Vojtech huser-data-warehouse-evaluation-2010-04-idr-snapshot014c
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Vojtech huser-data-warehouse-evaluation-2010-04-idr-snapshot014c
- 1. A Methodology for Quantitative Measurement of Quality and Comprehensiveness of a Research Data Repository (IDR Snapshot) Vojtech Huser MD PhD
- 10. Data Structures (VDW) linkedin.com/in/vojtechhuser
- 11. Data Structures (i2b2) linkedin.com/in/vojtechhuser
- 12. Data Structures (HealthFlow) linkedin.com/in/vojtechhuser healthcareworkflow.wordpress.com
- 14. Event table (IDR Snapshot v1) code.google.com/p/IDRSnapshot (Event-DOB) + ‘3000-01-01’
- 19. Cross institution comparison code.google.com/p/IDRSnapshot
Notes de l'éditeur
- open Tset
- Completeness. Modeling all relevant performance factors to provide a holistic measurement of the concept. Concision. A calculation that is as simple and straightfoward as possible, making it understandable and logical to users. Measurability. Using direct performance data rather than relying too heavily on proxies or subjective measures. And from a practical perspective, if you can’t reliably gather valid data, the exercise is futile. Independence. The components of the measure need to be independent so that variation in one component doesn’t directly drive another.
- Completeness. Modeling all relevant performance factors to provide a holistic measurement of the concept. Concision. A calculation that is as simple and straightfoward as possible, making it understandable and logical to users. Measurability. Using direct performance data rather than relying too heavily on proxies or subjective measures. And from a practical perspective, if you can’t reliably gather valid data, the exercise is futile. Independence. The components of the measure need to be independent so that variation in one component doesn’t directly drive another.
- Completeness. Modeling all relevant performance factors to provide a holistic measurement of the concept. Concision. A calculation that is as simple and straightfoward as possible, making it understandable and logical to users. Measurability. Using direct performance data rather than relying too heavily on proxies or subjective measures. And from a practical perspective, if you can’t reliably gather valid data, the exercise is futile. Independence. The components of the measure need to be independent so that variation in one component doesn’t directly drive another.
- Completeness. Modeling all relevant performance factors to provide a holistic measurement of the concept. Concision. A calculation that is as simple and straightfoward as possible, making it understandable and logical to users. Measurability. Using direct performance data rather than relying too heavily on proxies or subjective measures. And from a practical perspective, if you can’t reliably gather valid data, the exercise is futile. Independence. The components of the measure need to be independent so that variation in one component doesn’t directly drive another.
- Completeness. Modeling all relevant performance factors to provide a holistic measurement of the concept. Concision. A calculation that is as simple and straightfoward as possible, making it understandable and logical to users. Measurability. Using direct performance data rather than relying too heavily on proxies or subjective measures. And from a practical perspective, if you can’t reliably gather valid data, the exercise is futile. Independence. The components of the measure need to be independent so that variation in one component doesn’t directly drive another.