Patient-Level Data Tutorial
Here are some of the questions we will be addressing.
q Just what is patient-level data? Whats 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 cant?
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. Whats 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.? Whats 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.
This tutorial is a solid 8:45 a.m. to 4:30 p.m. day and covers the topics listed below. After this tutorial, youll 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. Youll have a good appreciation of the offering of the major data vendors and whats brewing in the industry. Youll 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, youll 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.
8:45 9:00 Introduction.
9:00 9:50 Whats the Deal? Definitions.
10:00 10:50 Industry Offering, Trends, and Perspectives
11:00 11:50 Compliance, Persistency, Switching, etc. (Fundamental Analyses)
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. JPs 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 email@example.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 Einsteins 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 firstname.lastname@example.org.
This is what the attendees of the previous sessions are saying:
Should be required for anyone using this type of data!
JPs 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
JPs humor most welcome! Great overview and explanation of what various databases contain.
JP and Igor were so knowledgeable about specific vendors offerings!
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.
First off, youll 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. Well 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:
Below is a selection of conferences where we uncovered some insights regarding patient-level data:
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
brilliant and gets it right away.
and I are a great team. I get all kinds of ideas and he gets them
does quality work.
amount of knowledge that they bought, not only about their tools but about
the industry & tool applications.
am very pleased with Baysers work.
real guru at Excel. Taught me everything I know about spreadsheets.
to a demo of Baysers Rx Tracker:
am having an out-of-body experience right now. --VP,
Large Pharmaceutical Company
am having an out-of-body experience right now.
--VP, Large Pharmaceutical Company
All contents copyright 2002 Bayser Consulting, (847) 920-1000