Understanding transitional care programmes for older adults who experience delayed discharge: a scoping review protocol

Leaving the hospital on time: hospital bed utilization and reasons for discharge delay in the Netherlands

Inappropriate bed occupancy due to delayed hospital discharge affects both physical and psychological well-being in patients and can disrupt patient flow. The Dutch healthcare system is facing ongoing pressure, especially during the current coronavirus disease pandemic, intensifying the need for optimal use of hospital beds. The aim of this study was to quantify inappropriate patient stays and describe the underlying reasons for the delays in discharge. The Day of Care Survey (DoCS) is a validated tool used to gain information about appropriate and inappropriate bed occupancy in hospitals. Between February 2019 and January 2021, the DoCS was performed five times in three different hospitals within the region of Amsterdam, the Netherlands. All inpatients were screened, using standardized criteria, for their need for in-hospital care at the time of survey and reasons for discharge delay. A total of 782 inpatients were surveyed. Of these patients, 94 (12%) were planned for definite discharge that day. Of all other patients, 145 (21%, ranging from 14% to 35%) were without the need for acute in-hospital care. In 74% (107/145) of patients, the reason for discharge delay was due to issues outside the hospital; most frequently due to a shortage of available places in care homes (26%, 37/145). The most frequent reason for discharge delay inside the hospital was patients awaiting a decision or review by the treating physician (14%, 20/145). Patients who did not meet the criteria for hospital stay were, in general, older [median 75, interquartile range (IQR) 65-84 years, and 67, IQR 55-75 years, respectively, P < .001] and had spent more days in hospital (7, IQR 5-14 days, and 3, IQR 1-8 days respectively, P < .001). Approximately one in five admitted patients occupying hospital beds did not meet the criteria for acute in-hospital stay or care at the time of the survey. Most delays were related to issues outside the immediate control of the hospital. Improvement programmes working with stakeholders focusing on the transfer from hospital to outside areas of care need to be further developed and may offer potential for the greatest gain. The DoCS can be a tool to periodically monitor changes and improvements in patient flow.

Hospital to Home: Supporting the Transition From Hospital to Home for Older Adults

Background

An increasing proportion of older adults experience avoidable hospitalizations, and some are potentially entering long-term care homes earlier and often unnecessarily. Older adults often lack adequate support to transition from hospital to home, without access to appropriate health services when they are needed in the community and resources to live safely at home.

Purpose

This study collaborated with an existing enhanced home care program called Home Again in Nova Scotia, to identify factors that contribute to older adult patients being assessed as requiring long-term care when they could potentially return home with enhanced supports.

Methods

Using a case study design, this study examined in-depth experiences of multiple stakeholders, from December 2019 to February 2020, through analysis of nine interviews for three focal patient cases including older adult patients, their family or friend caregivers, and healthcare professionals.

Results

Findings indicate home care services for older adults are being sought too late, after hospital readmission, or a rapid decline in health status when family caregivers are already experiencing caregiver burnout. Limitations in home care services led to barriers preventing family caregivers from continuing to care for older adults at home.

Conclusions

This study contributes knowledge about gaps within home care and transitional care services, highlighting the importance of investing in additional home care services for rehabilitation and prevention of rapidly deteriorating health.

Examining the predictability and prognostication of multimorbidity among older Delayed-Discharge Patients: A Machine learning analytics

BACKGROUND

Patient complexity among older delayed-discharge patients complicates discharge planning, resulting in a higher rate of adverse outcomes, such as readmission and mortality. Early prediction of multimorbidity, as a common indicator of patient complexity, can support proactive discharge planning by prioritizing complex patients and reducing healthcare inefficiencies.

OBJECTIVE

We set out to accomplish the following two objectives: 1) to examine the predictability of three common multimorbidity indices, including Charlson-Deyo Comorbidity Index (CDCI), the Elixhauser Comorbidity Index (ECI), and the Functional Comorbidity Index (FCI) using machine learning (ML), and 2) to assess the prognostic power of these indices in predicting 30-day readmission and mortality.

MATERIALS AND METHODS

We used data including 163,983 observations of patients aged 65 and older who experienced discharge delay in Ontario, Canada, during 2004 - 2017. First, we utilized various classification ML algorithms, including classification and regression trees, random forests, bagging trees, extreme gradient boosting, and logistic regression, to predict the multimorbidity status based on CDCI, ECI, and FCI. Second, we used adjusted multinomial logistic regression to assess the association between multimorbidity indices and the patient-important outcomes, including 30-day mortality and readmission.

RESULTS

For all ML algorithms and regardless of the predictive performance criteria, better predictions were established for the CDCI compared with the ECI and FCI. Remarkably, the most predictable multimorbidity index (i.e., CDCI with Area Under the Receiver Operating Characteristic Curve = 0.80, 95% CI = 0.79 - 0.81) also offered the highest prognostications regarding adverse events (RRR_mortality = 3.44, 95% CI = 3.21 - 3.68 and RRR_readmission = 1.36, 95% CI = 1.31 - 1.40).

CONCLUSIONS

Our findings highlight the feasibility and utility of predicting multimorbidity status using ML algorithms, resulting in the early detection of patients at risk of mortality and readmission. This can support proactive triage and decision-making about staffing and resource allocation, with the goal of optimizing patient outcomes and facilitating an upstream and informed discharge process through prioritizing complex patients for discharge and providing patient-centered care.

Understanding transitional care programs for older adults who experience delayed discharge: a scoping review

Background

Many hospitalized older adults cannot be discharged because they lack the health and social support to meet their post-acute care needs. Transitional care programs (TCPs) are designed to provide short-term and low-intensity restorative care to these older adults experiencing or at risk for delayed discharge. However, little is known about the contextual factors (i.e., patient, staff and environmental characteristics) that may influence the implementation and outcomes of TCPs. This scoping review aims to answer: 1) What are socio-demographic and/or clinical characteristics of older patients served by TCPs?; 2) What are the core components provided by TCPs?; and 3) What patient, caregiver, and health system outcomes have been investigated and what changes in these outcomes have been reported for TCPs?

Methods

The six-step scoping review framework and PRISMA-ScR checklist were followed. Studies were included if they presented models of TCPs and evaluated them in community-dwelling older adults (65+) experiencing or at-risk for delayed discharge. The data synthesis was informed by a framework, consistent with Donabedian’s structure-process-outcome model.

Results

TCP patients were typically older women with multiple chronic conditions and some cognitive impairment, functionally dependent and living alone. The review identified five core components of TCPs: assessment; care planning and monitoring; treatment; discharge planning; and patient, family and staff education. The main outcomes examined were functional status and discharge destination. The results were discussed with a view to inform policy makers, clinicians and administrators designing and evaluating TCPs as a strategy for addressing delayed hospital discharges.

Conclusion

TCPs can influence outcomes for older adults, including returning home. TCPs should be designed to incorporate interdisciplinary care teams, proactively admit those at risk of delayed discharge, accommodate persons with cognitive impairment and involve care partners. Additional studies are required to investigate the contributions of TCPs within integrated health care systems.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12877-021-02099-9.