TABLE 1.

Differences When Modeling the Budgetary Impact of New Technologies Entering the Market (Traditional Framework) Versus Payer UM Strategies (Adapted Framework)

Topic	What to consider when modeling the budgetary impact of new technologies entering the market (Traditional Framework)4	What to consider when modeling the budgetary impact of payer UM policies (Adapted Framework)
Evaluates impact of…	New drug or technology.	Payer “interventions” or approaches to managing utilization.
Step 1. Characterize Eligible Population
	Begin by estimating the number covered by the locally approved indications for the new technology.	Payer claims data provide an accurate initial estimate of the number of members using the technologies affected by the payer interventions.
	Base on whether the new intervention increases time on treatment, slows disease progression, or reduces mortality without curing the condition.	Past historical claims data help inform whether the population size and disease severity mix should be assumed to be constant after the payer intervention.
Step 2. Select Time Horizon
	One to five years is common, with the results presented for each budget period after the new intervention is covered.	One to two years with annual estimates may be sufficient.
Step 3. Determine Intervention Mix and Use
	BIA compares scenarios with sets of interventions.	Evaluation of a step therapy change may require inclusion of more unique drugs and NDC numbers than what is typically seen for BIAs evaluating new technologies.
	Interventions used off-label in the eligible population may be included and should not be seen as promotion of off-label use. However, for the new intervention, since there will be little effectiveness or safety data on such off-label use and promotion of off-label use should be avoided, inclusion in the BIA is not recommended unless the budget holder specifically requests its inclusion.	Past claims data provide a baseline estimate of all use, including off-label use of a technology, if the technology was previously available. If the technology was not available, look to similar scenarios (e.g., same drug class or similar situation for a similar disease state) for a range of estimates.
		When predicting utilization changes due to payer intervention for base case and sensitivity analyses, consider the disease state, entry and withdrawal of drugs or technologies, new evidence, provider loyalty to individual interventions, differences in effectiveness, dosing, types of evidence among drugs or technologies, and implementation delays.
		When predicting utilization changes due to payer intervention for base case and sensitivity analyses, consider other payer policy changes.
		Make the budget impact model flexible to changes in the availability of drugs or technologies, disease state, evidence, guidelines, clinician preference in treatment, and policy changes.
Step 4. Estimate Intervention Costs
	The cost of the current or new intervention mix is determined by multiplying the budget holder’s price for each intervention by the proportion of the eligible population using that intervention and by the number of people in the eligible population.	Payer cost data allow for more accurate budget impact estimates. Models should include details important to payers, such as dispensing fees, other health insurance, plan types, and points of service. When in doubt regarding assumptions, payers prefer underestimating the savings.
Step 5. Estimate Changes in Condition-Related Costs
	Provide intervention and condition-related costs separately, so the end user can see results with and without condition-related costs.	Agree with original guidance.Provide pharmacy and medical costs separately, if appropriate for payer.
Step 6. Present Budget Impacts and Health Outcomes
		Present results for subgroups relevant to the payer.
Time dependencies and discounting
	Forecasting changes, such as value of currency, uptake, new interventions entering mix, changes in price, and changes in understanding of disease, indications, and management practices, over time can be challenging. However, an attempt should be made for the study time horizon as long as the assumptions are clear, justified, and supported by evidence as feasible.No discounting.	Agree with original guidance.
Choice of computing framework
	Cost calculator approach is the preferred option because it is more easily understood by budget holders.	Agree with original guidance.
Uncertainty and scenario analyses
	Evaluate parameter uncertainty (input values) and structural uncertainty (assumptions in framing BIA). One-way sensitivity analyses and scenario analyses should be performed. Probabilistic sensitivity analyses are not recommended.	Agree with original guidance.
Validation
	1. Face validity.2. Internal verification.3. Where possible, the observed costs in a health plan with the current interventions should be compared with the initial-year estimates.	Agree with original guidance.More emphasis should be placed on validating budget impact models.

BIA = budget impact analysis; NDC=National Drug Code; UM = utilization management.