Computable phenotype for diagnostic error: developing the data schema for application of symptom-disease pair analysis of diagnostic error (SPADE).

Objectives: Diagnostic errors are the leading cause of preventable harm in clinical practice. Implementable tools to quantify and target this problem are needed. To address this gap, we aimed to generalize the Symptom-Disease Pair Analysis of Diagnostic Error (SPADE) framework by developing its computable phenotype and then demonstrated how that schema could be applied in multiple clinical contexts.

Cerebrovascular disease hospitalizations following emergency department headache visits: A nested case-control study.

Background: Delayed diagnosis of cerebrovascular disease (CVD) among patients can result in substantial harm. If diagnostic process failures can be identified at emergency department (ED) visits that precede CVD hospitalization, interventions to improve diagnostic accuracy can be developed.

Methods: We conducted a nested case-control study using a cohort of adult ED patients discharged from a single medical center with a benign headache diagnosis from October 1, 2015 to March 31, 2018.

Bed downtime: the novel use of a quality metric allows inpatient providers to improve patient flow from the emergency department.

Background: Emergency department (ED) boarding time is associated with increased length of stay (LOS) and inpatient mortality. Despite the documented impact of ED boarding on inpatient outcomes, a disparity continues to exist between the attention paid to the issue by inpatient and ED providers. A perceived lack of high yield strategies to address ED boarding from the perspective of the inpatient provider may discourage involvement in improvement initiatives on the subject.

Use of Systems Engineering to Design a Hospital Command Center.

Background: In hospitals and health systems across the country, patient flow bottlenecks delay care delivery-emergency department boarding and operating room exit holds are familiar examples. In other industries, such as oil, gas, and air traffic control, command centers proactively manage flow through complex systems.

Methods: A systems engineering approach was used to analyze and maximize existing capacity in one health system, which led to the creation of the Judy Reitz Capacity Command Center.

An Electronic Dashboard to Monitor Patient Flow at the Johns Hopkins Hospital: Communication of Key Performance Indicators Using the Donabedian Model.

Efforts to monitoring and managing hospital capacity depend on the ability to extract relevant time-stamped data from electronic medical records and other information technologies. However, the various characterizations of patient flow, cohort decisions, sub-processes, and the diverse stakeholders requiring data visibility create further overlying complexity. We use the Donabedian model to prioritize patient flow metrics and build an electronic dashboard for enabling communication.