BACKGROUND: Hospitals' risk-standardized mortality rates and outlier status (significantly higher/lower rates) are reported by the Centers for Medicare and Medicaid Services (CMS) for acute myocardial infarction (AMI) patients using Medicare claims data. New York now has AMI claims data with blood pressure and heart rate added. OBJECTIVE: The objective of this study was to see whether the appended database yields different hospital assessments than standard claims data. METHODS: New York State clinically appended claims data for AMI were used to create 2 different risk models based on CMS methods: 1 with and 1 without the added clinical data. Model discrimination was compared, and differences between the models in hospital outlier status and tertile status were examined. RESULTS: Mean arterial pressure and heart rate were both significant predictors of mortality in the clinically appended model. The C statistic for the model with the clinical variables added was significantly higher (0.803 vs. 0.773, P<0.001). The model without clinical variables identified 10 low outliers and all of them were percutaneous coronary intervention hospitals. When clinical variables were included in the model, only 6 of those 10 hospitals were low outliers, but there were 2 new low outliers. The model without clinical variables had only 3 high outliers, and the model with clinical variables included identified 2 new high outliers. CONCLUSION: Appending even a small number of clinical data elements to administrative data resulted in a difference in the assessment of hospital mortality outliers for AMI. The strategy of adding limited but important clinical data elements to administrative datasets should be considered when evaluating hospital quality for procedures and other medical conditions.
BACKGROUND: Hospitals' risk-standardized mortality rates and outlier status (significantly higher/lower rates) are reported by the Centers for Medicare and Medicaid Services (CMS) for acute myocardial infarction (AMI) patients using Medicare claims data. New York now has AMI claims data with blood pressure and heart rate added. OBJECTIVE: The objective of this study was to see whether the appended database yields different hospital assessments than standard claims data. METHODS: New York State clinically appended claims data for AMI were used to create 2 different risk models based on CMS methods: 1 with and 1 without the added clinical data. Model discrimination was compared, and differences between the models in hospital outlier status and tertile status were examined. RESULTS: Mean arterial pressure and heart rate were both significant predictors of mortality in the clinically appended model. The C statistic for the model with the clinical variables added was significantly higher (0.803 vs. 0.773, P<0.001). The model without clinical variables identified 10 low outliers and all of them were percutaneous coronary intervention hospitals. When clinical variables were included in the model, only 6 of those 10 hospitals were low outliers, but there were 2 new low outliers. The model without clinical variables had only 3 high outliers, and the model with clinical variables included identified 2 new high outliers. CONCLUSION: Appending even a small number of clinical data elements to administrative data resulted in a difference in the assessment of hospital mortality outliers for AMI. The strategy of adding limited but important clinical data elements to administrative datasets should be considered when evaluating hospital quality for procedures and other medical conditions.
Authors: Sara J Deakyne Davies; Robert W Grundmeier; Diego A Campos; Katie L Hayes; Jamie Bell; Evaline A Alessandrini; Lalit Bajaj; James M Chamberlain; Marc H Gorelick; Rene Enriquez; T Charles Casper; Beth Scheid; Marlena Kittick; J Michael Dean; Elizabeth R Alpern Journal: Appl Clin Inform Date: 2018-05-23 Impact factor: 2.342