Patient flow enabled forecasting

Patient-flow enabled forecasting IM Associates IM Associates | Leuven – Paris – Dusseldorf | www.im-associates.eu

Agenda Introduction to patient flow models • What is a patient flow model? • Why are they useful? • Patient flow models vs. historical trending analysis • Our role in your S&M excellence The forecasting process Case study • Description • The forecasting process deliverables Q&A © 2010 IM Associates: Patient-flow enabled forecasting 2

Introduction to patient flow models What is a patient flow model? A patient flow model captures the treatment process of a patient suffering from a particular disease It includes all relevant drivers & decision makers that determine which path of care will be followed It provides an holistic view on the specific healthcare environment, and takes into account a wide variety of input parameters that may change dynamically over time The patient flow will finally be translated into a revenue flow, enabling management to see the impact of input assumptions and treatment choices on their financials © 2010 IM Associates: Patient-flow enabled forecasting 4

Introduction to patient flow models Why are they useful? Patient flow models can be useful for several reasons: • Ideally, forecasting takes into account an appropriate range of different variables – History is no predictor of future performance, so historical trending will not suffice For example: Units ? Which years are Y1+Y2+Y3+Y4+Y5 best representative for the current Y3+Y4+Y5 market conditions? Y4+Y5 Y1 Y2 Y3 Y4 Y5 Y6 – What is more useful, is examining whether the drivers of historical performance are still applicable today, and which new performance drivers arise in the micro- and macro environment – Patient flow models are a complementary tool to traditional trending analyses, as they uncover these drivers and quantify their effects • Using the right market research information guarantees a best-estimate of future sales © 2010 IM Associates: Patient-flow enabled forecasting 6

Introduction to patient flow models Patient flow models vs. historical trending analysis The distinction between traditional trending analyses and their complementary patient flow models can be represented as follows Severe pain… PRODUCT A PRODUCT B Acute pain PRODUCT C PRODUCT D Chronic pain PRODUCT E PRODUCT F Trending YOUR PRODUCT analyzing trends within each product, optionally taking into account ex-trend effects Holistic view on market dynamics Ex-trend effects are automatically taken into account when a good patient-based forecasting model with correct assumptions is used © 2010 IM Associates: Patient-flow enabled forecasting 7

Introduction to patient flow models Patient flow models vs. historical trending analysis TREND-ANALYSIS SUPPORTED PATIENT-MODEL SUPPORTED Sales (€) P.1 P.2 1 line P.3 P.A 2 line P.B t P.C Forecasting Based on historical trending analyses of in-market Based on market research, we uncover the patient flow products we try to forecast the sales evolution and its underlying assumptions. The result of this exercise in the new product’s life cycle. is a series of future sales figures. © 2010 IM Associates: Patient-flow enabled forecasting 8

Introduction to patient flow models Patient flow models vs. historical trending analysis Patient flow models PROs CONs Best-estimate future sales taking into account Ext(p)ensive market research needed multitude of information Client gains insight into the market dynamics Danger of drawing false conclusions because of wrong input assumptions Sales improvement opportunities are Still validation needed with trending analyses in discovered order to check the accuracy of the input assumptions Relevant decision makers are identified Regular updates of input parameters needed Trending analyses PROs CONs For in-market products the availability of For new products, the data scarcity is a serious historical data is mostly no issue bottleneck Relatively simple approach Risk of not taking into account relevant future aspects by being overly focused on historical performance © 2010 IM Associates: Patient-flow enabled forecasting 9

Introduction to patient flow models Our role in your S&M excellence In the fast evolving healthcare industry an increasing number of interrelated drivers influence future performance. Simple spreadsheet forecasting models do not suffice in capturing the integrity of the various drivers. IM.ahead meets the growing need for elaborate forecasting in healthcare companies by using a unique methodology blend between traditional trending models and patient flow dynamics, always with a keen eye on implementation. © 2010 IM Associates: Patient-flow enabled forecasting 10

The forecasting process Strategic plan of action: 1. Discover the different drivers and/or influences in the healthcare market that determine commercial success 2. Determine the drivers that can be influenced NOT INFLUENCABLE DIFFICULT TO INFLUENCE EASY TO INFLUENCE Disease parameters Healthcare regulations Patients or patient groupings (incidence / prevalence %) (e.g. lobbying efforts which (e.g. using social marketing will be time-consuming) campaigns on radio or TV) 3. Model the process flow between the different drivers 4. Quantify their effects in realistic scenarios © 2010 IM Associates: Patient-flow enabled forecasting 12

The forecasting process Tactical plan of action • By using a stepwise approach, we deliver a unique methodology blend • Our excellence in traditional trend forecasting is catalyzed and extended with crucial insights in dynamic patient flows: – Not only do we identify all relevant drivers – The interrelations between the broad range of drivers are also identified – Next to this, we simulate the effect of all possible futures on commercial performance STEP 3 STEP 1 STEP 2 STEP 4 STEP 5 Preliminary Desk Market Validation Modeling the Process Research Research Workshop Future Design © 2010 IM Associates: Patient-flow enabled forecasting 13

The forecasting process Step 1: Desk research (secondary research) Desktop Research / Web Research • Scan available market research studies • Search for existing information on the Web • Identify the gap for a complete picture Step 2: Market research – if needed (primary research) Interview physicians in a stratified sample: “You diagnose a patient as having an allergic reaction to grass polls. Which drug do you prescribe and what makes you prescribe drug X?” “Would you consider other treatments? Why (won’t) you choose them?” … © 2010 IM Associates: Patient-flow enabled forecasting 14

The forecasting process Step 3: Preliminary Process Design • High-level visualisation of the conclusions from our primary & secondary research, Step 4: Validation workshop with the client • Validate our process design with client’s feedback • Agree on a final process design © 2010 IM Associates: Patient-flow enabled forecasting 15

The forecasting process Step 5: Modelling possible futures • Starting from the final process design, we will simulate different forecast scenarios and outcomes Timeframe New patients % Patients treated % Patients to 2° Line 1° line INI period 530,00 pts/m o 0,00 pts New patients visiting Patients treated by Patients treated by GP with disease X Patients diagnosed GP in 1° Line 1° Line treatment SP in 2° Line 2° Line treatment Product choice Stop after 1° Line treatment Patients start on Patients start on Product 1 Product 2 Treatment stop after 1° Line % Patients continue on © 2010 IM Associates: Patient-flow enabled forecasting 16

The forecasting process Step 5: Modelling possible futures • Starting from the final process design, we will simulate different forecast scenarios and outcomes Distribution of patients between 1° line & 2° line treatment pts 1.000 T ota l nbr o f P a tie nts Nbr of pa tie nts in 2° line tre a tm e nt Nbr of Pa tie nts in 1° line tre a tm e nt 500 0 1 ja n 2010 1 ja n 2012 1 jan 2014 1 ja n 2016 1 ja n 2018 1 jan 2020 Time © 2010 IM Associates: Patient-flow enabled forecasting 17

The forecasting process Step 5: Modelling possible futures • Starting from the final process design, we will simulate different forecast scenarios and outcomes Increase in revenues (i.e. actual revenues at each point in time) € per m o 300.000 200.000 Increa se in re ve nue s @ SP Increa se in re ve nue s @ GP Increa se in re ve nue s TO TAL SALES OBJECTIVES @ TARGET GROUPS 100.000 Time Increase in revenues @ SP Increase in revenues @ GP 1 fe b 2015 49.857,21 234.063,39 5 1 m rt 2015 49.161,24 241.108,27 1 a pr 2015 46.443,82 247.651,81 1 m e i 2015 43.273,33 254.332,49 1 jun 2015 41.322,86 261.676,62 1 jul 2015 40.425,65 268.534,78 1 a ug 2015 38.864,62 273.586,32 1 se p 2015 38.043,02 278.513,59 1 ok t 2015 37.328,55 282.707,74 0 1 nov 2015 34.299,08 285.867,23 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 1 de c 2015 32.373,88 288.909,99 1 ja n 2016 31.932,92 291.672,04 Time 1 fe b 2016 30.025,01 293.156,25 1 m rt 2016 29.588,87 294.377,81 1 a pr 2016 29.479,85 295.258,13 © 2010 IM Associates: Patient-flow enabled forecasting 18

The forecasting process Step 6: Validation workshop with the client • The set of most realistic forecast scenarios is chosen by the client. Based on this choice we determine the most probable forecast outcome. • Based on our market research, we also define the trigger points together with the client – These are the drivers on which they can work to increase their sales performance. © 2010 IM Associates: Patient-flow enabled forecasting 19

The forecasting process Summarized pay-off of this forecasting exercise: • The client gains insight into the business processes – thanks to the first workshop which is the culmination of our market research • Best-estimate of future sales – thanks to the core forecasting simulations • The client gains insight into sales improvement opportunities – thanks to the second workshop where we define the trigger points Keep in mind however: A forecast is only as good as its underlying assumptions © 2010 IM Associates: Patient-flow enabled forecasting 20

Case study Description Company XYZ produces a drug (e.g. Product 1) that can be prescribed in the second line treatment of patients with severe chronic pain. The general flow is as follows: • A person with regular pain complaints consults his GP. After doing some tests, the diagnosis of the GP is that it concerns a patient suffering from severe chronic pain. The GP prescribes a drug in 1° line treatment. • After 1 month there has been no progression booked. The patients returns to the GP and he asks a 2nd opinion by referring the patient to the SP. The SP decides to start the treatment in 2° line. • The SP considers 2 alternatives (Product 1 – from company XYZ, and Product 2 – from a competitor) and prescribes one of them for a short initiation period. • After the initiation period, the patient returns to the GP in the maintenance period and the GP follows the prescription choice of the SP. • If the patient still has the same complaints after a while, the GP refers him back to the SP and the latter reconsiders the Rx choice: stay on the same product or switch to the competitor. • Afterwards the patient again returns to the GP – who again follows the decision of the SP – for maintenance prescription. © 2010 IM Associates: Patient-flow enabled forecasting 22

Case study The forecasting process deliverables STEP 3 STEP 1 STEP 2 STEP 4 STEP 5 Preliminary Desk Market Validation Modeling the Process Research Research Workshop Future Design • Available information within the company XYZ • Internet search results from secondary research – Google – Scientific fora – National health insurances – EMA – … © 2010 IM Associates: Patient-flow enabled forecasting 24

Case study The forecasting process deliverables STEP 3 STEP 1 STEP 2 STEP 4 STEP 5 Preliminary Desk Market Validation Modeling the Process Research Research Workshop Future Design • Interviews: – With physicians – With field experts – With health insurers – … • Investigation of the treatment flow @ hospitals / treatment centers – Notion of formulary? © 2010 IM Associates: Patient-flow enabled forecasting 25

Case study The forecasting process deliverables STEP 3 STEP 1 STEP 2 STEP 4 STEP 5 Preliminary Desk Market Validation Modeling the Process Research Research Workshop Future Design General patient flow with best-estimates for each parameter © 2010 IM Associates: Patient-flow enabled forecasting 26

Case study The forecasting process deliverables STEP 3 STEP 1 STEP 2 STEP 4 STEP 5 Preliminary Desk Market Validation Modeling the Process Research Research Workshop Future Design Validation of the preliminary patient flow Validation of the best-estimates of the parameters • Are they realistic given the customer’s expertise in the field? • Do they have any more specific data that we can use? • How will the different parameter influences and/or values evolve over time? • … © 2010 IM Associates: Patient-flow enabled forecasting 27

Case study The forecasting process deliverables STEP 3 STEP 1 STEP 2 STEP 4 STEP 5 Preliminary Desk Market Validation Modeling the Process Research Research Workshop Future Design Using enhanced development software, we will build the validated patient & revenue flows with system dynamics © 2010 IM Associates: Patient-flow enabled forecasting 28

Case study The forecasting process deliverables STEP 3 STEP 1 STEP 2 STEP 4 STEP 5 Preliminary Desk Market Validation Modeling the Process Research Research Workshop Future Design Using enhanced development software, we will build the validated patient & revenue flows with system dynamics Patients start on Product 2 (in 2° line treatment) Patients start on Product 1 (in 2° line treatment) pts/m o pts/m o 150 30 Patients start on Product 2 20 100 Patients switch to Product 1 Patients start on Product 1 Patients stay on Product 2 Patients switch to Product 2 Patients stay on Product 1 10 50 0 1 jan 2010 1 jan 2014 1 jan 2018 0 1 jan 2010 1 jan 2014 1 jan 2018 © 2010 IM Associates: Patient-flow enabled forecasting 29

Case study The forecasting process deliverables Step 6: Final validation workshop with the client • The most probable forecast outcome is chosen • The trigger points can be defined – “Visit GP”; “Diagnosis”; “Referral to SP”; “Rx choice of SP”; … • Important conclusions can be drawn thanks to the patient flow – Although the GP treats more patients than the SP, it is the SP who decides among Product 1 and Product 2 – Because GPs will not switch the treatment it only pays off to visit the SPs – for GPs communication via digital channels is preferred Hereby the message to bring to the GPs is not that Product 1 is better than Product 2, but that they need to refer their patients to SPs if they don’t know how to proceed This will increase the market – Furthermore, it may also be useful to convince the patients to visit their GP in case of pain complaints This is another action that may increase the market All these points indicate that a patient flow can provide crucial additional insights © 2010 IM Associates: Patient-flow enabled forecasting 30

Q&A Feel free to contact us in case of questions IM Associates IM Associates Brusselsesteenweg 52 B-3000 Leuven (Belgium) http://www.im-associates.eu +32 16 22 47 43 info@im-associates.eu Follow-us on: © 2010 IM Associates: Patient-flow enabled forecasting 31

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Patient flow enabled forecasting

by IM Associates on Jun 15, 2010

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