Do Collaborative Case Management Models Decrease Hospital Readmission Rates Among High-Risk Patients?

Abstract Purpose Pharmacists are well positioned to provide transitions of care (TOC) services to patients with heart failure (HF); however, hospitalizations for patients with HF likely exceed the capacity of a TOC pharmacist. We developed and validated a tool to help pharmacists efficiently identify high-risk patients with HF and maximize their potential impact by intervening on patients at the highest risk for 30-day all-cause readmission. Methods We conducted a retrospective cohort study including adults with HF admitted to a health system between October 1, 2016, and October 31, 2019. We randomly divided the cohort into development (n = 2,114) and validation (n = 1,089) subcohorts. Nine models were applied to select the most important predictors of 30-day readmission. The final tool, called the Tool for Pharmacists to Predict 30-day hospital readmission in patients with Heart Failure (ToPP-HF) relied upon multivariable logistic regression. We assessed discriminative ability using the C statistic and calibration using the Hosmer-Lemeshow goodness-of-fit test. Results The risk of 30-day all-cause readmission was 15.7% (n = 331) and 18.8% (n = 205) in the development and validation subcohorts, respectively. The ToPP-HF tool included 13 variables: number of hospital admissions in previous 6 months; admission diagnosis of HF; number of scheduled medications; chronic obstructive pulmonary disease diagnosis; number of comorbidities; estimated glomerular filtration rate; hospital length of stay; left ventricular ejection fraction; critical care requirement; renin-angiotensin-aldosterone system inhibitor use; antiarrhythmic use; hypokalemia; and serum sodium. Discriminatory performance (C statistic of 0.69; 95% confidence interval [CI], 0.65-0.73) and calibration (Hosmer-Lemeshow P = 0.28) were good. Conclusions The ToPP-HF performs well and can help pharmacists identify high-risk patients with HF most likely to benefit from TOC services.

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Abstract 169: Targeting Patients with Multiple Readmissions in the Year after Non-ST-Elevation Myocardial Infarction Could Lower Hospital Readmission Rates

Circulation Cardiovascular Quality and Outcomes ◽

10.1161/circoutcomes.6.suppl_1.a169 ◽

2013 ◽

Vol 6 (suppl_1) ◽

Author(s):

Gwen Bernacki ◽

Karen Alexander ◽

Matthew Roe ◽

Shuang Li ◽

Laine Thomas ◽

...

Keyword(s):

High Risk ◽

Hospital Readmission ◽

Transitional Care ◽

Multivariable Analysis ◽

Patient Characteristics ◽

High Risk Group ◽

Billing Data ◽

Readmission Rates ◽

Bundled Payment ◽

Hospital Readmission Rates

Background: Bundled payment policies have focused on 30-day readmission rates after AMI, yet these are likely to lengthen over time. Identifying patients with multiple readmissions in the year after AMI could help focus transitional care efforts on these high risk patients. Methods: Data from the CRUSADE registry linked to Medicare billing data was used to examine longitudinal outcomes of 32,776 NSTEMI patients ≥ 65 years between 2003 and 2006 with 12-month follow-up. Defining frequent readmissions as ≥3 hospitalizations in 12 months, we compared characteristics of patients frequently readmitted vs. not. The association between frequent readmissions and patient characteristics was examined using multivariable logistic regression. Results: Readmission within 12 months after NSTEMI occurred: once (N=8,830, 26.9%); twice (N=4334, 13.2%); 3 times (N=2,319, 7.1%); ≥4 times (N=2470, 7.5%). Those with multiple (≥3) readmissions (14.6%) were older with recent prior hospitalization and greater prevalence of comorbidities. In multivariable analysis, these factors increased discrimination of patients with frequent readmissions, (c-statistic=0.714). Conclusions: Comorbidities and recent prior hospitalization can predict patients with frequent readmissions. Better understanding of the influence of these clinical factors in this high-risk group presents an opportunity to lower hospital readmission rates.

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Does an all-condition case management program for high-risk patients reduce health care utilization in medicaid and medicare beneficiaries with diabetes?

Journal of Diabetes and its Complications ◽

10.1016/j.jdiacomp.2018.12.011 ◽

2019 ◽

Vol 33 (6) ◽

pp. 445-450

Author(s):

Linh Phuong Bui ◽

Felicia Hill-Briggs ◽

Nola Durkin ◽

Ariella Apfel ◽

Patti L. Ephraim ◽

...

Keyword(s):

Health Care ◽

High Risk ◽

Case Management ◽

Health Care Utilization ◽

Management Program ◽

Care Utilization ◽

Medicare Beneficiaries ◽

Case Management Program ◽

High Risk Patients ◽

Risk Patients

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Differences in transitional care processes among high-performing and low-performing hospital-SNF pairs: a rapid ethnographic approach

BMJ Quality & Safety ◽

10.1136/bmjqs-2020-011204 ◽

2020 ◽

pp. bmjqs-2020-011204

Author(s):

Kirstin A Manges ◽

Roman Ayele ◽

Chelsea Leonard ◽

Marcie Lee ◽

Emily Galenbeck ◽

...

Keyword(s):

High Risk ◽

Best Practices ◽

Transitional Care ◽

Readmission Rates ◽

High Performing ◽

High Risk Patients ◽

Ethnographic Approach ◽

Care Processes ◽

Risk Patients ◽

Postacute Care

BackgroundDespite the increased focus on improving patient’s postacute care outcomes, best practices for reducing readmissions from skilled nursing facilities (SNFs) are unclear. The objective of this study was to observe processes used to prepare patients for postacute care in SNFs, and to explore differences between hospital-SNF pairs with high or low 30-day readmission rates.DesignWe used a rapid ethnographic approach with intensive multiday observations and key informant interviews at high-performing and low-performing hospitals, and their most commonly used SNF. We used flow maps and thematic analysis to describe the process of hospitals discharging patients to SNFs and to identify differences in subprocesses used by high-performing and low-performing hospitals.Setting and participantsHospitals were classified as high or low performers based on their 30-day readmission rates from SNFs. The final sample included 148 hours of observations with 30 clinicians across four hospitals (n=2 high performing, n=2 low performing) and corresponding SNFs (n=5).FindingsWe identified variation in five major processes prior to SNF discharge that could affect care transitions: recognising need for postacute care, deciding level of care, selecting an SNF, negotiating patient fit and coordinating care with SNF. During each stage, high-performing sites differed from low-performing sites by focusing on: (1) earlier, ongoing, systematic identification of high-risk patients; (2) discussing the decision to go to an SNF as an iterative team-based process and (3) anticipating barriers with knowledge of transitional and SNF care processes.ConclusionIdentifying variations in processes used to prepare patients for SNF provides critical insight into the best practices for transitioning patients to SNFs and areas to target for improving care of high-risk patients.

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The heterogeneous health state profiles of high-risk healthcare utilizers and their longitudinal hospital readmission and mortality patterns

10.21203/rs.2.12113/v2 ◽

2019 ◽

Author(s):

Shawn Choon Wee Ng ◽

Yu Heng Kwan ◽

Shi Yan ◽

Chuen Seng Tan ◽

Lian Leng Low

Keyword(s):

High Risk ◽

Hospital Readmission ◽

Latent Class ◽

Resource Planning ◽

Transitions Of Care ◽

High Risk Population ◽

Health State ◽

High Risk Patients ◽

Class 1 ◽

Risk Patients

Abstract Background: High-risk patients are most vulnerable during transitions of care. Due to the high burden of resource allocation for such patients, we hypothesize that segmentation of this heterogeneous population into distinct subgroups will enable improved healthcare resource planning. In this study, we segmented a high-risk population with the aim to identify and characterize a patient subgroup with the highest 30-day and 90-day hospital readmission and mortality. Methods: We extracted data from our transitional care program (TCP) from June to November 2018. Latent class analysis (LCA) was used to determine the optimal number and characteristics of latent subgroups, assessed based on model fit and clinical interpretability. Regression analysis was performed to assess the association of class membership on 30- and 90-day all-cause readmission and mortality. Results: Among 752 patients, a 3-class best fit model was selected: Class 1 “Frail, cognitively impaired and physically dependent”, Class 2 “Pre-frail, but largely physically independent” and Class 3 “Physically independent”. The 3 classes have distinct demographics, medical and socioeconomic characteristics (p<0.05), 30- and 90-day readmission (p<0.05) and mortality (p<0.01). Class 1 patients have the highest age-adjusted 90-day readmission (OR=2.04, 95%CI: 1.21-3.46, p= 0.008), 30- (OR=6.92, 95%CI: 1.76-27.21, p=0.006) and 90-day mortality (OR=11.51, 95%CI: 4.57-29.02, p<0.001). Conclusions: We identified a subgroup with the highest readmission and mortality risk amongst high-risk patients. We also found a lack of interventions in our TCP that specifically addresses increased frailty and poor cognition, which are prominent features in this subgroup. These findings will help to inform future program modifications and strengthen existing transitional healthcare structures currently utilized in this patient cohort.

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The heterogeneous health state profiles of high-risk healthcare utilizers and their longitudinal hospital readmission and mortality patterns

10.21203/rs.2.12113/v4 ◽

2019 ◽

Author(s):

Shawn Choon Wee Ng ◽

Yu Heng Kwan ◽

Shi Yan ◽

Chuen Seng Tan ◽

Lian Leng Low

Keyword(s):

High Risk ◽

Hospital Readmission ◽

Latent Class ◽

Resource Planning ◽

Transitions Of Care ◽

High Risk Population ◽

Health State ◽

High Risk Patients ◽

Class 1 ◽

Risk Patients

Abstract Background: High-risk patients are most vulnerable during transitions of care. Due to the high burden of resource allocation for such patients, we propose that segmentation of this heterogeneous population into distinct subgroups will enable improved healthcare resource planning. In this study, we segmented a high-risk population with the aim to identify and characterize a patient subgroup with the highest 30-day and 90-day hospital readmission and mortality. Methods: We extracted data from our transitional care program (TCP), a Hospital-to-Home program launched by the Singapore Ministry of Health, from June to November 2018. Latent class analysis (LCA) was used to determine the optimal number and characteristics of latent subgroups, assessed based on model fit and clinical interpretability. Regression analysis was performed to assess the association of class membership on 30- and 90-day all-cause readmission and mortality. Results: Among 752 patients, a 3-class best fit model was selected: Class 1 “Frail, cognitively impaired and physically dependent”, Class 2 “Pre-frail, but largely physically independent” and Class 3 “Physically independent”. The 3 classes have distinct demographics, medical and socioeconomic characteristics (p<0.05), 30- and 90-day readmission (p<0.05) and mortality (p<0.01). Class 1 patients have the highest age-adjusted 90-day readmission (OR=2.04, 95%CI: 1.21-3.46, p= 0.008), 30- (OR=6.92, 95%CI: 1.76-27.21, p=0.006) and 90-day mortality (OR=11.51, 95%CI: 4.57-29.02, p<0.001). Conclusions: We identified a subgroup with the highest readmission and mortality risk amongst high-risk patients. We also found a lack of interventions in our TCP that specifically addresses increased frailty and poor cognition, which are prominent features in this subgroup. These findings will help to inform future program modifications and strengthen existing transitional healthcare structures currently utilized in this patient cohort.

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The heterogeneous health state profiles of high-risk healthcare utilizers and their longitudinal hospital readmission and mortality patterns

10.21203/rs.2.12113/v3 ◽

2019 ◽

Author(s):

Shawn Choon Wee Ng ◽

Yu Heng Kwan ◽

Shi Yan ◽

Chuen Seng Tan ◽

Lian Leng Low

Keyword(s):

High Risk ◽

Hospital Readmission ◽

Latent Class ◽

Resource Planning ◽

Transitions Of Care ◽

High Risk Population ◽

Health State ◽

High Risk Patients ◽

Class 1 ◽

Risk Patients

Abstract Background: High-risk patients are most vulnerable during transitions of care. Due to the high burden of resource allocation for such patients, we hypothesize that segmentation of this heterogeneous population into distinct subgroups will enable improved healthcare resource planning. In this study, we segmented a high-risk population with the aim to identify and characterize a patient subgroup with the highest 30-day and 90-day hospital readmission and mortality. Methods: We extracted data from our transitional care program (TCP) from June to November 2018. Latent class analysis (LCA) was used to determine the optimal number and characteristics of latent subgroups, assessed based on model fit and clinical interpretability. Regression analysis was performed to assess the association of class membership on 30- and 90-day all-cause readmission and mortality. Results: Among 752 patients, a 3-class best fit model was selected: Class 1 “Frail, cognitively impaired and physically dependent”, Class 2 “Pre-frail, but largely physically independent” and Class 3 “Physically independent”. The 3 classes have distinct demographics, medical and socioeconomic characteristics (p<0.05), 30- and 90-day readmission (p<0.05) and mortality (p<0.01). Class 1 patients have the highest age-adjusted 90-day readmission (OR=2.04, 95%CI: 1.21-3.46, p= 0.008), 30- (OR=6.92, 95%CI: 1.76-27.21, p=0.006) and 90-day mortality (OR=11.51, 95%CI: 4.57-29.02, p<0.001). Conclusions: We identified a subgroup with the highest readmission and mortality risk amongst high-risk patients. We also found a lack of interventions in our TCP that specifically addresses increased frailty and poor cognition, which are prominent features in this subgroup. These findings will help to inform future program modifications and strengthen existing transitional healthcare structures currently utilized in this patient cohort.

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