Preventability of Early Versus Late Hospital Readmissions in a National Cohort of General Medicine Patients

Hospital readmissions among adults living with and without HIV in the US: findings from the Nationwide Readmissions Database

Background

Thirty-day hospital readmission measures quality of care, but there are limited data among people with HIV (PWH) and people without HIV (PWoH) in the era of universal recommendation for antiretroviral therapy. We descriptively compared 30-day all-cause, unplanned readmission risk between PWH and PWoH.

Methods

A retrospective cohort study was conducted using the 2019 Nationwide Readmissions Database (2019/01/01-2019/12/31), an all-payer database that represents all US hospitalizations. Index (initial) admissions and readmissions were determined using US Centers for Medicare & Medicaid Services definitions. Crude and age-adjusted risk ratios (aRR) comparing the 30-day all-cause, unplanned readmission risk between PWH to PWoH were estimated using random effect logistic regressions and predicted marginal estimates. Survey weights were applied to all analyses.

Findings

We included 24,338,782 index admissions from 18,240,176 individuals. The median age was 52(IQR = 40-60) years for PWH and 61(IQR = 38-74) years for PWoH. The readmission risk was 20.9% for PWH and 12.2% for PWoH (age-adjusted-RR:1.88 [95%CI = 1.84-1.92]). Stratified by age and sex, young female (age 18-29 and 30-39 years) PWH had a higher readmission risk than young female PWoH (aRR = 3.50 [95%CI = 3.11-3.88] and aRR = 4.00 [95%CI = 3.67-4.32], respectively). While the readmission risk increased with age among PWoH, the readmission risk was persistently high across all age groups among PWH. The readmission risk exceeded 30% for PWH admitted for hypertensive heart disease, heart failure, and chronic kidney disease.

Interpretation

PWH have a disproportionately higher risk of readmission than PWoH, which is concerning given the aging profile of PWH. More efforts are needed to address readmissions among PWH.

Funding

US National Institutes of Health.

Patient engagement with an automated postdischarge text messaging program for improving care transitions

Automated text messaging is a promising approach to monitor patients after hospital discharge and avert readmissions; however, it is not known to what extent patients would engage with this type of program and whether engagement may vary based on patients' characteristics. Using data from a 30-day postdischarge texting program at a large university hospital, we examined engagement over time (operationalized as response rate to text messages) and patient characteristics associated with engagement. Of the 1324 patients in the study sample, 838 (63%) stayed in the program for the full duration. Among those retained, the median response rate was 33% (interquartile range: 11%-77%) and decreased over time. Patients who were male (p < .05), were Black/African American (p < .001), had lower health literacy (p < .01), or had not recently logged into the patient portal (p < .001), all had lower response rates. Results support closer examinations of patient engagement in hospital-based texting programs and who is positioned to benefit.

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.

Improving the Discharge Process at the VA Palo Alto Through Change Management and Implementation of Project Re-Engineered Discharge

ABSTRACT

This quality improvement project demonstrates that nursing leadership with Project Re-Engineered Discharge can effect change in the discharge process and improve patient outcomes.

Length of stay in the clinical wards in a hospital after introducing a multiprofessional discharge team: An effectiveness improvement report

INTRODUCTION

Emergency overcrowding is a problem in hospitals worldwide. The expansion of wards has limitations. Hospital administrative leaders are constantly looking for opportunities to improve the efficiency of resource use.

METHODS

This is a care improvement study with a quasi-experimental design. We created a hospital discharge team (HDT) to solve the issues of prolonged hospital stays. The main interventions were active search and resolution of prolongation of stay and multi-disciplinary huddles. We developed strategies with different hospital units to expedite the processing of patients near discharge. Length of stay (LOS), morning hospital discharges, readmission rates, and bed usage were compared before (2018) and after (2019) HDT implementation.

RESULTS

There was a reduction in the mean LOS of 1.8 days (95% confidence interval [CI] -0.9 to -2.6; p < .001). The rate of hospital discharges before noon increased by 7.0% (95% CI 4%-11%; p < .001). The readmission rate was similar between 2018 and 2019 (+0.7%; 95% CI -0.1% to 1.9%; p = .358). We observed higher bed turnover, with 0.5 more hospitalizations per bed per month (95% CI 0.1-0.7; p = .01; mean of 3.7 ± 0.3 in 2018 and 4.1 ± 0.3 in 2019).

CONCLUSION

HDT brought benefits to our hospital, reducing the length of stay and increasing bed turnover. However, there is a need for a team focused on the project and support from managers to overcome resistance and integrate units until they are fully operational.

Understanding Potentially Preventable 7-day Readmission Rates in Hospital Medicine Patients at a Comprehensive Cancer Center

This study aimed to describe the potentially preventable 7-day unplanned readmission (PPR) rate in medical oncology patients. A retrospective analysis of all unplanned 7-day readmissions within Hospital Medicine at MD Anderson Cancer Center from September 1, 2020 to February 28, 2021, was performed. Readmissions were independently analyzed by 2 randomly selected individuals to determine preventability. Discordant reviews were resolved by a third reviewer to reach a consensus. Statistical analysis included 138 unplanned readmissions. The estimated PPR rate was 15.94%. The median age was 62.50 years; 52.90% were female. The most common type of cancer was noncolon GI malignancy (34.06%). Most patients had stage 4 cancer (69.57%) and were discharged home (64.93%). Premature discharge followed by missed opportunities for goals of care discussions were the most cited reasons for potential preventability. These findings highlight areas where care delivery can be improved to mitigate the risk of readmission within the medical oncology population.

Association between hospital admission either directly or via the emergency department, and readmission rates at 30 days in older adults in two rural hospitals: a retrospective cohort study

INTRODUCTION

Older patients are frequently re-admitted to the hospital after attending the emergency department (ED). We investigated whether direct admission to the hospital was associated with a lower risk of readmission at 30 days compared to admission via the ED, in patients aged ≥ 75 years.

METHODS

Retrospective multicenter cohort study from 01/01/2018 to 31/12/2019, including patients aged ≥ 75 years from two hospitals. Patients admitted directly were matched 1:1 with patients admitted via the ED for center, age category, sex, major diagnosis category, type of stay (medical/surgical), and severity. We compared readmission at 30 days (primary outcome) and length of stay (secondary outcome) between groups.

RESULTS

A total of 1486 matched patients with an available outcome measure were included for analysis. We observed no significant difference in 30-day readmission rate between those admitted directly (102/778, 13.1%) and those admitted via the ED (87/708, 12.3%, p = 0.63). There was a significant difference in length of stay between both groups: median 5 days [Q1-Q3: 2-8] vs 6 days [2-11] for direct and ED admissions, respectively (effect size: 0.11, p < 0.001). By multivariate analysis, only moderate to severe denutrition was associated with the risk of readmission at 30 days (Odds Ratio 2.133, 95% Confidence Interval 1.309-3.475).

CONCLUSION

The mode of entry to the hospital of patients aged 75 years and older was not associated with the risk of readmission at 30 days. However, those admitted directly had a significantly shorter length of stay than those admitted via the ED.

Opportunities for Diagnostic Improvement Among Pediatric Hospital Readmissions

OBJECTIVES

Diagnostic errors, termed "missed opportunities for improving diagnosis" (MOIDs), are known sources of harm in children but have not been well characterized in pediatric hospital medicine. Our objectives were to systematically identify and describe MOIDs among general pediatric patients who experienced hospital readmission, outline improvement opportunities, and explore factors associated with increased risk of MOID.

PATIENTS AND METHODS

Our retrospective cohort study included unplanned readmissions within 15 days of discharge from a freestanding children's hospital (October 2018-September 2020). Health records from index admissions and readmissions were independently reviewed and discussed by practicing inpatient physicians to identify MOIDs using an established instrument, SaferDx. MOIDs were evaluated using a diagnostic-specific tool to identify improvement opportunities within the diagnostic process.

RESULTS

MOIDs were identified in 22 (6.3%) of 348 readmissions. Opportunities for improvement included: delay in considering the correct diagnosis (n = 11, 50%) and failure to order needed test(s) (n = 10, 45%). Patients with MOIDs were older (median age: 3.8 [interquartile range 1.5-11.2] vs 1.0 [0.3-4.9] years) than patients without MOIDs but similar in sex, primary language, race, ethnicity, and insurance type. We did not identify conditions associated with higher risk of MOID. Lower respiratory tract infections accounted for 26% of admission diagnoses but only 1 (4.5%) case of MOID.

CONCLUSIONS

Standardized review of pediatric readmissions identified MOIDs and opportunities for improvement within the diagnostic process, particularly in clinician decision-making. We identified conditions with low incidence of MOID. Further work is needed to better understand pediatric populations at highest risk for MOID.

Thirty-Day Unplanned Hospital Readmissions in Patients With Cancer and the Impact of Social Determinants of Health: A Machine Learning Approach

PURPOSE

Develop a cancer-specific machine learning (ML) model that accurately predicts 30-day unplanned readmissions in patients with solid tumors.

METHODS

The initial cohort included patients 18 years or older diagnosed with a solid tumor. Two distinct cohorts were generated: one with and one without detailed social determinants of health (SDOHs) data. For each cohort, data were temporally partitioned in 70% (training), 20% (validation), and 10% (testing). Tree-based ML models were developed and validated on each cohort. The metrics used to evaluate the model's performance were receiver operating characteristic curve (ROC), area under the ROC curve, precision, recall (R), accuracy, and area under the precision-recall curve.

RESULTS

We included 13,717 patients in this study in two cohorts (5,059 without SDOH data and 8,658 with SDOH data). Unplanned 30-day readmission occurred in 21.3% of the cases overall. The five main non-SDOH factors most highly associated with an unplanned 30-day readmission (R, 0.74; IQR, 0.58-0.76) were: number of previous unplanned readmissions; higher Charlson comorbidity score; nonelective index admission; discharge to anywhere other than home, hospice, or nursing facility; and higher anion gap during the admission. Neighborhood crime index, neighborhood median home values, annual income, neighborhood median household income, and wealth index were the main five SDOH factors important for predicting a high risk for an unplanned hospital readmission (R, 0.66; IQR, 0.56-0.72). The models were not directly comparable.

CONCLUSION

Key drivers of unplanned readmissions in patients with cancer are complex and involve both clinical factors and SDOH. We developed a cancer-specific ML model that with reasonable accuracy identified patients with cancer at high risk for an unplanned hospital readmission.

Why do we evaluate 30-day readmissions in general medicine? A historical perspective and contemporary data

Reducing preventable readmissions is important to help manage current strains on healthcare systems. The metric of 30-day readmissions is commonly cited in discussions regarding this topic. While such thresholds have contemporary funding implications, the rationale for individual cut-off points is partially historical in nature. Through the examination of the basis for the analysis of 30-day readmissions, greater insight into the possible benefits and limitations of such a metric may be obtained.

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.

Non-alcoholic fatty liver disease is associated with greater risk of 30-day hospital readmission in the United States (U.S.)

INTRODUCTION AND OBJECTIVES

Data about 30-day readmission for patients with chronic liver disease (CLD) and their contribution to CLD healthcare burden are sparse. Patterns, diagnoses, timing and predictors of 30-day readmissions for CLD from 2010-2017 were assessed.

MATERIALS AND METHODS

Nationwide Readmission Database (NRD) is an all-payer, all-ages, longitudinal administrative database, representing 35 million discharges in the US population yearly. We identified unique patients discharged with CLD including hepatitis B (HBV) and C (HCV), alcoholic liver disease (ALD) and non-alcoholic fatty liver disease (NAFLD) from 2010 through 2017. Survey-weight adjusted multivariable were used.

RESULTS

From 2010 to 2017, the 30-day readmission rate for CLD decreased from 18.4% to 17.8% (p=.008), while increased for NAFLD from 17.0% to 19. 9% (p<.001). Of 125,019 patients discharged with CLD (mean age 57.4 years, male 59.0%) in 2017, the most common liver disease was HCV (29.2%), followed by ALD (23.5%), NAFLD (17.5%), and HBV (4.3%). Readmission rates were 20.5% for ALD, 19.9% for NAFLD, 16.8% for HCV and 16.7% for HBV. Compared to other liver diseases, patients with NAFLD had significantly higher risk of 30-day readmission in clinical comorbidities adjusted model (Hazard ratio [HR]=1.08 [95% confidence interval 1.03-1.13]). In addition to ascites, hepatic encephalopathy, higher number of coexisting comorbidities, comorbidities associated with higher risk of 30-day readmission included cirrhosis for NALFD and HCV; acute kidney injury for NAFLD, HCV and ALD; HCC for HCV, and peritonitis for ALD. Cirrhosis and cirrhosis-related complications was the most common reason for 30-day readmission, followed by sepsis. However, a large proportion of patients (43.7% for NAFLD; 28.4% for HCV, 39.0% for HBV, and 29.1% for ALD) were readmitted for extrahepatic reasons. Approximately 20% of those discharged with CLD were readmitted within 30 days but the majority of readmissions occurred within 15 days of discharge (62.8% for NAFLD, 63.7% for HCV, 74.3% for HBV, and 72.9% for ALD). Among readmitted patients, patients admitted ≤30-day had significantly higher cost and risk of in-hospital mortality for patients with NAFLD (+5.69% change [95% confidence interval, 2.54%-8.93%] and odds ratio (OR)=1.58 [1.28-1.95]) and HCV (+9.85% change [6.96%-12.82%] and OR=1.31, 1.08-1.59.

CONCLUSIONS

Early readmissions for CLD are prevalent causing economic and clinical burden to the US healthcare system, especially NAFLD readmissions. Closer surveillance and attention to both liver and extrahepatic medical conditions immediately after CLD discharge is encouraged.

Partnering with patients and families living with chronic conditions to coproduce diagnostic safety through OurDX: a previsit online engagement tool

OBJECTIVE

Patients and families are key partners in diagnosis, but methods to routinely engage them in diagnostic safety are lacking. Policy mandating patient access to electronic health information presents new opportunities. We tested a new online tool ("OurDX") that was codesigned with patients and families, to determine the types and frequencies of potential safety issues identified by patients/families with chronic health conditions and whether their contributions were integrated into the visit note.

METHODS

Patients/families at 2 US healthcare sites were invited to contribute, through an online previsit survey: (1) visit priorities, (2) recent medical history/symptoms, and (3) potential diagnostic concerns. Two physicians reviewed patient-reported diagnostic concerns to verify and categorize diagnostic safety opportunities (DSOs). We conducted a chart review to determine whether patient contributions were integrated into the note. We used descriptive statistics to report implementation outcomes, verification of DSOs, and chart review findings.

RESULTS

Participants completed OurDX reports in 7075 of 18 129 (39%) eligible pediatric subspecialty visits (site 1), and 460 of 706 (65%) eligible adult primary care visits (site 2). Among patients reporting diagnostic concerns, 63% were verified as probable DSOs. In total, probable DSOs were identified by 7.5% of pediatric and adult patients/families with underlying health conditions, respectively. The most common types of DSOs were patients/families not feeling heard; problems/delays with tests or referrals; and problems/delays with explanation or next steps. In chart review, most clinician notes included all or some patient/family priorities and patient-reported histories.

CONCLUSIONS

OurDX can help engage patients and families living with chronic health conditions in diagnosis. Participating patients/families identified DSOs and most of their OurDX contributions were included in the visit note.

Comparison of 30-day planned and unplanned readmissions in a tertiary teaching hospital in China

PURPOSE

The purpose of this study was to analyze and compare the clinical characteristics of patients with 30-day planned and unplanned readmissions and to identify patients at high risk for unplanned readmissions. This will facilitate a better understanding of these readmissions and improve and optimize resource utilization for this patient population.

METHODS

A retrospective cohort descriptive study was conducted at the West China Hospital (WCH), Sichuan University from January 1, 2015, to December 31, 2020. Discharged patients (≥ 18 years old) were divided into unplanned readmission and planned readmission groups according to 30-day readmission status. Demographic and related information was collected for each patient. Logistic regression analysis was used to assess the association between unplanned patient characteristics and the risk of readmission.

RESULTS

We identified 1,118,437 patients from 1,242,496 discharged patients, including 74,494 (6.7%) 30-day planned readmissions and 9,895 (0.9%) unplanned readmissions. The most common diseases of planned readmissions were antineoplastic chemotherapy (62,756/177,749; 35.3%), radiotherapy sessions for malignancy (919/8,229; 11.2%), and systemic lupus erythematosus (607/4,620; 13.1%). The most common diseases of unplanned readmissions were antineoplastic chemotherapy (2038/177,747; 1.1%), age-related cataract (1061/21,255; 5.0%), and unspecified disorder of refraction (544/5,134; 10.6%). There were statistically significant differences between planned and unplanned readmissions in terms of patient sex, marital status, age, length of initial stay, the time between discharge, ICU stay, surgery, and health insurance.

CONCLUSION

Accurate information on 30-day planned and unplanned readmissions facilitates effective planning of healthcare resource allocation. Identifying risk factors for 30-day unplanned readmissions can help develop interventions to reduce readmission rates.

Effects of a transitional care intervention on readmission among older medical inpatients: a quasi-experimental study

PURPOSE

To evaluate the effect of a transitional care intervention (TCI) on readmission among older medical inpatients.

METHODS

This non-randomised quasi-experimental study was conducted at Horsens Regional Hospital in Denmark from 1 February 2017 to 31 December 2018. Inclusion criteria were patients ≥ 75 years old admitted for at least 48 h. First, patients were screened for eligibility. Then, the allocation to the intervention or control group was performed according to the municipality of residence. Patients living in three municipalities were offered the hospital-based intervention, and patients living in a fourth municipality were allocated to the control group. The intervention components were (1) discharge transportation with a home visit, (2) a post-discharge cross-sectorial video conference and (3) seven-day telephone consultation. The primary outcome was 30-day unplanned readmission. Secondary outcomes were 30- and 90-day mortality and days alive and out of hospital (DAOH).

RESULTS

The study included 1205 patients (intervention: n = 615; usual care: n = 590). In the intervention group, the median age was 84.3 years and 53.7% were females. In the control group, the median age was 84.9 years and 57.5% were females. The 30-day readmission rates were 20.8% in the intervention group and 20.2% in the control group. Adjusted relative risk was 1.00 (95% confidence interval: 0.80, 1.26; p = 0.99). No significant difference was found between the groups for the secondary outcomes.

CONCLUSION

The TCI did not impact readmission, mortality or DAOH. Future research should conduct a pilot test, address intervention fidelity and consider real-world challenges.

TRIAL REGISTRATION

Clinical trial number: NCT04796701. Registration date: 24 February 2021.

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 trends before and after a national reconfiguration of emergency departments in Denmark

OBJECTIVE

In order to achieve better and more efficient emergency health care, the Danish public hospital system has been reconfigured, with hospital emergency care being centralised into extensive and specialised emergency departments. This article examines how this reconfiguration has affected patient readmission rates.

METHODS

We included all unplanned hospital admissions (aged ≥18 years) at public, non-psychiatric hospitals in four geographical regions in Denmark between 1 January 2007 and 24 December 2017. Using an interrupted time-series design, we examined trend changes in the readmission rates. In addition to analysing the overall effect, analyses stratified according to admission time of day and weekdays/weekends were conducted. The analyses were adjusted for patient characteristics and other system changes.

RESULTS

The seven-day readmission rate increased from 2.6% in 2007 to 3.8% in 2017, and the 30-day rate increased from 8.1% to 11.5%. However, the rates were less than what they would have been had the reconfiguration not been introduced. The reconfiguration reduced the seven-day readmission rate by 1.4% annually (hazard ratio [CI 95%] 0.986 [0.981-0.991]) and the 30-day rate by 1% annually (hazard ratio [CI 95%] 0.99 [0.987-0.993]).

CONCLUSIONS

Reconfiguration reduced the rate of increase in readmissions, but nevertheless readmissions still increased across the study period. It seems hospitals and policymakers will need to identify further ways to reduce patient loads.

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.

The Effect of Nurse Staffing on Patient Outcomes in Acute Care Hospitals in Korea

Nurse staffing is an important factor influencing patient health outcomes. This study aimed to analyze the effects of nurse staffing on patient health outcomes, such as length of stay, mortality within 30 days of hospitalization, and readmission within 7 days of discharge, in acute care hospitals in Korea. Data from the first quarter of 2018 were collected using public and inpatient sample data from the Health Insurance Review and Assessment Service. The data of 46,196 patients admitted to 536 general wards of acute care hospitals were analyzed. A multilevel logistic analysis was performed for the patients' mortality and early readmission, and a multilevel zero-truncated negative binomial analysis was performed for the length of stay. The average length of stay in acute care hospitals was 6.54 ± 6.03 days, the mortality rate was 1.1%, and the early readmission rate was 7.1%. As the nurse staffing level increased, the length of stay and number of early readmissions were likely to decrease. It can be concluded that interventions to improve nurse staffing are required; for example, a policy that compels medical institutions to comply with Korea's medical law standards should be implemented. Additionally, continuous research and interventions are needed to establish an appropriate nurse staffing level according to patient severity.

Readmission Reduction as a Hospital Quality Measure: Time to Move on to More Pressing Concerns?

The authors of this Viewpoint argue that the focus on hospital readmission rates as a measure of quality during the past decade, although undoubtedly leading to some improvements in care, has had minimal demonstrable benefit and has even distracted clinicians and health system leaders from other crucial quality concerns.

Secular trend and risk factors of 30-day COPD-related readmission in Beijing, China

Readmission due to chronic obstructive pulmonary disease (COPD) exacerbation contributes significantly to disease burden. Trend in readmission rate among COPD patients in China is not well characterized. We described the secular trend and identify risk factors of COPD-related 30-day readmission in Beijing during 2012–2017. In this retrospective cohort study, we used data from a citywide hospital discharge database in Beijing. We included patients ≥ 40 years with a primary diagnosis of COPD from 2012 to 2017. A total of 131 591 index admissions were identified. COPD-related 30-day readmission was defined as the initial admission with a primary diagnosis of COPD that occurs within 30 days from the discharge date of an index admission. Overall and annual 30-day readmission rates were calculated in the total population and subgroups defined by patient characteristics. We used multivariable logistic models to investigate risk factors for readmission and in-hospital mortality within 30 days. The overall 30-day COPD-related readmission rate was 15.8% (n = 20 808). The readmission rate increased from 11.5% in 2012 to 17.2% in 2017, with a multivariable-adjusted OR (95% CI) for annual change to be 1.08 (1.06–1.09) (P trend < 0.001). The upward trend in readmission rate levelled off at about 17% since 2014. The readmission rate of men was higher and increased faster than women. Comorbid osteoporosis, coronary heart disease, congestive heart failure, and cancer were associated with an increased risk of 30-day COPD-related readmission. The 30-day COPD-related readmission rate in Beijing showed an overall increasing trend from 2012 to 2017. Future efforts should be made to further improve care quality and reduce early readmissions of COPD patients.

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.

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.

Why did you come back to the hospital? A qualitative analysis of 72-hour readmissions

OBJECTIVES

Readmissions occurring within a few days of discharge are more likely due to a problem from the patient's original admission and may be preventable by interventions in the hospital setting. As part of a quality improvement project intended to reduce readmissions within 72 hours of discharge our objective was to explore patient and physician perspectives of reasons for readmissions and to identify potential indicators of readmission during the index admission.

METHODS

A retrospective chart review of all readmissions within 72 hours between 2/1/2019 and 6/7/2019 in our healthcare system comprised of an academic medical center and 2 smaller community hospitals. As part of a hospital protocol, patients readmitted within 30 days were interviewed by a social worker regarding reasons for readmission and their perspective on what might have prevented it. These answers, physician notes relevant to the reason for readmission and the clinical course of the index admission were abstracted from patient charts. For the subset of patients identified by themselves or their physicians as potentially benefitting from a longer hospitalization, their index admission was reviewed for indicators of readmission. Reasons for readmission, potential preventive measures, and indicators of readmission were independently reviewed by two authors then grouped into common themes by consensus.

RESULTS

One hundred and thirty-one patients readmitted within 72 hours were identified. Most patients were readmitted for infection related, cardiac or pulmonary reasons. Extending the initial admission was the most common factor suggested by both patients and physicians to prevent readmission. Focusing on 70 patients who may have benefited from a longer admission, indicators included patients not returning to their baseline health status, inadequate management of a known issue, or new symptoms developing during the index admission.

CONCLUSIONS

Patients should be evaluated for indicators of readmission, which may help guide decisions to discharge patients and decrease rates of 72-hour readmissions.

Miller N, L. Pecina J, D. Decker M, I. Wi C, Garrison GM. Association of Early and Late Hospital Readmissions with a Novel Housing-Based Socioeconomic Measure

Background

While socioeconomic status has been linked to hospital readmissions for several conditions, reliable measures of individual socioeconomic status are often not available. HOUSES, a new measure of individual socioeconomic status based upon objective public data about one's housing unit, is inversely associated with overall hospitalization rate but it has not been studied with respect to readmissions.

Purpose

To determine if patients in the lowest HOUSES quartile are more likely to be readmitted within 30 days (short-term) and 180 days (long-term).

Methods

A retrospective cohort study of 11 993 patients having 21 633 admissions was conducted using generalized linear mixed-effects models.

Results

HOUSES quartile did not show any significant association with early readmission. However, when compared to the lowest HOUSES quartile, the second quartile (OR = 0.90, 95%CI 0.83-0.98) and the third quartile (OR = 0.91, 95%CI 0.83-0.99) were associated with lower odds of late readmission while the highest quartile (OR = 0.91, 95%CI 0.82-1.01) was not statistically different.

Conclusion

HOUSES was associated with late readmission, but not early readmission. This may be because early readmissions are influenced by medical conditions and hospital care while late readmissions are influenced by ambulatory care and home-based factors. Since HOUSES relies on public county assessor data, it is generally available and may be used to focus interventions on those at highest risk for late readmission.

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.

Incidence and causes of early unplanned readmission after hospitalisation with peripheral arterial disease in Australia and New Zealand

OBJECTIVE

To evaluate the characteristics and predictors of unplanned readmission within 30 days of hospitalisation for the treatment of peripheral arterial disease (PAD) in Australia and New Zealand.

DESIGN

Analysis of hospitalisations data in the Admitted Patient Collection for each Australian state and territory and the New Zealand National Minimum Dataset (Hospital Events).

SETTING

All public and 80% of private hospitals in Australia and New Zealand.

PARTICIPANTS

Adults (18 years or older) hospitalised with a primary or conditional secondary diagnosis of PAD during 1 January 2010 - 31 December 2015.

MAIN OUTCOME MEASURE

Rate of unplanned readmission (any cause) within 30 days of hospitalisation with PAD.

RESULTS

Of 104 979 admissions included in our analysis (mean patient age, 73.7 years; SD, 12.4 years), 9765 were followed by at least one unplanned readmission within 30 days of discharge (9.3%): 3395 within one week (34.8%) and 7828 within three weeks (80.2%). The most frequent readmission primary diagnoses were atherosclerosis (1477, 15.3%), type 2 diabetes (1057, 10.8%), and "complications of procedures not elsewhere classified" (963, 9.9%). Readmission was more frequent after acute (4830 of 26 304, 18.4%) than elective PAD hospitalisations (4935 of 78 675, 6.3%), but the readmission characteristics were similar. Factors associated with greater likelihood of readmission included acute PAD hospitalisations (odds ratio [OR], 2.04; 95% CI, 1.96-2.17), surgical intervention during the PAD hospitalisation (OR, 1.74; 95% CI, 1.64-1.84), and chronic limb-threatening ischaemia (OR, 1.55; 95% CI, 1.47-1.63).

CONCLUSION

Unplanned readmissions within 30 days of hospitalisation for PAD are often for potentially preventable reasons. Their number should be reduced to improve clinical outcomes for people with PAD.

Association Rules Mining for Hospital Readmission: A Case Study

As an indicator of healthcare quality and performance, hospital readmission incurs major costs for healthcare systems worldwide. Understanding the relationships between readmission factors, such as input features and readmission length, is challenging following intricate hospital readmission procedures. This study discovered the significant correlation between potential readmission factors (threshold of various settings for readmission length) and basic demographic variables. Association rule mining (ARM), particularly the Apriori algorithm, was utilised to extract the hidden input variable patterns and relationships among admitted patients by generating supervised learning rules. The mined rules were categorised into two outcomes to comprehend readmission data; (i) the rules associated with various readmission length and (ii) several expert-validated variables related to basic demographics (gender, race, and age group). The extracted rules proved useful to facilitate decision-making and resource preparation to minimise patient readmission.

Predictive modeling for 14-day unplanned hospital readmission risk by using machine learning algorithms

BACKGROUND

Early unplanned hospital readmissions are associated with increased harm to patients, increased medical costs, and negative hospital reputation. With the identification of at-risk patients, a crucial step toward improving care, appropriate interventions can be adopted to prevent readmission. This study aimed to build machine learning models to predict 14-day unplanned readmissions.

METHODS

We conducted a retrospective cohort study on 37,091 consecutive hospitalized adult patients with 55,933 discharges between September 1, 2018, and August 31, 2019, in an 1193-bed university hospital. Patients who were aged < 20 years, were admitted for cancer-related treatment, participated in clinical trial, were discharged against medical advice, died during admission, or lived abroad were excluded. Predictors for analysis included 7 categories of variables extracted from hospital's medical record dataset. In total, four machine learning algorithms, namely logistic regression, random forest, extreme gradient boosting, and categorical boosting, were used to build classifiers for prediction. The performance of prediction models for 14-day unplanned readmission risk was evaluated using precision, recall, F1-score, area under the receiver operating characteristic curve (AUROC), and area under the precision-recall curve (AUPRC).

RESULTS

In total, 24,722 patients were included for the analysis. The mean age of the cohort was 57.34 ± 18.13 years. The 14-day unplanned readmission rate was 1.22%. Among the 4 machine learning algorithms selected, Catboost had the best average performance in fivefold cross-validation (precision: 0.9377, recall: 0.5333, F1-score: 0.6780, AUROC: 0.9903, and AUPRC: 0.7515). After incorporating 21 most influential features in the Catboost model, its performance improved (precision: 0.9470, recall: 0.5600, F1-score: 0.7010, AUROC: 0.9909, and AUPRC: 0.7711).

CONCLUSIONS

Our models reliably predicted 14-day unplanned readmissions and were explainable. They can be used to identify patients with a high risk of unplanned readmission based on influential features, particularly features related to diagnoses. The operation of the models with physiological indicators also corresponded to clinical experience and literature. Identifying patients at high risk with these models can enable early discharge planning and transitional care to prevent readmissions. Further studies should include additional features that may enable further sensitivity in identifying patients at a risk of early unplanned readmissions.

Clinical characteristics and risk factors of preventable hospital readmissions within 30 days

Knowledge regarding preventable hospital readmissions is scarce. Our aim was to compare the clinical characteristics of potentially preventable readmissions (PPRs) with non-PPRs. Additionally, we aimed to identify risk factors for PPRs. Our study included readmissions within 30 days after discharge from 1 of 7 hospital departments. Preventability was assessed by multidisciplinary meetings. Characteristics of the readmissions were collected and 23 risk factors were analyzed. Of the 1120 readmissions, 125 (11%) were PPRs. PPRs occurred equally among different departments (p = 0.21). 29.6% of PPRs were readmitted by a practitioner of a different medical specialty than the initial admission (IA) specialist. The PPR group had more readmissions within 7 days (PPR 54% vs. non-PPR 44%, p = 0.03). The median LOS was 1 day longer for PPRs (p = 0.16). Factors associated with PPR were higher age (p = 0.004), higher socio-economic status (p = 0.049), fewer prior hospital admissions (p = 0.004), and no outpatient visit prior to readmission (p = 0.025). This study found that PPRs can occur at any department in the hospital. There is not a single type of patient that can easily be pinpointed to be at risk of a PPR, probably due to the multifactorial nature of PPRs.

Post Discharge mHealth and Teach-Back Communication Effectiveness on Hospital Readmissions: A Systematic Review

Hospital readmissions pose a threat to the constrained health resources, especially in resource-poor low-and middle-income countries. In such scenarios, appropriate technologies to reduce avoidable readmissions in hospitals require innovative interventions. mHealth and teach-back communication are robust interventions, utilized for the reduction in preventable hospital readmissions. This review was conducted to highlight the effectiveness of mHealth and teach-back communication in hospital readmission reduction with a view to provide the best available evidence on such interventions. Two authors independently searched for appropriate MeSH terms in three databases (PubMed, Wiley, and Google Scholar). After screening the titles and abstracts, shortlisted manuscripts were subjected to quality assessment and analysis. Two authors checked the manuscripts for quality assessment and assigned scores utilizing the QualSyst tool. The average of the scores assigned by the reviewers was calculated to assign a summary quality score (SQS) to each study. Higher scores showed methodological vigor and robustness. Search strategies retrieved a total of 1932 articles after the removal of duplicates. After screening titles and abstracts, 54 articles were shortlisted. The complete reading resulted in the selection of 17 papers published between 2002 and 2019. Most of the studies were interventional and all the studies focused on hospital readmission reduction as the primary or secondary outcome. mHealth and teach-back communication were the two most common interventions that catered for the hospital readmissions. Among mHealth studies (11 out of 17), seven studies showed a significant reduction in hospital readmissions while four did not exhibit any significant reduction. Among the teach-back communication group (6 out of 17), the majority of the studies (5 out of 6) showed a significant reduction in hospital readmissions while one publication did not elicit a significant hospital readmission reduction. mHealth and teach-back communication methods showed positive effects on hospital readmission reduction. These interventions can be utilized in resource-constrained settings, especially low- and middle-income countries, to reduce preventable readmissions.

Early (0-7 day) and late (8-30 day) readmission predictors in acute coronary syndrome, atrial fibrillation, and congestive heart failure patients

OBJECTIVES

Thirty-day readmission following hospitalization for acute coronary syndrome (ACS), atrial fibrillation (AF), or congestive heart failure (CHF) is common, and many occur within one week of discharge. Using a cohort of patients hospitalized for ACS, AF, or CHF, we sought to identify predictors of 30-day, early (0-7 day), and late (8-30 day) all-cause readmission.

METHODS

We identified 3531 hospitalizations for ACS, AF, or CHF at a large academic medical center between 2008 and 2018. Multivariable logistic regression models were created to identify predictors of 30-day, early, and late unplanned, all-cause readmission, adjusting for discharge diagnosis and other demographics and comorbidities.

RESULTS

Of 3531 patients hospitalized for ACS, AF, or CHF, 700 (19.8%) were readmitted within 30 days, and 205 (29.3%) readmissions were early. Of all 30-day readmissions, 34.8% of ACS, 16.8% of AF, and 26.0% of the CHF cohorts' readmissions occurred early. Higher hemoglobin was associated with lower 30-day readmission [adjusted (adj) OR 0.92, 95% CI 0.88-0.97] while patients requiring intensive care unit (ICU) admission were more likely readmitted within 30 days (adj OR 1.31, 95% CI 1.03-1.67). Among patients with a 30-day readmission, females (adj OR 1.73, 95% CI 1.22, 2.47) and patients requiring ICU admission (adj OR 2.03, 95% CI 1.27, 3.26) were more likely readmitted early than late. Readmission predictors did not vary substantively by discharge diagnosis.

CONCLUSION

Patients admitted to the ICU were more likely readmitted in the early and 30-day periods. Other predictors varied between readmission groups. Since outpatient follow-up often occurs beyond 1 week of discharge, early readmission predictors can help healthcare providers identify patients who may benefit from particular post-discharge services.

Man vs. Machine: Comparing Physician vs. Electronic Health Record-Based Model Predictions for 30-Day Hospital Readmissions

BACKGROUND

Electronic health record (EHR)-based readmission risk prediction models can be automated in real-time but have modest discrimination and may be missing important readmission risk factors. Clinician predictions of readmissions may incorporate information unavailable in the EHR, but the comparative usefulness is unknown. We sought to compare clinicians versus a validated EHR-based prediction model in predicting 30-day hospital readmissions.

METHODS

We conducted a prospective survey of internal medicine clinicians in an urban safety-net hospital. Clinicians prospectively predicted patients' 30-day readmission risk on 5-point Likert scales, subsequently dichotomized into low- vs. high-risk. We compared human with machine predictions using discrimination, net reclassification, and diagnostic test characteristics. Observed readmissions were ascertained from a regional hospitalization database. We also developed and assessed a "human-plus-machine" logistic regression model incorporating both human and machine predictions.

RESULTS

We included 1183 hospitalizations from 106 clinicians, with a readmission rate of 20.8%. Both clinicians and the EHR model had similar discrimination (C-statistic 0.66 vs. 0.66, p = 0.91). Clinicians had higher specificity (79.0% vs. 48.9%, p < 0.001) but lower sensitivity (43.9 vs. 75.2%, p < 0.001) than EHR model predictions. Compared with machine, human was better at reclassifying non-readmissions (non-event NRI + 30.1%) but worse at reclassifying readmissions (event NRI - 31.3%). A human-plus-machine approach best optimized discrimination (C-statistic 0.70, 95% CI 0.67-0.74), sensitivity (65.5%), and specificity (66.7%).

CONCLUSION

Clinicians had similar discrimination but higher specificity and lower sensitivity than EHR model predictions. Human-plus-machine was better than either alone. Readmission risk prediction strategies should incorporate clinician assessments to optimize the accuracy of readmission predictions.

Inpatient Outcomes Following a Return Visit to the Emergency Department: A Nationwide Cohort Study

Introduction

Emergency department (ED) revisits are traditionally used to measure potential lapses in emergency care. However, recent studies on in-hospital outcomes following ED revisits have begun to challenge this notion. We aimed to examine inpatient outcomes and resource use among patients who were hospitalized following a return visit to the ED using a national database.

Methods

This was a retrospective cohort study using the National Health Insurance Research Database in Taiwan. One-third of ED visits from 2012–2013 were randomly selected and their subsequent hospitalizations included. We analyzed the inpatient outcomes (mortality and intensive care unit [ICU] admission) and resource use (length of stay [LOS] and costs). Comparisons were made between patients who were hospitalized after a return visit to the ED and those who were hospitalized during the index ED visit.

Results

Of the 3,019,416 index ED visits, 477,326 patients (16%) were directly admitted to the hospital. Among the 2,504,972 patients who were discharged during the index ED visit, 229,059 (9.1%) returned to the ED within three days. Of them, 37,118 (16%) were hospitalized. In multivariable analyses, the inpatient mortality rates and hospital LOS were similar between the two groups. Compared with the direct-admission group, the return-admission group had a lower ICU admission rate (adjusted odds ratio, 0.78; 95% confidence interval [CI], 0.72–0.84), and lower costs (adjusted difference, −5,198 New Taiwan dollars, 95% CI, −6,224 to −4,172).

Conclusion

Patients who were hospitalized after a return visit to the ED had a lower ICU admission rate and lower costs, compared to those who were directly admitted. Our findings suggest that ED revisits do not necessarily translate to poor initial care and that subsequent inpatient outcomes should also be considered for better assessment.

Factors associated with early 14-day unplanned hospital readmission: a matched case-control study

Background/Purpose

Early unplanned hospital readmissions are burdensome health care events and indicate low care quality. Identifying at-risk patients enables timely intervention. This study identified predictors for 14-day unplanned readmission.

Methods

We conducted a retrospective, matched, case–control study between September 1, 2018, and August 31, 2019, in an 1193-bed university hospital. Adult patients aged ≥ 20 years and readmitted for the same or related diagnosis within 14 days of discharge after initial admission (index admission) were included as cases. Cases were 1:1 matched for the disease-related group at index admission, age, and discharge date to controls. Variables were extracted from the hospital’s electronic health records.

Results

In total, 300 cases and 300 controls were analyzed. Six factors were independently associated with unplanned readmission within 14 days: previous admissions within 6 months (OR = 3.09; 95 % CI = 1.79–5.34, p < 0.001), number of diagnoses in the past year (OR = 1.07; 95 % CI = 1.01–1.13, p = 0.019), Malnutrition Universal Screening Tool score (OR = 1.46; 95 % CI = 1.04–2.05, p = 0.03), systolic blood pressure (OR = 0.98; 95 % CI = 0.97–0.99, p = 0.01) and ear temperature within 24 h before discharge (OR = 2.49; 95 % CI = 1.34–4.64, p = 0.004), and discharge with a nasogastric tube (OR = 0.13; 95 % CI = 0.03–0.60, p = 0.009).

Conclusions

Factors presented at admission (frequent prior hospitalizations, multimorbidity, and malnutrition) along with factors presented at discharge (clinical instability and the absence of a nasogastric tube) were associated with increased risk of early 14-day unplanned readmission.

Preventability of 7-Day Versus 30-Day Readmissions at an Academic Children's Hospital

BACKGROUND AND OBJECTIVES

The 30-day readmission rate is a common quality metric used by Medicare for adult patients. However, studies in pediatrics have shown lower readmission rates and potentially less preventability. Therefore, some question the utility of the 30-day readmission time frame in pediatrics. Our objective was to describe the characteristics of patients readmitted within 30 days of discharge over a 1-year period and determine the preventability of readmissions occurring 0 to 7 vs 8 to 30 days after discharge from a pediatric hospitalist service at an academic children's hospital.

METHODS

Retrospective chart review and hospital administrative data were used to gather medical characteristics, demographics, and process-level metrics for readmitted patients between July 1, 2015, and June 30, 2016. All readmissions were reviewed by 2 senior authors and assigned a preventability category. Subgroup analysis comparing preventability in 0-to-7- and 8-to-30-day readmissions groups was performed. Qualitative thematic analysis was performed on readmissions deemed preventable.

RESULTS

Of 1523 discharges that occurred during the study period, 49 patients, with 65 distinct readmission encounters, were readmitted for an overall 30-day readmission rate of 4.3% (65 of 1523). Twenty-eight percent (9 of 32) of readmissions within 7 days of discharge and 12.1% (4 of 33) occurring 8 to 30 days after discharge were deemed potentially preventable (P = .13). Combined, the 30-day preventable readmission rate was 20% (13 of 65).

CONCLUSIONS

We identified a possible association between preventability and time to readmission. If confirmed by larger studies, the 7-day, rather than 30-day, time frame may represent a better quality metric for readmitted pediatric patients.

Impact of collaborative clinician visits on postdischarge total cost of care in a polypharmacy population

PURPOSE

To evaluate the impact of a collaborative intervention by pharmacists and primary care clinicians on total cost of care, including costs of inpatient readmissions, emergency department visits, and outpatient care, at 30, 60, and 180 days after hospital discharge in a population of patients at high risk for readmission due to polypharmacy.

METHODS

A retrospective study of cost outcomes in a cohort of adult patients discharged from a single institution from July 1, 2013 to March 25, 2016, was conducted. All patients had at least 10 medications listed on their discharge list, including at least 1 drug frequently associated with adverse events leading to hospital readmission. About half of the cohort (n = 496) attended a postdischarge visit involving both a pharmacist and a primary care clinician (a physician, physician assistant, or licensed nurse practitioner); this was designated the pharmacist/clinician collaborative (PCC) group. The remainder of the cohort (n = 500) attended a visit without pharmacist involvement; this was designated as the usual care (UC) group. Costs were compared using a quantile regression to assess the potential heterogeneous impacts of the PCC intervention across different parts of the cost distribution. All outcomes were adjusted for differences in baseline characteristics.

RESULTS

At 30 days post index discharge, there was a significant decrease in total costs in the 10th and 90th cost quantiles in the PCC cohort vs the UC cohort, without a statistically significant decrease in the 25th, 50th or 75th quantiles. The difference was significant in the 75th and 90th quantiles at 60 days and in the 25th, 50th, and 75th quantiles at 180 days. There was a nonsignificant cost reduction in all other quantiles.

CONCLUSION

Medically complex patients had a significantly lower total cost of care in approximately half of the adjusted cost quantiles at 30, 60, and 180 days after hospital discharge when they had a PCC visit. PCC visits can improve patient clinical outcomes while improving cost metrics.

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.

Dyadic Disruption Theory

Aspects of couples' romantic relationships are some of the most powerful psychosocial forces shaping mental and physical health, but even high-quality relationships are not universally beneficial for patients. Dyadic health theories have largely focused on chronic illness management that occurs after the couple understands the disease and prognosis, rather than focusing on couples' interdependence in the days and weeks following a sudden and disruptive medical event (e.g., an acute coronary syndrome [ACS] or a stroke). To address this gap, I propose Dyadic Disruption Theory to guide research on couples' reactions to acute medical events and their consequences for individual and dyadic mental health, physical health, and behavior. I propose that dyadic processes of social support, shared reality, and co-rumination can precipitate harmful patient and partner dynamics when couples are distressed early post-event and offer three propositions that inform testable hypotheses. Finally, I discuss implications for early dyadic intervention and future directions for research.

The prevalence, characteristics, and risk factors of frequently readmitted patients to an internal medicine service

AIMS

To determine the prevalence, characteristics and risk factors associated with frequent readmissions to an internal medicine service at a tertiary public hospital.

METHOD

A retrospective observational study was conducted at an internal medicine service in a tertiary teaching hospital between 1st January 2010 and the 30th June 2016. Frequent readmission was defined as four or more readmissions within 12 months of discharge from the index admission. Demographic and clinical characteristics, and potential risk factors were evaluated.

RESULTS

50 515 patients were included, 1657 (3.3%) had frequent readmissions and were associated with nearly 2.5 times higher in 12-month mortality rates. They were older, had higher rates of Indigenous Australians (3.2%), more disadvantaged status (Index of Relative Socio-Economic Disadvantage decile of 5.3), and more comorbidities (mean Charlson comorbidity index 1.4) in comparison, to infrequent readmission group. The mean length of hospital stay during the index admission was 6 days for frequent readmission group (21.4% staying more than 7 days) with higher incidence of discharge against medical advice (2.0% higher). Intensive care unit admission rate was 6.6% for frequent readmission group compared to 3.9% for infrequent readmission group. Multivariate analysis showed mental disease and disorders, neoplastic, and alcohol/drug use and alcohol/drug induced organic mental disorders are associated with frequent readmission.

CONCLUSION

The risk factors associated with frequent readmission were older age, indigenous status, being socially disadvantaged, having higher comorbidities, and discharging against medical advice. Conditions that lead to frequent readmissions were mental disorders, alcohol/drug use and alcohol/drug induced organic mental disorders, and neoplastic disorders.

Examination of Post-discharge Follow-up Appointment Status and 30-Day Readmission

BACKGROUND

Post-hospital discharge follow-up appointments are intended to evaluate patients' recovery following a hospitalization, but it is unclear how appointment statuses are associated with readmissions.

OBJECTIVE

To examine the association between post-discharge ambulatory follow-up status, (1) having a scheduled appointment and (2) arriving to said appointment, and 30-day readmission.

DESIGN AND SETTING

A retrospective cohort study of patients hospitalized at 12 hospitals in an Integrated Delivery Network and their ambulatory appointments in that same network.

PATIENTS AND MAIN MEASURES

We included 50,772 patients who had an ambulatory appointment within 18 months of an inpatient admission in 2018. Primary outcome was readmission within 30 days post-discharge.

KEY RESULTS

There were 32,108 (63.2%) patients with scheduled follow-up appointments and 18,664 (36.8%) patients with no follow-up; 28,313 (88.2%) patients arrived, 3149 (9.8%) missed, and 646 (2.0%) were readmitted prior to their scheduled appointments. Overall 30-day readmission rate was 7.3%; 6.0% [5.75-6.31] for those who arrived, 8.8% [8.44-9.25] for those without follow-up, and 10.3% [9.28-11.40] for those who missed a scheduled appointment (p < 0.001). After adjusting for covariates, patients who arrived at their appointment in the first week following discharge were significantly less likely to be readmitted than those not having any follow-up scheduled (medical adjusted hazard ratio (aHR) 0.57 [0.47-0.69], p < 0.001; surgical aHR 0.58 [0.44-0.75], p < 0.001) There was an increased risk at weeks 3 and 4 for medical patients who arrived at a follow-up compared to those with no follow-up scheduled (week 3 aHR 1.29 [1.10-1.51], p = 0.001; week 4 aHR 1.46 [1.26-1.70], p < 0.001).

CONCLUSIONS

The benefit of patients arriving to their post-discharge appointments compared with patients who missed their follow-up visits or had no follow-up scheduled, is only significant during first week post-discharge, suggesting that coordination within 1 week of discharge is critical in reducing 30-day readmissions.

A Quality Improvement Approach to Decreasing Postdischarge Acute Care Reuse Among Children With Asthma

OBJECTIVES

To reduce 7-day acute care reuse among children with asthma after discharge from an academic children's hospital by standardizing the delivery of clinical care and patient education.

METHODS

A diverse group of stakeholders from our tertiary care children's hospital and local community agencies used quality improvement methods to implement a series of interventions within inpatient, emergency department (ED), and outpatient settings. These interventions were designed to improve admission, inpatient care, and discharge processes for children hospitalized because of asthma and included a focus on (1) resident education, (2) patient access to medication and asthma education, and (3) gaps in existing asthma clinical care pathways in the ED and ICU. The primary outcome was the rate of 7-day acute care reuse (combined hospital readmissions and ED revisits) after discharge from an index hospitalization for asthma, measured through a monthly review of electronic health record data and compared with a 6-month baseline period of reuse data.

RESULTS

The mean 7-day reuse rate for asthma after discharge was 3.7% during the 6 months baseline period (n = 107) and 1.0% during the 15-month intervention period (n = 302). This included a shift in our median from 3.3% to 0% with an 8-month period of no 7-day reuse.

CONCLUSIONS

An interprofessional quality improvement team successfully achieved and sustained a 73% reduction in mean 7-day asthma-related acute care reuse after discharge by standardizing provider training, care processes, and patient education.

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.

Prevalence and predictors of mortality for older adults referred to hospital avoidance program

AIM

Following discharge from a hospital avoidance program, to examine the prevalence of patient mortality, demographic characteristics associated with risk of mortality up to 33 months, patient demographic and health characteristics associated with mortality within 1 year.

METHODS

A retrospective data linkage study of older adults with mean age of 80.5 years discharged from a hospital avoidance program between January 2017 and January 2018. The prevalence of death at 3, 6, 12, 18 and 33 months was calculated. Patient demographic and health characteristics associated with participant mortality within 12 (n = 195) and 33 (n = 185) months of discharge was examined using Cox multivariable regression for patients with complete health characteristic data.

RESULTS

The mortality prevalence was 17% at 6 months and cumulative prevalence at 1 year, 18 months and 33 months post-discharge were 24%, 29% and 36% respectively. Characteristics associated with mortality within 12 months of discharge were lower cognition, increased burden of comorbidity, decreased physical function, weight <55 kg and male sex. The same variables were associated with death up to 33 months as well as age, interaction between household arrangement and time, and albumin.

CONCLUSIONS

The establishment of potential risk indicators allows greater specificity for identifying older people at risk of dying in the next 12 months and an opportunity to discuss their advanced care planning. Geriatr Gerontol Int 2021; ••: ••-••.

Timing of Readmissions After Elective Total Hip and Knee Arthroplasty: Does a 30-Day All-Cause Rate Capture Surgically Relevant Readmissions?

BACKGROUND

The 30-day all-cause readmission rate is a widely used metric of hospital performance. However, there is lack of clarity as to whether 30 days is an appropriate time frame following surgical procedures. Our aim is to determine whether a 90-day time window is superior to a 30-day time window in capturing surgically relevant readmissions after total hip arthroplasty (THA) and total knee arthroplasty (TKA).

METHODS

We analyzed readmissions following all primary THAs and TKAs recorded in the English National Health Service Hospital Episode Statistics database from 2008 to 2018. We compared temporal patterns of 30- and 90-day readmission rates for the following types of readmission: all-cause, surgical, return to theater, and those related to specific surgical complications.

RESULTS

A total of 1.47 million procedures were recorded. After THA and TKA, over three-quarters of 90-day surgical readmissions took place within the first 30 days (78.5% and 75.7%, respectively). All-cause and surgical readmissions both peaked at day 4 and followed a similar temporal course thereafter. The ratio of surgical to medical readmissions was greater for THA than for TKA, with THA dislocation both being one of the most common surgical complications and clustering early after discharge, with 73.5% of 90-day dislocations occurring within the first 30 days.

CONCLUSION

The 30-day all-cause readmission rate is a good reflection of surgically relevant readmissions that take place in the first 90 days after THA and TKA.

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.