Unplanned readmission prevention by a geriatric emergency network for transitional care (URGENT): a prospective before-after study

Background: Unplanned 30-day rehospitalization rates for AMI (19.9%) and CHF (24.4%) represent a huge health care burden for patients and providers. Delays in follow-up and lack of adherence to standardized guidelines, by providers and patients, contribute to these findings. The hospital-to-home transition is one area with the potential to effect changes in this complex problem. Specially trained outpatient cardiovascular nurse practitioners (NP) aim to “bridge” the transitional care gap in the Cardiovascular Medicine Bridge Program (BRIDGE). NPs, acting as an extension of the inpatient team, adjust treatments depending on patient status, educate patients, and ensure adherence to lifestyle and therapeutic guidelines. Purpose: To assess differences between patients who attended the BRIDGE clinic and those who did not. Methods: This was a retrospective study of all patients referred to BRIDGE, from June 2008 to February 2009. Univariate techniques were used to compare those who attended BRIDGE and those who did not, in terms of age, diagnoses, comorbidities, time to follow-up visit with a cardiologist, and unplanned readmission. Results: Of 359 patients, 239 (67%) attended BRIDGE, mean time from discharge to BRIDGE follow-up was 19.8 days. Mean age of attendees was 63.9, non-attendees M = 61.2, P = .110; 66.6% were male. Patients were more likely to attend BRIDGE if they had greater than two comorbidities (≤ 2 comorbidities 10.5% vs. > 2 comorbidities 18.3%, P = .046). Primary cardiac diagnoses accounted for 217 (60.6%) BRIDGE referrals (ACS 21.2%, CAD 13.7%, CHF 13.4%, other cardiac 12.3%); cardiac was a secondary diagnosis or complication for the remaining 39.4%. Mean days from discharge to first cardiology appointment was 73.0 for attendees and 53.6 for non-attendees, P = .018. BRIDGE attendees had significantly lower 30-day readmission and ED rates than those who did not attend (readmit: attend 8.7 % vs. not attend 21.7%, P = .001, ED visits: attend 13.5% vs. not attend 28.2%, P = .005). Conclusion: Individuals who attended the BRIDGE clinic had fewer unplanned readmissions, when compared to patients who did not take advantage of this opportunity. These preliminary findings suggest that this strategy can improve efficiency of acute cardiac care in the US.

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Abstract 095: Lessons Learned from the BRIDGE Registry: Major Findings From 8 Years of BRIDGE

Circulation Cardiovascular Quality and Outcomes ◽

10.1161/circoutcomes.10.suppl_3.095 ◽

2017 ◽

Vol 10 (suppl_3) ◽

Author(s):

Morgan Bradford ◽

Rachel Krallman ◽

Colin McMahon ◽

Daniel Montgomery ◽

Eva Kline-Rogers ◽

...

Keyword(s):

Transitional Care ◽

The United States ◽

Lessons Learned ◽

Unplanned Readmission ◽

Post Discharge ◽

Care Clinic ◽

Coronary Syndrome ◽

Patient Reported ◽

Comorbidity Scores

Background: Readmissions after cardiac hospitalizations are frequent and costly in the United States. Delays in follow-up and lack of adherence to guidelines may contribute to high unplanned readmission rates. Bridging the Discharge Gap Effectively (BRIDGE) is a nurse practitioner (NP) led, transitional care clinic for cardiac patients, aimed at reducing readmissions. Data on patients referred to BRIDGE has been collected since 2009; herein we report a summary of significant findings from these data. Methods: A qualitative review of results and conclusions from all published abstracts, oral presentations, and papers from the BRIDGE registry (June 2008-August 2015) was conducted. Content analysis was used to synthesize findings across studies. Results: Data from 3982 patients referred to BRIDGE have been collected. Seven themes were identified in the analysis of BRIDGE publications. During BRIDGE, NPs focused on medical history, symptoms, medication management (in 24.8% of visits), patient education, and referrals. In addition to addressing provider priorities, addressing patient concerns (daily living and clinical questions, feelings and fears) was highly salient, resulting in a high level of patient-NP connectedness as evidenced by high patient-reported scores on the Consultation and Relational Empathy scale (mean 43.5 ± 2.8; possible range 0, 50) and the Patient-Doctor Relationship Questionnaire (mean 43.05 ± 3.1; possible range 5, 45). Readmissions within 30 days were consistently lower for acute coronary syndrome (ACS) patients who attended BRIDGE compared to those who did not (6.4% v. 13.1%; p<0.01); similar results were not seen in heart failure (HF) (15.4% v. 15.7%; p=0.944) or atrial fibrillation (AF) (8.5% v. 5.2%; p=0.343) patients. A spike in HF readmissions was seen between 8-14 days post-discharge, suggesting the need for a sooner appointment. However, follow-up within 7 days of discharge did not show reduced readmissions in HF patients. AF readmissions were also difficult to avoid; in a subset of AF patients readmitted within 30 days, 51.1% (n=23) were readmitted for non-AF diagnoses. High risk patients (i.e. those with an adverse event before BRIDGE) were older, had higher Charlson comorbidity scores, and were more likely to have depression. However, marriage was associated with fewer readmissions. Conclusions: Data from the BRIDGE registry have shown that clinic attendance reduced ACS readmissions; has characterized older, depressed patients with higher Charlson comorbidity scores as being those most likely to be readmitted; and has identified areas for improvement in transitional care (e.g. AF and HF) where readmissions are difficult to avoid. Continuous quality improvement and real-time monitoring of patient outcomes have translated this research into more prompt transitional care, illustrating the importance of registry-based research.

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Abstract TMP107: Impact of a Stroke Transitional Care Navigator at a Comprehensive Stroke Center

Stroke ◽

10.1161/str.51.suppl_1.tmp107 ◽

2020 ◽

Vol 51 (Suppl_1) ◽

Author(s):

Darren T Larsen ◽

Alexandra Lesko ◽

Elizabeth Baraban

Keyword(s):

Transitional Care ◽

Positive Impact ◽

Outpatient Setting ◽

Unplanned Readmission ◽

Post Discharge ◽

Individualized Care ◽

Ed Visits ◽

Comprehensive Stroke Center ◽

Stroke Center

Background: The Stroke Transitional Care Navigator (STCN), was implemented at our Comprehensive Stroke Center (CSC) in January 2017 in order to bridge care from the inpatient to outpatient setting. The STCN nurse meets with patients prior to discharge to address secondary stroke risk factors and discuss the follow up plan in an effort to improve patient outcomes. The purpose of this study was to determine whether implementation of a STCN improved compliance with follow up stroke neurology care and reduced unplanned readmissions and Emergency Department (ED) visits. Methods: Retrospective data review, included ischemic stroke or ICH patients, 18 or over, discharged from February 2017 through February 2018. Subarachnoid hemorrhages and hospice discharges were excluded. Patients were grouped into a “Followed’ cohort if they had documented contact with the STCN prior to discharge or within 30 days; otherwise they were categorized as “Not Followed”. Outcomes of interest were percentage of patients compliant with attending an outpatient visit with a stroke provider within 45 or 120 days post-discharge and percentage of unplanned readmission and ED visits 30 days post-discharge. Analyses comparing those with and without STCN contact were performed using Fisher’s Exact test and Pearson’s chi square test. Results: There were 689 patients that met inclusion criteria with 47.2% (n=325) in the Followed and 52.8% (n=364) in the Not-Followed cohorts. The Followed cohort was more likely to comply with attending a follow-up visit within 45-days (67.2% vs. 32.8%, p<.001) as well as 120 days of discharge (61.0% vs 39.0%, p<.001). No differences were found between the Followed and Not Followed cohorts for readmissions (9.5% vs. 11.5%, p=.394) or ED visits (9.5% vs. 10.2%, p=.783). Conclusion: The STCN had a significant positive impact on patients returning to clinic for follow up stroke neurology care. Though follow up care has been shown to reduce readmission rates in some studies, in this study there was no impact on 30-day readmissions or ED visits. Given the unique, individualized care and coordination provided by the STCN, which is very well received by patients and providers, qualitative measures may be more useful in the future to determine the effectiveness of the STCN.

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

BMC Medical Informatics and Decision Making ◽

10.1186/s12911-021-01639-y ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Yu-Tai Lo ◽

Jay Chie-hen Liao ◽

Mei-Hua Chen ◽

Chia-Ming Chang ◽

Cheng-Te Li

Keyword(s):

Machine Learning ◽

High Risk ◽

Prediction Models ◽

Transitional Care ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Gradient Boosting ◽

Unplanned Readmission ◽

Extreme Gradient Boosting ◽

Readmission Risk

Abstract 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.

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Abstract 126: Inside Transitional Care: What Actually Happens During These Visits?

Circulation Cardiovascular Quality and Outcomes ◽

10.1161/circoutcomes.8.suppl_2.126 ◽

2015 ◽

Vol 8 (suppl_2) ◽

Author(s):

Sherry M Bumpus ◽

Minnie Bluhm ◽

Rachel Sylvester ◽

Joshua Dean ◽

Michaela Steinbacher ◽

...

Keyword(s):

Transitional Care ◽

Medical Condition ◽

Daily Life ◽

Stress Test ◽

Cost Savings ◽

Care Program ◽

Clinical History ◽

Physical Symptoms ◽

Unplanned Readmission ◽

Post Discharge

Background: Bridging the Discharge Gap Effectively (BRIDGE) is a nurse practitioner (NP) delivered transitional care program designed to provide cardiac patients a timely, guideline-based, first post-discharge visit. BRIDGE follow-up within 14 days of discharge has been shown to reduce early adverse events, including rehospitalization, for ACS patients, at a cost savings. Despite this success, there is little evidence documenting what occurs during these visits. The purpose of this study is to examine the content of first post-discharge visits. Methods: Mixed methods design was used to examine content of BRIDGE visits and assess patient perceptions of rapport with their NP. Visits with 17 ACS patients were audio-recorded and transcribed verbatim. Transcripts were coded and analyzed using conventional content analysis to identify themes within and across visits. Patients completed the Consultation and Relational Empathy (CARE) scale and a modified Patient-Doctor Relationship Questionnaire (PDRQ9). Demographic information and details of 30-day readmissions were abstracted from patient charts. Results: Nineteen patients consented; 17 (89%) completed the study. Most were male (14, 73.7%) and white (15, 78.9%). Average age was 61.6 years. One (5%) had an unplanned readmission. NP priorities during visits included clinical history, medication reconciliation, patient education, and referrals. Patients were screened for guideline-driven secondary prevention queues such as physical symptoms, diet, physical activity, and smoking. Patient priorities included questions about daily life (can I play catch with my grandson); clinical questions (can a stress test cause a heart attack); feelings (he feels like dying; I feel helpless), and fear of death (I’m afraid if I go to sleep I might not wake up). On average, NPs contributed 59% of the verbal content of the visits. Patients felt highly connected with NPs (mean PDRQ9 43.05 + 3.1; possible range 5, 45, α=.95) and viewed them as deeply empathic (mean CARE 43.5 + 2.8); possible range 0, 50, α=.94). Discussion: A qualitative approach resulted in nuanced understandings of the content of first post-discharge visits. Patients and NPs have overlapping priorities for these visits. Both concern themselves with managing the medical condition. In addition, patients reveal other priorities, such as how to carry on with daily life and manage feelings and fears. Notably, assessment of psychosocial issues and mental health were absent, suggesting an opportunity to enhance patient care. NPs may be ideally suited to begin filling this gap given their excellent rapport with patients and expertise in motivational interviewing. It is plausible that these factors also contribute to the success of the BRIDGE program in reducing 30-day readmissions. Further research is needed with larger sample sizes and other types of providers to fully assess their impact.

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