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.

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 %-9 9 . 7 % ( d e n o m i n a t o r s ) a n d 8 7 . 7 % -9 7 . 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.

Background: Despite advances in stroke care, many patients do not receive recommended care processes. Quality indicator (QI) reporting programs, like GWTG-Stroke, have been shown to improve care. We sought to determine whether training plus QI feedback was more effective than QI feedback alone in improving two stroke QIs. Methods: We conducted a cluster randomized trial in 11 VA hospitals. Sites were randomized to a quality improvement training program plus QI feedback vs. QI feedback alone to improve DVT prophylaxis and dysphagia screening. Intervention sites received face-to-face training, developed individualized improvement plans, and had 6 months of post-training facilitation. Both groups received monthly QI feedback. Eligibility and passing for the two stroke QIs, plus nine other stroke QIs, was determined by centralized chart review. We compared pre-intervention (pre-I) to post-intervention (post-I) performance on the two stroke QIs and on defect-free care (DF, a binary patient-level variable including all QIs) in intervention vs. control sites. We constructed logistic models of the two QIs and DF care, adjusting for patient variables, time, intervention group, and time-group interaction. Results: The five intervention sites had 1147 admissions and the six control sites had 1017 admissions during the study period. DVT prophylaxis was similar pre-I (85% vs. 90%) and improved in both groups (post-I rates 90% intervention and 94% control, ratio of ORs 0.89, p = 0.75). Dysphagia screening was higher pre-I in intervention sites (51% vs. 37%), and improved more in the control sites (post-I 56% and 52%, ratio of ORs 0.67, p=0.04). In logistic models, DVT, Dysphagia, and DF performance were associated with baseline performance and post-I time. Dysphagia performance was also associated with NIHSS and time-group interaction, and DF care was also associated with the presence of a baseline data collection program. Conclusion: Quality improvement training did not add to the impact of data feedback in sites already motivated to participate in QI initiatives. Defect-free stroke care is associated with an ongoing stroke data collection program, emphasizing the importance of audit and feedback to achieve the highest quality stroke care.