TY - JOUR

T1 - Anticipating care needs of patients after discharge from hospital

T2 - Frail and elderly patients without physiological abnormality on day of admission are more likely to require social services input

AU - Subbe, C. P.

AU - Goulden, Nia

AU - Mawdsley, K.

AU - Smith, R.

N1 - Copyright © 2017. Published by Elsevier B.V.

PY - 2017/11

Y1 - 2017/11

N2 - INTRODUCTION: Acute admissions to hospital are rising. As a part of a service evaluation we examined pathways of patients following hospital discharge depending on data available on admission to hospital.METHODS: We merged data available on admission to the Wrexham Maelor hospital from an existing data-base in the Acute Medical Unit with follow up data from local social services as part of a data sharing agreement. Patients requiring support by social services post-discharge were matched with patients not requiring social services from the same post-code.RESULTS: Stepwise logistic regression analysis identified candidate variables predicting likely support need. Decision tree analysis identified sub-groups of patients with higher likelihood to require support by social services after discharge from hospital. We found patients with normal physiology on admission as evidenced by a value of zero for the National Early Warning Score who were frail or older than 85years were most likely to require support after discharge.CONCLUSIONS: Information available on admission to hospital might inform long term care needs. Prospective testing is needed. The algorithms are prone to be dependent on availability of local services but our methodology is expected to be transferable to other organizations.

AB - INTRODUCTION: Acute admissions to hospital are rising. As a part of a service evaluation we examined pathways of patients following hospital discharge depending on data available on admission to hospital.METHODS: We merged data available on admission to the Wrexham Maelor hospital from an existing data-base in the Acute Medical Unit with follow up data from local social services as part of a data sharing agreement. Patients requiring support by social services post-discharge were matched with patients not requiring social services from the same post-code.RESULTS: Stepwise logistic regression analysis identified candidate variables predicting likely support need. Decision tree analysis identified sub-groups of patients with higher likelihood to require support by social services after discharge from hospital. We found patients with normal physiology on admission as evidenced by a value of zero for the National Early Warning Score who were frail or older than 85years were most likely to require support after discharge.CONCLUSIONS: Information available on admission to hospital might inform long term care needs. Prospective testing is needed. The algorithms are prone to be dependent on availability of local services but our methodology is expected to be transferable to other organizations.

KW - Journal Article

U2 - 10.1016/j.ejim.2017.09.029

DO - 10.1016/j.ejim.2017.09.029

M3 - Article

C2 - 28974330

VL - 45

SP - 74

EP - 77

JO - European Journal of Internal Medicine

JF - European Journal of Internal Medicine

SN - 0953-6205

ER -