Leveraging EMR Data to Better Understand Local Market Potential and the Deployment of Commercial Resources

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1 Leveraging EMR Data to Better Understand Local Market Potential and the Deployment of Commercial Resources James Charnetski, Practice Leader Commercial Analytics & Effectiveness Quintiles Integrated Healthcare Solutions Copyright 2014 Quintiles 1

2 The New World of Value-Based Healthcare In a perfect world payers would not buy drugs and pharma would not sell drugs. They would buy and sell what the drugs do; improve health outcomes. 2

3 The New World of Value-Based Healthcare What if we could measure the impact of medicines by the improved outcomes they enable? What if we could allocate the deployment and measurement of commercialization efforts by these outcomes? 3

4 Pharma: Greater focus on the patient & enhancing QOL We aspire to improve the health and wellness of people around the world Merck Our mission is to help people do more, feel better, live longer GSK Transforming the lives of patients biogen idec Our mission is to care and cure Novartis We re passionate about improving quality of life and healthcare globally Teva 4

5 Value-based Commercial Model Framework An Outcomes-Based Commercial Model Built Around Five Key Questions 1 Real world data & analytics help us understand which treatments actually are prescribed to patients defined by: Condition being treated Demographics (age, sex) Clinical characteristics Line of use in treatment cascade What Drugs Are Prescribed? What Are the Prescribing Behaviors? 3 Treatment guidelines & comparative effectiveness research (CER) help us understand which treatments should be prescribed to patients defined by: Condition being treated Demographics (age, sex) Clinical characteristics Line of use in treatment cascade What Drugs Should Be Prescribed? How to Alter Prescribing Behavior? 5 RWD collected on changes in prescribing patterns & patient outcomes Via EMR systems & registries Changes assessed & fed into performance management system Linked to changes in market share How to Assess Change & Measure Outcomes? 2 Real world data & analytics help us gain deeper insights on how physicians prescribing behaviors vary: By patient characteristics (clinical, demographic) By specialty (primary care, specialist) By geography 4 Real world data, analytics & IT combine to create tools to inform prescribing decisions, improve prescribing behavior Predictive modeling of outcomes based on current prescribing behavior Simulation of improved outcomes based on changed prescribing behavior 5

6 Agenda Overview of QEMR platform Influence on commercial applications Case study Impact on targeting & resource deployment 6

7 Profile & Key Benefits of Q-EMR Platform Over 36 Million Unique Patients in an Ambulatory Care Setting 652 MQIC Institutions* 39,900 Providers (M.D., D.O., D.P.M., PA-C, RN-NP) 49 States and DC Rich clinical information QOL measures PHQ (depression), HAQ (RA/arthritis) and Wong-Baker FACES (pain) scores Diagnostic tests, lab values & vital signs OTC recommendations Covers all insurance types Personal and family history of medical conditions; co-morbidities; lifestyle data 7

8 Influence on Commercial Applications EMR drives additional insights across a number of commercial applications Treatment pathway/patient journey Understand treatment selection by line of therapy within specific patient cohorts and specialty segments Achieve better patient outcomes Improving patient care using realworld QOL outcomes measures and engagement Market Sizing & Patient Segmentation Draw upon a wealth of patient clinical demographics to gain insights into market potential Commercial Deployment Align commercial resources to local market potential by disease state to improve the probability of commercial success Performance Tracking Fine-tune brand performance measurement activities across several patient and practice dimensions Prospective Market Research Connect with physicians and patients to further draw upon their treatment and therapy experience. 8

9 Case Study: Atrial Fibrillation 9

10 Case study Issue Understand if treatment selection in the A-Fib market is optimal based on certain patient risk factors Opportunity Identify and prioritize geographies where A-Fib treatment selection is suboptimal Action Deploy specific field-based resources and messaging within select geographies to drive the clinical benefits and value of newer therapy options on these patient populations 10

11 Inputs into Statistical Analysis Data Sources Q-EMR National network of ambulatory care offices 36+ million patients, 30,000 providers,» ~350K A-fib patients (Dx )» 1.1 million patients Cardiac Dysrhythmias Q-EMR / Truven Market Scan linked database 1.9 million Q-EMR patients linked to Truven claims» ~30K linked A-fib patients on anti-coagulant IMS prescriber-level data TRx at the 5-digit zip Physician specialty, decile and state Market Definition Warfarin, Pradaxa, Xarelto, Eliquis Key Data Elements Stroke risk assessment score (CHA2DS2-VASc). Contraindication for warfarin: (artificial heart valves, significant valve disease, severe renal failure, advanced liver disease, pregnancy) Clinical measures (e.g., BP, BMI, lipids) INR, prothrombin, and platelet levels Current and past therapies for anticoagulation, antihypertension, antiplatelet, and lipid-lowering: start/stop dates, cost/fill, compliance Current and past therapies for rate and rhythm control AF outcomes: Ischemic stroke, transient ischemic attack Anticoagulation side effects: Hemorrhage Patient characteristics and demographics (including state of residence), Provider specialty and decile Current product share at 3 digit zip level Concentration of top deciles at 3 digit zip level Time since AF diagnosis Other comorbidities 11

12 Claims vs. Rx-level information Source: Q-EMR, Truven, IMS Health 12

13 Brand Performance EMR + Rx-level data = a more accurate depiction of the A-Fib market ~53% of total prescriptions are written for A-Fib What Drugs Are Prescribed? 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% 21.8M TRxs 11.7M TRxs 78% 71% Rx-level Rx-level (for Dx only) Warfarin Xarelto Pradaxa Eliquis Source: IMS Health, Q-EMR 13

14 A-Fib Brand Performance by Specialty Slower adoption of newer agents by PCPs in the management of A-Fib What Are the Prescribing Behaviors? 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% 6.3M TRxs 4.9M TRxs 83% 55% Rx share for A-Fib (PCP) Rx share for A-Fib (Cards) Warfarin Xarelto Pradaxa Eliquis Source: IMS Health, Q-EMR 14

15 A-Fib TRx Performance by State Warfarin A-Fib State-level TRx Share ranges from 45%-92% What Are the Prescribing Behaviors? Evaluating product use by Dx from EMR data at the state and specialty level in conjunction with Rx-level claims data, we are able to estimate warfarin market share for A-Fib. Source: IMS Health, Q-EMR 15

16 What Are the Prescribing Behaviors? Does A-Fib treatment vary locally in NC? 9 local geographies had Warfarin use greater than the state average (65%), ranging from 67%-85% Source: IMS Health, Q-EMR 16

17 Are certain A-Fib patient types at higher risk? Warfarin patients are at greater risk of experiencing a bleeding or ischemic stroke event over newer therapy options. What drugs should be prescribed? Source: Q-EMR 17

18 A-Fib HR for Bleeding and Ischemic Stroke Events Both Xarelto and Pradaxa are superior to warfarin for reduction of bleeding risk. Pradaxa is superior to warfarin in the prevention of ischemic stroke. Ischemic Stroke 0.64** 0.94* Bleeding warfarin (n=20448) Xarelto (n=2517) Pradaxa (n=4524) * Xarelto vs. warfarin Rocket AF Study HR = 0.88 ( ) ** Pradaxa vs. warfarin RE-LY study HR = 0.65 Source: Q-EMR 18

19 Can future stroke event risk be reduced? Through predictive modeling, we estimate that ~30% of Warfarin A-Fib patients would benefit from an alternative therapy that would reduce their future risk of a stroke or bleed event. How to Alter Prescribing Behavior? n = 11.7M Prescriptions Source: IMS Health, Q-EMR 19

20 Patients, Pharma & the Health System all Benefit Patient outcomes improve with less overall reported events; brand share improves across newer and safer agents, and the overall costs of healthcare are reduced with the decline of more costly ischemic stroke events. How to Alter Prescribing Behavior? Source: IMS Health, Q-EMR 20

21 Treatment Model Summary In addition to less reported events and greater patient QOL, manufacturers also benefit via incremental gains in brand performance. How to Alter Prescribing Behavior? 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% A-Fib TRx Share N. Carolina Pre & Post Treatment Deficit 66% Current 40% Modeled Eliquis Xarelto Pradaxa Warfarin Source: IMS Health, Q-EMR 21

22 How to Alter Prescribing Behavior? Impact on targeting Local geographies were prioritized based on a combination of current treatment patterns and modeled results, incorporating other local health system and payer influences Identify geographic areas with high concentrations of targeted diagnoses with greatest upside potential for a comprehensive IDN/ACO cross stakeholder model Deliver prime targets for focused analysis on local market behaviors and opportunities for improvement 22

23 Impact on resource deployment How to Alter Prescribing Behavior? Field-based and other integrated channel resources were deployed locally to capitalize on the opportunity. Local market analytics and performance reporting monitor the impact of activities. Channels Analytics Solutions 23

24 Questions? 24

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