Preventability and Causes of Readmissions in a National Cohort of General Medicine Patients

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.

Implementing Transitions of Care Services in a Primary Care Clinic: Role of the Pharmacist

IntroductionPharmacists in primary care clinics improve medication adherence and reduce medication errors but these require further investigation to determine its potential impact on health care readmission rates. Methods: This review assessed the addition of a clinical pharmacist to the primary care provider's transitions of care appointment. The primary outcome of this review was the change in acute health care encounters (hospital readmissions and/or Emergency Department visits) in the 6-month period before to the 6 months following initial clinical pharmacist intervention. Each patient served as their own control. Secondary outcomes included the number and types of pharmacist interventions, percentage of pharmacist recommendations accepted, number and categories of drug-related problems (DRPs), nonadherence reasons, 30-day hospital readmission rate, 30-day Emergency Department (ED) rate, time to first hospital readmission, time to first ED readmission, and preventable readmission rate. Results: The total number of combined acute health care encounters in 6 months decreased by 15% from 280 at baseline to 238 after pharmacist intervention (P = .087) for the 206 patients included in the final analysis. The 30-day hospital readmission rate was 9.7% and the median time between first hospital readmission was 54.5 days (IQR 63.5). We identified a total number of 310 DRPs with a mean of 1.5 DRPs (SD 1.3) identified per patient. The providers accepted 88% of the pharmacists' recommendations. Conclusions: Clinical pharmacists embedded in a primary care setting demonstrated improved patient care during transitions of care by identifying and resolving drug-related problems, with a trend in decreasing acute health care utilization.

The effect of occupational therapy services on hospital readmission for patients with cancer in acute care settings: a retrospective data analysis

PURPOSE

This study examined how the use of occupational therapy services affected the likelihood of hospital readmission within 30 days for patients with cancer diagnoses.

METHODOLOGY

This was a retrospective observational study. Patient medical records were analyzed from a National Cancer Institute Hospital over a 5-year period with a sample size of 6614 patients included for analysis in an unadjusted logistic regression model and 1920 patients analyzed in an adjusted logistic regression model. Various factors, including the use of occupational therapy services as well as individual factors such as pain levels, cancer stage, and living environment, were considered in relation to readmission status. Logistic regression analyses were used to assess the provision of occupational therapy service's association with 30-day hospital readmissions.

RESULTS

Patients who received occupational therapy services had a statistically significant decrease in their risk of a 30-day hospital readmission compared to patients with cancer who did not receive occupational therapy services. In an unadjusted analysis, patients with cancer who had occupational therapy services were 33.5% (OR = 0.665) less likely to be readmitted within 30 days compared to a patient who did not have occupational therapy services (p < 0.001). In an analysis after adjusting for patient health-related factors, patients with cancer who had occupational therapy services were 22.2% (OR = 0.778) less likely to readmit to a hospital compared to a patient who did not have occupational therapy services (p < 0.046).

CONCLUSION

The results of the study are intended to contribute to the body of knowledge on the benefits of occupational therapy services on an individual as well as a health systems-based level for patients with cancer diagnoses while hospitalized.

IMPLICATIONS FOR CANCER SURVIVORS

The knowledge of the utility of occupational therapy services for patients with cancer diagnoses while in the hospital can assist providers, patients, and hospital leadership in understanding some of the potential benefits for patient care and healthcare systems at large while seeking to avoid the deleterious effects from a hospital readmission.

Evaluation of hospital-onset bacteraemia and fungaemia in the USA as a potential healthcare quality measure: a cross-sectional study

BACKGROUND

Hospital-onset bacteraemia and fungaemia (HOB) is being explored as a surveillance and quality metric. The objectives of the current study were to determine sources and preventability of HOB in hospitalised patients in the USA and to identify factors associated with perceived preventability.

METHODS

We conducted a cross-sectional study of HOB events at 10 academic and three community hospitals using structured chart review. HOB was defined as a blood culture on or after hospital day 4 with growth of one or more bacterial or fungal organisms. HOB events were stratified by commensal and non-commensal organisms. Medical resident physicians, infectious disease fellows or infection preventionists reviewed charts to determine HOB source, and infectious disease physicians with training in infection prevention/hospital epidemiology rated preventability from 1 to 6 (1=definitely preventable to 6=definitely not preventable) using a structured guide. Ratings of 1-3 were collectively considered 'potentially preventable' and 4-6 'potentially not preventable'.

RESULTS

Among 1789 HOB events with non-commensal organisms, gastrointestinal (including neutropenic translocation) (35%) and endovascular (32%) were the most common sources. Overall, 636/1789 (36%) non-commensal and 238/320 (74%) commensal HOB events were rated potentially preventable. In logistic regression analysis among non-commensal HOB events, events attributed to intravascular catheter-related infection, indwelling urinary catheter-related infection and surgical site infection had higher odds of being rated preventable while events with neutropenia, immunosuppression, gastrointestinal sources, polymicrobial cultures and previous positive blood culture in the same admission had lower odds of being rated preventable, compared with events without those attributes. Of 636 potentially preventable non-commensal HOB events, 47% were endovascular in origin, followed by gastrointestinal, respiratory and urinary sources; approximately 40% of those events would not be captured through existing healthcare-associated infection surveillance.

DISCUSSION

Factors identified as associated with higher or lower preventability should be used to guide inclusion, exclusion and risk adjustment for an HOB-related quality metric.

Optimizing the Hospital Discharge Process: Perspectives of the Health Care Team

Background

Prior research capturing pharmacists' perspectives on the discharge process has shown that their involvement is essential. Given the multidisciplinary nature of the hospital environment, it is important to understand the perspectives of nonpharmacist health care providers.

Objectives

To explore the perspectives of nonpharmacist health care providers concerning current discharge practices, components of an effective discharge plan, and perceived barriers to an optimal discharge, and to explore their expectations of pharmacists at discharge.

Methods

This qualitative study used key informant interviews of allied health professionals and prescribers at Vancouver General Hospital and North Island Hospital Comox Valley (British Columbia). Participants primarily working on general medicine, family practice, or hospitalist wards were invited to participate.

Results

A total of 16 health care providers participated, consisting of 12 allied health professionals and 4 prescribers. Thematic analysis of the interview transcripts revealed 5 themes for each group. The following 3 themes were common to both groups: systems-related barriers to an optimal discharge; patient- and community-related barriers to an optimal discharge; and patient involvement and education. For allied health professionals, themes of prioritization of patients for discharge and direct communication/teamwork were also key for an optimal discharge. Prescriber-specific themes were limitations related to technology infrastructure and inefficiency of existing collaborative processes. Key responsibilities expected of the pharmacist at discharge included preparing the discharge medication reconciliation and prescriptions, addressing medication-related cost concerns, organizing adherence aids/tools, and providing medication counselling.

Conclusions

Further studies are warranted to investigate optimization of the discharge process through implementation of standardized discharge protocols and electronic health record-related tools. The primary responsibilities of the pharmacist at discharge, as perceived by study participants, were consistent with previous literature.

Birth Weight and Gestational Age as Modifiers of Rehospitalization after Neonatal Intensive Care Unit Admission

OBJECTIVE

 This study aimed to assess interaction effects between gestational age and birth weight on 30-day unplanned hospital readmission following discharge from the neonatal intensive care unit (NICU).

STUDY DESIGN

 This is a retrospective study that uses the study site's Children's Hospitals Neonatal Database and electronic health records. Population included patients discharged from a NICU between January 2017 and March 2020. Variables encompassing demographics, gestational age, birth weight, medications, maternal data, and surgical procedures were controlled for. A statistical interaction between gestational age and birth weight was tested for statistical significance.

RESULTS

 A total of 2,307 neonates were included, with 7.2% readmitted within 30 days of discharge. Statistical interaction between birth weight and gestational age was statistically significant, indicating that the odds of readmission among low birthweight premature patients increase with increasing gestational age, whereas decrease with increasing gestational age among their normal or high birth weight peers.

CONCLUSION

 The effect of gestational age on odds of hospital readmission is dependent on birth weight.

KEY POINTS

· Population included patients discharged from a NICU between January 2017 and March 2020.. · A total of 2,307 neonates were included, with 7.2% readmitted within 30 days of discharge.. · The effect of gestational age on odds of hospital readmission is dependent on birth weight..

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.

Early repeat hospitalization for fluid overload in individuals with cardiovascular disease and risks: a retrospective cohort study

AIMS

Fluid overload is a common manifestation of cardiovascular and kidney disease and a leading cause of hospitalizations. To identify patients at risk of recurrent severe fluid overload, we evaluated the incidence and risk factors associated with early repeat hospitalization for fluid overload among individuals with cardiovascular disease and risks.

METHODS

Single-center retrospective cohort study of 3423 consecutive adults with an index hospitalization for fluid overload between January 2015 and December 2017 and had cardiovascular risks (older age, diabetes mellitus, hypertension, dyslipidemia, kidney disease, known cardiovascular disease), but excluded if lost to follow-up or eGFR < 15 ml/min/1.73 m2. The outcome was early repeat hospitalization for fluid overload within 30 days of discharge.

RESULTS

The mean age was 73.9 ± 11.6 years and eGFR was 54.1 ± 24.6 ml/min/1.73 m2 at index hospitalization. Early repeat hospitalization for fluid overload occurred in 291 patients (8.5%). After adjusting for demographics, comorbidities, clinical parameters during index hospitalization and medications at discharge, cardiovascular disease (adjusted odds ratio, OR 1.66, 95% CI 1.27-2.17), prior hospitalization for fluid overload within 3 months (OR 2.52, 95% CI 1.17-5.44), prior hospitalization for any cause in within 6 months (OR 1.33, 95% CI 1.02-1.73) and intravenous furosemide use (OR 1.58, 95% CI 1.10-2.28) were associated with early repeat hospitalization for fluid overload. Higher systolic BP on admission (OR 0.992, 95% 0.986-0.998) and diuretic at discharge (OR 0.50, 95% CI 0.26-0.98) reduced early hospitalization for fluid overload.

CONCLUSION

Patients at-risk of early repeat hospitalization for fluid overload may be identified using these risk factors for targeted interventions.

Thirty-Day Readmissions Are Largely Not Preventable in Patients With Cirrhosis

INTRODUCTION

Hospital readmissions are common in patients with cirrhosis, but there are few studies describing readmission preventability. We aimed to describe the incidence, causes, and risk factors for preventable readmission in this population.

METHODS

We performed a prospective cohort study of patients with cirrhosis hospitalized at a single center between June 2014 and March 2020 and followed up for 30 days postdischarge. Demographic, clinical, and socioeconomic data, functional status, and quality of life were collected. Readmission preventability was independently and systematically adjudicated by 3 reviewers. Multinomial logistic regression was used to compare those with (i) preventable readmission, (ii) nonpreventable readmission/death, and (iii) no readmission.

RESULTS

Of 654 patients, 246 (38%) were readmitted, and 29 (12%) were preventable readmissions. Reviewers agreed on preventability for 70% of readmissions. Twenty-two (including 2 with preventable readmission) died. The most common reasons for readmission were hepatic encephalopathy (22%), gastrointestinal bleeding (13%), acute kidney injury (13%), and ascites (6%), and these reasons were similar between preventable and nonpreventable readmissions. Preventable readmission was often related to paracentesis timeliness, diuretic adjustment monitoring, and hepatic encephalopathy treatment. Compared with nonreadmitted patients, preventable readmission was independently associated with racial and ethnic minoritized individuals (odds ratio [OR] 5.80; 95% CI, 1.96-17.13), nonmarried marital status (OR 2.88; 95% CI, 1.18-7.05), and admission in the prior 30 days (OR 3.45; 95% CI, 1.48-8.04).

DISCUSSION

For patients with cirrhosis, readmission is common, but most are not preventable. Preventable readmissions are often related to ascites and hepatic encephalopathy and are associated with racial and ethnic minorities, nonmarried status, and prior admissions.

Diagnostic Errors in Hospitalized Adults Who Died or Were Transferred to Intensive Care

Importance

Diagnostic errors contribute to patient harm, though few data exist to describe their prevalence or underlying causes among medical inpatients.

Objective

To determine the prevalence, underlying cause, and harms of diagnostic errors among hospitalized adults transferred to an intensive care unit (ICU) or who died.

Design, Setting, and Participants

Retrospective cohort study conducted at 29 academic medical centers in the US in a random sample of adults hospitalized with general medical conditions and who were transferred to an ICU, died, or both from January 1 to December 31, 2019. Each record was reviewed by 2 trained clinicians to determine whether a diagnostic error occurred (ie, missed or delayed diagnosis), identify diagnostic process faults, and classify harms. Multivariable models estimated association between process faults and diagnostic error. Opportunity for diagnostic error reduction associated with each fault was estimated using the adjusted proportion attributable fraction (aPAF). Data analysis was performed from April through September 2023.

Main Outcomes and Measures

Whether or not a diagnostic error took place, the frequency of underlying causes of errors, and harms associated with those errors.

Results

Of 2428 patient records at 29 hospitals that underwent review (mean [SD] patient age, 63.9 [17.0] years; 1107 [45.6%] female and 1321 male individuals [54.4%]), 550 patients (23.0%; 95% CI, 20.9%-25.3%) had experienced a diagnostic error. Errors were judged to have contributed to temporary harm, permanent harm, or death in 436 patients (17.8%; 95% CI, 15.9%-19.8%); among the 1863 patients who died, diagnostic error was judged to have contributed to death in 121 (6.6%; 95% CI, 5.3%-8.2%). In multivariable models examining process faults associated with any diagnostic error, patient assessment problems (aPAF, 21.4%; 95% CI, 16.4%-26.4%) and problems with test ordering and interpretation (aPAF, 19.9%; 95% CI, 14.7%-25.1%) had the highest opportunity to reduce diagnostic errors; similar ranking was seen in multivariable models examining harmful diagnostic errors.

Conclusions and Relevance

In this cohort study, diagnostic errors in hospitalized adults who died or were transferred to the ICU were common and associated with patient harm. Problems with choosing and interpreting tests and the processes involved with clinician assessment are high-priority areas for improvement efforts.

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.

Comparison of clinical outcomes of venous thromboembolic disease between outpatient and inpatient management

Objectives

To compare the occurrence of death, bleeding, and recurrence according to inpatient or outpatient management of venous thromboembolic disease (VTE).

Materials and methods

. Retrospective cohort that included a consecutive sampling of VTE consultations between 2016 and 2019 diagnosed in the Emergency Center of a private hospital in Argentina.

Results

There were 1202 cases, 908 with isolated deep vein thrombosis (DVT), 205 with isolated pulmonary embolism (PE), and 89 cases of combined DVT - PE. 66% were women, with a median age of 77 years; 72% of cases were managed on an outpatient basis (n= 862). Comorbidities associated with hospitalization were obesity (p=0.03), chronic obstructive pulmonary disease (COPD) (p=0.01), heart failure (CHF) (p=0.01), chronic renal failure (CKD) (p=0.01), and cancer (p=0.01). At 90 days, the cumulative incidence of bleeding was 2.6% in inpatient compared to 2.9% in outpatient management (p=0.81); recurrence was 0% versus 0.9% (p=0.07), and mortality was 42.9% versus 18.9%, respectively (p=0.01). The HR for 90-day mortality in hospitalized patients adjusted for confounders (sex, age, type of VTE, obesity, CKD, CHF, COPD, and cancer) was 1.99 (95% CI 1.49-2.64; p=0.01).

Conclusions

In this elderly, and predominantly female Argentine population, the 90-day mortality in patients hospitalized for VTE was higher than mortality in patients with outpatient management, without differences in recurrence or major bleeding.

Charlson and Elixhauser Comorbidity Indices for Prediction of Mortality and Hospital Readmission in Patients With Acute Pulmonary Embolism

Several risk stratification systems aid clinicians in classifying pulmonary embolism (PE) severity and prognosis. We compared 2 clinical PE scoring systems, the PESI and sPESI scores, with 2 comorbidity indices, the Charlson Comorbidity Index (CCI) and the val Walraven Elixhauser Comorbidity Index (ECI), to determine the utility of each in predicting mortality and hospital readmission. Information was collected from 436 patients presenting with PE via retrospective chart review. The PESI, sPESI, CCI, and ECI scores were calculated for each patient. Multivariate analysis was used to determine each system's ability to predict in-hospital mortality, 90-day mortality, overall mortality, and all-cause hospital readmission. The impact of various demographic and clinical characteristics of each patient on these outcomes was also assessed. The PESI score was found to be an independent predictor of in-hospital mortality and 90-day mortality. The PESI score and the CCI were able to independently predict overall mortality. None of the 4 risk scores independently predicted hospital readmission. Other factors including hypoalbuminemia, serum BNP, coagulopathy, anemia, and diabetes were associated with increased mortality and readmission at various endpoints. The PESI score was the best tool for predicting mortality at any endpoint. The CCI may have utility in predicting long-term outcomes. Further work is needed to better determine the roles of the CCI and ECI in predicting patient outcomes in PE. The potential prognostic implications of low serum albumin and anemia at the time of PE also warrant further investigation.

Reduced Readmission and Increased Patient Satisfaction in Post-Cardiac Arrhythmia Ablation: A Randomized Pilot Study

BACKGROUND

Hospital readmissions within 30 days post-cardiac arrhythmia ablation are typically related to postoperative complications and arrhythmia recurrence and considered mostly preventable.

PURPOSE

To evaluate the impact of a cardiac ablation patient education program on hospital readmissions and patient satisfaction.

METHODS

An education intervention was established for patients who underwent cardiac ablation based on the Project RED framework. Hospital readmissions rates 30 days postprocedure and satisfaction via a single-blinded posttest design were assessed to evaluate the program.

RESULTS

Those in the intervention group had a significantly lower rate of 30-day readmissions (7.1% vs 53.3%, P = .014). A large magnitude of effect and higher total patient satisfaction scores were also seen in the intervention group ( M = 633, SD = 78) than in the control group ( M = 508, SD = 137, P = .005).

CONCLUSIONS

Results of this study support the implementation of an enhanced cardiac arrhythmia patient education intervention with consideration of identified facilitators and barriers.

The impact of a multidisciplinary goals-of-care program on unplanned readmission rates at a comprehensive cancer center

PURPOSE

This study examined the 30-day unplanned readmission rate in the medical oncology population before and after the implementation of an institution-wide multicomponent interdisciplinary goals of care (myGOC) program.

METHODS

This retrospective study compared the 30-day unplanned readmission rates in consecutive medical patients during the pre-implementation period (May 1, 2019, to December 31, 2019) and the post-implementation period (May 1, 2020, to December 31, 2020). Secondary outcomes included 7-day unplanned readmission rates, inpatient do-not-resuscitate (DNR) orders, and palliative care consults. We randomly selected a hospitalization encounter for each unique patient during each study period for statistical analysis. A multivariate analysis model was used to examine the association between 30-day unplanned readmission rates and implementation of the myGOC program.

RESULTS

There were 7028 and 5982 unique medical patients during the pre- and post-implementation period, respectively. The overall 30-day unplanned readmission rate decreased from 24.0 to 21.3% after implementation of the myGOC program. After adjusting for covariates, the myGOC program implementation remained significantly associated with a reduction in 30-day unplanned readmission rates (OR [95% CI] 0.85 [0.77, 0.95], p = 0.003). Other factors significantly associated with a decreased likelihood of a 30-day unplanned readmission were an inpatient DNR order, advanced care planning documentation, and an emergent admission type. We also observed a significant decrease in 7-day unplanned readmission rates (OR [95% CI] 0.75 [0.64, 0.89]) after implementation of the myGOC program.

CONCLUSION

The 30-day and 7-day unplanned readmission rates decreased in our hospital after implementation of a system-wide multicomponent GOC intervention.

Care pathways of sepsis survivors: sequelae, mortality and use of healthcare services in France, 2015-2018

BACKGROUND

Individuals who survive sepsis are at high risk of chronic sequelae, resulting in significant health-economic costs. Several studies have focused on aspects of healthcare pathways of sepsis survivors but comprehensive, longitudinal overview of their pathways of care are scarce. The aim of this retrospective, longitudinal cohort study is to identify sepsis survivor profiles based on their healthcare pathways and describe their healthcare consumption and costs over the 3 years following their index hospitalization.

METHODS

The data were extracted from the French National Hospital Discharge Database. The study population included all patients above 15 years old, with bacterial sepsis, who survived an incident hospitalization in an acute care facility in 2015. To identify survivor profiles, state sequence and clustering analyses were conducted over the year following the index hospitalization. For each profile, patient characteristics and their index hospital stay and sequelae were described, as well as use of care and its associated monetary costs, both pre- and post-sepsis.

RESULTS

New medical (79.2%), psychological (26.9%) and cognitive (18.5%) impairments were identified post-sepsis, and 65.3% of survivors were rehospitalized in acute care. Cumulative mortality reached 36.6% by 3 years post-sepsis. The total medical cost increased by 856 million € in the year post-sepsis. Five patient clusters were identified: home (65.6% of patients), early death (12.9%), late death (6.8%), short-term rehabilitation (11.3%) and long-term rehabilitation (3.3%). Survivors with early and late death clusters had high rates of cancer and primary bacteremia and experienced more hospital-at-home care post-sepsis. Survivors in short- or long-term rehabilitation clusters were older, with higher percentage of septic shock than those coming back home, and had high rates of multiple site infections and higher rates of new psychological and cognitive impairment.

CONCLUSIONS

Over three years post-sepsis, different profiles of sepsis survivors were identified with different mortality rates, sequels and healthcare services usage and cost. This study confirmed the importance of sepsis burden and suggests that strategies of post-discharge care, in accordance with patient profile, should be further tested in order to reduce sepsis burden.

Experiences of in-hospital care among dementia caregivers in the context of high neighborhood-level disadvantage

BACKGROUND

Persons living with dementia (PLWD) experience high rates of hospitalization and rehospitalization, exposing them to added risk for adverse outcomes including delirium, hastened cognitive decline, and death. Hospitalizations can also increase family caregiver strain. Despite disparities in care quality surrounding hospitalizations for PLWD, and evidence suggesting that exposure to neighborhood-level disadvantage increases these inequities, experiences with hospitalization among PLWD and family caregivers exposed to greater levels of neighborhood disadvantage are poorly understood. This study examined family caregiver perspectives and experiences of hospitalizations among PLWD in the context of high neighborhood-level disadvantage.

METHODS

We analyzed data from the Stakeholders Understanding of Prevention Protection and Opportunities to Reduce HospiTalizations (SUPPORT) study, an in-depth, multisite qualitative study examining hospitalization and rehospitalization of PLWD in the context of high neighborhood disadvantage, to identify caregiver perspectives and experiences of in-hospital care. Data were analyzed using rapid identification of themes; duplicate transcript review was used to enhance rigor.

RESULTS

Data from N = 54 individuals (47 individual interviews, 2 focus groups with 7 individuals) were analyzed. Sixty-three percent of participants identified as Black/African American, 35% as non-Hispanic White, and 2% declined to report. Caregivers' experiences were largely characterized by PLWD receiving suboptimal care that caregivers viewed as influenced by system pressures and inadequate workforce competencies, leading to communication breakdowns and strain. Caregivers described poor collaboration between clinicians and caregivers with regard to in-hospital care delivery, including transitional care. Caregivers also highlighted the lack of person-focused care and the exclusion of the PLWD from care.

CONCLUSIONS

Caregiver perspectives highlight opportunities for improving hospital care for PLWD in the context of neighborhood disadvantage and recognition of broader issues in care structure that limit their capacity to be actively involved in care. Further work should examine and develop strategies to improve caregiver integration during hospitalizations across diverse contexts.

Intensive Case Management to Reduce Hospital Readmissions: A Pilot Quality Improvement Project

PURPOSE OF STUDY

Hospital readmissions burden the U.S. health care system, and they have negative effects on patients and their families. The primary aim of this study was to pilot an intensive case management (ICM) intervention to reduce 30-day hospital readmissions. A secondary aim was to obtain patient- and caregiver-reported reasons for readmission.

PRIMARY PRACTICE SETTING

The setting was a vertically integrated health care system located in Northern California.

METHODOLOGY AND SAMPLE

This pilot quality improvement project occurred over a 4-month period. The intervention was delivered by master's degree students in nurse case management through an academic-clinical partnership. Patients hospitalized with a 30-day readmission were offered the ICM intervention. A total of 36 patients were identified and 20 accepted. Patient and/or caregiver was interviewed to identify reasons for their readmission. Data were collected about pre-/post-health care utilization including subsequent 30-day readmission. Mixed methods were used to analyze the findings.

RESULTS

Thirteen of 20 enrolled patients received the weekly ICM intervention for at least 30 days. Seven declined further contact before 30 days. Patient-reported reasons for readmission included being discharged too soon, poor communication among providers and with patients/families, lack of understanding about disease management and/or treatment options, and inadequate support. Several patients believed that their readmission was unavoidable due to the complexity of their illnesses. We compared 30-day readmissions for those who participated in and those who declined the ICM intervention, finding that those who received the ICM intervention had a lower readmission rate than those who did not receive the intervention (35% vs. 37.5%).

Evaluating an intervention to improve the safety and experience of transitions from hospital to home for older people (Your Care Needs You): a protocol for a cluster randomised controlled trial and process evaluation

BACKGROUND

Older patients often experience safety issues when transitioning from hospital to home. The 'Your Care Needs You' (YCNY) intervention aims to support older people to 'know more' and 'do more' whilst in hospital so that they are better prepared for managing at home.

METHODS

A multi-centre cluster randomised controlled trial (cRCT) will evaluate the effectiveness and cost-effectiveness of the YCNY intervention. Forty acute hospital wards (clusters) in England from varying medical specialities will be randomised to deliver YCNY or care-as-usual on a 1:1 basis. The primary outcome will be unplanned hospital readmission rates within 30 days of discharge. This will be extracted from routinely collected data of at least 5440 patients (aged 75 years and older) discharged to their own homes during the 4- to 5-month YCNY intervention period. A nested cohort of up to 1000 patients will be recruited to the study to collect secondary outcomes via follow-up questionnaires at 5-, 30- and 90-day post-discharge. These will include measures of patient experience of transitions, patient-reported safety events, quality of life and healthcare resource use. Unplanned hospital readmission rates at 60 and 90 days of discharge will be collected from routine data. A process evaluation (primarily interviews and observations with patients, carers and staff) will be conducted to understand the implementation of the intervention and the contextual factors that shape this, as well as the intervention's underlying mechanisms of action. Fidelity of intervention delivery will also be assessed across all intervention wards.

DISCUSSION

This study will establish the effectiveness and cost-effectiveness of the YCNY intervention which aims to improve patient safety and experience for older people during transitions of care. The process evaluation will generate insights about how the YCNY intervention was implemented, what elements of the intervention work and for whom, and how to optimise its implementation so that it can be delivered with high fidelity in routine service contexts.

TRIAL REGISTRATION

UK Clinical Research Network Portfolio: 44559; ISTCRN: ISRCTN17062524. Registered on 11/02/2020.

Transition of care of patients with chronic diseases and its relation with clinical and sociodemographic characteristics

OBJECTIVE

evaluate the transition of care from the perspective of people living with chronic diseases and identify its relation with clinical and sociodemographic characteristics.

METHOD

cross-sectional study with 487 patients who were discharged from a hospital. Clinical and sociodemographic characterization instruments were used, as well as the Care Transitions Measure-15, which measures Preparation for self-management, Secured preferences, Understanding about medications and Care plan factors. Descriptive and inferential statistical analysis.

RESULTS

the transition of care was satisfactory (76.8±10.4). Average of the factors: Preparation for self-management (82.2±10.8), Secured preferences (84.7±14.3), Understanding about medications (75.7±13.7) and Care plan (64.5±13.2). Female patients had a higher average in the understanding about medications factor. Whites and residents in the urban area better evaluated the Care plan factor. The highest mean was observed for the Secured preferences factor (84.7±14.3) and the lowest for the Care plan factor (64.5±13.2). In all factors, significant differences were found in the variables (surgical patient, carrying clinical artifacts and not being hospitalized for COVID-19). Patients hospitalized for up to five days showed statistical difference in Preparation for self-management and Understanding about medications factors. In patients who were not readmitted within 30 days of discharge, Preparation for self-management was better. The better the Preparation for self-management, the lower the 30-day readmission rates.

CONCLUSION

in patients living with chronic diseases, sociodemographic and clinical variables are associated with the transition of care. Patients who better evaluated preparation for self-management had fewer readmissions within 30 days. (1) Brazilian study that evaluated the transition of care of patients with CNCDs. (2) Women had a higher average in the understanding about medications factor. (3) Whites and residents in the urban area better evaluated the care plan. (4) Better preparation for self-management reduces length of stay and readmissions. (5) Better preparation for understanding about medications reduces hospitalization time.

Postdischarge Video Visits for Adherence to Hospital Discharge Recommendations: A Randomized Clinical Trial

Objective

To determine whether a postdischarge video visit with patients, conducted by hospital medicine advanced practice providers, improves adherence to hospital discharge recommendations.

Patients and Methods

We conducted a single-institution 2-site randomized clinical trial with 1:1 assignment to intervention vs control, with enrollment from August 10, 2020, to June 23, 2022. Hospital medicine patients discharged home or to an assisted living facility were randomized to a video visit 2-5 days postdischarge in addition to usual care (intervention) vs usual care (control). During the video visit, advanced practice providers reviewed discharge recommendations. Both intervention and control groups received telephone follow-up 3-6 days postdischarge to ascertain the primary outcome of adherence to all discharge recommendations for new and chronic medication management, self-management and action plan, and home support.

Results

Among 1190 participants (594 intervention; 596 control), the primary outcome was ascertained in 768 participants (314 intervention; 454 control). In intervention vs control, there was no difference in the proportion of participants with the primary outcome (76.7% vs 72.5%; P=.19) or in the individual domains of the primary outcome: new and chronic medication management (94.1% vs 92.8%; P=.50), self-management and action plan (76.5% vs 71.5%; P=.18), and home support (94.1% vs 94.3%; P=.94). Women receiving intervention vs control had higher adherence to recommendations (odds ratio, 1.77; 95% CI, 1.08-2.91).

Conclusion

In hospital medicine patients, a postdischarge video visit did not improve adherence to discharge recommendations. Potential gender differences in adherence require further investigation.Clinicaltrials.gov number, NCT04547803.

Identifying older adults at increased risk of medication-related readmission to hospital within 30 days of discharge: development and validation of a risk assessment tool

OBJECTIVE

Developing and validating a risk assessment tool aiming to identify older adults (≥65 years) at increased risk of possibly medication-related readmission to hospital within 30 days of discharge.

DESIGN

Retrospective cohort study.

SETTING

The risk score was developed using data from a hospital in southern Sweden and validated using data from four hospitals in the mid-eastern part of Sweden.

PARTICIPANTS

The development cohort (n=720) was admitted to hospital during 2017, whereas the validation cohort (n=892) was admitted during 2017-2018.

MEASURES

The risk assessment tool aims to predict possibly medication-related readmission to hospital within 30 days of discharge. Variables known at first admission and individually associated with possibly medication-related readmission were used in development. The included variables were assigned points, and Youden's index was used to decide a threshold score. The risk score was calculated for all individuals in both cohorts. Area under the receiver operating characteristic (ROC) curve (c-index) was used to measure the discrimination of the developed risk score. Sensitivity, specificity and positive and negative predictive values were calculated using cross-tabulation.

RESULTS

The developed risk assessment tool, the Hospitalisations, Own home, Medications, and Emergency admission (HOME) Score, had a c-index of 0.69 in the development cohort and 0.65 in the validation cohort. It showed sensitivity 76%, specificity 54%, positive predictive value 29% and negative predictive value 90% at the threshold score in the development cohort.

CONCLUSION

The HOME Score can be used to identify older adults at increased risk of possibly medication-related readmission within 30 days of discharge. The tool is easy to use and includes variables available in electronic health records at admission, thus making it possible to implement risk-reducing activities during the hospital stay as well as at discharge and in transitions of care. Further studies are needed to investigate the clinical usefulness of the HOME Score as well as the benefits of implemented activities.

Profile of and risk factors for early unplanned readmissions in patients with acute necrotizing pancreatitis

INTRODUCTION

Acute necrotizing pancreatitis (ANP) complicates up to 15% of acute pancreatitis cases. ANP has historically been associated with a significant risk for readmission, but there are currently no studies exploring factors that associate with risk for unplanned, early (<30-day) readmissions in this patient population.

METHODS

We performed a retrospective review of all consecutive patients presenting to hospitals in the Indiana University (IU) Health system with pancreatic necrosis between December 2016 and June 2020. Patients younger than 18 years of age, without confirmed pancreatic necrosis and those that suffered in-hospital mortality were excluded. Logistic regression was performed to identify potential predictors of early readmission in this group of patients.

RESULTS

One hundred and sixty-two patients met study criteria. 27.7% of the cohort was readmitted within 30-days of index discharge. The median time to readmission was 10 days (IQR 5-17 days). The most frequent reason for readmission was abdominal pain (75.6%), followed by nausea and vomiting in (35.6%). Discharge to home was associated with 93% lower odds of readmission. We found no additional clinical factors that predicted early readmission.

CONCLUSION

Patients with ANP have a significant risk for early (<30 days) readmission. Direct discharge to home, rather than short or long-term rehabilitation facilities, is associated with lower odds of early readmission. Analysis was otherwise negative for independent, clinical predictors of early unplanned readmissions in ANP.

Management of Adrenal Deficiency and Shock in a Patient With Polyglandular Autoimmune Syndrome Type II

Polyglandular autoimmune syndrome (PAS) is a rare disorder characterized by the autoimmune destruction of multiple endocrine glands. Type II PAS is the most common of the PAS subtypes and is characterized by Addison's disease, autoimmune thyroid disease, and type I diabetes mellitus. Disease manifestations are predominantly seen in young adulthood with an emerging endocrine disorder; however, a host of other autoimmune conditions can also be present before endocrine organ dysfunction. Due to the complex nature of presentation and management, an important consideration in patient care involves a multidisciplinary team with the addition of an endocrinologist. A 21-year-old African American woman with a medical history of PAS-II presented during three hospitalizations with adrenal crisis, diabetic ketoacidosis (DKA), and myxedema. The common theme across admissions entails a spectrum of adrenal dysfunction, including shock, as well as glucose and thyroid abnormalities. During her first hospitalization, the patient presented with hypotension, hyperglycemia, and hypothyroidism. She received aggressive IV fluid resuscitation, an insulin drip, electrolyte repletion, an up-titration of levothyroxine, and stress-dose corticosteroids. In the second hospitalization, she also had hypotension and electrolyte derangements, along with hypoglycemia and myxedema. She received glucose management, thyroid hormone replacement, and stress steroids again. The third hospitalization involved flu-like symptoms and a positive SARS-CoV-2 test. She was managed similarly for hypotension, hyponatremia, and hyperglycemia. In this case, she presented with non-gap metabolic acidosis and required a bicarbonate drip for a short period. She did not receive antibiotics across these three admissions. We present three hospitalizations where adrenal, pancreatic, and thyroid derangements were seen and managed. It is known that most general providers other than endocrinologists are not comfortable with the management of disease manifestations of PAS-II; therefore, we provide a case review to address the standard of care management and guidelines with further discussion. This patient's maintenance care was complicated by a lack of adherence to outpatient medications, leading to recurrent hospitalizations. We also endorse the importance of doctors pursuing endocrinology fellowships, especially due to the observed waning number of graduates. An endocrinologist's availability and involvement in the care of patients with complex endocrine issues lead to improved outcomes.

Predictors of readmission in a medical department of a tertiary university hospital in the Philippines

BACKGROUND

Identifying factors that increase the risk for hospital readmission helps in determining potential targets for quality improvement efforts. The main objective of this study was to examine factors that predict increased risk of hospital readmission within 30 days of hospital discharge of patients under the General Medicine service of a tertiary government hospital in Manila, Philippines.

METHODS

We performed a retrospective cohort study which included service patients 19 years old and above readmitted within 30 days following discharge. A total of 324 hospital readmissions within 30 days of discharge from January 1 to December 31, 2019 were reviewed. We estimated the rate of 30-day readmission and identified factors associated with preventable readmissions using multivariable logistic regression.

RESULTS

Of the 4,010 hospitalizations under General Medicine service in 2019, 602 (18%) were readmissions within 30 days of discharge, majority of which were related to the index admission (90%) and unplanned (68%). Predictors of preventable readmission were emergency readmission (OR 3.37, 95% CI 1.72 to 6.60), having five to ten medications at discharge (OR 1.78, 95% CI 1.10 to 2.87), and presence of nosocomial infection (OR 1.86, 95% CI 1.09 to 3.17). The most frequent reason for readmission among preventable ones is health-care related infection (42.9%).

CONCLUSIONS

We identified factors which increased the likelihood of preventable readmissions such as type of readmission, number of medications per day, and presence of nosocomial infections. We propose that these issues be addressed to improve healthcare delivery and reduce readmission-related expenditures. Further studies should be pursued to identify impactful evidence-based practices.

Prevalence and Causes of Diagnostic Errors in Hospitalized Patients Under Investigation for COVID-19

BACKGROUND

The COVID-19 pandemic required clinicians to care for a disease with evolving characteristics while also adhering to care changes (e.g., physical distancing practices) that might lead to diagnostic errors (DEs).

OBJECTIVE

To determine the frequency of DEs and their causes among patients hospitalized under investigation (PUI) for COVID-19.

DESIGN

Retrospective cohort.

SETTING

Eight medical centers affiliated with the Hospital Medicine ReEngineering Network (HOMERuN).

TARGET POPULATION

Adults hospitalized under investigation (PUI) for COVID-19 infection between February and July 2020.

MEASUREMENTS

We randomly selected up to 8 cases per site per month for review, with each case reviewed by two clinicians to determine whether a DE (defined as a missed or delayed diagnosis) occurred, and whether any diagnostic process faults took place. We used bivariable statistics to compare patients with and without DE and multivariable models to determine which process faults or patient factors were associated with DEs.

RESULTS

Two hundred and fifty-seven patient charts underwent review, of which 36 (14%) had a diagnostic error. Patients with and without DE were statistically similar in terms of socioeconomic factors, comorbidities, risk factors for COVID-19, and COVID-19 test turnaround time and eventual positivity. Most common diagnostic process faults contributing to DE were problems with clinical assessment, testing choices, history taking, and physical examination (all p < 0.01). Diagnostic process faults associated with policies and procedures related to COVID-19 were not associated with DE risk. Fourteen patients (35.9% of patients with errors and 5.4% overall) suffered harm or death due to diagnostic error.

LIMITATIONS

Results are limited by available documentation and do not capture communication between providers and patients.

CONCLUSION

Among PUI patients, DEs were common and not associated with pandemic-related care changes, suggesting the importance of more general diagnostic process gaps in error propagation.

Patient Perspectives of the Hospital Discharge Process: A Qualitative Study

Care transitions after hospitalization require communication across care teams, patients, and caregivers. As part of a quality improvement initiative, we conducted qualitative interviews with a diverse group of 53 patients who were recently discharged from a hospitalization within a safety net hospital to explore how patient preferences were included in the hospital discharge process and differences in the hospital discharge experience by race/ethnicity. Four themes emerged from participants regarding desired characteristics of interactions with the discharge team: (1) to feel heard, (2) inclusion in decision-making, (3) to be adequately prepared to care for themselves at home through bedside teaching, (4) and to have a clear and updated discharge timeline. Additionally, participants identified patient-level factors the discharge planning team should consider, including the social context, family involvement, health literacy, and linguistic barriers. Lastly, participants identified provider characteristics, such as a caring and empathetic bedside manner, that they found valuable in the discharge process. Our findings highlight the need for shared decision-making in the discharge planning process to improve both patient safety and satisfaction.

Effects of a Multimodal Transitional Care Intervention in Patients at High Risk of Readmission: The TARGET-READ Randomized Clinical Trial

Importance

Hospital readmissions are frequent, costly, and sometimes preventable. Although these issues have been well publicized and incentives to reduce them introduced, the best interventions for reducing readmissions remain unclear.

Objectives

To evaluate the effects of a multimodal transitional care intervention targeting patients at high risk of hospital readmission on the composite outcome of 30-day unplanned readmission or death.

Design, Setting, and Participants

A single-blinded, multicenter randomized clinical trial was conducted from April 2018 to January 2020, with a 30-day follow-up in 4 medium-to-large-sized teaching hospitals in Switzerland. Participants were consecutive patients discharged from general internal medicine wards and at higher risk of unplanned readmission based on their simplified HOSPITAL score (≥4 points). Data were analyzed between April and September 2022.

Interventions

The intervention group underwent systematic medication reconciliation, a 15-minute patient education session with teach-back, a planned first follow-up visit with their primary care physician, and postdischarge follow-up telephone calls from the study team at 3 and 14 days. The control group received usual care from their hospitalist, plus a 1-page standard study information sheet.

Main Outcomes and Measures

Thirty-day postdischarge unplanned readmission or death.

Results

A total of 1386 patients were included with a mean (SD) age of 72 (14) years; 712 (51%) were male. The composite outcome of 30-day unplanned readmission or death was 21% (95% CI, 18% to 24%) in the intervention group and 19% (95% CI, 17% to 22%) in the control group. The intention-to-treat analysis risk difference was 1.7% (95% CI, -2.5% to 5.9%; P = .44). There was no evidence of any intervention effects on time to unplanned readmission or death, postdischarge health care use, patient satisfaction with the quality of their care transition, or readmission costs.

Conclusions and Relevance

In this randomized clinical trial, use of a standardized multimodal care transition intervention targeting higher-risk patients did not significantly decrease the risks of 30-day postdischarge unplanned readmission or death; it demonstrated the difficulties in preventing hospital readmissions, even when multimodal interventions specifically target higher-risk patients.

Trial Registration

ClinicalTrials.gov Identifier: NCT03496896.

Assessment of Patient Education Delivered at Time of Hospital Discharge

Importance

Patient education at time of hospital discharge is critical for smooth transitions of care; however, empirical data regarding discharge communication are limited.

Objective

To describe whether key communication domains (medication changes, follow-up appointments, disease self-management, red flags, question solicitation, and teach-back) were addressed at the bedside on the day of hospital discharge, by whom, and for how long.

Design, Setting, and Participants

This quality improvement study was conducted from September 2018 through October 2019 at inpatient medicine floors in 2 urban, tertiary-care teaching hospitals and purposefully sampled patients designated as "discharge before noon." Data analysis was performed from September 2018 to May 2020.

Exposures

A trained bedside observer documented all content and duration of staff communication with a single enrolled patient from 7 am until discharge.

Main Outcomes and Measures

Presence of the key communication domains, role of team members, and amount of time spent at the bedside.

Results

Discharge days for 33 patients were observed. Patients had a mean (SD) age of 63 (18) years; 14 (42%) identified as White, 15 (45%) were female, and 6 (18%) had a preferred language of Spanish. Thirty patients were discharged with at least 1 medication change. Of these patients, 8 (27%) received no verbal instruction on the change, while 16 of 30 (53%) were informed but not told the purpose of the changes. About half of the patients (15 of 31, 48%) were not told the reason for follow-up appointments, and 18 of 33 (55%) were not given instructions on posthospital disease self-management. Most patients (27 of 33, 81%) did not receive guidance on red-flag signs. While over half of the patients (19 of 33, 58%) were asked if they had any questions, only 1 patient was asked to teach back his understanding of the discharge plan. Median (IQR) total time spent with patients on the day of discharge by interns, senior residents, attending physicians, and nurses was 4.0 (0.75-6.0), 1.0 (0-2.0), 3.0 (0.5-7.0), and 22.5 (15.5-30.0) minutes, respectively. Most of the time was spent discussing logistics rather than discharge education.

Conclusions and Relevance

In this quality improvement study, patients infrequently received discharge education in key communication domains, potentially leaving gaps in patient knowledge. Interventions to improve the hospital discharge process should address the content, method of delivery, and transparency among team members regarding patient education.

Development and Internal Validation of an Interpretable Machine Learning Model to Predict Readmissions in a United States Healthcare System

(1) One in four hospital readmissions is potentially preventable. Machine learning (ML) models have been developed to predict hospital readmissions and risk-stratify patients, but thus far they have been limited in clinical applicability, timeliness, and generalizability. (2) Methods: Using deidentified clinical data from the University of California, San Francisco (UCSF) between January 2016 and November 2021, we developed and compared four supervised ML models (logistic regression, random forest, gradient boosting, and XGBoost) to predict 30-day readmissions for adults admitted to a UCSF hospital. (3) Results: Of 147,358 inpatient encounters, 20,747 (13.9%) patients were readmitted within 30 days of discharge. The final model selected was XGBoost, which had an area under the receiver operating characteristic curve of 0.783 and an area under the precision-recall curve of 0.434. The most important features by Shapley Additive Explanations were days since last admission, discharge department, and inpatient length of stay. (4) Conclusions: We developed and internally validated a supervised ML model to predict 30-day readmissions in a US-based healthcare system. This model has several advantages including state-of-the-art performance metrics, the use of clinical data, the use of features available within 24 h of discharge, and generalizability to multiple disease states.

Evaluation, Description and Magnitude of Readmission Phenomenon in Azienda Ospedaliero Universitaria Pisana (AOUP) for Chronic-Degenerative Diseases in the Period 2018-2021

BACKGROUND

Readmissions are hospitalizations following a previous hospitalization (called index hospitalization) of the same patient that occurred in the same facility or nursing home. They may be a consequence of the progression of the natural history of a disease, but they may also reveal a previous suboptimal stay, or ineffective management of the underlying clinical condition. Preventing avoidable readmissions has the potential to improve both a patient's quality of life, by avoiding exposure to the risks of re-hospitalization, and the financial well-being of health care systems.

METHODS

We investigated the magnitude of 30 day repeat hospitalizations for the same Major Diagnostic Category (MDC) in the Azienda Ospedaliero Universitaria Pisana (AOUP) over the period from 2018 to 2021. Records were divided into only admissions, index admissions and repeated admission. The length of the stay of all groups was compared using analysis of variance and subsequent multi-comparison tests.

RESULTS

Results showed a reduction in readmissions over the period examined (from 5.36% in 2018 to 4.46% in 2021), likely due to reduced access to care during the COVID-19 pandemic. We also observed that readmissions predominantly affect the male sex, older age groups, and patients with medical Diagnosis Related Groups (DRGs). The length of stay of readmissions was longer than that of index hospitalization (difference of 1.57 days, 95% CI 1.36-1.78 days, p < 0.001). The length of stay of index hospitalization is longer than that of single hospitalization (difference of 0.62 days, 95% CI 0.52-0.72 days, p < 0.001).

CONCLUSIONS

A patient who goes for readmission thus has an overall hospitalization duration of almost two and a half times the length of the stay of a patient with single hospitalization, considering both index hospitalization and readmission. This represents a heavy use of hospital resources, about 10,200 more inpatient days than single hospitalizations, corresponding to a 30-bed ward working with an occupancy rate of 95%. Knowledge of readmissions is an important piece of information in health planning and a useful tool for monitoring the quality of models of patient care.

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.

[Predictors for Early Unplanned Readmissions]

Predictors for Early Unplanned Readmissions Abstract. Unplanned rehospitalizations represent a major burden for patients, their relatives and the healthcare system. Since the introduction of the SwissDRG in 2012, financial incentives for hospitals have been promoted to forestall readmissions. Not every patient is at risk for rehospitalization. Affected patients can be identified by predictors from various areas in order to implement adequate interventions and avoid readmissions. Predictors can be directly related to patients as in the case of polypharmacy, multiple comorbidities or related to gender, but also provider-related and system-related. Early follow-up visits or a pre-discharge medication review are cited as effective interventions.

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.

Navigator programme for hospitalised adults experiencing homelessness: protocol for a pragmatic randomised controlled trial

INTRODUCTION

People experiencing homelessness suffer from poor outcomes after hospitalisation due to systemic barriers to care, suboptimal transitions of care, and intersecting health and social burdens. Case management programmes have been shown to improve housing stability, but their effects on broad posthospital outcomes in this population have not been rigorously evaluated. The Navigator Programme is a Critical Time Intervention case management programme that was developed to help homeless patients with their postdischarge needs and to link them with community-based health and social services. This randomised controlled trial examines the impact of the Navigator Programme on posthospital outcomes among adults experiencing homelessness.

METHODS AND ANALYSIS

This is a pragmatic randomised controlled trial testing the effectiveness of the Navigator Programme at an urban academic teaching hospital and an urban community teaching hospital in Toronto, Canada. Six hundred and forty adults experiencing homelessness who are admitted to the hospital will be randomised to receive support from a Homeless Outreach Counsellor for 90 days after hospital discharge or to usual care. The primary outcome is follow-up with a primary care provider (physician or nurse practitioner) within 14 days of hospital discharge. Secondary outcomes include postdischarge mortality or readmission, number of days in hospital, number of emergency department visits, self-reported care transition quality, and difficulties meeting subsistence needs. Quantitative outcomes are being collected over a 180-day period through linked patient-reported and administrative health data. A parallel mixed-methods process evaluation will be conducted to explore intervention context, implementation and mechanisms of impact.

ETHICS AND DISSEMINATION

Ethics approval was obtained from the Unity Health Toronto Research Ethics Board. Participants will be required to provide written informed consent. Results of the main trial and process evaluation will be reported in peer-reviewed journals and shared with hospital leadership, community partners and policy makers.

TRIAL REGISTRATION NUMBER

NCT04961762.

Multiple Electronic Health Record-Based Measures of Social Determinants of Health to Predict Return to the Emergency Department Following Discharge

Health care systems continue to struggle with preventing 30-day readmissions to their institutions. Social determinants of health (SDOH) are important predictors of repeat visits to the hospital. In many health systems, SDOH data are limited to those variables that are most relevant to care delivery or payment (eg, race, gender, insurance status). Despite calls for integrating a more robust set of measures (eg, measures of health behaviors and living conditions) into the electronic health record (EHR), these data often have missing values necessitating the use of imputation to build a comprehensive picture of patients who are likely to return to the health system. Using logistic regression analyses and imputation of missing data from 2017 to 2018, this study uses measures found in the EHR (eg, tobacco use, living situation, problems at home, education) to assess those SDOH that might predict a return to the emergency department within 30 days of discharge from a health system. In both imputed and raw data, the total number of recorded health conditions was the most important predictor and collectively SDOH variables made a relatively small contributions in determining the likelihood of a return to the hospital. Although SDOH variables might be important in the design of programs aimed at preventing readmissions, they may not be useful in readmission predictive models.

Post-discharge early assessment with remote video link (PEARL) initiative for patients discharged from hospital medicine services

OBJECTIVES

The COVID-19 pandemic impacted the availability and accessibility of outpatient care following hospital discharge. Hospitalists (physicians) and hospital medicine advanced practice providers (HM-APPs) coordinate discharge care of hospitalized patients; however, it is unknown if they can deliver post-discharge virtual care and overcome barriers to outpatient care. The objective was to develop and provide post-discharge virtual care for patients discharged from hospital medicine services.

METHODS

We developed the Post-discharge Early Assessment with Remote video Link (PEARL) initiative for HM-APPs to conduct a post-discharge video visit (to review recommendations) and telephone follow-up (to evaluate adherence) with patients 2-6 days following hospital discharge. Participants included patients discharged from hospital medicine services at an institution's hospitals in Rochester (May 2020-August 2020) and Austin (November 2020-February 2021) in Minnesota, US. HM-APPs also interviewed patients about their experience with the video visit and completed a survey on their experience with PEARL.

RESULTS

Of 386 eligible patients, 61.4% were enrolled (n = 237/386) including 48.1% women (n = 114/237). In patients with complete video visit and telephone follow-up (n = 141/237), most were prescribed new medications (83.7%) and took them as prescribed (93.2%). Among five classes of chronic medications, patient-reported adherence ranged from 59.2% (narcotics) to 91.5% (anti-hypertensives). Patient-reported self-management of 12 discharge recommendations ranged from 40% (smoking cessation) to 100% (checking rashes). Patients reported benefit from the video visit (agree: 77.3%) with an equivocal preference for video visits over clinic visits. Among HM-APPs who responded to the survey (88.2%; n = 15/17), 73.3% reported benefit from visual contact with patients but were uncertain if video visits would reduce emergency department visits.

CONCLUSION

In this novel initiative, HM-APPs used video visits to provide care beyond their hospital role, reinforce discharge recommendations for patients, and reduce barriers to outpatient care. The effect of this initiative is under evaluation in a randomized controlled trial.

Association of Diagnosed Dementia with Post-discharge Mortality and Readmission Among Hospitalized Medicare Beneficiaries

BACKGROUND

Patients with dementia are frequently hospitalized and may face barriers in post-discharge care.

OBJECTIVE

To determine whether patients with dementia have an increased risk of adverse outcomes following discharge.

DESIGN

Retrospective cohort study.

SUBJECTS

Medicare beneficiaries hospitalized in 2016.

MAIN MEASURES

Co-primary outcomes were mortality and readmission within 30 days of discharge. Multivariable logistic regression models were estimated to assess the risk of each outcome for patients with and without dementia accounting for demographics, comorbidities, frailty, hospitalization factors, and disposition.

KEY RESULTS

The cohort included 1,089,109 hospitalizations of which 211,698 (19.3%) were of patients with diagnosed dementia (median (IQR) age 83 (76-89); 61.5% female) and 886,411 were of patients without dementia (median (IQR) age 76 (79-83); 55.0% female). At 30 days following discharge, 5.7% of patients with dementia had died compared to 3.1% of patients without dementia (adjusted odds ratio (aOR) 1.21; 95% CI 1.17 to 1.24). At 30 days following discharge, 17.7% of patients with dementia had been readmitted compared to 13.1% of patients without dementia (aOR 1.02; CI 1.002 to 1.04). Dementia was associated with an increased odds of readmission among patients discharged to the community (aOR 1.07, CI 1.05 to 1.09) but a decreased odds of readmission among patients discharge to nursing facilities (aOR 0.93, CI 0.90 to 0.95). Patients with dementia who were discharged to the community were more likely to be readmitted than those discharged to nursing facilities (18.9% vs 16.0%), and, when readmitted, were more likely to die during the readmission (20.7% vs 4.4%).

CONCLUSIONS

Diagnosed dementia was associated with a substantially increased risk of mortality and a modestly increased risk of readmission within 30 days of discharge. Patients with dementia discharged to the community had particularly elevated risk of adverse outcomes indicating possible gaps in post-discharge services and caregiver support.

Cohort design to assess the association between post-hospital primary care physician follow-up visits and hospital readmissions

While multifaceted post-hospitalization interventions can succeed in preventing hospital readmissions, many of these interventions are labor-intensive and costly. We hypothesized that a timely post-discharge primary care physician (PCP) visit alone might prevent hospital readmission. We conducted a retrospective cohort study to assess whether post-hospitalization PCP visits within 14 days of discharge were associated with lower rates of 30-day hospital readmission. In a secondary analysis we also assessed: whether visits with a PCP at 7-days post-discharge changed rates of hospital readmissions and whether post-hospitalization PCP visits were associated with decreased 90-day hospital readmissions. We included all adults with a PCP who were discharged from an inpatient medical service in a large, urban integrated academic health system from January 1, 2019 to September 9, 2019 in our analysis. We performed unadjusted bivariate analyses to measure the associations between having a PCP visit within 14 and 7 days of discharge and hospital readmission within 30 and 90 days. Then we constructed multivariate logistic regression models including patient medical and utilization characteristics to estimate the adjusted odds of a patient with a post-hospitalization PCP visit experiencing a 30-day hospital readmission (primary outcome) and 90-day readmission (secondary outcome). A total of 9236 patients were discharged; mean age was 57.9 years and 59.7% were female. Of the study population, 35.6% (n = 3284) and 24.1% (n = 2224) of patients had a post-hospitalization PCP visit within 14 days and or 7 days, respectively. Overall, 1259 (13.6%) and 2153 (23.3%) of discharged patients were readmitted at 30 and 90 days, respectively. In unadjusted analyses, having a post discharge PCP visit was not associated with decreased hospital readmission rates, but after adjusting for sociodemographic, medical and utilization characteristics, having a post-hospitalization PCP visit at 14 and 7 days was associated with lower hospital readmission rates at 30 days: 0.68 (95% CI 0.59-0.79) and 0.76 (95% CI 0.66-0.89), respectively; and 90 days: 0.76 (95% CI 0.68-0.85) and 0.80 (95% CI 0.70-0.91), respectively. In this large integrated urban academic health system, having a post-hospitalization PCP visit within 14- and 7-days of hospital discharge was associated with lower rates of readmission at 30 and 90 days. Further studies should examine whether improving access to PCP visits post hospitalization reduces readmissions rates.

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.

Improving the safety and experience of transitions from hospital to home: a cluster randomised controlled feasibility trial of the 'Your Care Needs You' intervention versus usual care

Background

The ‘Your Care Needs You’ (YCNY) intervention aims to increase the safety and experience of transitions for older people through greater patient involvement during the hospital stay.

Methods

A cluster randomised controlled feasibility trial was conducted on NHS inpatient wards (clusters) where ≥ 40% of patients were routinely ≥ 75 years. Wards were randomised to YCNY or usual care using an unequal allocation ratio (3:2). We aimed to recruit up to 20 patients per ward. Follow-up included routine data collection and questionnaires at 5-, 30-, and 90-days post-discharge. Eligible patients were ≥ 75 years, discharged home, stayed overnight on participating wards, and could read and understand English.

The trial assessed the feasibility of delivering YCNY and the trial methodology through recruitment rates, outcome completion rates, and a qualitative evaluation. The accuracy of using routinely coded data for the primary outcome in the definitive trial was assessed by extracting discharge information for up to ten nonindividual consenting patients per ward.

Results

Ten wards were randomised (6 intervention, 4 control). One ward withdrew, and two wards were unable to deliver the intervention. Seven-hundred twenty-one patients were successfully screened, and 161 were recruited (95 intervention, 66 control). The patient post-discharge attrition rate was 17.4% (n = 28). Primary outcome data were gathered for 91.9% of participants with 75.2% and 59.0% providing secondary outcome data at 5 and 30 days post-discharge respectively. Item completion within questionnaires was generally high. Post-discharge follow-up was terminated early due to the COVID-19 pandemic affecting 90-day response rates (16.8%). Data from 88 nonindividual consenting patients identified an error rate of 15% when using routinely coded data for the primary outcome. No unexpected serious adverse events were identified.

Most patients viewed YCNY favourably. Staff agreed with it in principle, but ward pressures and organisational contexts hampered implementation. There was a need to sustain engagement, provide clarity on roles and responsibilities, and account for fluctuations in patients’ health, capacity, and preferences.

Conclusions

If implementation challenges can be overcome, YCNY represents a step towards involving older people as partners in their care to improve the safety and experience of their transitions from hospital to home.

Trial registration

ISRCTN: 51154948.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40814-022-01180-3.

Readmission rates and predictors of avoidable readmissions in older adults in a tertiary care centre

Context

Thirty-day readmissions are used to gauge health care accountability, which occurs as part of the natural course of the illness or due to avoidable fallacies during the index admission. The utility of this metric is unknown in older adults from developing countries.

Aim

To ascertain the unplanned 30-day readmission rate and enumerate predictors of avoidable hospital readmission among early (0-7 days) and late (8-30 days) readmissions.

Settings and Design

A retrospective chart audit of 140 older adults who were readmitted to a premier tertiary care teaching hospital under Geriatrics from the neighboring states of Tamil Nadu, Andhra Pradesh, and Kerala were undertaken.

Methods and Materials

Data from health records were collected from the hospital electronic database from May 2015 to May 2020. The data was reviewed to determine the 30-day readmission rate and to ascertain the predictors of avoidable readmissions among both early and late readmissions.

Results

Out of 2698 older adults admitted to the geriatric wards from the catchment areas, the calculated 30-day hospital readmission rate was 5.18%, and 41.4% of these readmissions were potentially avoidable. The median duration from discharge to the first readmission was ten days (Interquartile range: 5-18 days). Patients had to spend INR 44,000 (approximately 602 USD) towards avoidable readmission. The most common causes for readmission included an exacerbation, reactivation, or progression of a previously existing disease (55.7%), followed by the emergence of a new disease unrelated to index admission (43.2%). Fifty-eight patients (41.4%) were readmitted within seven days following discharge. Early readmissions were seen in patients with malignancies [8 (13.5%) vs. 4 (4.8%); P = 0.017], on insulin (P = 0.04) or on antidepressants (P = 0.01). Advanced age was found to be an independent predictor of avoidable early readmission (OR 2.99 95%CI 1.34-6.62, P = 0.007), and admission to a general ward (as compared to those admitted in a private ward) was an independent predictor of early readmissions (OR 2.99 95%CI 1.34-6.62, P = 0.007).

Conclusion

The 30-day readmission rate in a geriatric unit in a tertiary care hospital was 5.2%. Advanced age was considered to be an independent predictor of avoidable early readmission. Future prospective research on avoidable readmissions should be undertaken to delineate factors affecting 30-day avoidable hospital readmissions in developing nations.

Differential Impact of Work Overload on Physicians' Attention: A Comparison Between Residential Fields

OBJECTIVES

Medical errors cause tens of thousands of deaths annually and have a major impact on quality of care and management; however, it receives scant research and public awareness. This study aimed to examine the relation between workload-induced lack of sleep and attention failure, as indications for medical errors risk, among young residents.

METHODS

We performed an evaluation of young physicians by the Test of Variables of Attention, before and after a 24-hour shift.

RESULTS

Workload was manifested by 13% overall attention impairment at baseline, which increased to 34% with deficiencies below the normal range after the shift. Attention measures differed between physicians of each residential field at baseline, but to greater extent after the shift.

CONCLUSIONS

Traditional working schedule is strongly associated with attention failure. Based on the literature linking attention failures to medical errors, we suggest a regulatory change regarding residents' shift duration to decrease preventable errors.

Dismissal disagreement and discharge delays: Associations of patient-clinician plan of care agreement with discharge outcomes

BACKGROUND

Many hospitalized patients do not understand or agree with their clinicians about their discharge plan. However, the effect of disagreement on discharge outcomes is unknown.

OBJECTIVE

To measure the correlation between patient-clinician care agreement and discharge outcomes.

DESIGN

A prospective cohort study was performed from September 2019 to March 2020 (Rochester, MN, USA).

SETTING AND PARTICIPANTS

Internal medicine patients and their primary clinician (resident, advanced practice clinician or attending) hospitalized from September 2019-March 2020 at Mayo Clinic Hospital. Participants were independently surveyed following hospital day #3 ward rounds regarding the goals of the hospitalization and discharge planning.

MAIN OUTCOME AND MEASURES

Patient-clinician agreement for main diagnosis, patient's main concern, and four domains of discharge planning was assessed. Readiness for hospital discharge, delayed discharge, and 30-day readmission was measured. Then, associations between patient-clinician agreement, delayed discharge, and 30-day readmissions were analyzed using multivariable logistic regression.

RESULTS

Of the 436 patients and clinicians, 17.7% completely agreed about what needs to be accomplished before dismissal, 40.8% agreed regarding discharge date, and 71.1% agreed regarding discharge location. In the multivariable model, patient-clinician agreement scores were not significantly correlated with discharge outcomes. Patient-clinician agreement on discharge location was higher for those discharged to home (81.5%) versus skilled nursing facility (48.5%) or assisted living (42.9%) (p < .0001). The agreement on the expected length of stay was highest for home-goers (45.9%) compared to skilled nursing (32.0%) or assisted living (21.4%) (p = .004).

CONCLUSIONS

Patients and their clinicians frequently disagree about when and where a patient will go after hospitalization, particularly for those discharged to a skilled nursing facility. While disagreement did not predict discharge outcomes, our findings suggest opportunities to improve effective communication and promote shared mental models regarding discharge earlier in the hospital stay.

Explainable machine learning models for predicting 30-day readmission in pediatric pulmonary hypertension: A multicenter, retrospective study

Background

Short-term readmission for pediatric pulmonary hypertension (PH) is associated with a substantial social and personal burden. However, tools to predict individualized readmission risk are lacking. This study aimed to develop machine learning models to predict 30-day unplanned readmission in children with PH.

Methods

This study collected data on pediatric inpatients with PH from the Chongqing Medical University Medical Data Platform from January 2012 to January 2019. Key clinical variables were selected by the least absolute shrinkage and the selection operator. Prediction models were selected from 15 machine learning algorithms with excellent performance, which was evaluated by area under the operating characteristic curve (AUC). The outcome of the predictive model was interpreted by SHapley Additive exPlanations (SHAP).

Results

A total of 5,913 pediatric patients with PH were included in the final cohort. The CatBoost model was selected as the predictive model with the greatest AUC for 0.81 (95% CI: 0.77–0.86), high accuracy for 0.74 (95% CI: 0.72–0.76), sensitivity 0.78 (95% CI: 0.69–0.87), and specificity 0.74 (95% CI: 0.72–0.76). Age, length of stay (LOS), congenital heart surgery, and nonmedical order discharge showed the greatest impact on 30-day readmission in pediatric PH, according to SHAP results.

Conclusions

This study developed a CatBoost model to predict the risk of unplanned 30-day readmission in pediatric patients with PH, which showed more significant performance compared with traditional logistic regression. We found that age, LOS, congenital heart surgery, and nonmedical order discharge were important factors for 30-day readmission in pediatric PH.

Decision support through risk cost estimation in 30-day hospital unplanned readmission

Unplanned hospital readmissions mean a significant burden for health systems. Accurately estimating the patient’s readmission risk could help to optimise the discharge decision-making process by smartly ordering patients based on a severity score, thus helping to improve the usage of clinical resources. A great number of heterogeneous factors can influence the readmission risk, which makes it highly difficult to be estimated by a human agent. However, this score could be achieved with the help of AI models, acting as aiding tools for decision support systems. In this paper, we propose a machine learning classification and risk stratification approach to assess the readmission problem and provide a decision support system based on estimated patient risk scores.