136 Readmissions to Hospital Following A Decision to Eat and Drink with Acknowledged Risk with Support From the Forward Care Bundle

2021 ◽  
Vol 50 (Supplement_1) ◽  
pp. i12-i42
Author(s):  
N Soar ◽  
J Birns ◽  
P Sommerville ◽  
A Lang ◽  
A Fitzgerald ◽  
...  
Keyword(s):  
Hospital Readmissions ◽  
Oral Intake ◽  
Oral Route ◽  
Care Bundle ◽  
Discharge Destination ◽  
Post Discharge ◽  
Exact Test ◽  
Trust Hospital ◽  
Discharge Location ◽  
Private Homes

Abstract Introduction The FORWARD care bundle (Feeding via the Oral Route With Acknowledged Risk of Deterioration) is used to support patients with dysphagia eating and drinking with acknowledged risk (EDAR) at our Trust. Key aims of FORWARD include improving advanced care planning (ACP) and avoiding unnecessary readmissions. This study aimed to determine the incidence of EDAR related readmissions (RR-EDAR) after FORWARD, and the effects of ACP and discharge location. Methods Retrospective review of all patients supported by FORWARD during admissions between January 2018 and December 2019. Data were collected on number and reasons for in-Trust hospital readmissions 6 months post-discharge, preferred place of care ACPs in event of EDAR related deterioration and discharge destination. Readmission reasons were classified as RR-EDAR (e.g. chest infection, reduced oral intake) and no relation to EDAR. Means (SD) and percentages are presented with comparisons using Fishers Exact Test. Results 316 patients were included; mean (SD) age 81(12). 64% (n = 202) of patients were discharged alive, 36% (n = 114) were alive at 6 months. 38% of live discharges (n = 75) were readmitted and 52% (n = 39) of these patients were RR-EDAR. Mean (SD) RR-EDAR number was 11, and 18% (n = 7) of patients had RR-EDAR >1 (range 1–5). RR-EDAR was only 7% (n = 4) in patients wishing to remain at home vs 25% (n = 33) in those without a documented place of care (p < 0.01). RR-EDAR was 23% (n = 29) in patients discharged to a private home vs 10% (n = 6) discharged to nursing/care homes (p < 0.05). Conclusions The majority of FORWARD patients are not readmitted. RR-EDAR comprises half of all readmitted patients and some have multiple admissions. Fewer patients with ACPs were RR-EDAR suggesting these are effective. Most patients RR-EDAR were from private homes, suggesting residential care may provide more support. Further work includes increasing ACPs, supporting patients with multiple RR-EDAR and those discharged to private homes.

Discharge destination following pancreaticoduodenectomy: A NSQIP analysis of predictive factors and post-discharge outcomes

2019 ◽  
Vol 218 (2) ◽  
pp. 342-348 ◽  
Author(s):  
David A. Mahvi ◽  
Linda M. Pak ◽  
Richard D. Urman ◽  
Jason S. Gold ◽  
Edward E. Whang
Keyword(s):  
Predictive Factors ◽  
Discharge Destination ◽  
Post Discharge ◽  
Discharge Outcomes

Abstract TP308: Transitional Care in Stroke Certified and Non-certified Hospitals: The Comprehensive Post-Acute Stroke Services (COMPASS) Study

Stroke ◽  
2017 ◽  
Vol 48 (suppl_1) ◽  
Author(s):  
Janet Prvu Bettger ◽  
Sara Jones ◽  
Anna Kucharska-Newton ◽  
Janet Freburger ◽  
Walter Ambrosius ◽  
...  
Keyword(s):  
Transitional Care ◽  
Discharge Planning ◽  
Care Transitions ◽  
Cluster Randomized Trial ◽  
Stroke Patients ◽  
Care Needs ◽  
Post Discharge ◽  
Exact Test ◽  
Chi Squared

Background: Greater than 50% of stroke patients are discharged home from the hospital, most with continuing care needs. In the absence of evidence-based transitional care interventions for stroke patients, procedures likely vary by hospital even among stroke-certified hospitals with requirements for transitional care protocols. We examined the standard of transitional care among NC hospitals enrolled in the COMPASS study comparing stroke-certified and non-certified hospitals. Methods: Hospitals completed an online, self-administered, web-based questionnaire to assess usual care related to hospitals’ transitional care strategy, stroke program structural components, discharge planning processes, and post-discharge patient management and follow-up. Response frequencies were compared between stroke certified versus non-certified hospitals using chi-squared statistics and Fisher’s exact test. Results: As of July 2016, the first 27 hospitals enrolled (of 40 expected) completed the survey (67% certified as a primary or comprehensive stroke center). On average, 54% of stroke patients were discharged home. Processes supporting hospital-to-home care transitions, such as timely follow-up calls and follow-up with neurology, were infrequent and overall less common for non-certified hospitals (Table). Assessment of post-discharge outcomes was particularly infrequent among non-certified sites (11%) compared with certified sites (56%). Uptake of transitional care management billing codes and quality metrics was low for both certified and non-certified hospitals. Conclusion: Significant variation exists in the infrastructure and processes supporting care transitions for stroke patients among COMPASS hospitals in NC. COMPASS as a pragmatic cluster-randomized trial will compare outcomes among hospitals that implement a CMS-directed model of transitional care with those hospitals that provide highly variable transitional care services.

Potentially Preventable Hospital Readmissions After Patients’ First Stroke—A National Population-Based Study In Taiwan

2021 ◽  
Author(s):  
Ling-Jan Chiou ◽  
Hui-Chu Lang
Keyword(s):  
Hospital Readmission ◽  
Discharge Planning ◽  
Multinomial Logistic Regression ◽  
Hospital Readmissions ◽  
Care Program ◽  
Severity Index ◽  
Stroke Severity ◽  
Stroke Patients ◽  
Post Discharge ◽  
Important Indicator

Abstract Readmission is an important indicator of the quality of care. The purpose of this study was to explore the probabilities and predictors of 30-day and 1-year potentially preventable hospital readmission (PPR) after a patient’s first stroke. We used claims data from the National Health Insurance (NHI) from 2010 to 2018. Multinomial logistic regression was used to assess the predictors of 30-day and 1-year PPR. A total of 41,921 discharged stroke patients was identified. We found that hospital readmission rates were 15.48% within 30-days and 47.25% within 1-year. The PPR and non-PPR were 9.84% (4,123) and 5.65% (2,367) within 30-days, and 30.65% (12,849) and 16.60% (6,959) within 1-year, respectively. The factors of older patients, type of stroke, shorter length of stay, higher Charlson Comorbidity Index (CCI), higher stroke severity index (SSI), hospital level, hospital ownership, and urbanization level were associated significantly with the 30-day PPR. In addition, the factors of gender, hospitalization year, and monthly income were associated significantly with 1-year PPR. The results showed that better discharge planning and post-discharge follow-up programs could reduce PPR substantially. Also, implementing a post-acute care program for stroke patients has helped reduce the long-term PPR in Taiwan.

Abstract TP288: Overcoming Obstacles Making People Healthy (OOMPH): A Patient Centered Post Discharge Follow-up Program for Stroke Patients

Stroke ◽  
2017 ◽  
Vol 48 (suppl_1) ◽  
Author(s):  
Jonathan Muller ◽  
Barbara Gatton ◽  
Linda Fox ◽  
Joseph A Bove ◽  
Johanna Donovan Turner ◽  
...  
Keyword(s):  
Support Groups ◽  
Medical Information ◽  
Patient Adherence ◽  
Hospital Readmissions ◽  
Phone Call ◽  
Stroke Patients ◽  
Post Discharge ◽  
Phone Calls ◽  
Stroke Support Groups

Background and Purpose: At least 12% of stroke patients are readmitted to a hospital within 30 days of discharge. We know that patients hospitalized for other conditions are less likely to be readmitted within 30 days if they are seen by their PCP shortly after discharge. However, less than a third of patients in the New York metropolitan area admitted for heart failure, heart attacks, and pneumonia see their PCP within 14 days after discharge and nearly 40% of patients do not adhere to their prescribed regimen. In the case of cerebrovascular diseases, outpatient follow-up may prevent the majority of avoidable readmissions. The purpose of this project is to identify and reduce unnecessary, unplanned hospital readmissions after stroke. Our goal is to encourage patient adherence to prescribed medication and other therapies, as well as to ensure timely follow-up with their PCP. Methods: Stroke and transient ischemic attack (TIA) patients with a disposition of either home or short-term rehabilitation are visited and offered enrollment. Participants are given a kit which includes a personalized binder (to manage essential medical information) and a 28-slot pill box. Each patient then receives 3 phone call interviews at 7, 21 and 32 days after discharge. The aim of the phone calls is to identify obstacles to compliance with treatment regimen and follow-up care. Results: From January 2015 to June 2016, 247 patients were enrolled and followed up. Within 30 days of discharge, 10% were readmitted and 50% of all readmissions occurred within the first 7 days. Of those readmitted, 19% were due to an injury from physical therapy. Data from follow-up phone calls revealed that 83% were taking all prescribed medications, 89% had completed a follow-up with any physician, 69% were using the binder, and 61% had done all three. Conclusions: While we have not enrolled enough patients to see a statistically significant reduction in readmissions, our interviews showed that weather, depression, as well as a lack of insurance, family support, and a home health aide are all determinants on how patients will follow their prescribed regimen. The results of this study have allowed us to begin implementing stroke support groups and pre-discharge follow-up appointment scheduling.

Abstract 14996: Healthcare Resource Utilization and Costs in Congestive Heart Failure 90-day Episodes Within the Bundled Payment for Care Initiative (BPCI) Model 2, 2016-2018

Circulation ◽  
2020 ◽  
Vol 142 (Suppl_3) ◽  
Author(s):  
Xian Shen ◽  
Gabriel Sullivan ◽  
Mark Adelsberg ◽  
Martins Francis ◽  
Taylor T Schwartz ◽  
...  
Keyword(s):  
Heart Failure ◽  
Congestive Heart Failure ◽  
Hospital Stay ◽  
Hospital Readmission ◽  
Hospital Readmissions ◽  
Angiotensin Receptor ◽  
Post Discharge ◽  
Readmission Rates ◽  
Hospital Readmission Rates ◽  
Initial Hospital

Introduction: Congestive heart failure (HF) is the fourth most commonly selected clinical episode among Model 2 participants of the Medicare Bundled Payments for Care Improvement (BPCI) Initiative. This study describes utilization of pharmacologic therapies, hospital readmission rates, and HF episode costs within the BPCI framework. Methods: The 100% sample of Medicare FFS enrollment/claims were used to identify acute hospital stays with a MS-DRG 291/292/293 between 1JAN2016 and 31DEC2018. A HF episode consisted of the initial hospital stay and all Part A & B covered services up to 90-days post-discharge. Prescription fills for angiotensin-converting enzyme inhibitors (ACEIs), angiotensin receptor blockers (ARBs), or angiotensin receptor-neprilysin inhibitors (ARNI) during the 90 days post-discharge were captured. Rates of all-cause and HF readmissions were reported per 10,000 episodes during the 30-, 60-, and 90-days post-discharge period. Total episode costs were defined as the sum of Medicare payments for the initial hospital stay plus all Part A & B covered medical services in the 90-day post-discharge. Results: The sample included 634,307 HF episodes. Patients received ARNIs in 3%, ACEIs/ARBs in 45%, and neither in 52% of the episodes, respectively. All-cause hospital readmission rates were 2,503, 4,465, and 6,368 per 10,000 episodes during the 30-, 60-, and 90-day periods. The 30-, 60-, and 90-day HF readmission rates were 958, 1,696, and 2,394 per 10,000 episodes. Total mean 90-day episode cost was $20,122, of which $8,002 was attributable to hospital readmissions. Conclusions: Hospital readmissions are frequent for HF patients and contribute a notable proportion of overall HF BPCI episode costs. BPCI participants may consider improving utilization of guideline directed medical therapies for HF, including ACEIs/ARBs and ARNI, as a strategy for reducing hospital readmissions and associated costs.

Impact of Visual-Spatial Neglect on Stroke Functional Outcomes, Discharge Destination and Maintenance of Improvement Post-Discharge

2009 ◽  
Vol 72 (5) ◽  
pp. 219-225 ◽  
Author(s):  
Maria Stella Stein ◽  
David Maskill ◽  
Louise Marston
Keyword(s):  
Occupational Therapy ◽  
Functional Outcomes ◽  
Discharge Planning ◽  
Inpatient Rehabilitation ◽  
Functional Mobility ◽  
Spatial Neglect ◽  
Discharge Destination ◽  
Post Discharge ◽  
Visual Spatial

This study evaluated basic functional mobility in 25 patients with stroke and visual-spatial neglect during inpatient rehabilitation and early follow-up. Seven patients with neglect and 12 patients without neglect were discharged home and the rest to institutions. Patients without neglect achieved higher outcomes in a shorter time (mean 52 and 79 days respectively). All patients discharged home continued to improve at least up to 5 weeks post-discharge. The patients discharged to institutions achieved lower outcomes overall and quickly deteriorated to admission levels post-discharge. The results inform occupational therapy practice in the areas of assessment, discharge planning, destination and expected functional mobility outcomes in the community.

scholarly journals A high impact intervention for a high impact intervention: Improving documentation of peripheral venous access insertion in theatre

2017 ◽  
Vol 19 (1) ◽  
pp. 43-45 ◽  
Author(s):  
Kavita Upadhyaya ◽  
Heidy Hendra ◽  
Nick Wilson
Keyword(s):  
Venous Access ◽  
High Impact ◽  
Surgical Patients ◽  
Care Bundle ◽  
Post Intervention ◽  
Exact Test ◽  
Intravenous Cannula ◽  
Fisher's Exact Test ◽  
Peripheral Venous Access ◽  
Fisher’S Exact Test

The Department of Health’s ‘High Impact Intervention (HII) – Peripheral intravenous cannula care bundle’ lists six actions to be performed at the time of peripheral intravenous cannulation. Audit of compliance to these requires documentation. We assessed documentation on the anaesthetic charts of 50 surgical patients. Purpose-made stickers were then placed on all anaesthetic charts. Re-assessment of a further 50 patients’ charts demonstrated a significant improvement in documentation of the bundle post intervention (Fisher’s exact test P < 0.0001). This is an example of how a low-tech intervention can produce a high impact improvement in documentation.

scholarly journals Machine learning modeling for predicting hospital readmission following lumbar laminectomy

2019 ◽  
Vol 30 (3) ◽  
pp. 344-352 ◽  
Author(s):  
Saisanjana Kalagara ◽  
Adam E. M. Eltorai ◽  
Wesley M. Durand ◽  
J. Mason DePasse ◽  
Alan H. Daniels
Keyword(s):  
Machine Learning ◽  
Predictive Models ◽  
Hospital Readmission ◽  
Hospital Readmissions ◽  
Influential Factors ◽  
Gradient Boosting ◽  
Unplanned Readmission ◽  
Discharge Destination ◽  
Improvement Program ◽  
Patient Variables

OBJECTIVEHospital readmission contributes substantial costs to the healthcare system. The purpose of this investigation was to create a predictive machine learning model to identify lumbar laminectomy patients at risk for postoperative hospital readmission.METHODSPatients who had undergone a lumbar laminectomy procedure in the period from 2011 to 2014 were isolated from the American College of Surgeons National Surgical Quality Improvement Program (ACS NSQIP) database. Demographic characteristics and clinical factors, including complications, comorbidities, length of stay, age, and body mass index, were analyzed in relation to whether or not the patients had been readmitted to the hospital within 30 days after their procedure by utilizing independent-samples t-tests. Supervised gradient boosting machine learning was then used to create two models to predict readmission—one with all collected patient variables and one with only the variables known prior to hospital discharge.RESULTSA total of 26,869 patients were evaluated, 5.59% (1501 patients) of whom had an unplanned readmission to the hospital within 30 days of their procedure. Readmitted patients were older and had a greater number of complications and comorbidities, longer operative time, longer hospital stay, higher BMI, and higher work relative value unit (RVU) operation score (p < 0.01). They also had a worse health status prior to surgery (p < 0.01) and were more likely to be sent to a skilled discharge destination postoperatively (p < 0.01). The model with all patient variables accurately identified 49.6% of readmissions with an overall accuracy of 95.33% (area under the curve [AUC] = 0.8059), with postdischarge complications and comorbidities as the most important predictors. The predictive model built with only clinical information known predischarge identified 40.5% of readmitted patients with an accuracy of 79.55% (AUC = 0.6901), with discharge destination, comorbidities, and American Society of Anesthesiologists (ASA) classification as the most influential factors in identifying readmitted patients.CONCLUSIONSIn this study, the authors analyzed hospital readmissions following laminectomy and developed predictive models to identify readmitted patients with an accuracy of over 95% using all variables and over 79% when using only predischarge variables. Using only the variables available predischarge, the authors created a model capable of predicting 40% of the readmitted patients. This study provides data that will assist in the development of predictive models for readmission and the creation of interventions to prevent readmission in high-risk patients.

Sign in / Sign up

Export Citation Format

Share Document