{"doi":"10.2196/72709","title":"Association of Qualified Clinical Data Registry Clinician Dashboard Engagement With Performance on Quality-of-Care Measures: Cross-Sectional Analysis","abstract":"Background: Qualified Clinical Data Registries (QCDRs) have proliferated across many medical specialties, facilitating quality measure performance monitoring and reporting in programs like the CMS Merit-based Incentive Payment System. Many of these QCDRs offer web-based, clinician-facing dashboards to support quality improvement. However, it is unknown whether engagement with such dashboards is associated with improvements in quality of care. Objective: We investigated the cross-sectional relationship between engagement with a QCDR dashboard and quality measure performance. Methods: Data derived from a rheumatology QCDR (\"Rheumatology Informatics System for Effectiveness [RISE]\") and audit log data from the dashboard (exposure) and Merit-based Incentive Payment System submission data (outcome) from 2020-2022 were included. Among practices participating in RISE, we assessed aggregated engagement with the QCDR dashboard and quality performance for 8 rheumatology-specific measures at the practice level. For each measure, the binomial generalized linear model was used to examine the association between dashboard engagement and measure performance, adjusting for EHR vendor, study year, and clustering at the practice level to account for repeated measures. Two types of engagement were analyzed: (1) measure-specific (interactions with patient-level information for a particular measure) and (2) global (interactions with any feature of the dashboard, classified into 4 profiles). Linear trends between the level of dashboard engagement and performance were also tested in the global analysis. Results: In total, 211 practices were included in the study; over half were single-specialty practices. During their first year in the study, 65% of the practices had \"most\" or \"moderate\" levels of global engagement. In measure-specific analyses, we observed a positive but nonsignificant association of each individual and \"any\" actions with performance on 6-8 measures. However, having a ≥90th percentile number of drill-down views on 1 measure (rheumatoid arthritis (RA) periodic disease activity assessment) was statistically significant (odds ratio [OR] 2.3, 95% CI 1.2-4.3). In global analyses, we observed a similar pattern, where practices \"most\" engaged with the dashboard had higher odds of better performance compared to those with \"none.\" In total, 4 measures (osteoporosis screening, RA functional status assessment, RA periodic disease activity assessment, and gout serum urate target) had a statistically significant association with engagement and exhibited a \"dose-response\" relationship (P=.004, .02, <.001, and .04, respectively, for trend). Practices with \"any\" global engagement had higher performance on 6 out of 8 measures, again, with RA periodic disease activity assessment being statistically significant (OR 2.9, 95% CI 1.3-6.6). Conclusions: We found that higher levels of engagement were associated with higher performance on some, but not all, rheumatology-specific quality measures. Additional work is needed to understand whether the dashboard facilitates quality improvement or is merely a marker for high-performing practices.","journal":"Journal of Medical Internet Research","year":2025,"id":554312,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":1,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9577,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2025-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":648367,"name":"Jing Li","orcid":"0000-0002-3298-2112","position":1,"is_corresponding":false},{"id":482305,"name":"Julia Adler‐Milstein","orcid":"0000-0002-0262-6491","position":2,"is_corresponding":false},{"id":24102,"name":"Jinoos Yazdany","orcid":"0000-0002-3508-4094","position":3,"is_corresponding":false},{"id":417286,"name":"Stephen Shiboski","orcid":"0000-0002-9257-2609","position":4,"is_corresponding":false},{"id":58625,"name":"Gabriela Schmajuk","orcid":"0000-0003-2687-5043","position":5,"is_corresponding":false},{"id":1096596,"name":"Emma Kersey","orcid":"0009-0001-5637-7515","position":0,"is_corresponding":true}],"reference_count":35,"raw_metadata":null,"created_at":"2026-07-19T02:54:50.112989Z","pmid":"41055052","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}