Improving patient flow during infectious disease outbreaks using machine learning for real-time prediction of patient readiness for discharge

BACKGROUND

Delays in patient flow and a shortage of hospital beds are commonplace in hospitals during periods of increased infection incidence, such as seasonal influenza and the COVID-19 pandemic. The objective of this study was to develop and evaluate the efficacy of machine learning methods at identifying and ranking the real-time readiness of individual patients for discharge, with the goal of improving patient flow within hospitals during periods of crisis.

METHODS AND PERFORMANCE

Electronic Health Record data from Oxford University Hospitals was used to train independent models to classify and rank patients' real-time readiness for discharge within 24 hours, for patient subsets according to the nature of their admission (planned or emergency) and the number of days elapsed since their admission. A strategy for the use of the models' inference is proposed, by which the model makes predictions for all patients in hospital and ranks them in order of likelihood of discharge within the following 24 hours. The 20% of patients with the highest ranking are considered as candidates for discharge and would therefore expect to have a further screening by a clinician to confirm whether they are ready for discharge or not. Performance was evaluated in terms of positive predictive value (PPV), i.e., the proportion of these patients who would have been correctly deemed as 'ready for discharge' after having the second screening by a clinician. Performance was high for patients on their first day of admission (PPV = 0.96/0.94 for planned/emergency patients respectively) but dropped for patients further into a longer admission (PPV = 0.66/0.71 for planned/emergency patients still in hospital after 7 days).

CONCLUSION

We demonstrate the efficacy of machine learning methods at making operationally focused, next-day discharge readiness predictions for all individual patients in hospital at any given moment and propose a strategy for their use within a decision-support tool during crisis periods.

Duplication of cervical oesophagus: a case report and review of literatures

Foregut duplication is commonly found in the posterior mediastinum. 10-20% of these anomalies are associated with oesophageal duplication. It can occur in all parts of oesophageal length. Although duplication of cervical oesophagus has been previously reported, but a majority of them were found in thoracic oesophagus. Infants with oesophageal duplication usually manifested by respiratory distress or asymptomatic thoracic mass, casually, detected in X-ray. A 7-month-old infant weighing about 7.5 kg, with the signboard of respiratory distress, fever and nutritional intolerance was admitted to our hospital. Physical examination showed dehydration, stridor, tachypnoea, intercostal retraction and neck stiffness. Plain chest radiogram showed dubious cystic mass in the distal neck. Cervical and chest computed tomography scan showed neck cystic lesion disseminated to posterior mediastinum, probably propagated to the respiratory system. Cystic lesion connected to oesophagus was partially resected, oesophagus was repaired and remaining mucous of a cyst was removed, then gastrostomy tube was applied. Although cervical oesophageal duplication cysts are rare, but they must be considered as one of the differential diagnoses of cervical mass with respiratory distress in infants.