Lecture 6. Economic Models for Managed Care Pharmacy, Shukla

Model

use past and present costs and outcomes to predict future costs and outcomes and better define the "value" of an intervention

Value

what I want vs what I get

Budget Impact Models (BIMs) are developed by

drug manufacturers to aid a decision-maker in resource planning

BIM

accounting models used to calculate how a healthcare budget will be impacted by new drugs or a change in utilization of existing drugs (for example, an indication being expanded to both COPD and asthma, now there is a larger pt population)

How a BIM works

enter population characteristics (population size, target population size, current intervention, new intervention, drug costs and other costs) → model calculates projected future expenditures for current AND future scenario

Model Calculation is expressed as

PMPM (per member per month) OR PMPY (per member per year)

BIM advantage

simplest model

BIM assesses

financial consequences in a short-term time frame to estimate cost

CEM assesses

value of a new drug in a long-term time frame to estimate cost per life year or quality-adjusted life years (QALY)

Calculation in CEM

ICER (evaluates cost and effectiveness)

ICER

incremental cost-effectiveness and ratio

Quadrant 1

high cost, high efficacy (use lambda to evaluate)

Quadrant 2

high cost, low efficacy (bad)

Quadrant 3

low cost, low efficacy (use lambda to evaluate)

Quadrant 4

low cost, high efficacy (good)

Name of the HTA agency in the UK

NICE

CEM advantages

established quantitative approach, allows for consideration of costs and outcomes

CEM disadvantages

complicated to explain to clinicians and healthcare decision-makers, transparency might be questioned, dependent on the quality of data

Key Parameters for Model Development

Type (BIM or CEM), Perspective, what costs to include, time horizon, relevant population, comparators

Good models

model inputs and managing with assumptions

One-Way Sensitivity Analysis

change one input at a time of assumptions

Probabilistic Sensitivity Analysis

change all variables at the same time of assumptions