Tracking Discharge Delays

Holland, Diane E.; Pacyna, Joel E.; Gillard, Kristine L.; Carter, Lisa C.

doi:10.1097/ncq.0000000000000141

Cited by 6 publications

(3 citation statements)

References 22 publications

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“…However, it is a factor that can be acted upon earlier on in the patient's hospital stay (even as early as the admission phase). 35 In fact, a specifically designed computer program to track delayed discharges in real-time in a large academic medical centre in the United States was utilized for this purpose. 35 The study uncovered a tendency for inadequacies in the discharge process (ranging across the patient's overall stay to the actual day of discharge) to be mainly responsible for incurred delays (mainly in the form of delayed paperwork and organisation related to transfer of care).…”

Section: Causes and Effects Of Delayed Dischargesmentioning

confidence: 99%

“…35 In fact, a specifically designed computer program to track delayed discharges in real-time in a large academic medical centre in the United States was utilized for this purpose. 35 The study uncovered a tendency for inadequacies in the discharge process (ranging across the patient's overall stay to the actual day of discharge) to be mainly responsible for incurred delays (mainly in the form of delayed paperwork and organisation related to transfer of care). These findings also seem to support the commonly shared perspective that acute hospital facilities attach limited importance to the discharge planning process, as they are more focused on the medical treatment provided rather than on what happens after treatment ends.…”

Section: Causes and Effects Of Delayed Dischargesmentioning

confidence: 99%

See 1 more Smart Citation

Defining Delayed Discharges of Inpatients and Their Impact in Acute Hospital Care: A Scoping Review

Micallef

Buttiġieġ

Tomaselli

et al. 2020

Int J Health Policy Manag

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Background: With the ever-increasing demand on acute healthcare, the hospital discharge process and delayed discharges are considered relevant in achieving optimal performance in clinical settings. The purpose of this paper is to review the literature to identify conceptual and operational definitions of delayed discharges, identify causes and effects of delayed discharges, and also to explore the literature for interventions aimed at decreasing the impact (in terms of reducing the number/rate of delays) of delayed discharges in acute healthcare settings. Methods: An extensive literature search yielded a total of 26 248 records. Sixty-four research articles were included in the scoping review after considering inclusion/exclusion criteria and the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) search strategy. The following databases were utilized: Cochrane, EBSCO, PubMed, PubMed Central, Medline, and Web of Science. The search was carried out between January 2017 and March 2020 and covered literature ranging from 1990 to 2019. Results were reviewed by authors for duplicates and filtered using the inclusion/ exclusion criteria. Tables were created to classify the chosen articles (n = 64), allowing us to organise findings and results. Results: Conceptual and operational definitions were analysed. In turn, causes and effects of delayed discharges were extracted and represented in diagrammatic format, together with specific interventions used in acute healthcare settings to lessen the effect of delayed discharges. Operational definitions of delayed discharges were found to be more difficult to establish, particularly in the light of the vast number of different scenarios and workplace interventions uncovered in the literature. The main causes of delayed discharges were faulty organisational management, inadequate discharge planning, transfer of care problems, and age. The main effects were bed-blocking, A&E (Accident & Emergency) overcrowding, and financial implications. The main interventions included ‘discharge before noon’ initiative, ‘discharge facilitation tools,’ ‘discharge delay tracking’ mechanisms, and the role of general practitioners and social care staff. Conclusion: This paper fills a gap in the fragmented literature on delayed inpatient discharges by providing a researchbased perspective on conceptual and operational definitions, causes and effects, as well as interventions to minimize their impact. The findings and definitions are intended as points of reference for future research.

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Section: Causes and Effects Of Delayed Dischargesmentioning

confidence: 99%

Section: Causes and Effects Of Delayed Dischargesmentioning

confidence: 99%

Defining Delayed Discharges of Inpatients and Their Impact in Acute Hospital Care: A Scoping Review

Micallef

Buttiġieġ

Tomaselli

et al. 2020

Int J Health Policy Manag

View full text Add to dashboard Cite

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“…[6,22,32,33] reviewed the literature to identify the causes of discharge delay. The reasons for discharge delays include lack of rehab/nursing home space [9,34,35], social isolation/lack of family support [13,17,[36][37][38], inadequate social services [18,39], conflict of interest [40,41], procedural delays [41,42], confusion and redundancy [42,43], inadequate discharge planning [44], and patient's age [1,8].…”

Section: Introductionmentioning

confidence: 99%

Phase-Type Survival Trees to Model a Delayed Discharge and Its Effect in a Stroke Care Unit

et al. 2022

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The problem of hospital patients’ delayed discharge or ‘bed blocking’ has long been a challenge for healthcare managers and policymakers. It negatively affects the hospital performance metrics and has other severe consequences for the healthcare system, such as affecting patients’ health. In our previous work, we proposed the phase-type survival tree (PHTST)-based analysis to cluster patients into clinically meaningful patient groups and an extension of this approach to examine the relationship between the length of stay in hospitals and the destination on discharge. This paper describes how PHTST-based clustering can be used for modelling delayed discharge and its effects in a stroke care unit, especially the extra beds required, additional cost, and bed blocking. The PHTST length of stay distribution of each group of patients (each PHTST node) is modelled separately as a finite state continuous-time Markov chain using Coxian-phase-type distributions. Delayed discharge patients waiting for discharge are modelled as the Markov chain, called the ‘blocking state’ in a special state. We can use the model to recognise the association between demographic factors and discharge delays and their effects and identify groups of patients who require attention to resolve the most common delays and prevent them from happening again. The approach is illustrated using five years of retrospective data of patients admitted to the Belfast City Hospital with a stroke diagnosis.

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