Background: Clinically stable inpatients may receive potentially unnecessary care, such as overnight vital sign assessment. Nighttime vital signs can disrupt sleep and adversely affect patient satisfaction and contribute to delirium. However, it may be difficult for individual clinicians to determine which patients could safely forego overnight vital signs. Purpose: We developed a predictive algorithm designed [...]

Background: Mortality prediction models are increasingly being adopted in the clinical setting, both to retrospectively assess quality of care and to prospectively inform clinical practice. An open question is whether a particular hospital should employ a model trained using a diverse nationwide dataset or use a model developed primarily from local data. The Veterans Affairs [...]

Background: Diagnostic errors (DEs) represent ongoing threats to patient safety in the hospital. Little is known about the factors present on admission that can predict DE during the hospital encounter. Such knowledge is essential for prospective identification of hospitalized patients at risk for DE who can be targeted for early intervention. The purpose of this [...]

Background: The HCAHPS survey is used nationally by health systems as a standard to measure the patient experience and identify opportunities for improvement. Historically, process improvement initiatives based on the survey are implemented retroactively, attempting to prevent negative experiences for future patients. However, given the increasing complexity of patient care, proactively addressing the identified concerns [...]

Background: Clinically stable inpatients may receive potentially unnecessary care, such as overnight vital sign assessment. Nighttime vital signs can disrupt sleep and adversely affect patient satisfaction and contribute to delirium. However, it may be difficult for individual clinicians to determine which patients could safely forego overnight vital signs. Purpose: We developed a predictive algorithm designed [...]

Background: Assessing severity of illness using available electronic medical record (EMR) data on admission and predicting inpatient mortality is very challenging. Lacking standardized practices around end of life issues, hospitalists use their clinical judgment in making these crucial decisions. Prolonged discussions may be needed in medically complex patients to direct an optimal plan of care [...]

Background: Mortality prediction models are increasingly being adopted in the clinical setting, both to retrospectively assess quality of care and to prospectively inform clinical practice. An open question is whether a particular hospital should employ a model trained using a diverse nationwide dataset or use a model developed primarily from local data. The Veterans Affairs [...]

Background: Diagnostic errors (DEs) represent ongoing threats to patient safety in the hospital. Little is known about the factors present on admission that can predict DE during the hospital encounter. Such knowledge is essential for prospective identification of hospitalized patients at risk for DE who can be targeted for early intervention. The purpose of this [...]

Background: Discharge to post-acute care settings (PACs), such as skilled nursing facilities (SNFs), requires significant, complex discharge planning which often needs to be started early during hospitalization to be complete by time of discharge. This study sought to identify and model factors which predict a given patient’s likelihood of requiring PAC after discharge, using routinely [...]

Background: Acute heart failure (AHF) is the major cause of hospital admission and death. Furthermore, patients admitted with AHF have high readmission rates and  postdischarge mortality. Although there have been some reports, which demonstrated the predictive factors of death among patients of acute heart failure in the USA and Europe, there has been no studies [...]