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Leveraging APLD and Other Data Assets to Broaden Organizational InfluenceCynthia DilleyUCB
PREMISE
Finding more audiences for your data assets gives you better access and influence across the organization. Knowledge is power – leverage it in as many places as possible.
AGENDA
• What do you have?
• Make others covet your belongings (slap it with a designer label)
• Share the parts of your lunch that you don’t eatAnd some parts that you do eat
• Reap the rewards of your marketing gain a cushy seat at the table – which lunch table do you want to join? HEOR? Clinical? Marketing? Sales Ops?
CHALLENGE
Market research is the best career…
• You have more knowledge about your market place and its customers than anyone else in the building
• You have more ideas about how to have strategic impact than anyone else on the extended brand team BECAUSE you know everything about the market
• You learn something new every day
• You have many tools at your disposal which makes answering questions an intriguing mental exercise:
• primary, secondary, advanced analytics
CHALLENGE
… and the worst career
• While you know that you know more about the market than anyone else, does anyone else know that?
• You put in all of the work, marketing gets all of the glory
• You are perceived as a project manager or worse, a librarian
• Your opportunity to get in front of the organization outside of the brand team is limited or non-existent
BUT YOU LOVE MARKET RESEARCH
Or you don’t. Either way it is imperative that you begin to influence others outside of your immediate sphere of influence.
The obvious:• Influencing others leads to job opportunities in other
areas
And less obvious:• If others covet you, your brand team will appreciate you
more(Yes it worked with your high school boyfriend, yes it still works now)
Getting Others to Covet You – Skills Discussion
• Market research personality types – has your team ever done a Myers Briggs analysis?
• Finding different ground… big picture thinking
• Slowly but surely – assert, don’t ask
• Research – you are great at it, do it!!
TOOLS AT YOUR DISPOSAL
§ Layout your own positioning statement§ Messaging§ Visual Aid
YOU HAVE FEATURES AND BENEFITS, TOO
BEGIN BY TAKING A FULL INVENTORY
§ APLD?§ EHR/EMR?§ Syndicated Survey Data?§ Prescriber Dynamics?
I will focus on APLD data today but have also leveraged these other assets in my company
What data do you already own? What data does the organization own?
KNOW EVERYTHING ABOUT YOUR DATA…
Be source agnostic
• The market leader for commercial data may have cache to investors and brand teams but name dropping to clinical won’t get you anywhere
• Look at your data with fresh eyes – yes, it might be the same old IMS data you’ve always used (or Wolters Kluwer or SDI) but do you understand everything about the data or do you just trust it?
AND THEN KNOW MORE ABOUT IT
So you own APLD data?
• What are all of the other sources of APLD data available to the industry?
• What are the strengths and weakness of your data sources vs other?
• If other departments are using this data, what are they using? Why?
FIND THE RIGHT CONTACTS AT YOUR VENDORS
Find the person at the vendor that will educate you about the data. Seek out the most academic person you can find.
If you stay in market research for a long time, your brand teams will change over and over but your vendor contacts will remain much more steady – these are the relationships that you want to build – they determine your success and your future.
(Note to vendors: the same applies to you, always be honest about your data and know your competition – you may lose the sale today but you will gain a long term client no matter which company you end up at in the future)
DEMONSTRATE YOUR KNOWLEDGE
§ Develop some materials to show them how to work with open data sets§ Show them good publications that have leveraged open
data sets§ Explain the advantages/disadvantages of these data § More geographically representative§ Has Medicare and Medicaid claims represented§ Can be linked across changes in insurance reducing
bias
Example: Your Health Outcomes Group Only Comfortable With Closed Data Sets? Do they even know the difference between a closed and open set? Do you?
FACTOID: Even closed data sets aren’t truly closed
BRAINSTORM ABOUT YOUR VALUE PROPOSITION
§ “It takes so long for us to complete a clinical trial, I don’t understand it!”
§ “Doctors keep asking our reps about the product in the elderly but we don’t have data”
§ We’ve heard our drug works better in this patient population but we don’t know…
§ Are patients in the South being treated better?
Find out what the leaders of your brand team bellyache about – not the questions they ask you to solve… the other questions
CASE STUDIES:BUILDING AN APLD DATA MART
APLD
§ Commercial: Our market research group had been buying more and more analysis from APLD data sets. Some of these were monthly reports from IMS (pharmetrics) and SDI. However, we always had more questions, but not more money.
The golden opportunity (find yours, too)
§ Health Outcomes: Their team was ok with their current data set but they couldn’t see which drug was being used in the hospital nor could they see how our launch product was going because of the data delay
My AHA moment
§ At UCB our medical directors and publications groups are the primary consumers of non-commercial APLD data output. I struck up conversations with them after meetings, in meetings, in the lunch line, in the bathroom etc… to ask about their challenges with data.
§ Eventually, they put me in touch with the decision makers in global regarding the data and its output. I asked that group to formally evaluate all of the potential database choices prior to their next renewal.
What I really wanted was an ad hoc query based platform for the APLD data, so I could slice and dice it. But how to make it happen….
A YEAR AND A HALF LATER WE HAVE A CONTRACT
T=0Discussions with medical/pubs
T=2Discussions with global HEOR
T=5Discussions with Commercial Ops
T=7Vendor Evaluation Committee Initiated and Scorecard developed
T=14Vendor Presentations and Committee Meetings Begin
T=16Vendor Selected and Contract Process/Negotiations Begin
T=8Leadership ChangesProcess Stalls
T=18Contract CompleteWork Can Begin
BUILDING OUT THE ASSET
My Considerations
My SAS skills are extremely rusty (and I don’t want to improve them)
Speed is important*
More data is better
* Speed turns out to be a key advantage
KNOW WHAT YOU WANT TO ANSWER Reporting Question/Need On-LineNeed standard metrics by Diagnosis Yes
Source of Business by Diagnosis, including a Switch/Add breakout Yes
Line of Therapy : Time on line, time between lines Yes
Compliance and Persistency by Regimen Yes
Compliance and Persistency by Line of Therapy Yes
Concomitant Therapy Yes
Physician Pathway – How long does it take to see a specialist? No
Does patient compliance change when switching from a branded molecule? No
Track patients across different locations of care. When a change in therapy is made, where is it made and what is it changed to?
No
Source of Business by channel? No
What is the initial diagnosis by Drug? No
If a patient switches or is new, does the number of claims decrease by therapy? (i.e. What is the drug’s ability to control the disease?)
No
Cross titration: How many physicians titrate down the current drug when a new drug is added? No
How often do side effects occur from drug use? Do treated patients develop a cardiovascular condition? Does this vary from untreated patients?
No
Populating the Data Mart
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ProjectedOn-line
Reporting
SDI Dx Claims
SDI Rx Claims
“Driver” Product
List
“Driver” Diagnosis
ListOR
Rx Dx
CDM EMR* Lab*
All Diagnoses
All Procedures
All Products
“Master” Patient List
UCB Data Mart
OutcomesData Extract
Data Assets
*Outcomes Only
Source of Business
Standard Dimensions Potential Additional Cuts Projected Metrics
Product•Market•Class•Product Group
Time•Month•Quarter•Rolling Quarter•Year•MAT
Patient Type•New•Continue•Switch•Add*
Script Type•NRx•TRx
Indication/Disease StateEthnicity
Age Group
Gender
Specialty Group
Pay Type•Cash•Medicaid•Third Party•Medicare
Previous Fill Days (Days since previous fill “0-30”, “31-60”, etc)
Patient CountPatient ShareNumber of RxRx Share---------------------------------------Extended UnitsAvg SizeTherapy DaysMean Therapy DaysDaily Consumption---------------------------------------Acquisition CostAcq Cost / RxRetail DollarsRetail Dollars / RxCost Per DayCost Per Unit-----------------------------------------Switch To RxsSwitch From RxsNet Switch RxsAdd Rxs
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Compliance and PersistencyStandard Dimensions Potential Additional Cuts Projected Metrics
Product Group
Patient Type•New•Continue•Switch•Add
Indication/Disease State
Line of Therapy
Regimen (2-product Combos, subject to sample size)
Cohort Time Period (Subject to Sample Size)
Time Period (Month 1, Month 2, etc)
Age GroupGenderSpecialty GroupPay Type
•Cash•Medicaid•Third Party•Medicare
Persistency Metrics•Number of Continuing Patients•Number of Discontinued Patients•Number of Restart Patients
Compliance Metrics•Average Length of Therapy•Days Per Claim•Claims per patient•Compliance Rate
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Cohort Builder – The Cool and Unusual
• The report will show metrics for a cohort of patients the Rx, Hospital, and Physician Office channels
• Users will be able to compare patients’ experience across products and answer questions such as:– Do patients who switch to Product A have less ER visits than those
who switch to Product B?– Do patients who are new to Product A see a specialist more than a
Patient who is new to Product B?
• Report will use “processing on demand” technology
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Define CohortUser Prompt Response
RequiredMultiple Selection
Product(s)
Disease condition
Co-morbid disease condition
Patient Age Group
Patient Gender
Patient type (New, Continue, Switch, Add)
Rx out of pocket cost range
Line of therapy
Cohort start month
Cohort end month
Number of months to track
Pay Type
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Cohort OutputDimensions Metrics
Cohort Product* Patient Count Hosp Visits In-Patient Total
Cohort Disease Condition* Total Rxs/Patient (All Markets)
Hosp Visits In-Patient ER
Cohort Co-morbid Disease Condition*
Rx Count – Market Hosp Visits In-Patient Non-ER
Cohort Age Group* Rx Count Product Total Hosp Visits Out-Patient Total
Cohort Gender* Rx Count Product Specialist Hosp Visits Out-Patient ER
Cohort Patient Type* Rx Count Product PCP Hosp Visits Out-Patient Non-ER
Cohort Out-of-Pocket Cost* Office Visits Specialist
Cohort Line of Therapy* Office Visits PCP
Cohort Mono/Combo Distinct Products Market
Pre/Post “Month”
Cohort Pay Type
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OUTPUT #1
§ One key finding we were able to achieve from this data is the average hospitalization rate per patient was variant for those using older drugs vs newer drugs even though their clinical data shows equivalent efficacy
§ Interested parties: medical, policy team, marketing team
Hospitalization rates differ for drug cohorts
OUTPUT #2
§ Across all copay buckets and products in the market, there is little variation in compliance and persistence rates
§ Interested parties: managed markets, policy, marketing, medical
Copay amounts do not affect compliance and persistency rates
OUTPUT #3
§ When using comparative cohorts across 3, 6 and 9 months, our product consistently bested the other products
§ Interested Parties: CEO, leadership team, marketing, medical, clinical, HEOR, managed care
Our product has the highest days of therapy and compliance rates
OUTPUT #4
§ We provided patient counts, leading therapies, and treating specialties to help our clinical team decide on whether to pursue a clinical study in an elderly population
§ Interested Parties: Medical made the request and sent data to clinical
Assessment for an elderly trial
CASE STUDIES:LEVERAGE OTHER
LONGITUDINAL DATA
OUTPUT #5
§ Produced New Patient Population Density Maps and Add On Population Density Maps to assist clinical in choosing site locations
§ Interested Parties: Clinical
Prescriber level dynamic data
OUTPUT #6
§ Produced Population Densities of Patient Incidence to Uncover Whether Environmental Factors Could Be at Play
§ Interested Parties: Medical
Prescriber level dynamic data
CASE STUDIES:LINK YOUR DATA
• Do you know if it is actual managed care access OR the perception of managed care access that is giving your product a barrier?
• However, these data streams are typically looked at separately:• Primary Data = Perceptions• Secondary Data = Behaviors
Leverage Your Data – (AGAIN)
Primary Data•ATU/Brand Equity Data•Message Recall Data•Segmentation Data•SF Effectiveness Data•Employee Engagement•Other Primary Data
Secondary Data•Managed Care Data•Patient Longitudinal Data•Sales Force Activity Data•Promotional Spend Data•Prescribing Data (TRx/NRx)•PAP•Tenure•Other Secondary Data
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Macro Level Opportunity Assessment
• Evaluation of Tactical Opportunities Often Takes Place in a Limited Data Set– Promotion Response Curves: Detailing, Sampling, Managed Care– Non Personal Promotion: Physician participation– Messaging, Platforms: Qual/Quant Research– Sales Rep Engagement: qual metrics
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HYPOTHESIS: Evaluating opportunities in a more dynamic optimization model will yield better resource allocation AND eliminate downstream work. The more we can eliminate constraints (ie. Ceteris Paribus) the better our model can direct us. Unfortunately we are seldom in an “all things equal” environment.
CAUTION: We must assess the opportunity cost of each piece of data – will the energy to include it be offset by the additional rigor
A MULTIVARIATE REMINDER
§ Who Loves Dancing With the Stars?§ If I only include age cohorts as a variable, I might come
out with an answer that says 45+
§ If I add in gender, I find out that age itself is relatively unimportant as it is overwhelmed by gender which contributes MOST of the explanation
The more you can control for known variables, the better you can explain variation
Provide more control
• These modeling efforts allow us to effectively link perceptions with behaviors - we can use the data to tell us what specific perceptions are driving behavior or prescribing.
• More importantly, the output also allows us to simulate the expected change in TRx/NRx output based on a brand’s ability to change perceptions upstream.
• By monetizing the costs of programs and their anticipated return, we can calculate strategic program ROIs…therefore determining how to direct resources optimally in an effort to most effectively change perceptions.
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Specialty Segment 1 Model shows the relationship between all variables included in the model
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What Levers Are Most Important Across ALL Data?
• Higher utilization of voucher programs, higher share• Higher utilization of co-pay program, higher share•Attendance at dinner meetings, CME symposia, web/teleconference, and e-details, higher share
• Perception of low incidence of weight gain/loss, higher share• Perception of low incidence of cognitive side effects, higher share
• Higher percentage of male patients, lower share• Higher percentage of younger patients, higher share• Higher average number of days in hospital higher share
• Perception of appropriateness for pediatric patients, lower share• Perception of low incidence of drug interactions higher share• Higher percentage of prescriptions for
sodium therapies, higher share• Higher percentage of new generation prescriptions, higher share• Lower number of monotherapy trials before adjunctive usage, higher share
Patient CharacteristicsCompany Resources
Prescribing Characteristics
Safety Profile
Side Effect Profile
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Specific High Impact Strategies For Physicians
These can be rank ordered and simulated
Output #7
§ Indexed Levers are provided to agencies, brand teams, managed market colleagues and leadership
Using this linkage model we provide rank ordered strategies to use for planning budgets and business models for the following planning year.
RESULTS
All of this work yielded some wonderful results:
• Inclusion on the HEOR monthly planning meetings• Inclusion on results presentations for HEOR projects• Consultative requests from other marketing, medical and
clinical colleagues• More interactive discussions with brand and medical
leadership• Inclusion in high profile global project teams
