Study groups to Received actions labeled as reciprocal determine - - PowerPoint PPT Presentation
Study groups to Received actions labeled as reciprocal determine - - PowerPoint PPT Presentation
Received their first-ever action from a board Group 1 Represent 45% of disciplined physicians in 2017 Have 1.3 board orders and 2.5 actions per physician Study groups to Received actions labeled as reciprocal determine if board
Study groups to determine if board
- rder narratives
provide additional insight beyond the Conclusions of Law.
─Received their first-ever action from a board ─Represent 45% of disciplined physicians in 2017 ─Have 1.3 board orders and 2.5 actions per physician
Group 1
─Received actions labeled as reciprocal ─Represent 13% of disciplined physicians in 2017 ─Have 4.6 board orders and 7.7 actions per physician
Group 2
─Received multiple actions all from the same board ─Represent 26% of disciplined physicians in 2017 ─Have 3.3 board orders and 5.8 actions per physician
Group 3
─Received multiple actions from different boards ─Represent 45% of disciplined physicians in 2017 ─Have 6.1 board orders and 9.8 actions per physician
Group 4
Board order narratives usually give more complete information than ‘Conclusions of Law’ therefore it is appropriate to move ahead with a categorization project. Documents received from boards vary in quality, format and detail. This makes automation non-feasible at this time. Challenges with reciprocal actions remain. For statistical purposes, if an action is known to be reciprocal, it will be classified as such, regardless of associated narrative. ‘Not applicable’ basis code will be classified as ‘No reason given.’
Observations
Results
Bayesian method yielded the highest accuracy for entire documents. Accuracy on three classes: averages 40%, up to 60%
- n certain training sets.
fastText provides the highest accuracy for slices which could be used to assist a human evaluator.
These findings seem low – but are statistically significant compared to random guessing. These models were trained
- n limited data pools and could improve with more data.