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Objective: Develop and validate a surveillance model to identify outpatient surgical adverse events (AEs) based on previously developed electronic triggers.Data Sources: Veterans Health Administration's Corporate Data Warehouse.Study Design: Six surgical AE triggers, including postoperative emergency room visits and hospitalizations, were applied to FY2012-2014 outpatient surgeries (n = 744,355). We randomly sampled trigger-flagged and unflagged cases for nurse chart review to document AEs and measured positive predictive value (PPV) for triggers. Next, we used chart review data to iteratively estimate multilevel logistic regression models to predict the probability of an AE, starting with the six triggers and adding in patient, procedure, and facility characteristics to improve model fit. We validated the final model by applying the coefficients to FY2015 outpatient surgery data (n = 256,690) and reviewing charts for cases at high and moderate probability of an AE.Principal Findings: Of 1,730 FY2012-2014 reviewed surgeries, 350 had an AE (20 percent). The final surveillance model c-statistic was 0.81. In FY2015 surgeries with >0.8 predicted probability of an AE (n = 405, 0.15 percent), PPV was 85 percent; in surgeries with a 0.4-0.5 predicted probability of an AE, PPV was 38 percent.Conclusions: The surveillance model performed well, accurately identifying outpatient surgeries with a high probability of an AE. [ABSTRACT FROM AUTHOR]

Objective: Electronic clinical quality measures (eCQMs) rely on computer algorithms to extract data from electronic health records (EHRs). On behalf of the American College of Rheumatology (ACR), we sought to develop and test eCQMs for rheumatoid arthritis (RA).Methods: Drawing from published ACR guidelines, a working group developed candidate RA process measures and subsequently assessed face validity through an interdisciplinary panel of health care stakeholders. A public comment period followed. Measures that passed these levels of review were electronically specified using the quality data model, which provides standard nomenclature for data elements (category, datatype, and value sets) obtained through an EHR. For each eCQM, 3 clinical sites using different EHR systems tested the scientific feasibility and validity of measures. Measures appropriate for accountability were presented for national endorsement.Results: Expert panel validity ratings were high for all measures (median 8-9 of 9). Health system performance on the eCQMs was 53.6% for RA disease activity assessment, 69.1% for functional status assessment, 93.1% for disease-modifying antirheumatic drug (DMARD) use, and 72.8% for tuberculosis screening. Kappa statistics, which evaluated whether the eCQM validly captured data obtained from manual EHR chart review, demonstrated moderate to substantial agreement (0.54 for functional status assessment, 0.73 for tuberculosis screening, 0.84 for disease activity, and 0.85 for DMARD use).Conclusion: Four eCQMs for RA have achieved national endorsement and are recommended for use in federal quality reporting programs. Implementation and further refinement of these measures is ongoing in the ACR's registry, the Rheumatology Informatics System for Effectiveness (RISE). [ABSTRACT FROM AUTHOR]