Older medical outliers on surgical wards: impact on 6-month outcomes

Abstract

BackgroundMedical patients are on occasion admitted transiently to surgical wards when more appropriate wards are at capacity, potentially leading to suboptimal care. The aim of this study was to compare 6-month outcomes in older adults diagnosed with medical conditions in the ED then admitted inappropriately to surgical wards (defined as outliers), with outcomes in comparable patients admitted to medical wards (controls).MethodsIn a matched cohort study, 100 consecutive medical outliers from the ED aged 75 years and over were matched according to age, sex and diagnosis to 200 controls. Collected data included number of diagnoses reported in acute care, level of patient illness severity, length of stay, mortality and destination of patients discharged from acute care units (home, rehabilitation facility, nursing home or palliative care facility). An assessment was made of patient vital status and living environment (home, nursing home or hospital) at 6 months post-ED admission.ResultsMean age was 85.6 years. The most common ED diagnoses were gait disorders/falls (18%), neurological disorders (17%) and exhaustion (16%). Outliers displayed lower illness severity levels (0.001) and shorter lengths of stay from ED admission to acute care discharge (p=0.040). Subsequent to acute care, outliers were less commonly discharged home (45% vs 59%) and more commonly discharged to rehabilitation facilities (42% vs 28%). At 6 months post-ED admission, multivariable regression analysis showed that outlier status (OR=0.44 (0.25–0.83); p=0.011) and numbers of diagnoses reported in acute care (OR=0.87 (0.76–0.98); p=0.028) were independently associated with lower probability of living at home.ConclusionOutlying of older patients to surgical wards negatively affects their prospects of living at home at 6 months after hospital admission. Older patients hospitalised via the ED are entitled to appropriate medical care.

Machine learning for real-time aggregated prediction of hospital admission for emergency patients

“…Arrivals to the ED show diurnal and seasonal variations, with predicted peaks in the morning and early evening, but workflows elsewhere in a hospital mean that discharges from the hospital happen late in the day, creating flow problems ( https://www.nuffieldtrust.org.uk/resource/understanding-patient-flow-in-hospitals ). This mismatch of cadence between different parts of the hospital results in patients boarding in ED, or being admitted to inappropriate wards, with adverse consequences including longer stays 5 , greater risk of medical errors 6 and worse long-term outcomes in elderly patients 7 .…”

Machine Learning for Real-Time Aggregated Prediction of Hospital Admission for Emergency Patients

“…Arrivals to the ED show diurnal and seasonal variations, with predicted peaks in the morning and early evening, but workflows elsewhere in a hospital mean that discharges from the hospital happen late in the day, creating flow problems 5 . This mismatch of cadence between different parts of the hospital results in patients boarding in ED, or being admitted to inappropriate wards, with adverse consequences including longer stays 6 , greater risk of medical errors 7 and worse long-term outcomes in elderly patients 8 . Hospital services can be managed more efficiently if accurate short-term forecasts for emergency demand are available 9,10 .…”