Validation of Stroke Meaningful Use Measures in a National Electronic Health Record System.

BACKGROUND: The Meaningful Use (MU) program has increased the national emphasis on electronic measurement of hospital quality. OBJECTIVE: To evaluate stroke MU and one VHA stroke electronic clinical quality measure (eCQM) in national VHA data and determine sources of error in using centralized electronic health record (EHR) data. DESIGN: Our study is a retrospective cross-sectional study of stroke quality measure eCQMs vs. chart review in a national EHR. We developed local SQL algorithms to generate the eCQMs, then modified them to run on VHA Central Data Warehouse (CDW) data. eCQM results were generated from CDW data in 2130 ischemic stroke admissions in 11 VHA hospitals. Local and CDW results were compared to chart review. MAIN MEASURES: We calculated the raw proportion of matching cases, sensitivity/specificity, and positive/negative predictive values (PPV/NPV) for the numerators and denominators of each eCQM. To assess overall agreement for each eCQM, we calculated a weighted kappa and prevalence-adjusted bias-adjusted kappa statistic for a three-level outcome: ineligible, eligible-passed, or eligible-failed. KEY RESULTS: In five eCQMs, the proportion of matched cases between CDW and chart ranged from 95.4 %-99.7 % (denominators) and 87.7 %-97.9 % (numerators). PPVs tended to be higher (range 96.8 %-100 % in CDW) with NPVs less stable and lower. Prevalence-adjusted bias-adjusted kappas for overall agreement ranged from 0.73-0.95. Common errors included difficulty in identifying: (1) mechanical VTE prophylaxis devices, (2) hospice and other specific discharge disposition, and (3) contraindications to receiving care processes. CONCLUSIONS: Stroke MU indicators can be relatively accurately generated from existing EHR systems (nearly 90 % match to chart review), but accuracy decreases slightly in central compared to local data sources. To improve stroke MU measure accuracy, EHRs should include standardized data elements for devices, discharge disposition (including hospice and comfort care status), and recording contraindications.

A cluster-randomised quality improvement study to improve two inpatient stroke quality indicators.

BACKGROUND: Quality indicator collection and feedback improves stroke care. We sought to determine whether quality improvement training plus indicator feedback was more effective than indicator feedback alone in improving inpatient stroke indicators. METHODS: We conducted a cluster-randomised quality improvement trial, randomising hospitals to quality improvement training plus indicator feedback versus indicator feedback alone to improve deep vein thrombosis (DVT) prophylaxis and dysphagia screening. Intervention sites received collaborative-based quality improvement training, external facilitation and indicator feedback. Control sites received only indicator feedback. We compared indicators pre-implementation (pre-I) to active implementation (active-I) and post-implementation (post-I) periods. We constructed mixed-effect logistic models of the two indicators with a random intercept for hospital effect, adjusting for patient, time, intervention and hospital variables. RESULTS: Patients at intervention sites (1147 admissions), had similar race, gender and National Institutes of Health Stroke Scale scores to control sites (1017 admissions). DVT prophylaxis improved more in intervention sites during active-I period (ratio of ORs 4.90, p<0.001), but did not differ in post-I period. Dysphagia screening improved similarly in both groups during active-I, but control sites improved more in post-I period (ratio of ORs 0.67, p=0.04). In logistic models, the intervention was independently positively associated with DVT performance during active-I period, and negatively associated with dysphagia performance post-I period. CONCLUSION: Quality improvement training was associated with early DVT improvement, but the effect was not sustained over time and was not seen with dysphagia screening. External quality improvement programmes may quickly boost performance but their effect may vary by indicator and may not sustain over time.