Reliability and accuracy of medication-based performance measures for assessment of outpatient cardiology clinicians
Chow, C.; Gunzburger, E.; Wilson, C.; Salahuddin, T.; Hess, P. L.; Essien, U.; Rodriguez, F.; Yang, K.-C.; Ziaeian, B.; Doll, J. A.
Show abstract
Background: Multiple performance measures are used to assess the management of common cardiovascular conditions, such as coronary artery disease (CAD), heart failure with reduced ejection fraction (HFrEF), and atrial fibrillation (AF). These measures are sometimes used to assess individual clinicians, despite lack of evidence of reliability and accuracy. We used national Veteran Affairs (VA) data to test medication-based performance measures for assessment of clinician performance in outpatient cardiology care. Methods: This retrospective study examined VA cardiology clinician/Veteran dyads from 2017-2023 to evaluate 10 medication-based performance measures for CAD, HFrEF, or AF with elevated stroke risk. Achievement of each performance measure was defined as an active prescription of an indicated medication for each eligible patient encounter, summarized at the clinician level. Reliability was calculated using the beta-binomial method. A Bayesian causal model was used to assess the impact of case complexity on performance measure achievement. Finally, we performed peer review of a subset of cases (n=952) to assess the accuracy of three representative performance measures. Results: Of 456,071 unique cardiology encounters, 312,331 included CAD, 224,599 included HFrEF, and 178,678 included AF. These patients were treated by 1,043 cardiology clinicians. Median performance measure achievement ranged from 0.23 (SGLT2i for HFrEF) to 0.99 (beta blocker for HFrEF). Measure reliability varied from 0.63 to 0.90. Case complexity did not significantly affect performance measure achievement. Using peer review as the gold standard, accuracy was poor to moderate (balanced accuracy 0.51-0.73), and measures underestimated performance relative to peer review. Conclusions: Ten medication-based performance measures for CAD, HFrEF, and AF can be efficiently measured using electronic health record data, and most have sufficient reliability to support clinician benchmarking and the identification of potential care gaps. However, suboptimal accuracy compared with a peer review indicates that these measures may be insufficient as a standalone assessment of clinician quality.
Matching journals
The top 6 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Learning Decision Thresholds for Risk-Stratification Models from Aggregate Clinician Behavior 92%
- Assessing the quality of clinical and administrative data extracted from hospitals: The General Medicine Inpatient Initiative (GEMINI) experience 91%
- Measure what matters: counts of hospitalized patients are a better metric for health system capacity planning for a reopening 91%
Similar papers in this journal
- The Impact of COVID-19 Pandemic on Cardiology Services 92%
- Machine learning approaches to predict 30-day mortality following percutaneous coronary intervention in an Australian population 91%
- OpenSAFELY: impact of national guidance on switching from warfarin to direct oral anticoagulants (DOACs) in early phase of COVID-19 pandemic in England 91%
Similar papers in this journal
- Applications of Machine Learning in Decision Analysis for Dose Management for Dofetilide 93%
- External validation of a claims-based model to predict left ventricular ejection fraction class in patients with heart failure 93%
- Predicting 30-Day and 1-Year Mortality in Heart Failure with Preserved Ejection Fraction (HFpEF) 92%
Similar papers in this journal
- A Systematic Process for Assessing Fitness-for-Purpose of Health Outcomes for Computable Phenotyping with Electronic Health Record Data 91%
- Development and application of an algorithm for statin-induced myopathy based on electronic health record-derived structured elements 91%
- Using Natural Language Processing of Clinical Notes to Supplement Structured Electronic Health Record Data for Phenotyping Smoking and Obesity in a Healthcare System 90%
Similar papers in this journal
- Evaluation of Indicators of Reproducibility and Transparency in Published Cardiology Literature 93%
- Evaluation of antithrombotic use and COVID-19 outcomes in a nationwide atrial fibrillation cohort 90%
- Predicting the risk of motor vehicle crash in the first year after cardioverter-defibrillator implantation 88%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.