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Patient-Level Data Tutorial

Sample of Questions Addressed

Here are some of the questions we will be addressing.

q       Just what is patient-level data? What’s the difference between patient-level and patient-centric data? What does patient-level data really boil down to? What can it achieve that good old physician-level data such as IMS Xponent or NDC Source Prescriber can’t?

q       How does the offering of data vendors such as IMS, NDC/ArcLight, Dendrite, Verispan, PharMetrics, Ingenix, Solucient, Medstat, Premier, AdvancePCS/Caremark to mention the most conspicuous ones differ from one another? What are the implications of working with pharmacy/switch, claims, discharge, or stay data? In what respects do databases from one-plan, multi-plan, PBM, and employer group claims data differ? Why am I better off using one database over another when I am charged with answering certain business questions or analyzing certain therapeutic areas?

q       Some data vendors swear only by cubes. What’s a cube anyway? How does the cube measure up with actual patient-level transactions? Pros and Cons? Why should I care about UB-92, Form 1500, ICD-9, CPT-4, HCPCS, APC, J-code, etc.? What’s the deal with timeliness, coverage, and connectivity?

q       How are pharmaceutical companies using patient-level data today? What is the state of the art? What are the current challenges? What are the emerging trends? What does the industry have in store for us? Is it true patient-level data allows us to measure hospital-retail spillover, identify influencers, leverage system targeting, in addition to performing bread-and-butter persistency, compliance, and switching analyses?

q       Is HIPAA nipping patient-level data in the bud? How come the Bush administration has decided to increase spending by 53% to boost health connectivity and CPOE in the hospitals? How does that jibe with IOM and the Markle Group?

q       Your mandate is to boost the skills and competence of your department. You may even have been charged with equipping your company with patient-level data and turning around patient-level analyses on a regular basis? But which patient-level database to acquire? What questions to ask? Running an evaluation sounds like a good idea. But you have not done that before. How to pull this off without jeopardizing or limiting your career?

q       You are planning on attending patient-level data conferences. Good idea! But how to make sure you get the most out of those events. Indeed, you want an objective lay of the land: vocabulary, state-of-the art, industry trends and perspectives, success stories, failures, etc. More importantly you have a stash of well-articulated and inchoate questions alike that you want answered and put in perspective.

Agenda

This tutorial is a solid 8:45 a.m. to 4:30 p.m. day and covers the topics listed below. After this tutorial, you’ll have a solid grasp of what patient-level data is, of the nuances among different strands of patient level data and the bearing on the analyses performed. You’ll have a good appreciation of the offering of the major data vendors and what’s brewing in the industry. You’ll have the lowdown on fundamental analyses such as compliance and persistency (adherence), switching, dosing, and the like. You will also be exposed to dramatically novel applications of patient-level data (e.g.. spillover, influencers, molecular targeting, etc.). Last but not least, you’ll be better equipped back in the ranch to advise your company as to the best patient data course to embark on, to vet and select patient-level databases, to build the case for patient-level data to upper management, and to enthuse analysts already overwhelmed with too much data.

Morning

8:45 – 9:00         Introduction.

9:00 – 9:50         What’s the Deal? Definitions.

10:00 – 10:50     Industry Offering, Trends, and Perspectives

11:00 – 11:50     Compliance, Persistency, Switching, etc.  (Fundamental Analyses) 

Afternoon 

12:00 – 1:00       Lunch

1:00 – 1:50         Review of Material Covered and Open Discussion

2:00 – 2:50         Measuring Spillover, Identifying Influencers, Deploying Molecular Targeting (Advanced Analyses)

3:00 – 4:20         Case Studies

4:20 – 4:30         Evaluation & Conclusion

About the Tutorial Leaders

Jean-Patrick Tsang, PhD & MBA (INSEAD)

Jean-Patrick Tsang is the Founder and President of Bayser, a Chicago-based consulting firm dedicated to pharmaceuticals sales and marketing. JP started up Bayser in April 1996 after a two-year stint in a larger consulting firm. JP’s contribution to the industry includes: adaptive targeting based on individual promotion response curves, “Hood Robin” principle for portfolio optimization, a probabilistic approach to the managed care masking problem, a quota reallocation scheme to re-level the managed care playing field, a four-point evaluation scheme to assess co-promotion opportunities, a music-like notation to capture the terms of a deal, and a CRM tool to enhance communication and planning between the district  manager  and the sales rep. JP is a big proponent of patient-level data, not only for the longitudinal analyses, but also for loftier endeavors it supports such as measuring hospital-retail spillover, identifying influencers & spheres of influence, and opening up new targeting paradigms such as system targeting. JP publishes in Pharmaceutical Executive, Product Management Today, and other trade magazines, and talks at PMSA, PMRG, CBI, SRI, and IIRUSA.  In a previous life, JP worked on the automation of the design process of payloads for satellites, methaners/ethaners, and cruise-liners.  JP earned a Ph.D. in Artificial Intelligence from Grenoble University and an MBA from INSEAD in France. He can be reached at (847) 920-1000 or bayser@bayser.com.

Igor Rudychev, PhD

Igor Rudychev is currently a senior consultant with Bayser. He has intimate working knowledge of patient-level and patient-centric data, having completed several client engagements involving measurement of hospital-retail spillover, hospital/retail switching, identification of influencers and referral patterns, mapping of institutional neighborhoods, and the like. In his patient-level data analyses, Igor leverages techniques from advanced mathematics, statistics, artificial intelligence, and elementary particle physics. In addition to performing and supervising client projects, Igor is involved in fundamental research  pertaining  to  the  deployment  of  patient-level data to solve key business questions of the pharmaceutical industry. Igor recently co-authored an article with JP titled  “Distilling Influence Networks and Referral Patterns using Patient-Level Data” that appeared in Product Management Today (April 2003 issue). Prior to joining Bayser, Igor conducted research in optimization techniques applied to super string theory, the most promising avenue to the unification of quantum mechanics and Einstein’s theory of gravity. Igor has a PhD in Theoretical Physics from Texas A&M. His hobbies include guitar and chess (Russian Chess candidate Master at 13). He can be reached at (847) 679-8278 or igor@bayser.com.

Testimonials

This is what the attendees of the previous sessions are saying:

       “Sign up!”

       “Should be required for anyone using this type of data!”

       “JP’s insights into where the industry is heading, the motivation of the data vendors and the pitfalls of using that data are most interesting. Very helpful!”

       “Excellent review of various vendor capabilities and approach to working with vendors for quality, substance, content.”

       “Review of concepts and appendix thorough!”

       “Provides a complete understanding of the key differences in what elements are included in which sources.”

       “Very good! Should be required for market research department.”

       “Great job! This course applies to all levels of understanding/ knowledge”

       “JP’s humor most welcome! Great overview and explanation of what various databases contain.”

       “JP and Igor were so knowledgeable about specific vendors’ offerings!”

       “Intellectually challenging!”

       “Excellent class notes – plentiful and useful”

Companies that attended

More than 20 companies have attended our tutorials. They include: Abbott, AstraZeneca, Aventis, Bayer Pharma, BMS, Boehringer-Ingelheim, Centocor/J&J, Forest Labs, GSK, Janssen/J&J, Novartis, Novo Nordisk, Ortho Biotech/J&J, Pfizer, Takeda, Roche, Roche Diagnostics, Sanofi-Synthelabo, Solvay, Wyeth, and Yamanouchi. 

Why Bayser?

First off, you’ll have an objective evaluation account of the patient industry. We have no vested interest in any patient-level data vendor and will give you the untainted truth straight from the gut. We’ll tell you what data vendors are loath to admit.

Second, we have done considerable research and have solid client experience under the belt. Here is a list of recent articles we penned on patient-level data:

1. “Patient-level data come of age” that appeared in the May 2003 issue of Pharmaceutical Executive and will be reproduced in the Nov 2003 issue of the International Pharmaceutical Review,
2. “Distilling Influence Networks and Referral Patterns using Patient-Level Data” in the April 2003 issue of Product Management Today
3.  “Hospital to Home: The Rx Spillover effect”, Pharmaceutical Executive,               July 2003.
4. “Patient-Level Data: A New Level of Analysis”, Pharmaceutical Marketing      News, Aug 2003.
5.  “Molecular Targeting”, Medical Marketing Media, February 2004.

Below is a selection of conferences where we uncovered some insights regarding patient-level data:

1. “Measuring Hospital-Retail Spillover: Bridging and Holistic Techniques”,       PMSA 2003, Orlando, FL, May 4-7, 2003.

2.  “Patient-Level Data – Leverage emerging data to maximize market           potential”, Organizer and chairman of conference with CBI, Philadelphia,           PA, June 5-6, 2003.
3. “Identification of Influencers using patient-level data”, PMRG Fall 2003,            San Diego, Sep 21-23, 2003.
4. “Nail down true ROI”, PMC 2003 Washington DC, Oct 2003                         (co-presentation with Sanofi).
5. “Anonymous Patient-Level Data – Leverage emerging data to maximize        market potential”, Organizer and Chairman of conference with CBI,      Philadelphia, PA, Nov 16-17 2003.
6. “Building sociograms of physicians using upstream targeting”, IIRUSA     conference on “Using market research to advance innovation and new         product development for pharmaceuticals: Aligning marketing and         development for commercial success”, Sheraton Rittenhouse Square              Hotel, Philadelphia, PA, Feb 24-26, 2004 (co-presentation with Sepracor).
7. “Optimizing the use of patient-level data to enhance your marketing       capabilities”, chairman and leader of pre-conference workshop             “Introduction to classical and advanced patient-level analyses”, PEA      Conference, Park Hyatt, Philadelphia, PA, March 22-23, 2004.

“One of the brightest individuals that I've met in my career. JP has an incredible skill regarding simplifying issues, and preparing presentations for senior management." -- Director, Large Pharmaceutical Company

“Extremely brilliant and gets it right away.” --Director, Large Pharmaceutical Company

“Very professional!” --VP, Large Medical Devices Company

“JP and I are a great team. I get all kinds of ideas and he gets them implemented.” --VP, Large Pharmaceutical Company

“Always does quality work.” --Director, Large Medical Devices Company

"The amount of knowledge that they bought, not only about their tools but about the industry & tool applications.” --Market Researcher, Large Pharmaceutical Company

“I am very pleased with Bayser’s work. ” --Director, Large Diagnostics Company

“A real guru at Excel. Taught me everything I know about spreadsheets.” --Account Manager, Large Diagnostics Company

Reaction to a demo of Bayser’s Rx Tracker: “I am having an out-of-body experience right now.” --VP, Large Pharmaceutical Company