A pharmacist’s integral role in transitions of care as part of a patient care management team in decreasing re-hospitalizations in high-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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A Checklist-Based Tool for Head and Neck Reconstructive Surgery Record Keeping

Bulletin of The Royal College of Surgeons of England ◽

10.1308/147363512x13311314198337 ◽

2013 ◽

Vol 95 (1) ◽

pp. 1-3

Author(s):

G Roberts ◽

A Nawar ◽

H Schumacher ◽

B Mathur ◽

K Tzafetta

Keyword(s):

High Risk ◽

Patient Care ◽

Head And Neck ◽

Reconstructive Surgery ◽

Multidisciplinary Team ◽

Care Management ◽

Organ System ◽

Patient Care Management ◽

Technical Approach ◽

Record Keeping

Medicine has witnessed a revolution in care and outcomes over the last 50 years. This has resulted in an increasingly specialised and technical approach to patient care. Management of complicated and high-risk inpatients often requires consideration of multi-organ system disruption and the input of a large multidisciplinary team (Mdt). It would be fair to acknowledge that even the most diligent doctors may overlook details on occasion.

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

BMC Health Services Research ◽

10.1186/s12913-019-4769-7 ◽

2019 ◽

Vol 19 (1) ◽

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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Exploring and Overcoming the Challenges Primary Care Practices Face with Care Management of High-Risk Patients in CPC+: a Mixed-Methods Study

Journal of General Internal Medicine ◽

10.1007/s11606-020-06528-0 ◽

2021 ◽

Author(s):

Tricia Collins Higgins ◽

Ann S. O’Malley ◽

Rosalind E. Keith

Keyword(s):

Primary Care ◽

Mixed Methods ◽

High Risk ◽

Care Management ◽

Mixed Methods Study ◽

High Risk Patients ◽

Risk Patients ◽

Care Practices ◽

Primary Care Practices

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Care coordination and provider stress in primary care management of high-risk patients

Journal of General Internal Medicine ◽

10.1007/s11606-017-4186-8 ◽

2017 ◽

Vol 33 (1) ◽

pp. 65-71 ◽

Cited By ~ 7

Author(s):

Adeyemi Okunogbe ◽

Lisa S. Meredith ◽

Evelyn T. Chang ◽

Alissa Simon ◽

Susan E. Stockdale ◽

...

Keyword(s):

Primary Care ◽

High Risk ◽

Care Coordination ◽

Care Management ◽

High Risk Patients ◽

Risk Patients ◽

Primary Care Management

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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/v5 ◽

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.

Download Full-text