Risk Calculator Prevents Delayed Discharges in Hospitals

A novel prediction model that identifies patients occupying emergency departments (EDs) longer than needed could significantly reduce overcrowding, claims a new study.

Developed by researcher at University Hospitals of North Midlands (UHNM; Stoke-on-Trent, United Kingdom) and Staffordshire University (United Kingdom), the eight-variable predictive tool calculates the probability of a patient experiencing a delayed discharge already at admission. The researchers first interrogated administrative and clinical data to identify patient factors related to delayed transfer of care (DTOC) on discharge, and developed a predictive model for identifying such patients.

Three-years of data on 92,444 admissions were used to develop the eight-variable predictive model, and 39,877 admissions were used to validate it. The variables (age, gender, ethnicity, national early warning score (NEWS), Glasgow admission prediction score, Index of Multiple Deprivation (IMD) decile, arrival by ambulance, and admission within the last year) exhibited a 79% sensitivity, 69% specificity, and 70% overall accuracy for identifying patients who will experience DTOC. The study was published on September 29, 2021, in the International Journal for Quality in Health Care.

“We 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,” said senior author MD Asaduzzaman, PhD, of the Staffordshire University department of engineering. “We must develop a well-designed patient care pathway model for vulnerable patients, incorporating all stakeholders including acute care hospitals and social care centers alongside local governments.”

A DTOC occurs when an adult inpatient is medically ready to go home but is still occupying a hospital bed. Delays to discharge can have serious implications such as mortality, infections, depression, and reductions in patients’ mobility and their ability to undertake daily activities. In addition, there is a significant secondary effect on patients waiting for admission from emergency portals to the wards, as these “blocked beds” cause a bottleneck effect, which results in increased mortality, poor patient outcomes, and significantly higher consumption of hospital resources.

Related Links:
University Hospitals of North Midlands
Staffordshire University