BACKGROUND: The BrainIT group works collaboratively on developing standards for collection and analyses of data from brain injured patients towards providing a more efficient infrastructure for assessing new health technology. MATERIALS AND METHODS: Over a 2 year period, core dataset data (grouped by nine categories) were collected from 200 head-injured patients by local nursing staff. Data were uploaded by the BrainIT web and random samples of received data were selected automatically by computer for validation by data validation (DV) research nurse staff against gold standard sources held in the local centre. Validated data was compared with original data sent and percentage error rates calculated by data category. FINDINGS: Comparisons, 19,461, were made in proportion to the size of the data received with the largest number checked in laboratory data (5,667) and the least in the surgery data (567). Error rates were generally less than or equal to 6%, the exception being the surgery data class where an unacceptably high error rate of 34% was found. CONCLUSIONS: The BrainIT core dataset (with the exception of the surgery classification) is feasible and accurate to collect. The surgery classification needs to be revised.
BACKGROUND: The BrainIT group works collaboratively on developing standards for collection and analyses of data from brain injured patients towards providing a more efficient infrastructure for assessing new health technology. MATERIALS AND METHODS: Over a 2 year period, core dataset data (grouped by nine categories) were collected from 200 head-injured patients by local nursing staff. Data were uploaded by the BrainIT web and random samples of received data were selected automatically by computer for validation by data validation (DV) research nurse staff against gold standard sources held in the local centre. Validated data was compared with original data sent and percentage error rates calculated by data category. FINDINGS: Comparisons, 19,461, were made in proportion to the size of the data received with the largest number checked in laboratory data (5,667) and the least in the surgery data (567). Error rates were generally less than or equal to 6%, the exception being the surgery data class where an unacceptably high error rate of 34% was found. CONCLUSIONS: The BrainIT core dataset (with the exception of the surgery classification) is feasible and accurate to collect. The surgery classification needs to be revised.