Modelling methodology and assumptions Billing data for three financial years (2009/10, 2010/11 and 2011/12) was obtained at “item number” level showing: Date of service; Number of services; Provider number; Charge; MBS item number; Schedule fee; Bulk bill indicator; Benefit paid; and State; Out-of-pocket amount. Remoteness Area code; The data was further categorised to assist analysis according to the following Practice type (general practice, specialist, etc.); MBS item category and description (1 to 8); MBS item group (A1 to T10); MBS item sub-group (0 to 15); and Whether an MBS item was more likely to be for or “assessment” “treatment”; In addition to the above information, similar data was obtained from 2,271 consultant psychiatrist provider numbers as the comparator group for addiction medicine. Modelled estimations of current and future MBS expenditure were calculated according to the following methods: The number of services for items relating to and were identified ‘assessment’ ‘treatment’ from the data sample; Estimates were rounded up (dividing by the response rate: 0.486) to estimate a total proportion of services across all working addiction medicine Specialists; The estimated number of services for each of the three years was fitted to a linear prediction equation; Prediction intervals were calculated for the fitted equation to provide an upper and lower estimate of error (associated with the observed fit); and Fitted estimates were then used for estimating current and future MBS services. Assumptions underlying different modelling scenarios are presented in Chapter 6, but may be summarised according to the following: Basic scenario: Costs of all observed MBS items classified as and were ‘assessment’ ‘treatment’ summed;
Modelling methodology and assumptions Billing data for three financial years (2009/10, 2010/11 and 2011/12) was obtained at “item number” level showing: Date of service; Provider number; MBS item number; Bulk bill indicator; State; Remoteness Area code; Number of services; Charge; Schedule fee; Benefit paid; and Out-of-pocket amount. The data was further categorised to assist analysis according to the following Practice type (general practice, specialist, etc.); MBS item category and description (1 to 8); MBS item group (A1 to T10); MBS item sub-group (0 to 15); and Whether an MBS item was more likely to be for “assessment” or “treatment”; In addition to the above information, similar data was obtained from 2,271 consultant psychiatrist provider numbers as the comparator group for addiction medicine. Modelled estimations of current and future MBS expenditure were calculated according to the following methods: The number of services for items relating to ‘assessment’ and ‘treatment’ were identified from the data sample; Estimates were rounded up (dividing by the response rate: 0.486) to estimate a total proportion of services across all working addiction medicine Specialists; The estimated number of services for each of the three years was fitted to a linear prediction equation; Prediction intervals were calculated for the fitted equation to provide an upper and lower estimate of error (associated with the observed fit); and Fitted estimates were then used for estimating current and future MBS services. Assumptions underlying different modelling scenarios are presented in Chapter 6, but may be summarised according to the following: Basic scenario: Costs of all observed MBS items classified as ‘assessment’ and ‘treatment’ were summed;
1167-ContractedAssessmentReport%20accessibility.docx