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. METHODS: We created an information model for the SPADE processes, then mapped data fields from electronic health records (EHR) and claims data in use to that model to create the SPADE information model (intention) and the SPADE computable phenotype (extension). Later we validated the computable phenotype and tested it in four case studies in three different health systems to demonstrate its utility. RESULTS: We mapped and tested the SPADE computable phenotype in three different sites using four different case studies. We showed that data fields to compute an SPADE base measure are fully available in the EHR Data Warehouse for extraction and can operationalize the SPADE framework from provider and/or insurer perspective, and they could be implemented on numerous health systems for future work in monitor misdiagnosis-related harms. CONCLUSIONS: Data for the SPADE base measure is readily available in EHR and administrative claims. The method of data extraction is potentially universally applicable, and the data extracted is conveniently available within a network system. Further study is needed to validate the computable phenotype across different settings with different data infrastructures.

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. As such, further work is needed to identify inpatient metrics and strategies to address patient flow problems, and which may improve ED boarding time. METHODS: After initial system analysis, our multidisciplinary quality improvement (QI) group defined the process time metric 'bed downtime'-the time from which a bed is vacated by a discharged patient to the time an ED patient is assigned to that bed. Using the Lean Sigma QI approach, this metric was targeted for improvement on the internal medicine hospitalist service at a tertiary care academic medical centre. INTERVENTIONS: Interventions included improving inpatient provider awareness of the problem, real-time provider notification of empty beds, a weekly retrospective emailed performance dashboard and the creation of a guideline document for admission procedures. RESULTS: This package of interventions was associated with a 125 min reduction in mean bed downtime for incoming ED patients (254 min to 129 min) admitted to the intervention unit. CONCLUSION: Use of the bed downtime metric as a QI target was associated with marked improvements in process time during our project. The use of this metric may enhance the ability of inpatient providers to participate in QI efforts to improve patient flow from the ED. Further study is needed to determine if use of the metric may be effective at reducing boarding time without requiring alterations to LOS or discharge patterns.

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. Hospitalizations for CVD within 1 year of index ED visit were identified using a regional health information exchange. Patients with subsequent CVD hospitalization (cases) were individually matched to patients without subsequent hospitalization (controls) using patient age and visit date. Demographic, clinical, and ED process characteristics were assessed via detailed chart review. McNemar's test for categorical and paired t-test for continuous variables were used with statistical significance set at ≤0.05. RESULTS: Of the 9157 patients with ED headache visits, 57 (0.6%, 95% confidence interval [CI] = 0.5-0.8) had a subsequent CVD hospitalization. Median time from ED visit to hospitalization was 107 days. In 25 patients (43.9%, 25/57) the CVD hospitalization and the index ED visit were at different hospitals. Fifty-three cases and 53 matched controls were included in the final study analysis. Cases and controls had similar baseline demographic and headache characteristics. Cases more often had a history of stroke (32.1% vs. 13.2%, p = 0.02) and neurosurgery (13.2% vs. 1.9%, p = 0.03) prior to the index ED visit. Cases more often had less than two components of the neurologic examination documented (30.2% vs. 11.3%, p = 0.03). CONCLUSION: We found that 0.6% of patients with an ED headache visit had subsequent CVD hospitalization, often at another medical center. ED visits for headache complaints among patients with prior stroke or neurosurgical procedures may be important opportunities for CVD prevention. Documented neurologic examinations were poorer among cases, which may represent an opportunity for ED process improvement.

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. This article describes the key elements of this novel health system command center, which include strategic colocation of teams, automated visual displays of real-time data providing a global view, predictive analytics, standard work and rules-based protocols, and a clear chain of command and guiding tenets. Preliminary data are also shared. RESULTS: With proactive capacity management, subcycle times decreased and allowed the health system's flagship hospital to increase occupancy from 85% to 92% while decreasing patient delays. CONCLUSION: The command center was built with three primary goals-reducing emergency department boarding, eliminating operating room holds, and facilitating transfers in from outside facilities-but the command center infrastructure has the potential to improve hospital operations in many other areas.

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. Ten metrics were identified as key indicators including outcome (length of stay, 30-day readmission, operating room exit delays, capacity-related diversions), process (timely inpatient unit discharge, emergency department disposition), and structural metrics (occupancy, discharge volume, boarding, bed assignation duration). Dashboard users provided real-life examples of how the tool is assisting capacity improvement efforts, and user traffic data revealed an uptrend in dashboard utilization from May to October 2017 (26 to 148 views per month, respectively). Our main contributions are twofold. The former being the results and methods for selecting key performance indicators for a unit, department, and across the entire hospital (i.e., separating signal from noise). The latter being an electronic dashboard deployed and used at The Johns Hopkins Hospital to visualize these ten metrics and communicate systematically to hospital stakeholders. Integration of diverse information technology may create further opportunities for improved hospital capacity.