First, we examined math accuracy gains, comparing targeted problems expected to show change with embodied spatial training to those for which change would not be expected.
10, 11, 13, 15, 18, 21, 22, 24– 28 Attempts to rigidly control these CASs often worsen the targeted problems and lead to unintended negative consequences.
In the data set we found a total of 24160 ICD-9-CM code instances, and 15449 of them corresponded to one of our 80 targeted problems.
Regular expressions and a list of phrases representing possible ways of describing each of the 80 targeted problems were then used to select sentences with mentions of the targeted problems.
To estimate the proportion of coded diagnoses covered by our set of 80 targeted problems, all unique ICD-9-CM codes assigned to cardiovascular adult inpatients during 2003 in our institution (LDS Hospital, Salt Lake City, Utah) were retrieved.
To estimate the proportion of patient's coded diagnoses covered by our set of targeted problems, we retrieved ICD-9-CM codes assigned for administrative purposes to all cardiovascular adult inpatients during 2003 in our institution (LDS Hospital, Salt Lake City, Utah), and compared it with our set of problems.
The background application uses Natural Language Processing NLPP) to harvest potential problem list entries from the list of 80 targeted problems detected in the multiple free-text electronic documents available in our electronic medical record.
In user-centric models, the number of available active users plays an important role in providing interesting patterns for targeted problems [33, 34].
Our set of 80 targeted problems therefore covered only 5.2% of all possible codes.
The development and functions of the Automated Problem List system have been reported in this paper, along with results of two preliminary evaluations: The coverage of the set of 80 targeted problems was only about 5% of all possible diagnoses coded in ICD-9-CM, but about 64% of all instances of these coded diagnoses.
