A multifaceted study of hospital variables and interventions to improve inpatient satisfaction in a multi-hospital system.

Knowing the areas of service, actions, and parameters that can influence patient perception about a service provided can help hospital executives and healthcare workers to devise improvement plans, leading to higher patient satisfaction. To identify inpatient satisfaction determinants, assess their relationships with hospital variables, and improve patient satisfaction through interventions. We studied the inpatient population of an eight-hospital tertiary medical center in 2015.

Preventing inpatient falls with injuries using integrative machine learning prediction: a cohort study.

Patient falls during hospitalization can lead to severe injuries and remain one of the most vexing patient-safety problems facing hospitals. They lead to increased medical care costs, lengthened hospital stays, more litigation, and even death. Existing methods and technology to address this problem mostly focus on stratifying inpatients at risk, without predicting fall severity or injuries.