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It remains unclear whether the change from a manual to an automated coding system (ACS) for deaths has significantly affected the consistency of Australian mortality data. The underlying causes of 34,000 deaths registered in 1997 in Australia were dual coded, in ICD-9 manually, and by using an automated computer coding program. The diseases most affected by the change from manual to ACS were senile/presenile dementia, and pneumonia. The most common disease to which a manually assigned underlying cause of senile dementia was coded with ACS was unspecified psychoses (37.2%). Only 12.5% of codes assigned by ACS as senile dementia were coded the same by manual coders. This study indicates some important differences in mortality rates when comparing mortality data that have been coded manually with those coded using an automated computer coding program. These differences may be related to both the different interpretation of ICD coding rules between manual and automated coding, and different co-morbidities or co-existing conditions among demographic groups.

The aims of this study were to compare Sydney public hospitals regarding medical record coding times to compare observed coding times with coding times necessary to avoid backlog and to evaluate the impact on coding time of casemix complexity, coder age, experience, job satisfaction, employment status, and salary. Coding time (in minutes) for each medical record over a two-week period was documented by 61 coders employed in 13 hospitals: six principal referral (PR), six major metropolitan (MM), and one paediatric specialist (PS) hospitals. The mean coding time for each coder was estimated by averaging across coding times for all records during the two-week period. In order to compare hospital mean coding times, the hospitals were grouped into PR and MM/PS groups. The mean coding time necessary to avoid coding backlog (expected coding time) for each hospital group was based on the total number of annual separations and filled full-time equivalent coding positions. The observed mean coding time was longer in the PR group than in the MM/PS group (p = 0.019); however, the observed coding time was within the expected coding time limit in both the PR and MM/PS groups. Casemix complexity tended to influence coding time, but neither age, experience, job satisfaction, employment status nor salary had any impact. In conclusion, the expected coding times, if reliable, indicate that coders in the two hospital groups were keeping coding up-to-date. Thus, the variation between hospital groups in coding time is of little importance, given that the main objective in coding productivity is to maintain the coding workload.