Background: Innovation in graduate medical education is driven by the recognition of residents as adult learners. Increasing digitization provides for exciting opportunities to make the learning process more interactive. The use of audience response devices, or clickers, have been shown to be highly effective in higher education by improving engagement and participation in the classroom [...]

Background: Sepsis is a significant cause of morbidity and mortality in hospitalized patients . Early and appropriate therapy has been shown to improve outcomes, making early diagnosis and intervention critical . However, recognition and treatment of sepsis remains a challenge . In order to understand how to best deliver sepsis treatment in different hospitals within [...]

Background: Health information technology (HIT) has the potential to decrease rates of hospital-acquired conditions. The Patient Safety Learning Lab (PSLL) developed a suite of HIT tools to engage patients, families, and providers in identifying, assessing, and reducing patient safety threats. The goal of this current evaluation is to quantify the effects of this intervention on [...]

Background: Adverse events (AEs) are a major concern in the inpatient setting, with many considered preventable. The Patient Safety Learning Lab implemented a Patient Safety Dashboard integrated with our electronic health record as part of a suite of health information technology tools to reduce inpatient AEs. The goals of this evaluation were to understand patterns [...]

Background: Diagnostic error in acute care represents an unresolved safety issue: error rates range from 4.8 to 49.8%. If the diagnosis is delayed or incorrect, the patient may not get correct treatment in a timely manner. Underlying contributing factors include system flaws (e.g., communication barriers) and cognitive errors (e.g., anchoring), factors that are often overlooked [...]

Background: The EMR does not provide readily available information that conveys an at-a-glance understanding of discharge progress for a given patient. Healthcare workers have different workflows and need to manage the information in different ways, with a reliance on one-to-one conversations. We believe that optimizing patient length of stay is hindered by lack of: data [...]

Background: A somewhat under-discussed topic in EHR implementation is the safety and efficiency consequences of their lengthy build and implementation. After multiple years of stalled clinical innovation while IT staff dedicate their focus to EHR builds, hospital systems undergo an 8 – 16 hour downtime often followed by multiple planned and unplanned downtimes that can [...]

Background: In recent years, in large part due to reimbursement incentives, the healthcare industry has shifted focus from volume to quality of care, with patient satisfaction being a crucial part of the evaluation. There is growing recognition that patient satisfaction is at least in part linked to clinician satisfaction and burnout(1). Thus, there is a [...]

Background: Sepsis is one of the top causes of inpatient mortality and rapid detection presents numerous challenges. In March, 2016, an interdisciplinary team consisting of top clinicians, data scientists and machine learning experts at a large academic medical center (AMC) embarked on an innovation pilot to develop a novel machine learning model to detect sepsis. [...]