Risk calculator to prevent delayed discharges in hospitals

Date:
November 11, 2021
Source:
Staffordshire University
Summary:
New research could significantly reduce overcrowding in emergency
departments -- with great financial savings. Experts have developed
a prediction model to identify patients most at risk of taking up
hospital beds longer than needed.



FULL STORY ==========================================================================
New research could significantly reduce overcrowding in emergency
departments - - potentially saving the NHS millions of pounds.


==========================================================================
In a collaborative project between University Hospitals of North Midlands (UHNM) NHS Trust and Staffordshire University, experts have developed a prediction model to identify patients most at risk of taking up hospital
beds longer than needed.

A new study, funded by the North Staffordshire Medical Institute
(NSMI), details an eight-variable predictive tool which can calculate
the probability of a patient experiencing a delayed discharge at the
time of admission.

Delayed discharge from hospital is one of the major challenges facing
the NHS and has increased considerably over the last decade. According to
2019 data, delayed bed days cost the equivalent of -L-27,000 each hour and
the additional pressures of COVID-19 have since intensified the problem.

Dr Andrew Davy, GP Lead for Research and Development in A&E at UHNM,
explained: "A delayed transfer of care occurs when an adult inpatient is medically ready to go home but is unable to because other necessary care, support or accommodation is unavailable. These delays can have serious implications such as mortality, infections, depression and reductions
in patients' mobility and their ability to undertake daily activities.

"It also has a knock-on effect on patients in A&E departments who
cannot move into ward beds until current patients are discharged. This bottleneck effect on flow causes significant overcrowding within emergency departments and other emergency portals, which results in increased
mortality, poor patient outcomes and significantly higher consumption
of hospital resources." For the development of the predictive tool, Md Asaduzzaman, Associate Professor in Operational Research at Staffordshire University, explained: "Administration and clinical data from the Royal
Stoke University Hospital's emergency department was analysed for the
study, covering a three-year period from 2018 to 2020. The researchers
used information routinely collected when patients are admitted to
hospital from A&E to identify several demographic, socio-economic and
clinical factors associated with patients experiencing a delayed transfer
of care or not.



========================================================================== "Age, gender, ethnicity, national early warning score (NEWS), Glasgow
admission prediction score, Index of Multiple Deprivation decile, arrival
by ambulance and previous admission within the last year were all found
to have a statistically significant association with delayed transfers
of care." The prediction model and digital toolkit is currently being
piloted at Royal Stoke University Hospital with Thomas Hill, Technical
Business Intelligence Analyst/Developer at UHNM, developing how the
scoring system is visually displayed on A&E's live dashboards, to ensure patients at high risk of delayed transfers of care are flagged early
to reviewing teams. Further variables, felt likely to be causative in
delayed discharge, are currently being reviewed. The researchers believe
that eventually, this predictive model could easily be rolled out across
the country.

Sarahjane Jones, Associate Professor of Patient Safety, said: "Better
discharge planning would reduce a huge cost burden to the NHS and we
believe that this paper could have a major impact on managing patients.

"Understanding who is most statistically likely to experience a delayed discharge could help hospitals target patients for proactive discharge
planning early on in their care journey. This could be achieved by
alerting internal teams such as therapists earlier on as well as external partners, enabling more timely provision of community care plans and
placements in residential and nursing homes." Building on this study,
the research team now hope to improve the accuracy of the risk calculator
and work with local authorities to better understand the logistics of
patient aftercare. Dr Keira Watts and Dr Simon Lea from UHNM Research
and Innovation, who have been involved in the development of the project
are keen to take this work forward.

Dr Lea said: "This work is incredibly important for the wellbeing of our patients and for the delivery of our services and we are pleased that
this work, funded by a North Staffordshire Medical Institute grant,
has the potential to have such significant impact. We look forward to
working with Dr Davy and Prof Asaduzzaman on future grant applications
and research projects." Associate Professor Asaduzzaman added: "We have
based our model on data routinely collected in all hospitals which means
it has the potential to be adopted across the NHS. This problem is not
going to vanish and in the wake of COVID-19 it is more important than ever
to find solutions. We must develop a well-designed patient care pathway
model for vulnerable patients, incorporating all stakeholders including
acute care hospitals and social care centres alongside local governments." ========================================================================== Story Source: Materials provided by Staffordshire_University. Note:
Content may be edited for style and length.


========================================================================== Journal Reference:
1. Andrew Davy, Thomas Hill, Sarahjane Jones, Alisen Dube, Simon c Lea,
Keiar l Watts, M d Asaduzzaman. A predictive model for
identifying patients at risk of delayed transfer of care: a
retrospective, cross- sectional study of routinely collected
data. International Journal for Quality in Health Care, 2021; 33
(3) DOI: 10.1093/intqhc/mzab130 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2021/11/211111130335.htm

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