Although only a few variables were collected from each hospital on consecutive days, one major difficulty we had was the data presented by discontinuous data, i.e. data that sometimes be there and sometimes not. Sometimes data were repeated. To handle this problem, specific criteria of data cleansing were used for different variables, including the logic checks described above and double checks for possible presence of duplicate patient records. If data in the chief complaint field was written as "test" or the field was left empty or if the ICD-9 field was written as "test" or left empty, they were deleted before data analysis. Hospital ID, date of birth, admission time, gender, and home zip-code were used as key indicators of whether a listing is a duplicate listing and be deleted as repeated data. The system was capable of performing frequent and rapid checks of any subject of hospital identification code and time format of all time fields. It was capable of moving erroneous data to an "error table" for storage. Incorrectly formatted ICD-9-CM data were also moved to the error table. All deletion and removal operations were recorded in the log file for monitoring. In certain situa-tions in case possible systematic errors were found (i.e., aberrant number of ED visits on certain days or occasionally inconsistent formats of ICD-9 codes), the data examiner would contact the medical informatics officers of those specific hospitals to discuss improving data entry.