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The only way is mobile

Julia LangBill SalokarAnalystVP, Client SolutionsSKIMSKIM

Drug forecasting in the age of personalised medicine

1BILL START

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An innovative approach to drug forecasting in-the-moment mobile research enables collection of robust data close to the point of prescription. Drug forecasting in the age of personalised medicineThe only way is mobile

2Good morning everyone.

Im Bill Salokar with SKIM and this is my colleague, Julia Lang. Today were going to talk about a novel approach to addressing forecasting challenges.

The market environment is changing rapidly, with high patient turnover and increasingly personalised therapy.

And with the hectic schedules most physicians have today, its not surprising that it is difficult to remember accurately why they have behaved as they did or made a certain decision. Why they prescribed what they prescribed.

So tools and technologies are emerging to help us to get closer to respondents in order to collect data and interpret their behavior. The use of mobile technology has added an extra dimension, enabling greater confidence in outcomes at all levels.

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Crowded markets, increasingly diverse patient groupsSetting the context

ABC

3So what created this particular need that were going to address today?

With a wide number of treatments available to patients, the oncology arena is increasingly crowded, and in some market segments biomarker testing is becoming vital in understanding which agents or regimens works best for which patient groups. This makes it increasingly difficult to predict uptake, and important to tailor communications appropriately.BUILDWere going to look at the Non-Small Cell Lung Cancer arena, with prevalent biomarkers such as ALK and EGFR. With the availability of numerous agents and many tests to predict efficacy, every prescribing decision is highly individualised with treatment personalised to the patient and his or her specific genetic profile.

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Crowded markets, increasingly diverse patient groups

Forecast getsmore difficult

No moregeneralisationSetting the context

4In a crowded market it becomes increasingly challenging to understand how your drug will stand out from competition. Therefore, a forecast which is both robust and accurate is crucial to understanding how to allocate budget to aspects such as further clinical development, sales force resources and commercial promotion.

A good quality forecast takes into account the current market dynamics which impact prescribing decisions. This includes factors such as the vast array of competing drugs and combinations, the biomarker tests available, other patient characteristics and the patient groups this creates. In addition, future developments such as new drug classes add additional layers of complexity to treatment decision making. In an age of personalised medicine, this needs to be reflected and accounted for in our forecasting. So lets take a look how we approached this NSNCC forecasting project.

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Over-generalisation or excessive cognitive burden compromise dataConventional methods dont cut it

Too simpleToo complex

5So, in such a dynamic market, conventional methods can often fall short of collecting data in a reliable way. We need to balance the complexity of the market along with the need to prevent overburdening the respondent with so much detail that we end up with poor data quality.

At SKIM and elsewhere, we often see patient allocation employed as a method for collecting forecast data in a survey, with physicians allocating patients across all available options or patient types. This is a sound methodology, widely used, but doesnt really account for highly individualised treatment environments.

The risk is that the allocation exercise is over-generalized (as you see on the left here), or unfeasibly complex as you see on the right.Over-generalize and data generated doesnt provide the detail required for your forecast. Whereas a highly complex allocation exercise still risks over-generalisation due to excessive cognitive burden meaning that respondents become unable to accurately or consistently allocate patients across the many options.

So neither option is a fair reflection of actual physician choice, and leads to poor data quality. The problem is that the Too complex option is the closest to reality. So what is the big deal with cognitive burden?

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- Emotions wear off - Recall fades - Answer solely based on rational factors (e.g. hard clinical facts)+ Based on more softer factors+ Easier to recall why+ Answer based on a combination of soft (irrational) and hard (rational) factorsOCCURRENCEMEASUREMENT

OCCURRENCEMEASUREMENT

6Thats why its also important to collect data as close to the decision making point as possible - to incorporate the system 1 elements in the choice. The further away from the occurrence (in this case the prescription decision) the measurement is, the more the emotions involved fade, recall is more difficult, and over-rationalised factors become more dominant in the reporting of decisions.

Being close to the point of occurrence enables those soft factors to be accounted for, recall is easier, and the explanation is based on both soft and hard factors.So this case study will outline a technique which enables us to get close to these System 1 elements; with it being very easy and quick to complete, and providing both numbers and narrative. Just like a real prescribing decision!

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The only way is mobile

7The only way was mobile!

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Simplify data collection, but account for market complexity

Account for complexity simply

3 minutes of mobile closed-ended questions2 minutevoice memo

Forms via direct mail15 minute online survey

8JULIA START: The research process was structured around 4 simple steps.Firstly a short online survey to collect some basic data on demographics, patient population and current approach to prescription, our sample size for this was 83. *** NUMBER OF PATIENT CASESNext, we sent hard copies of the target product profiles (or TPPs) for the new products we wanted to forecast to all those physicians so they could familiarise themselves with them, or keep them as reference on their desk.Thirdly, we then asked physicians over a period of 2 weeks to enter key patient characteristics, such as age, gender, ethnicity, comorbidities, as well as current and past treatments using a mobile app for each relevant patient they saw (and we outlined what the inclusion criteria were). Importantly we asked them to do this immediately after each relevant consultation. The last few questions in the app asked if respondents would have treated this patient differently if the new products were available. The questions were short and all closed, and data entry took no more than 1 or 2 minutesFor this phase we did see quite a bit of sample drop off, and we had 42 of the 83 original physicians complete this phase. However, we saw a great deal of activity from those who did participate. They were asked to enter between 3 and 10 patient cases, and in the end from those 42 physicians, we got 401 patient cases, giving a huge and extremely detailed data set. ** DONT MENTION DROP OUT YETThe final step was the recording of an audio clip to explain their opinions on the new products. Which added further explanation to the prescription choices made and brought out great information on the key drivers and barriers of choice for the new products.

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Simple & quick to complete

9What is really crucial in this technique is to keep the app questions short and extremely focussed on what is really needed. 1-2 minutes worth can be completed between consultations. More than that, and the likelihood of completing the records drops massively.

**Ask the nitty gritty in Step 1 what setting they work in, etc. Then make exercise simple and quick

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Individual, unique & representative dataActual patients, no generalizations

10The huge advantage with this is that what you get in the end is pretty much a patient record-like data set, including actual patients, without any generalisation and completely focussed on the key information.

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Collection of focussed, relevant information

EGFR+EGFR+Valid outcomes

11In terms of the outcomes, we saw nice validation of the data we gathered. These charts show the current market, based on the left on data gathered from our 15minute mini-survey which included 83 physicians, and on the right from the 401 patient cases. As you can see, they are quite similar. So these charts shows that in this survey, stated and actual behaviour is not far off at all. This is encouraging, as it means that the patient case collection method is sound and gives a valid and consistent representation of practice reality.

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Core forecast data gatheredRobust forecasting based on high sample of cases

All 4 new products launchedPatient sharesFuture MarketFuture MarketOnly A & E launchedCurrent Market

12With the data from the patient cases we were then able of course to actually forecast use of the new products. In this project we looked at 2 patient populations EGFR+ and EGFR-/untested. What you see here is the current market on the left, in the middle the future market if all 4 new products were launched, and on the right the future market if only 2 of the new products we launched. It was possible to model different launch scenarios because while in the app we asked what physicians would do if all the new products were available, we also asked, after they made their initial choice for one of the new products, what if that were not available? So we could see what their second choices would be, or if they would switch back to a current treatment.So robust and accurate forecasting is possible using the data generated with this technique. But what else did we get?

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Collection of focussed, relevant informationPatient data gives further insight

Patient ComorbiditiesPatient ECOG Group

13We also gained insight into the patient types for which the new products would be considered. This is a real bonus, as with conventional forecasting methods this is not possible as the answers are given on an aggregate level, and or you are limited to the patient types or options you included in your allocation exercise. Whereas here, we were able to look at all relevant patient characteristics, and see which were most influential on prescription.What you can see here is how the new products were prescribed to different patient groups on the left different comorbidities, where you can see one product is favoured for particular comorbid patients. And on the right ECOG status (which is performance status as an indicator of patient fitness) and again you see product preferences in the higher, less fit ECOG group. With mobile forecasting each decision is directly linked to a real-life patient case, which can provide vital information about the influence of patient characteristics on prescription.

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Collection of focussed, relevant informationQualitative data provides depthProduct EProduct AProduct BProduct D

14The qualitative audio added further to the picture by drawing out the key drivers and barriers to prescribing each new product which can be useful for optimising communications both to maximise the benefits of your products and/or to counter message against other competition. You can see for example that Product E is faring well on both efficacy and safety benefits, and while product D fares less strongly on these elements, it has a wider span of perceived benefits. This could be crucial information to advise and direct communications strategy.

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Insight into drivers of choiceQualitative data provides depth

Physicians are impressed with Product Es efficacyProduct D - innovative therapy with good efficacy (in melanoma)

15Here are a couple of clips to give you a feel for the type of information gained:

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Benefits

Robust data patient records without associated problems

Applications in market potential, segmentation, Tx pathway

Less post-rationalisation responses close to point of prescription

Accounts for indications with wide variety of patient types, or highly individualised treatment (e.g. biomarker-based treatment)

16So to summarise the main benefits of this new technique:We are able to gather robust and detailed data effectively a patient record database, but without the logistical constraints of patient record research.In generating such a data set, this technique could also have applications in segmentation and treatment pathway research as well as forecasting and market potentialWe are collecting data close to the decision making moment, thereby reducing the post-rationalisation of responses. Techniques that reduce the role of memory in market research are known to make the insights more accurate. This approach requires minimal cognitive effort for the respondent, thereby achieving optimal data quality and high predictive power.And finally, this technique accounts for the diversity of the patient population and the wide variety of factors involved in decision making

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Practical considerationsSome over-recruitment needed

Incentives to maximise participationNot necessarily cheaper thancurrent methods

17Some practical considerations include over recruitment to account for drop-out as we are asking a lot of our respondents over a relatively long time period. This was especially important in this research where we wanted to recruit more senior physicians to be sure we were asking the true decision makers. In order to maximise participation, each physician received a follow up phone call soon after recruitment to clarify exactly what was expected of them as well as how to download the app. Test reminders and incremental incentives were also useful in maintaining participation throughout the stages of the research from mini-survey through to audio recording. The high levels of engagement required during fieldwork by respondents, combined with greater agency involvement in fieldwork management, means that this approach is not necessarily cheaper than current conventional methods but is sure to lead to a greater return on investment.

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Mobile increases accuracy of forecast by adding patient level insightsBusiness impact

Use data incomms

80%

18BILL START: The research has given us a much clearer picture of patient characteristics in NSCLC. Anecdotal feedback from physicians would suggest that some patient groups are not eligible for treatment with certain current agents, and would not be considered for novel therapies. However this research has clearly shown that 80% of patientsdo meet the criteria for existing chemotherapy and would therefore be eligible for our new product. This data will be incorporated into our sales materials and will form the foundation of our discussions with customers. It has also generated a great deal of excitement within the sales team which will translate into a more enthusiastic sell!

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Impact on positioning

Clear patient typesDifferentiationPositioning

19The research has given us a much clearer picture of patient characteristics in NSCLC. Anecdotal feedback from physicians would suggest that some patient groups are not eligible for treatment with certain current agents, and would not be considered for novel therapies. However this research has clearly shown that 80% of patientsdo meet the criteria for existing chemotherapy and would therefore be eligible for our new product. This data will be incorporated into our sales materials and will form the foundation of our discussions with customers. It has also generated a great deal of excitement within the sales team which will translate into a more enthusiastic sell!

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increase in confidence at every level

Business impact

20Better respondent engagement has led to more robust results, driving the confidence of the marketing team in the inputs to our forecasting process. The data generated from this project has enabled them to submit a revised forecast with a downside based on the impact of launch of a major competitor and they feel more confident in explaining the rationale behind this to Corporate.Alongside this, the outputs of this research have increased confidence in our product among the extended brand team, allowing us to refine our messaging. The robustness of the results provides a great basis on which the sales team can confidently challenge their customers based on validated data and leads to better customer engagement. This in turn will drive physician confidence in selecting the appropriate patients for the product.So an increase in confidence at every level!

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Questions?

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