Utilize Real World Data to Optimize Patient Savings Programs. Mason Tenaglia Vice President, IMS Institute, Payer and Managed Care Insights

Transcription

1 Utilize Real World Data to Optimize Patient Savings Programs Mason Tenaglia Vice President, IMS Institute, Payer and Managed Care Insights September

2 Today we will answer three major questions 1. How should we think about co-pay cards in today s landscape? 2. What are the guiding principles for co-pay card optimization? 3. How does real world evidence strengthen co-pay card strategy? 1

3 A holistic approach to Program optimization is necessary within an increasingly challenging environment Evolution of Patient Savings Programs Origination Coupons used to offset Tier 3 co-pays Brand Marketing Tool Compete with generics Build adherence and brand loyalty Incentivize switch to follow-on products Core Market Access Requirement Overcome PAs/NDC blocks Maintain share after LOE Reinforce formulary wins Blunt a competitive launch 2

4 For effective program optimization, a fully-integrated approach is recommended Evaluate Landscape Assess market need and competitive context Analyze Price Sensitivity Assess patient behavior, costsensitivity and ROI from programs in the market Measure & Adapt Assess ROI, evaluate market conditions, consider redesign Savings Program Optimization Implement & Execute Program Write and execute bus rules, implement Develop Options Create offer options, roll-out scheme, regional variations Forecast and Plan Create budget forecast, sensitivity, and tracking KPIs Fully Integrated Patient Savings Program Solution 3

5 What is optimization? After an a priori design, ongoing optimization is necessary for success Depending on brand goals, optimization priorities may focus on maximizing: Program Uptake Frequent and pervasive use of coupon program Paid Volume Encourage program use among high-value population Product Uptake Increased volume and market share for brand potentially spillover Program ROI Measure cost and revenue tradeoffs to identify the best design Profit Margin Target program inefficiencies to cut costs Geographic Performance Overcome cost access/sensitivity barriers in certain territories 4

6 What constraints affect optimization? Lifecycle Plan Products early into lifecycle may prioritize uptake Established brands may focus on margin Budget Budget constraints may eliminate certain optimization plans Product Uptake Profit Margin Program Uptake Geographic Performance Paid Volume Program ROI Strategy Formulary access changes program needs Unlike established, new brands need to encourage switching 5 Competition Competitor programs can change cost sensitivity in the market

7 What tools are required for optimization? $ Real World Evidence (RWE) using Longitudinal Patient Data and Lifecycle Claims in FIA* Measure patient behavior - adherence w/ or w/o co-pay card Visibility into abandonment behavior by commercial payer Therapeutic Class Universe in FIA Competitive or analog co-pay card performance and insights Primary and Secondary Cost-Sharing in FIA Capture initial cost exposure, secondary payer activity, final OOP Granular Geographic Performance (PlanTrak Xponent) Align patients and claims with physicians and their geographies Who is using the card? How are patients using the card? How effective is the program? New Patients Continuing Patients Switch Patients As Intended One-and-done Inconsistent Buy-Down vs. Exposure Competitive Response *Formulary Impact Analyzer 6

8 How does IMS/Amundsen optimize a co-pay card program? IMS leverages proprietary methodology to generate program cost and revenue estimates Establish a but for comparison between patients who would have each level of cost exposure and what their behavior would look like with a co-pay card Key driver of ROI is Patients saved and their value versus patients subsidized needlessly MODEL INPUTS Target Co-Pay Maximum Offset (Program Cap) Transaction Costs Patient Co-Pays Before Co-Pay Card Apply Proposed Program Design Adherence for Original Co-Pay Abandonment for Original Co-Pay Gross Revenue Before Co-Pay Card MODEL OUTPUTS Total Program Costs Gross Revenue Product Discounts/Rebates Product WAC Commercial Co- Pays Patient Co-Pays After Co-Pay Card Adherence for New Co-Pay Abandonment for New Co-Pay Buy Down Costs Plus Additional Transaction Costs Revenue After Co- Pay Card Net Revenue ROI 7

9 Days of therapy per year RWE A RWE, but for example for one diabetes program Impact of Co-Pay offset program on patient adherence days +49 days Increased Brand Loyalty Reduced Cost Exposure 50 0 $55 co-pay reduced to $5 w/ card Control 1: Pay $5 w/o card Control 2: Pay $55 w/o Card Total Adherence Improvement from Co-Pay Card $ Source: IMS Health Analysis 8

10 Total MG per Year % of Patients Abandoned Increasing max buy-down to $75 helps cost sensitive patients with $ co-pays in this case RWE Increase the maximum buy-down 50% 40% Brand X New Patient Commercial Adjusted Abandonment by Co-pay 41% Reduce abandonment by 50% or more for patients with copays of $100-$200 30% 20% 10% 0% 0% $0 2% $0.01- $ % 4% $ $ % $ $ % $ $ % $ $ % $ $ % $ $ % $ $ Increase the maximum buy-down 5,000 4,000 3,000 Brand X New Patient Commercial Adherence by Co-pay +28% No Coupon Coupon Increase adherence by 28% for patients with copays of $ ,000 1,000 0 $0 $0.01- $9.99 $ $19.99 $ $29.99 $ $39.99 $ $49.99 $ $74.99 $ $ $ $ Source: IMS Formulary Impact Analyzer; Amundsen Consulting analysis $ 9

11 RWE ROI is a function of RWE market dynamics Therapeutic Area e.x. Patients on lifestyle therapies are more likely to stop therapy if they face cost increases or any cost at all Generic Alternatives e.x. In therapeutic areas that have a lot of generic competition, patients may be even more cost sensitive Product Lifecycle e.x. The spillover and long-term uptake effects of co-pay cards on a launch product could change the ROI calculation Competitor Behavior e.x. Competitors may respond to co-pay cards with programs that are even more aggressive, forcing a reaction 10

12 Program Effect ($000s) In a RWE example, returns are negative for co-pays < $50, balanced by returns in patients > $50 RWE Costs and Returns by Primary Copay per 1,000 patients Current Program (PNMT=$0; Buy-down=$50) $140 $130 $120 $122K Program Cost Program Benefit $110 Negative ROI $100 $90 $80 $70 $70K 4% ROI $60 $55K $50 $40 $30 $20 $10 $0 $3K $3K $10 - $19.99 $23K $18K $20 - $29.99 $31K $30 - $39.99 $43K $27K $40 - $49.99 $50 - $74.99 $21K $39K $75 - $99.99 $18K $11K $100 - $ $6K $7K $125 - $ $3K $2K $1K $0K $4K $2K $150 - $ $175 - $ $200 - $ $4K $0K $250+ Primary Copay Note: program costs and benefits are calculated based on the effect of the program on the average copay in each copay range Source: IMS Formulary Impact Analyzer; Amundsen Consulting analysis $ 11

13 Program Effect ($000s) Changing the benefit design to PNMT $25/$75 cap focuses spend on cost sensitive patients RWE Costs and Returns by Primary Copay per 1,000 patients, 2014 New Program (PNMT=$25; Buy-down=$75) $140 $130 $120 $110 $100 $90 $80 $70 $60 $50 $40 $30 $20 $10 $0 $0K $0K $10 - $19.99 $0K $0K $20 - $29.99 $13K $0K $30 - $39.99 $40 - $49.99 $44K $106K $50 - $74.99 Positive ROI $39K $75 - $99.99 $48K $24K $27K $17K $9K $10K $8K $4K $7K $3K $2K $1K $2K $100 - $ $125 - $ % ROI $150 - $ $175 - $ Program Cost Program Benefit $200 - $ $6K $0K $250+ Primary Copay Note: program costs and benefits are calculated based on the effect of the program on the average copay in each copay range Source: IMS Formulary Impact Analyzer; Amundsen Consulting analysis $ 12

14 Return on Investment With this re-design, the trade off between program cost and ROI benefit is optimized RWE PNMT $25 ROI with Varying Benefit Caps Assumes PNMT=$ % 40.00% 1.41x 1.42x 35.00% 30.00% 1.33x 25.00% 20.00% 15.00% 10.00% 5.00% 1.04x 0.00% Buy-down=$50 PNMT=$0 Buy-down=$50 PNMT=$25 Buy-down=$75 PNMT=$25 Buy-down=$100 PNMT=$25 Source: IMS Formulary Impact Analyzer; Amundsen Consulting analysis $ 13

15 RWE & Competitive Intelligence RWE example of competing voucher programs Patient Result After Voucher Usage 100% 90% 80% 70% 49% No product in class filled within one year of voucher use 42% Voucher and Dones Switchers Returners 60% 50% 40% 9% Switched to Brand B after Brand A voucher 10% 30% 20% 42% Brand A filled within one year after voucher 48% 10% 0% Brand A Source: IMS Formulary Impact Analyzer; Amundsen Consulting analysis Brand B $ 14

16 Average Buy-Down RWE of competitor buy-downs in Brand D are double those other brands RWE & Competitive Intelligence Average Buy-Down among Claims where Coupons are the Secondary Payer (Commercial) $150 Brand A Brand B Brand C Brand D Brand E $119 $100 $98 $78 $50 $53 $51 $0 Note: Averages are calculated among paid claims where a co-pay card is used as the secondary payer and normalized to 30 days Source: IMS Health Formulary Impact Analyzer, Jan 2014-Mar 2016; IMS Health Analysis $ 15

17 Commercial Claims (% TRxs) Brand D s co-pay card also program has highest penetration and as a primary payer Co-Pay Card Penetration by Brand (Commercial, 2015) RWE & Competitive Intelligence 75% Coupon as Primary Payer Coupon as Secondary Payer 50% 32% 25% 19% 20% 20% 10% 5% 0% Brand A Brand B Brand C Brand D Brand E Source: IMS Formulary Impact Analyzer, 2015; IMS Health Analysis $ 16

18 Ideally, RWE analysis is an ongoing process Immediate Monthly Long-Term Identify fraudulent claims Check design s impact on costs Manage exposure (i.e., deductibles) Measure and react to competitor response Minimize surprises Validate and quantify goal achievement Reassess optimization goals 17

19 Is optimization possible without RWE? $ Real World Evidence (RWE) using Longitudinal Patient Data and Lifecycle Claims in FIA* Measure patient behavior - adherence w/ or w/o co-pay card Visibility into abandonment behavior by commercial payer Therapeutic Class Universe in FIA Competitive or analog co-pay card performance and insights Primary and Secondary Cost-Sharing in FIA Capture initial cost exposure, secondary payer activity, final OOP Granular Geographic Performance (PlanTrak Xponent) Align patients and claims with physicians and their geographies Who is using the card? How are patients using the card? How effective is the program? New Patients Continuing Patients Switch Patients As Intended One-and-done Inconsistent Buy-Down vs. Exposure Competitive Response *Formulary Impact Analyzer 18

20 Today we answered three major questions 1. How should we think about co-pay cards in today s landscape? Co-pay cards improve adherence, abandonment and share retention They are no longer alternatives to contracting but often overused Brands should approach them as they do pricing ongoing and strategic 2. What are the guiding principles for co-pay card optimization? Prioritize the type of optimization Consider brand constraints to come up with optimization plan Use the right data tools and analytic frameworks (i.e. a but for analysis) 3. How does real world evidence strengthen co-pay card strategy? Provides longitudinal view of co-pay card use Measures patient price sensitivity for adherence and abandonment Provides performance relative to competition Identifies divergent competitive strategies and results 19

21 For questions, contact Dave MacDougall Practice Lead, Amundsen Consulting a Division of IMS Health amundsen@amundsen.imshealth.com