization. Multivariable logistic regression and 10-fold cross-validation were performed. For external validation, simulations based on prevalence of 30-day readmission, and the regression coefficients provided by referenced papers were conducted.

RESULTS

Eleven percent of 2355 patients reported 30-day readmission. The prediction model included 4 predictors: length of stay, ischemic heart disease, peripheral vascular disease, and number of drugs. C-statistics for the prediction model and 10-fold cross-validation were 0.68 (95% CI 0.66, 0.70) and 0.67 (95% CI 0.63 to 0.70), respectively. Those for the 3 simulated external validation data sets ranged from 0.64 to 0.68.

CONCLUSION

The prediction model performs well with good internal and external validity for identifying patients with DM at risk of unplanned 30-day readmission.

The Association Between Medication Non-Adherence and Early and Late Readmission Rates for Patients with Acute Coronary Syndrome

Purpose

Unplanned hospital readmission forms costly, but preventable burdens on healthcare system. This study was designed to evaluate cardiovascular-related readmission rate after discharge of acute coronary syndrome (ACS) patients and its relationship with medication adherence at a university hospital, Saudi Arabia.

Methods

A total of 370 consecutive patients presenting with ACS were involved. The inclusion criteria were clinical and coronary angiography diagnostic data of ACS. Exclusion criteria included heart valve disease, myocarditis, hepatic disease, and history of acute infection during the previous two weeks. Patients were divided into index admission group (n = 291) and unplanned readmission group (n = 79). Readmission and medication adherence rates were evaluated during 1–30, 31–180, 181–365, and 366–548 days post-ACS discharge. Medication adherence was estimated with a (yes/no) questionnaire.

Results

The overall readmission rate was 21.4%; individual rates were 30.4%, 38.0%, 27.8%, and 3.8% and the overall medication adherence rate was 62.03%, while individual rates were 54.2%, 70.0%, 63.6%, and 33.3% during the four periods, respectively. There were strong correlations between medication non-adherence and readmission rates. Heart failure, ST-elevated myocardial infarction, unstable angina, cerebrovascular accident, and arrhythmia represented the top causes. Body mass index was higher in readmission group. There were significant correlations between smoking, hypertension, cerebrovascular accident, ischemic heart disease, previous stent, instent restenosis, and LDL-cholesterol as predictor factors and readmission rate.

Conclusion

The cardiovascular-related unplanned readmission rate post-ACS discharge was 21.4%, and medication non-adherence rate was 37.97%. There were strong correlations between them in the time frames from 1-month to 1.5-year post-discharge. The individual rates decreased by time, but the first month showed lower rates than the following 5 months and this indicated the role of factors other than medication non-adherence in readmission. The rates are generally consistent with the international levels but utilizing technology can further improve medication adherence and reduce readmission rates.

Leveraging Advanced Data Analytics to Predict the Risk of All-Cause Seven-Day Emergency Readmissions

Introduction

Emergency readmissions have been a long-time, multifaceted, unsolved problem. Developing a predictive model calibrated with hospital-specific Electronic Health Record (EHR) data could give higher prediction accuracy and insights into high-risk patients for readmission. Thus, we need to proactively introduce the necessary interventions. This study aims to investigate the relationship between features that consider significant predictors of at-risk patients for seven-day readmission through logistic regression in addition to developing several machine learning models to test the predictability of those attributes using EHR data in a Saudi Arabia-specific ED context.

Methods

Univariate and multivariate logistic regression has been used to identify the most statistically significant features that contributed to classifying readmitted and not readmitted patients. Seven different machine learning models were trained and tested, and a comparison between the best-performing model was conducted in terms of five performance metrics. To construct the prediction model and internally validate it, the processed dataset was split into two sets: 70% for the training set and 30% for the test set or validation set.

Results

XGBoost achieved the highest accuracy (64%) in predicting early seven-day readmissions. Catboost was the second-best predictive model at 61%. XGBoost achieved the highest specificity at 70%, and all the models had a sensitivity of 57% except for XGBoost and Catboost at 32% and 38%, respectively. All predictive attributes, patient age, length of stay (LOS) in minutes, visit time (AM), marital status (married), number of medications, and number of abnormal lab results were significant predictors of early seven-day readmissions while marital status and number of vital-sign instabilities at discharge were not statistically significant predictors of seven-day readmission.

Conclusion

Although XGBoost and Catboost showed good accuracy, none of the models achieved good discriminative ability in terms of sensitivity and specificity. Thus, none can be clinically used for predicting early seven-day readmission. More predictive variables need to be fed into the model, specifically predictors approximate to the day of discharge, in order to optimize the model’s performance.

Effect Modification by Social Determinants of Pharmacogenetic Medication Interactions on 90-Day Hospital Readmissions within an Integrated U.S. Healthcare System

The present study builds on our prior work that demonstrated an association between pharmacogenetic interactions and 90-day readmission. In a substantially larger, more diverse study population of 19,999 adults tracked from 2010 through 2020 who underwent testing with a 13-gene pharmacogenetic panel, we included additional covariates to evaluate aggregate contribution of social determinants and medical comorbidity with the presence of identified gene-x-drug interactions to moderate 90-day hospital readmission (primary outcome). Univariate logistic regression analyses demonstrated that strongest associations with 90 day hospital readmissions were the number of medications prescribed within 30 days of a first hospital admission that had Clinical Pharmacogenomics Implementation Consortium (CPIC) guidance (CPIC medications) (5+ CPIC medications, odds ratio (OR) = 7.66, 95% confidence interval 5.45−10.77) (p < 0.0001), major comorbidities (5+ comorbidities, OR 3.36, 2.61−4.32) (p < 0.0001), age (65 + years, OR = 2.35, 1.77−3.12) (p < 0.0001), unemployment (OR = 2.19, 1.88−2.64) (p < 0.0001), Black/African-American race (OR 2.12, 1.47−3.07) (p < 0.0001), median household income (OR = 1.63, 1.03−2.58) (p = 0.035), male gender (OR = 1.47, 1.21−1.80) (p = 0.0001), and one or more gene-x-drug interaction (defined as a prescribed CPIC medication for a patient with a corresponding actionable pharmacogenetic variant) (OR = 1.41, 1.18−1.70). Health insurance was not associated with risk of 90-day readmission. Race, income, employment status, and gene-x-drug interactions were robust in a multivariable logistic regression model. The odds of 90-day readmission for patients with one or more identified gene-x-drug interactions after adjustment for these covariates was attenuated by 10% (OR = 1.31, 1.08−1.59) (p = 0.006). Although the interaction between race and gene-x-drug interactions was not statistically significant, White patients were more likely to have a gene-x-drug interaction (35.2%) than Black/African-American patients (25.9%) who were not readmitted (p < 0.0001). These results highlight the major contribution of social determinants and medical complexity to risk for hospital readmission, and that these determinants may modify the effect of gene-x-drug interactions on rehospitalization risk.

Variation in Cardiac Rehabilitation Participation During Aortic Valve Replacement Episodes of Care

BACKGROUND

Despite reported benefit in the setting of aortic valve replacement (AVR), cardiac rehabilitation (CR) utilization remains low, with few studies evaluating hospital and patient-level variation in CR participation. We explored determinants of CR variability during AVR episodes of care: transcatheter aortic valve replacement (TAVR) and surgical aortic valve replacement (SAVR).

METHODS

A cohort of 10 124 AVR episodes of care (TAVR n=5121 from 24 hospitals; SAVR n=5003 from 32 hospitals) were identified from the Michigan Value Collaborative statewide multipayer registry (2015-2019). CR enrollment was defined as the presence of a single professional or facility claim within 90 days of discharge: 93 797, 93 798, G0422, G0423. Annual trends and hospital variation in CR were described for TAVR, SAVR, and all AVR. Multilevel logistic regression was used to estimate effects of predictors and hospital risk-adjusted rates of CR enrollment.

RESULTS

Overall, 4027 (39.8%) patients enrolled in CR, with significant differences by treatment strategy: SAVR=50.9%, TAVR=28.9% (P<0.001). CR use after SAVR was significantly higher than after TAVR and increased over time for both modalities (P<0.001). There were significant differences in CR enrollment across age, gender, payer, and some comorbidities (P<0.05). At the hospital level, CR participation rates for all AVR varied 10-fold (4.8% to 68.7%) and were moderately correlated between SAVR and TAVR (Pearson r=0.56, P<0.01).

CONCLUSIONS

Substantial variation exists in CR participation during AVR episodes of care across hospitals. However, within-hospital CR participation rates were significantly correlated across treatment strategies. These findings suggest that CR participation is the product of hospital-specific practice patterns. Identifying hospital practices associated with higher CR participation can help assist future quality improvement efforts to increase CR use after AVR.

The impact of court-ordered psychiatric treatment on hospital length of stay: balancing legal and clinical concerns

PURPOSE

Psychiatric hospital length of stay (LOS) is not affected solely by socio-clinical factors but also by legal procedures. This study examined the associations between legal procedures and LOS.

METHODS

Data from 521 patients with psychiatric illnesses hospitalized over 2013-2015 were analyzed. Logistic regression was used to evaluate the predictors of longer (> 14 days) or prolonged (> 30) LOS with socio-clinical factors and legal procedures including court-ordered interventions (assisted outpatient treatment, medication over objection, and retention).

RESULTS

Longer LOS occurred in 246 patients and 99 had prolonged LOS. Legal procedures affected 57 patients, with 11 assisted outpatient treatments, 39 cases of medication over objection, and 16 retentions. Longer LOS was significantly associated with six factors including older age, unmarried status, non-Hispanic race, risk of violence, schizophrenia, and legal procedures. Legal procedures had the strongest association. Longer/prolonged LOS yielded qualitatively similar associations.

CONCLUSION

Among 521 psychiatric inpatients, approximately 11% were mandated to receive interventions/procedures by the courts. Court-ordered legal procedures were strongly associated with longer LOS. Mental health providers may consider legal procedures for patients at high treatment/medication noncompliance risk as early as patient admission to inpatient units to prevent, intervene or prepare for a longer or prolonged LOS.

Incidence of Avoidable 30-Day Readmissions Following Hospitalization for Community-Acquired Pneumonia in France

IMPORTANCE

Rates of 30-day readmissions following hospitalization for pneumonia are used to publicly report on hospital performance and to set financial penalties for the worst-performing hospitals. However, the rate of avoidable readmission following hospitalization for pneumonia is undefined.

OBJECTIVE

To assess how often 30-day readmissions following hospitalization for community-acquired pneumonia (CAP) are avoidable.

DESIGN, SETTING, AND PARTICIPANTS

This cohort study analyzed the results of an independent review of readmissions following hospitalization for CAP within 30 days among patients discharged from 2 large hospitals in France in 2014. Structured clinical records including clinical information (ie, baseline characteristics, physical examination, laboratory findings, x-ray or computed tomography scan findings, discharge plan, and treatments) for both index and readmission stays were independently reviewed by 4 certified board physicians. All consecutive adult patients hospitalized in 2014 with a diagnosis of CAP in our 2 eligible hospitals were eligible. All analyses presented were performed in March 2021.

MAIN OUTCOMES AND MEASURES

Avoidable readmission within 30 days of discharge from index hospitalization. The likelihood that a readmission was avoidable was quantified using latent class analysis based on the independent reviews. A readmission was considered avoidable if Bayes posterior probability exceeded 50%.

RESULTS

The total analytical sample consisted of 1150 index hospital stays with a diagnosis of CAP, which included 651 (56.6%) male patients. The median (IQR) age for all patients was 77.8 (IQR, 62.7-86.4) years. Out of the 1150 index hospital stays, 98 patients (8.5%) died in hospital, and 108 (9.4%) unplanned readmissions were found. Overall, 15 readmissions had a posterior probability of avoidability exceeding 0.50 (13.9% of the 108 unplanned readmissions; 95% CI, 8.0%-21.9%). The median (IQR) delay between the hospital discharge index and readmission was considerably shorter when readmission was deemed avoidable (4 [6-21] days vs 12 [2-18] days; P = .02).

CONCLUSIONS AND RELEVANCE

Only a small number of readmissions following hospitalization for CAP were deemed avoidable, comprising less than 10% of all readmissions. Shorter time interval between hospitalization discharge and readmission was associated with avoidability.

Applicability of predictive models for 30-day unplanned hospital readmission risk in paediatrics: a systematic review

OBJECTIVES

To summarise multivariable predictive models for 30-day unplanned hospital readmissions (UHRs) in paediatrics, describe their performance and completeness in reporting, and determine their potential for application in practice.

DESIGN

Systematic review.

DATA SOURCE

CINAHL, Embase and PubMed up to 7 October 2021.

ELIGIBILITY CRITERIA

English or German language studies aiming to develop or validate a multivariable predictive model for 30-day paediatric UHRs related to all-cause, surgical conditions or general medical conditions were included.

DATA EXTRACTION AND SYNTHESIS

Study characteristics, risk factors significant for predicting readmissions and information about performance measures (eg, c-statistic) were extracted. Reporting quality was addressed by the 'Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis' (TRIPOD) adherence form. The study quality was assessed by applying six domains of potential biases. Due to expected heterogeneity among the studies, the data were qualitatively synthesised.

RESULTS

Based on 28 studies, 37 predictive models were identified, which could potentially be used for determining individual 30-day UHR risk in paediatrics. The number of study participants ranged from 190 children to 1.4 million encounters. The two most common significant risk factors were comorbidity and (postoperative) length of stay. 23 models showed a c-statistic above 0.7 and are primarily applicable at discharge. The median TRIPOD adherence of the models was 59% (P₂₅-P₇₅, 55%-69%), ranging from a minimum of 33% to a maximum of 81%. Overall, the quality of many studies was moderate to low in all six domains.

CONCLUSION

Predictive models may be useful in identifying paediatric patients at increased risk of readmission. To support the application of predictive models, more attention should be placed on completeness in reporting, particularly for those items that may be relevant for implementation in practice.

Inpatient pharmacists using a readmission risk model in supporting discharge medication reconciliation to reduce unplanned hospital readmissions: a quality improvement intervention

Introduction

Reducing unplanned hospital readmissions is an important priority for all hospitals and health systems. Hospital discharge can be complicated by discrepancies in the medication reconciliation and/or prescribing processes. Clinical pharmacist involvement in the medication reconciliation process at discharge can help prevent these discrepancies and possibly reduce unplanned hospital readmissions.

Methods

We report the results of our quality improvement intervention at Duke University Hospital, in which pharmacists were involved in the discharge medication reconciliation process on select high-risk general medicine patients over 2 years (2018–2020). Pharmacists performed traditional discharge medication reconciliation which included a review of medications for clinical appropriateness and affordability. A total of 1569 patients were identified as high risk for hospital readmission using the Epic readmission risk model and had a clinical pharmacist review the discharge medication reconciliation.

Results

This intervention was associated with a significantly lower 7-day readmission rate in patients who scored high risk for readmission and received pharmacist support in discharge medication reconciliation versus those patients who did not receive pharmacist support (5.8% vs 7.6%). There was no effect on readmission rates of 14 or 30 days. The clinical pharmacists had at least one intervention on 67% of patients reviewed and averaged 1.75 interventions per patient.

Conclusion

This quality improvement study showed that having clinical pharmacists intervene in the discharge medication reconciliation process in patients identified as high risk for readmission is associated with lower unplanned readmission rates at 7 days. The interventions by pharmacists were significant and well received by ordering providers. This study highlights the important role of a clinical pharmacist in the discharge medication reconciliation process.

Factors associated with 14-day hospital readmission in frail older patients: A case-control study

Frailty is a key predictor of readmission among older patients. However, studies on the factors associated with readmission of frail older patients are lacking. This study aims to examine factors associated with 14-day hospital readmission in frail older patients. A retrospective case-control study was conducted. Patients were eligible for inclusion if they were age 65 and over and if their Clinical Frailty Scale (CFS) score was above 4. A total of 210 frail older patients were included. Patients who had partners, experienced a fall within 6 months before hospitalization, had pressure injuries, received surgery or chemotherapy, and received rehabilitation therapy from a physical therapist during hospitalization had increased odds of being readmitted to the hospital within 14 days. Moreover, patients receiving comprehensive geriatric assessment (CGA) services during hospitalization showed a significantly reduced risk of readmission. Adapting CGA and developing continuity care plans from hospitals to the community are crucial.

Did the Hospital Readmissions Reduction Program Reduce Readmissions? An Assessment of Prior Evidence and New Estimates

In this article, we provide a comprehensive, empirical assessment of the hypothesis that the Hospital Readmissions Reduction Program (HRRP) affected hospital readmissions. In doing so, we provide evidence as to the validity of prior empirical approaches used to evaluate the HRRP and we present results from a previously unused approach to study this research question-a regression-kink design. Results of our analysis document that the empirical approaches used in most prior research assessing the efficacy of the HRRP often lack internal validity. Therefore, results from these studies may not be informative about the causal consequences of the HRRP. Results from our regression-kink analysis, which we validate, suggest that the HRRP had little effect on hospital readmissions. This finding contrasts with the results of most prior studies, which report that the HRRP significantly reduced readmissions. Our finding is consistent with conceptual considerations related to the assumptions underlying HRRP penalty: in particular, the difficulty of identifying preventable readmissions, the highly imperfect risk adjustment that affects the penalty determination, and the absence of proven tools to reduce readmissions.

External validation of the PAR-Risk Score to assess potentially avoidable hospital readmission risk in internal medicine patients

BACKGROUND

Readmission prediction models have been developed and validated for targeted in-hospital preventive interventions. We aimed to externally validate the Potentially Avoidable Readmission-Risk Score (PAR-Risk Score), a 12-items prediction model for internal medicine patients with a convenient scoring system, for our local patient cohort.

METHODS

A cohort study using electronic health record data from the internal medicine ward of a Swiss tertiary teaching hospital was conducted. The individual PAR-Risk Score values were calculated for each patient. Univariable logistic regression was used to predict potentially avoidable readmissions (PARs), as identified by the SQLape algorithm. For additional analyses, patients were stratified into low, medium, and high risk according to tertiles based on the PAR-Risk Score. Statistical associations between predictor variables and PAR as outcome were assessed using both univariable and multivariable logistic regression.

RESULTS

The final dataset consisted of 5,985 patients. Of these, 340 patients (5.7%) experienced a PAR. The overall PAR-Risk Score showed rather poor discriminatory power (C statistic 0.605, 95%-CI 0.575-0.635). When using stratified groups (low, medium, high), patients in the high-risk group were at statistically significant higher odds (OR 2.63, 95%-CI 1.33-5.18) of being readmitted within 30 days compared to low risk patients. Multivariable logistic regression identified previous admission within six months, anaemia, heart failure, and opioids to be significantly associated with PAR in this patient cohort.

CONCLUSION

This external validation showed a limited overall performance of the PAR-Risk Score, although higher scores were associated with an increased risk for PAR and patients in the high-risk group were at significantly higher odds of being readmitted within 30 days. This study highlights the importance of externally validating prediction models.

Hospital Readmissions and Mortality Among Fee-for-Service Medicare Patients With Minor Stroke or Transient Ischemic Attack: Findings From the COMPASS Cluster-Randomized Pragmatic Trial

Background Mortality and hospital readmission rates may reflect the quality of acute and postacute stroke care. Our aim was to investigate if, compared with usual care (UC), the COMPASS-TC (Comprehensive Post-Acute Stroke Services Transitional Care) intervention (INV) resulted in lower all-cause and stroke-specific readmissions and mortality among patients with minor stroke and transient ischemic attack discharged from 40 diverse North Carolina hospitals from 2016 to 2018. Methods and Results Using Medicare fee-for-service claims linked with COMPASS cluster-randomized trial data, we performed intention-to-treat analyses for 30-day, 90-day, and 1-year unplanned all-cause and stroke-specific readmissions and all-cause mortality between INV and UC groups, with 90-day unplanned all-cause readmissions as the primary outcome. Effect estimates were determined via mixed logistic or Cox proportional hazards regression models adjusted for age, sex, race, stroke severity, stroke diagnosis, and documented history of stroke. The final analysis cohort included 1069 INV and 1193 UC patients (median age 74 years, 80% White, 52% women, 40% with transient ischemic attack) with median length of hospital stay of 2 days. The risk of unplanned all-cause readmission was similar between INV versus UC at 30 (9.9% versus 8.7%) and 90 days (19.9% versus 18.9%), respectively. No significant differences between randomization groups were seen in 1-year all-cause readmissions, stroke-specific readmissions, or mortality. Conclusions In this pragmatic trial of patients with complex minor stroke/transient ischemic attack, there was no difference in the risk of readmission or mortality with COMPASS-TC relative to UC. Our study could not conclusively determine the reason for the lack of effectiveness of the INV. Registration URL: https://www.clinicaltrials.gov; Unique identifier: NCT02588664.

Impact of a National Quality Collaborative on Pediatric Asthma Care Quality by Insurance Status

OBJECTIVE

To assess whether disparities in asthma care and outcomes based on insurance type existed before a national quality improvement (QI) collaborative, and to determine the effects of the collaborative on these disparities.

METHODS

Secondary analysis of data from Pathways for Improving Pediatric Asthma Care (PIPA), a national collaborative to standardize emergency department (ED) and inpatient asthma management. PIPA included children aged 2 to 17 with a diagnosis of asthma. Disparities were examined based on insurance status (public vs private). Outcomes included guideline adherence and health care utilization measures, assessed for 12 months before and 15 months after the start of PIPA.

RESULTS

We analyzed 19,204 ED visits and 11,119 hospitalizations from 89 sites. At baseline, children with public insurance were more likely than those with private insurance to receive early administration of corticosteroids (52.3% vs 48.9%, P= .01). However, they were more likely to be admitted (20.0% vs 19.4%, P = .01), have longer inpatient length of stay (31 vs 29 hours, P = .01), and have a readmission/ED revisit within 30 days (7.4% vs 5.6%, P = .02). We assessed the effects of PIPA on these disparities by insurance status and found no significant changes across 6 guideline adherence and 4 health care utilization measures.

CONCLUSION

At baseline, children with public insurance had higher asthma health care utilization than those with private insurance, despite receiving more evidence-based care. The PIPA collaborative did not affect pre-existing disparities in asthma outcomes. Future research should identify effective strategies for leveraging QI to better address disparities.

Association of Post-discharge Service Types and Timing with 30-Day Readmissions, Length of Stay, and Costs

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.

Prevalence and Nature of Medication Errors and Medication-Related Harm Following Discharge from Hospital to Community Settings: A Systematic Review

Background

Little is known about the epidemiology of medication errors and medication-related harm following transition from secondary to primary care. This systematic review aims to identify and critically evaluate the available evidence on the prevalence and nature of medication errors and medication-related harm following hospital discharge.

Methods

Studies published between January 1990 and March 2019 were searched across ten electronic databases and the grey literature. No restrictions were applied with publication language or patient population studied. Studies were included if they contained data concerning the rate of medication errors, unintentional medication discrepancies, or adverse drug events. Two authors independently extracted study data.

Results

Fifty-four studies were included, most of which were rated as moderate (39/54) or high (7/54) quality. For adult patients, the median rate of medication errors and unintentional medication discrepancies following discharge was 53% [interquartile range 33–60.5] (n = 5 studies) and 50% [interquartile range 39–76] (n = 11), respectively. Five studies reported adverse drug reaction rates with a median of 27% [interquartile range 18–40.5] and seven studies reported adverse drug event rates with a median of 19% [interquartile range 16–24]. For paediatric patients, one study reported a medication error rate of 66.3% and another an adverse drug event rate of 9%. Almost a quarter of studies (13/54, 24%) utilised a follow-up period post-discharge of 1 month (range 2–180 days). Drug classes most commonly implicated with adverse drug events were antibiotics, antidiabetics, analgesics and cardiovascular drugs.

Conclusions

This is the first systematic review to explore the prevalence and nature of medication errors and adverse drug events following hospital discharge. Targets for future work have been identified.

Electronic supplementary material

The online version of this article (10.1007/s40264-020-00918-3) contains supplementary material, which is available to authorized users.

Racial Disparities in 7-Day Readmissions from an Adult Hospital Medicine Service

Background

Health systems have targeted hospital readmissions to promote health equity as there may be racial and ethnic disparities across different patient groups. However, 7-day readmissions have been understudied in adult hospital medicine.

Design

This is a retrospective study. We performed multivariable logistic regression between patient race/ethnicity and 7-day readmission. Mediation analysis was performed for limited English proficiency (LEP) status. Subgroup analyses were performed for patients with initial admissions for congestive heart failure (CHF), chronic obstructive pulmonary disease (COPD), and cancer.

Patients

We identified all adults discharged from the adult hospital medicine service at UCSF Medical Center between July 2016 and June 2019.

Main Measures

The primary outcome was 7-day all-cause readmission back to the discharging hospital.

Results

There were 18,808 patients in our dataset who were discharged between July 2016 and June 2019. A total of 1,297 (6.9%) patients were readmitted within 7 days. Following multivariable regression, patients who identified as Black (OR 1.35, 95% CI 1.15–1.58, p <0.001) and patients who identified as Asian (OR 1.26, 95% CI 1.06–1.50, p = 0.008) had higher odds of readmission compared to white patients. Multivariable regression at the subgroup level for CHF, COPD, and cancer readmissions did not demonstrate significant differences between the racial and ethnic groups.

Conclusions

Black patients and Asian patients experienced higher rates of 7-day readmission than patients who identified as white, confirmed on adjusted analysis.

Reducing Time to Discharge after Chemotherapy by Standardizing Workflow and Providing Outpatient Intravenous Hydration

Introduction

Patients receiving cyclophosphamide or ifosfamide chemotherapy require intravenous fluid hydration to prevent hemorrhagic cystitis. In selected patients without medical contraindications (ie, excess nausea/vomiting), this hydration may be completed after discharge. We aimed to reduce the time to discharge after completing mesna in patients receiving cyclophosphamide or ifosfamide therapy on an inpatient chemotherapy service.

Methods

The quality improvement team performed a medical record review to capture the time to discharge after mesna therapy and the readmission rate and used quality improvement methods to redesign discharge workflow and increase patient involvement with the discharge process.

Results

From August 2017 through July 2018, there were 160 admission encounters (73 patients) for cyclophosphamide or ifosfamide on a dedicated chemotherapy service. Of those encounters, 89 (55.6%) were appropriate for outpatient hydration; 48 (53.9%) of these encounters involved a patient who elected to receive outpatient hydration. Although the median time to discharge for the whole cohort did not change, in encounters where patients chose intravenous outpatient hydration, the median time to discharge was reduced from 2.82 to 0.66 hours (76.6% reduction) after implementing the new discharge workflow. No patients experienced readmission within 48 hours.

Conclusions

Discharge workflow redesign and standardization reduced the time to discharge after chemotherapy in patients who chose outpatient hydration. Outpatient intravenous hydration after cyclophosphamide or ifosfamide appears safe and feasible in selected patient populations.

Hospital Readmissions and Mortality Among Intubated and Mechanically Ventilated Adult Subjects With Pneumonia Due to Gram-Negative Bacteria

BACKGROUND

Ventilator-associated pneumonia (VAP) is one of the most common hospital-acquired infections in ICUs and is associated with significant morbidity and mortality. Gram-negative bacteria cause 55-85% of hospital-acquired pneumonia and are associated with increased mortality.

METHODS

This study sought to describe mortality rates and 30-d readmission rates among intubated and mechanically ventilated subjects with Gram-negative pneumonia and to explore associated risk factors for mortality and rehospitalization using data from the 2013 Healthcare Cost and Utilization Project (HCUP) National Readmission Database. The study sample included adults age ≥ 18 y who were hospitalized with invasive, continuous mechanical ventilation; were discharged between February 1, 2013, and November 30, 2013; and had a primary or secondary diagnosis of Gram-negative bacterial pneumonia. Logistic regression was used to identify subject characteristics significantly associated with mortality and readmissions.

RESULTS

Using the HCUP projected sample of 32,683 intubated and mechanically ventilated subjects with Gram-negative pneumonia, the mortality rate during the index hospitalization was 24.3%. More than one fifth of subjects (22.9%) who survived the index hospitalization were readmitted within 30 d of discharge. Among subjects with readmissions, 18% occurred within 3 d of discharge, 39% occurred within 7 d of discharge, and 65% occurred within 14 d of discharge. Subjects with prior hospitalization within 30 d of the index hospitalization had a higher risk of readmission with an odds ratio of 1.70 (95% CI 1.48-1.94).

CONCLUSIONS

Mortality was high and readmissions were substantial among intubated and mechanically ventilated subjects with Gram-negative pneumonia.

Better nurse work environments associated with fewer readmissions and shorter length of stay among adults with ischemic stroke: A cross-sectional analysis of United States hospitals

Stroke is among the most common reasons for disability and death. Avoiding readmissions and long lengths of stay among ischemic stroke patients has benefits for patients and health care systems alike. Although reduced readmission rates among a variety of medical patients have been associated with better nurse work environments, it is unknown how the work environment might influence readmissions and length of stay for ischemic stroke patients. Using linked data sources, we conducted a cross-sectional analysis of 543 hospitals to evaluate the association between the nurse work environment and readmissions and length of stay for 175,467 hospitalized adult ischemic stroke patients. We utilized logistic regression models for readmission to estimate odds ratios (OR) and zero-truncated negative binomial models for length of stay to estimate the incident-rate ratio (IRR). Final models accounted for hospital and patient characteristics. Seven and 30-day readmission rates were 3.9% and 10.1% respectively and the average length of stay was 4.9 days. In hospitals with better nurse work environments ischemic stroke patients experienced lower odds of 7- and 30-day readmission (7-day OR, 0.96; 95% confidence interval [CI]: 0.93-0.99 and 30-day OR, 0.97; 95% CI: 0.94-0.99) and lower length of stay (IRR, 0.97; 95% CI: 0.95-0.99). The work environment is a modifiable feature of hospitals that should be considered when providing comprehensive stroke care and improving post-stroke outcomes.

Private choices, public costs: Evaluating cost-shifting between private and public health sectors in New Zealand

New Zealand's dual public-private health system allows individuals to purchase health services from the private sector rather than relying solely upon publicly-funded services. However, financial boundaries between the public and private sectors are not well defined and patients receiving privately-funded care may subsequently seek follow-up care within the public health system, in effect shifting costs to the public sector. This study evaluates this phenomenon, examining whether cost-shifting between the private and public hospital systems is a significant issue in New Zealand. We used inpatient discharge data from 2013/14 to identify private events with a subsequent admission to a public hospital within seven days of discharge. We examined the frequency of subsequent public admissions, the demographic and clinical characteristics of the patients and estimated the direct costs of inpatient care incurred by the public health system. Approximately 2% of private inpatient events had a subsequent admission to a public hospital. Overall, the costs to the public system amounted to NZ$11.5 million, with a median cost of NZ$2800. At least a third of subsequent admissions were related to complications of a medical procedure. Although only a small proportion of private events had a subsequent public admission, the public health system incurred significant costs, highlighting the need for greater understanding and discussion around the interface between the public and private health systems.

Can we predict early 7-day readmissions using a standard 30-day hospital readmission risk prediction model?

BACKGROUND

Despite focus on preventing 30-day readmissions, early readmissions (within 7 days of discharge) may be more preventable than later readmissions (8-30 days). We assessed how well a previously validated 30-day EHR-based readmission prediction model predicts 7-day readmissions and compared differences in strength of predictors.

METHODS

We conducted an observational study on adult hospitalizations from 6 diverse hospitals in North Texas using a 50-50 split-sample derivation and validation approach. We re-derived model coefficients for the same predictors as in the original 30-day model to optimize prediction of 7-day readmissions. We then compared the discrimination and calibration of the 7-day model to the 30-day model to assess model performance. To examine the changes in the point estimates between the two models, we evaluated the percent changes in coefficients.

RESULTS

Of 32,922 index hospitalizations among unique patients, 4.4% had a 7-day admission and 12.7% had a 30-day readmission. Our original 30-day model had modestly lower discrimination for predicting 7-day vs. any 30-day readmission (C-statistic of 0.66 vs. 0.69, p ≤ 0.001). Our re-derived 7-day model had similar discrimination (C-statistic of 0.66, p = 0.38), but improved calibration. For the re-derived 7-day model, discharge day factors were more predictive of early readmissions, while baseline characteristics were less predictive.

CONCLUSION

A previously validated 30-day readmission model can also be used as a stopgap to predict 7-day readmissions as model performance did not substantially change. However, strength of predictors differed between the 7-day and 30-day model; characteristics at discharge were more predictive of 7-day readmissions, while baseline characteristics were less predictive. Improvements in predicting early 7-day readmissions will likely require new risk factors proximal to day of discharge.

The Hospital Readmissions Reduction Program's Impact on Readmissions From Skilled Nursing Facilities

EXECUTIVE SUMMARY

Hospital readmissions have long served as an indicator of patient recovery and the effectiveness of care. The present study examines the Hospital Readmissions Reduction Program's (HRRP's) impact on hospital readmissions from skilled nursing facilities (SNFs) and the characteristics of SNFs that were predictive of lower readmission rates. Adjusted 30-day readmission rates among 14,666 SNFs in the United States from 2011 through 2015 were examined using linear regression with generalized estimating equations to determine the relationship of the HRRP mandate to readmission rates from SNFs. Findings indicate a significant downward trend in adjusted 30-day readmission rates over time, decreasing 1.4% from 2011 to 2015. Furthermore, lower readmission rates were associated with SNF characteristics including location in a hospital facility, rural designation, higher registered nurse-to-nurse ratios, and not-for-profit status. We found a substantial decrease in SNF-related readmissions associated with HRRP, which may limit the impact of the Protecting Access to Medicare Act. Policy-makers may consider these systemic and structural differences before drafting future legislation targeting hospital readmission from SNFs. In addition, acute care facility operators who do not have an SNF may consider adding one to their facility and/or consider partnering with SNFs to ensure that high-quality programs in these SNFs are in place to reduce 30-day readmissions to the acute care facilities.

Ability of the LACE index to predict 30-day hospital readmissions in patients with community-acquired pneumonia

Background and objective

Hospital readmissions within 30 days are used as an indicator of quality of hospital care. We aimed to evaluate the ability of the LACE (Length of stay, Acuity of admission, Comorbidities based on Charlson comorbidity score and number of Emergency visits in the last 6 months) index to predict the risk of 30-day readmissions in patients hospitalised for community-acquired pneumonia (CAP).

Methods

In this retrospective cohort study a LACE index score was calculated for patients with a principal diagnosis of CAP admitted to a tertiary hospital in Sydney, Australia. The predictive ability of the LACE score for 30-day readmissions was assessed using receiver operator characteristic curves with C-statistic.

Results

Of 3996 patients admitted to hospital for CAP at least once, 8.0% (n=327) died in hospital and 14.6% (n=584) were readmitted within 30 days. 17.8% (113 of 636) of all 30-day readmissions were again due to CAP, followed by readmissions for chronic obstructive pulmonary disease, heart failure and chest pain. The LACE index had moderate discriminative ability to predict 30-day readmission (C-statistic=0.6395) but performed poorly for the prediction of 30-day readmissions due to CAP (C-statistic=0.5760).

Conclusions

The ability of the LACE index to predict all-cause 30-day hospital readmissions is comparable to more complex pneumonia-specific indices with moderate discrimination. For the prediction of 30-day readmissions due to CAP, the performance of the LACE index and modified risk prediction models using readily available variables (sex, age, specific comorbidities, after-hours, weekend, winter or summer admission) is insufficient.

The LACE index is easy to use and its ability to predict all-cause 30-day hospital readmissions for patients hospitalised with community-acquired pneumonia is comparable to more complex pneumonia-specific indices with moderate discriminationhttps://bit.ly/2SYkxam

Assessing the Impacts of Misclassified Case-Mix Factors on Health Care Provider Profiling: Performance of Dialysis Facilities

Quantitative metrics are used to develop profiles of health care institutions, including hospitals, nursing homes, and dialysis clinics. These profiles serve as measures of quality of care, which are used to compare institutions and determine reimbursement, as a part of a national effort led by the Center for Medicare and Medicaid Services in the United States. However, there is some concern about how misclassification in case-mix factors, which are typically accounted for in profiling, impacts results. We evaluated the potential effect of misclassification on profiling results, using 20 744 patients from 2740 dialysis facilities in the US Renal Data System. In this case study, we compared 30-day readmission as the profiling outcome measure, using comorbidity data from either the Center for Medicare and Medicaid Services Medical Evidence Report (error-prone) or Medicare claims (more accurate). Although the regression coefficient of the error-prone covariate demonstrated notable bias in simulation, the outcome measure—standardized readmission ratio—and profiling results were quite robust; for example, correlation coefficient of 0.99 in standardized readmission ratio estimates. Thus, we conclude that misclassification on case-mix did not meaningfully impact overall profiling results. We also identified both extreme degree of case-mix factor misclassification and magnitude of between-provider variability as 2 factors that can potentially exert enough influence on profile status to move a clinic from one performance category to another (eg, normal to worse performer).

Pathways for Improving Inpatient Pediatric Asthma Care (PIPA): A Multicenter, National Study

BACKGROUND AND OBJECTIVES

Pathways guide clinicians through evidence-based care of specific conditions. Pathways have been demonstrated to improve inpatient asthma care but mainly in studies at large, tertiary children's hospitals. It remains unclear if these effects are generalizable across diverse hospital settings. Our objective was to improve inpatient asthma care by implementing pathways in a diverse, national sample of hospitals.

METHODS

We used a learning collaborative model. Pathway implementation strategies included local champions, external facilitators and/or mentors, educational seminars, quality improvement methods, and audit and feedback. Outcomes included length of stay (LOS) (primary), early administration of metered-dose inhalers, screening for secondhand tobacco exposure and referral to cessation resources, and 7-day hospital readmissions or emergency revisits (balancing). Hospitals reviewed a sample of up to 20 charts per month of children ages 2 to 17 years who were admitted with a primary diagnosis of asthma (12 months before and 15 months after implementation). Analyses were done by using multilevel regression models with an interrupted time series approach, adjusting for patient characteristics.

RESULTS

Eighty-five hospitals enrolled (40 children's and 45 community); 68 (80%) completed the study (n = 12 013 admissions). Pathways were associated with increases in early administration of metered-dose inhalers (odds ratio: 1.18; 95% confidence interval [CI]: 1.14-1.22) and referral to smoking cessation resources (odds ratio: 1.93; 95% CI: 1.27-2.91) but no statistically significant changes in other outcomes, including LOS (rate ratio: 1.00; 95% CI: 0.96-1.06). Most hospitals (65%) improved in at least 1 outcome.

CONCLUSIONS

Pathways did not significantly impact LOS but did improve quality of asthma care for children in a diverse, national group of hospitals.

A RCT evaluating a pragmatic in-hospital service to increase the quality of discharge prescriptions

OBJECTIVE

To improve discharge prescription quality and information transfer to improve post-hospital care with a pragmatic in-hospital service.

DESIGN

A single-centre, randomized controlled trial.

SETTING

Internal medicine wards in a Swiss teaching hospital.

PARTICIPANTS

Adult patients discharged to their homes, 76 each in the intervention and control group.

INTERVENTION

Medication reconciliation at discharge by a clinical pharmacist, a prescription check for formal flaws, interactions and missing therapy durations. Important information was annotated on the prescription.

MAIN OUTCOME MEASURES

At the time of medication dispensing, community pharmacy documented their pharmaceutical interventions when filling the prescription. A Poisson regression model was used to compare the number of interventions (primary outcome). The significance of the pharmaceutical interventions was categorized by the study team. Comparative analysis was used for the significance of interventions (secondary outcome).

RESULTS

The community pharmacy staff performed 183 interventions in the control group, and 169 in the intervention group. The regression model revealed a relative risk for an intervention of 0.78 (95% CI 0.62-0.99, p = 0.04) in the intervention group. The rate of clinically significant interventions was lower in the intervention group than in the control group (72 of 169 (42%) vs. 108 of 183 (59%), p < 0.01), but more economically significant interventions were performed (98, 58% vs. 80, 44%, p < 0.01).

CONCLUSIONS

The pragmatic in-hospital service increased the quality of prescriptions. The intervention group had a lower risk for the need for pharmaceutical interventions, and clinically significant interventions were less frequent. Overall, our pragmatic approach showed promising results to optimize post-discharge care.

Prediction of 7-Day Readmission Risk for Pediatric Trauma Patients

BACKGROUND

Pediatric patients admitted for trauma may have unique risk factors of unplanned readmission and require condition-specific models to maximize accuracy of prediction. We used a multicenter data set on trauma admissions to study risk factors and predict unplanned 7-day readmissions with comparison to the 30-day metric.

METHODS

Data from 28 hospitals in the United States consisting of 82,532 patients (95,158 encounters) were retrieved, and 75% of the data were used for building a random intercept, mixed-effects regression model, whereas the remaining were used for evaluating model performance. The variables included were demographics, payer, current and past health care utilization, trauma-related and other diagnoses, medications, and surgical procedures.

RESULTS

Certain conditions such as poisoning and medical/surgical complications during treatment of traumatic injuries are associated with increased odds of unplanned readmission. Conversely, trauma-related conditions, such as trauma to the thorax, knee, lower leg, hip/thigh, elbow/forearm, and shoulder/upper arm, are associated with reduced odds of readmission. Additional predictors include the current and past health care utilization and the number of medications. The corresponding 7-day model achieved an area under the receiver operator characteristic curve of 0.737 (0.716, 0.757) on an independent test set and shared similar risk factors with the 30-day version.

CONCLUSIONS

Patients with trauma-related conditions have risk of readmission modified by the type of trauma. As a result, additional quality of care measures may be required for patients with trauma-related conditions that elevate their risk of readmission.

On the relations between focus, experience, and hospital performance

BACKGROUND

In the United States, a long-standing debate has existed over advantages/disadvantages of general versus specialty hospitals. A recent stream of research has investigated whether general hospitals accrue performance benefits from a focus strategy; a strategy of specializing in certain clinical conditions while remaining a multiproduct firm. In contrast, a substantial and long-standing body of research on hospitals has been concerned with the absolute volume of cases in a service area as an indication of experience based largely on the idea that absolute volume confers learning opportunities.

PURPOSE

We investigated whether hospital focus and experience in a service area have complementary effects or are largely substitutive for hospital performance.

METHODOLOGY/APPROACH

Key data sources were patient discharge records and hospital discharge profiles from California's Office of Statewide Health Policy and Development for years 2010-2014. We specified hospital focus as the proportion of total cardiology-related discharges and hospital experience as the cumulative volume of cardiology-related discharges for each hospital. Performance was specified using quality (inpatient mortality and 30-day readmission) and efficiency (length of stay and cost) patient-level performance metrics. We analyzed the data using logistic and log-linear ordinary least squares regression models.

RESULTS

Study results generally supported our hypotheses that focus and experience are related to better quality and efficiency performance and that the effects are largely substitutive for hospitals.

CONCLUSION

Our study extends the literature by finding that hospitals exhibit distinct and stable patterns regarding their positioning on focus and experience and that these patterns have important implications for hospitals' performance in terms of quality and efficiency.

PRACTICE IMPLICATIONS

Many general hospitals in the United States may be stretched too thin across service areas for which they lack necessary patient volumes for clinical proficiency. A viable alternative is to select a limited set of service areas on which to focus.

Contribution of social factors to readmissions within 30 days after hospitalization for COPD exacerbation

BACKGROUND

To investigate whether, in patients hospitalized for COPD, the addition of social factors improves the predictive ability for the risk of overall 30-day readmissions, early readmissions (within 7 days after discharge), and late readmissions (8-30 days after discharge).

METHODS

Patients (aged ≥40 years) hospitalized for COPD were identified in the Medicare Current Beneficiary Survey from 2006 through 2012. With the use of 1000 bootstrap resampling from the original cohort (training-set), two prediction models were derived: 1) the reference model including age, comorbidities, and mechanical ventilation use, and 2) the optimized model including social factors (e.g., educational level, marital status) in addition to the covariates in the reference model. Prediction performance was examined separately for 30-day, early, and late readmissions.

RESULTS

Following 905 index hospitalizations for COPD, 18.5% were readmitted within 30 days. In the test-set, for overall 30-day readmissions, the discrimination ability between reference and optimized models did not change materially (C-statistic, 0.57 vs. 0.58). By contrast, for early readmissions, the optimized model had significantly improved discrimination (C-statistic, 0.57 vs. 0.63; integrated discrimination improvement [IDI], 0.018 [95%CI, 0.003-0.032]) and reclassification (continuous net reclassification index [NRI], 0.298 [95%CI 0.060-0.537]). Likewise, for late readmissions, the optimized model also had significantly improved discrimination (C-statistic, 0.65 vs. 0.68; IDI, 0.026 [95%CI 0.009-0.042]) and reclassification (continuous NRI, 0.243 [95%CI 0.028-0.459]).

CONCLUSIONS

In a nationally-representative sample of Medicare beneficiaries hospitalized for COPD, we found that the addition of social factors improved the predictive ability for readmissions when early and late readmissions were examined separately.

Perioperative Stroke and Readmissions Rates in Noncardiac Non-Neurologic Surgery

BACKGROUND AND AIM

Perioperative stroke is a feared and potentially disastrous complication of surgery. Postdischarge care, specifically hospital readmissions, can significantly impact postsurgical recovery and provides a useful metric for quality care. Our primary aim was examining 30-day readmissions for patients who had a perioperative stroke undergoing noncardiac non-neurosurgery.

METHODS

We analyzed data from the State Inpatient Database, a database of community hospital discharges, in California between 2008 and2011. Surgical patients undergoing one of the 10 highest-volume procedures were included; patients less than 18 years old, undergoing pregnancy-related procedures, or who died in-hospital were excluded. Our dataset covariates included demographic and clinical variables, comorbidities, and discharge location. After running an initial bivariate analysis using Chi-square and t-tests and testing for multicollinearity, logistical models were run to calculate adjusted odds ratios and confidence intervals for readmission predictors.

RESULTS

30-day readmissions for patients with perioperative stroke (n = 1613) occurred at a rate of 21.08% (340 patients), compared to 6.29% (63,856 patients) for patients without perioperative stroke (adjusted OR = 1.40, 95% CI 1.23-1.59, P < .0001). Demographic predictors of 30-day readmissions included male sex and African-American race. Clinical predictors of 30-day readmissions included several comorbidities (i.e. liver disease, hypertension), and discharge to a postacute care facility. Key 30-day readmission diagnoses for perioperative stroke patients included septicemia, stroke, aspiration pneumonitis, and urinary tract infections.

CONCLUSIONS

Patients with perioperative stroke have high 30-day readmissions rates. A number of demographic and clinical factors increase readmission risk in this population. Further research is warranted to better support patients with perioperative stroke undergoing care transitions.

Variability in Transitional Care Outcomes Across Hospitals Discharging Veterans to Skilled Nursing Facilities

BACKGROUND

The period after transition from hospital to skilled nursing facility (SNF) is high-risk, but variability in outcomes related to transitions across hospitals is not well-known.

OBJECTIVES

Evaluate variability in transitional care outcomes across Veterans Health Administration (VHA) and non-VHA hospitals for Veterans, and identify characteristics of high-performing and low-performing hospitals.

RESEARCH DESIGN

Retrospective observational study using the 2012-2014 Residential History File, which concatenates VHA, Medicare, and Medicaid data into longitudinal episodes of care for Veterans.

SUBJECTS

Veterans aged 65 or older who were acutely hospitalized in a VHA or non-VHA hospital and discharged to SNF; 1 transition was randomly selected per patient.

MEASURES

Adverse "transitional care" outcomes were a composite of hospital readmission, emergency department visit, or mortality within 7 days of hospital discharge.

RESULTS

Among the 365,942 Veteran transitions from hospital to SNF across 1310 hospitals, the composite outcome rate ranged from 3.3% to 23.2%. In multivariable analysis adjusting for patient characteristics, hospital discharge diagnosis and SNF category, no single hospital characteristic was significantly associated with the 7-day adverse outcomes in either VHA or non-VHA hospitals. Very few high or low-performing hospitals remained in this category across all 3 years. The increased odds of having a 7-day event due to being treated in a low versus high-performing hospital was similar to the odds carried by having an intensive care unit stay during the index admission.

CONCLUSIONS

While variability in hospital outcomes is significant, unmeasured care processes may play a larger role than currently measured hospital characteristics in explaining outcomes.