Transparency as a Good Business Practice

Transcription

1 Transparency as a Good Business Practice Exploring How Transparency Can Drive Best Practices in Business Strategy August 2017 Polaris Management Partners

2 Agenda Compliance Trends Deriving Strategic Commercial Value from Transparency Data Managing Your Analytics Life Cycle Use Case 1: Physician Sub-Specialization and FMV Validation Use Case 2: Speaker Bureau Travel Optimization 2

3 Polaris has been focused on key compliance trends for 2017 and 2018 Accountability Integration of compliance and business Business ownership of traditional compliance responsibilities Criticality Defining key cost drivers for fee-for-service activities with HCOs and Payers Establishing repeatable FMV methodologies across a wide range of service types Access Understanding the necessity of market and patient access programs to ensure therapies are accessible to patients, as well as the increased risks associated with emerging commercial strategies Complexity Mapping third-party relationships and understanding relational risk Mining strategic insight from complex, sometimes messy data 3

4 Increased use of compliance-related data by commercial stakeholders implicates accountability and complexity Accountability Integration of compliance and business Business ownership of traditional compliance responsibilities Criticality Defining key cost drivers for fee-for-service activities with HCOs and Payers Establishing repeatable FMV methodologies across a wide range of service types Access Understanding the necessity of market and patient access programs to ensure therapies are accessible to patients, as well as the increased risks associated with emerging commercial strategies Complexity Mapping third-party relationships and understanding relational risk Mining strategic insight from complex, sometimes messy data 4

5 Discussion questions Q 1. Have you partnered with your marketing or sales teams to use transparency data for commercial purposes? 2. How did you use the data? 3. What challenges did you experience? 5

6 There are risks associated with using transparency data for commercial purposes Policy considerations Healthcare Climate People are concerned about the effect of money on medicine People are concerned about conflicts of interest Compliance officers as well as potential users must consider both public perception as well as internal motive when deciding whether to use transparency data for commercial purposes Business and compliance considerations Strategic Value and Cost Commercial ethical maturity (e.g., compliance comprehension ) Delay in Release of Transparency Data Data considerations Data limitations Instances of strategic misclassification 6

7 Transparency data can support many different commercial workstreams Transparency data can support commercial analytics projects with many different strategic use cases, including: Relationship Management Program Efficiency Field Sales Optimization Market Understanding Transparency analytics can take many forms, including key risk indicators (KRIs), key performance indicators (KPIs), and in-stream decision-support metrics. It s important to define, as early as possible, what the anticipated analytics use case is and to make sure it: Furthers strategic objectives Provides actionable insight Aligns with company ethical expectations 7

8 Analytics projects frequently suffer from poorly defined objectives and mission creep Interesting Strategic Blog Post Business Value Trim the Fat Trim analytics projects to their core strategic objectives and then test and improve them over time. 8

9 Discussion questions Q 1. Has your use of transparency data been the result of ad hoc requests or defined projects? 2. How has your use of transparency data changed over time? 9

10 Understanding the analytics life-cycle supports the development of strategic use-cases Prepare Deliver Improve The Analytics Lifecycle Treat analytics projects as ongoing initiatives, not discrete events. 10

11 The most common reasons analytics projects fail occurs at the preparation and delivery stages Prepare Deliver Improve Prepare Bad metrics o Are your metrics aligned with strategic objectives? o Have you refined your questions so as to generate actionable answers? Flawed insights o Have you incorporated feedback from the right stakeholders (especially end-users)? o Have they validated that metrics will be actionable? Deliver Faulty execution o Do end-users have the right training to operationalize your metrics? 11

12 Transparency professionals are in a good position to support transparency analytics projects Manage expectations, support data collection, transformation, and translation, database management Prepare Deliver Improve Facilitate crossfunctional dialogue Advocate for iterative refinement of questions and metrics Manage expectations Support compliance in assessing the anticipated outcomes of analytics projects Prepare data sets Identify data gaps Integrate company data sets Support (or conduct) data analysis Support analysis or dashboard development Maintain historical databases Identify new capture fields (for internal use) Develop repository of institutional knowledge Identify possible external databases for future integration 12

13 Use case 1: physician sub-specialization and FMV validation Problem Statement: As a result of increasing physician sub-specialization, companies are challenged to develop FMV rates that capture emergent sub-specialties wherein physicians are requesting (or demanding) higher rates This includes, for example, more aesthetic -focused physicians working in the dermatology and plastic surgery fields This can lead to an increased number FMV exception requests, slow down the contracting process, and create tension between internal functional groups Use-Case Type(s): Relationship Management Market Understanding 13

14 What information does this graph provide us? What are some of its limitations? 14

15 We can assess payment differentiation within a specialty Cluster A Cluster B Cluster C Company s Current Range 15

16 The payment differentiation assessment must be context-aware and support clear next steps Cluster A Cluster B Cluster C Company s Current Range Manage Expectations, Translate Results: Data reflects aggregate per-instance speaking payments, not hourly rates Different speaking program types Cluster C may reflect market compensation related to KOL/thought leader status, not new sub-specialization type Identify Next Steps in Analytics Workflow: Sample sub-set of Cluster A, B, and C physicians Analyze physicians specialties Determine whether sub-specialization population emerges in high-payment clusters only, or if dispersed through all clusters (suggesting no FMV differentiation) Assess econometric data to determine FMV rate differentiation 16

17 Don t forget the last stage of the analytics life cycle Cluster A Cluster B Cluster C Company s Current Range Improve: Obtain feedback and evaluate results Decide whether to incorporate subspecialty analysis into similar annual market and competitor benchmarking analyses o Consider developing FMV exception request frequency triggers to initiate benchmarking review 17

18 Use case 2: speaker program travel optimization Problem Statement: In addition to contracting fees, travel and lodging expenses constitute a significant portion of overall speaker program budget, limiting, in some instances, a brand s desired programming scope In a tiered payment model, speakers within the same specialty or sub-specialty may command significantly different hourly rates, depending on KOL/thought leader status Therefore, sending an international/national thought leader by plane to a program versus using a regional/local physician can have significant budget implications, especially in the aggregate Use-Case Type(s): Relationship Management Program Efficiency Market Understanding 18

19 What are possible explanations for the different travel patterns observed for these two brands? How might brand teams operationalize this data? 19

20 Open Payments data can make speaker program planning a much more strategic process This does not need to involve potentially risky analytics surrounding prescriptions generated by speakers, representatives, or program types. Instead, companies can use program attendance metrics to determine: Physician attendance (Open Payments data) Physician and allied-hcp attendance (Open Payments and internal data) Do some regions show better attendance trends with local versus national speakers? Are there other speaker attributes associated with positive attendance trends or does differentiation seem to be associated with sales representatives or sales regions? The answer to each of these questions may significantly impact the structure of speaker program operations. Attendance is a means of assessing potential educational impact. Attendance is one metric to consider when deciding, at a programmatic level, which HCPs to use for which programs. 20

21 Data-enabled efficiency gains can deliver significant commercial savings to reinvest in operations Strategic analysis can reduce spend on both speakers and consultant fees as well as travel and lodging. At scale, this can mean more education for more HCPs Travel Costs Brand C Every instance of regional or national travel for speakers and consultants is an opportunity to evaluate local resources: 8.9% of longer travel instances (>$600) constitute 37% of total travel expenditure /2/2015 2/2/2015 3/2/2015 4/2/2015 5/2/2015 6/2/2015 7/2/2015 8/2/2015 9/2/ /2/ /2/ /2/

22 Summary Carefully analyze the risks associated with using transparency data from a compliance, commercial, and data perspective. Define, as early as possible, what your anticipated transparency analytics use case is and make sure it: Furthers strategic objectives Provides actionable insight Aligns with company ethical expectations Treat analytics projects as ongoing initiatives, not discrete projects. As a transparency professional, leverage your insight into the data and crossfunctional relationships to support transparency analytics. 22