Life Sciences - Incedo Inc.

What is Contract Pull Through?

The pharma sales team engages in contracts with brands, hospitals, clinics, infusion centers, doctor offices, IDNs, ONA, GPOs, and other networks. These networks are often referred to as pharma accounts, and contracts are lined up to improve overall sales, market share, and profitability. Contracts with these accounts are based on various factors such as rebate percentage, formulary tiers, and performance-based fees.

Now, the current size of pharma gross contracted sales is to the tune of 50B USD and projected to grow to 85B USD over the next 5 years^[1], making this a big area for the pharma commercial team to have a close look and improve sales effectiveness while engaging with these accounts. Pharma companies are interested in the contracted sales, rebates, terms, and tiers data from the accounts to measure effectiveness. Mainly to figure out which of the existing accounts are underperforming, and which are performing above benchmark – and this aspect of pulling contract data across accounts to measure account effectiveness is the key objective of Contract Pull Through.

We may define Contact Pull Through, as the analysis of –

How much an account has purchased (sales) by contract program and by brand;
How much they’ve received in discounts (rebates, chargebacks);
How they are doing against their baselines; and
Where are the opportunities to buy more and save more?

Why does pharma need to focus on Contract Pull Through?

Large and mid-size pharma companies have Contract Pull Through from the accounts, as the top-of-mind problem, as even increasing effectiveness by 2-5% would mean savings to the tune of millions of dollars.

Based on our experiences working with the client’s market access team – we realized that the organization delegated the task of supporting field pull-through entirely to its payer account managers. These executives reported spending 75% of their time creating and pulling reports, time that could have been better spent with customers or in more strategic dialogue with the field members.

The key stakeholders for Contract Pull Through are the field team members i.e., Business Engagement Managers (BEMs) and Healthcare Market Directors (HDs), who need to focus on Contract Pull Through for –

Generating contract awareness and pull through for major providers in their ecosystems
Creating awareness around the contracts/terms offered by pharma firms for the products
Engaging with customers to show their historical performance and current performance

Some specific Contract Pull Through use cases the pharma account team focuses on include –

A portfolio purchasing summary of the account, which enables the account to understand how much volume the account has bought, and the savings received from pharma products
Product contribution at an account level, which enables the understanding of how much volume is coming from each pharma product
Contract eligibility of an account, to understand which contracts are available at a certain account

Other business insights that Contract Pull Through data may help pharma companies are around –

How is the account performing as compared to others, in their ecosystem/region?
Which is the account’s dominant payer, and how does that payer work at a national level?
How much this account can purchase to reach the next tier?

Use case deep dive: Portfolio purchasing summary of an account

A portfolio purchasing summary of the account is one of the critical use cases handled through Contract Pull data – what the Contract Pull Through team is looking for is to understand a particular account within a regional ecosystem, across all periods of the contract or for a particular period –

What has been the number of gross sales? Has that gone up or down compared to its last quarter?
What part of the total gross sales is contracted vs non-contracted? What part of sales is attributed to specialty pharmacy? What percentage of account savings is attributed to contracting?

Also, another key insight to look at for portfolio purchasing summary for the account is to identify the product mix/segment mix (GPO, 340B, NCCN, etc.) across the portfolio, and double click on which product/ segment contribution has gone up, or down over the previous periods, and how does it fare against its anticipated baseline numbers.

Insights of this degree can immensely help the field team members understand how much volume the account has bought and the savings received from contracted pharma products, how are the accounts performing against their expectations, and where there are opportunities to buy more to save more.

How to generate the pull through business insights from data?

To arrive at these insights- the key data elements that must be looked into are contracts, chargebacks, 867 Sales, non-contracted sales, rebates, terms and tiers, account hierarchy, and zip to territory-mapping data.

These data sets coming from different source systems are ingested, assimilated, and presented as output for improved decision-making –

Firstly, it needs to be ingested into cloud-based or on-prem databases by using RPA tools like UIPath
The ingested data then goes through a set of data quality engine checks to ensure data is of expected quality.
The clean dataset then is transformed through the ETL process, where complex calculations through business-defined rules are applied, and
Presented through BI tools reports providing visualized/graphics and tabular data on gross sales, savings, performance, opportunities for an account, and other pull through insights.

Conclusion: CPT – Top of mind for strategic and financial objectives

BEMs/ HDs have Contract Pull Through insights generation as a huge part of their mind share. Thus, large and mid-pharma organizations are, or should invest in building a robust Contract Pull Through system to enable them to understand how the accounts are performing against their expectations, and in turn, identify the opportunities for them to sell more to optimize rebate payments. By doing that they have financial benefits to the tune of millions of dollars in terms of optimized rebates, and savings. As also the strategic incentive to understand the top accounts to concentrate on, and the key underperforming accounts to re-negotiate the contracting terms and tiers.

¹ U.S. Pharmacy Market Size, Share & Growth | Forecast [2028] (fortunebusinessinsights.com)

Self-service AI refers to the intelligence that business users (analysts and executives) can acquire on their own from the data without the extensive involvement of data scientists and engineers. It means enabling them to acquire the actionable intelligence to serve their business needs by leveraging the low-code paradigm. This results in reduced dependency on other skills such as core IT and programming and makes faster iterations possible at the hands of business users.

As the data inside and outside of the organizations grows in size, frequency and variety, the classical challenges such as hard-shareability across BUs, lack of single-ownership and quality issues (missing data, stale data, etc.) increase. For IT teams owning the data sources, this becomes an additional task to ensure provisioning of the data in requisite format, quality, frequency and volume for ever-growing analytics needs of various BU teams, each having its own request as a priority request. Think of the several dashboards floating in the organizations created at the behest of various BU teams, and even if with great effort they are kept updated, it is still tough to draw the exact insights that will help take direct actions based on critical insights and measure their impact on the ground. Different teams have different interaction patterns, workflows and unique output requirements – making the job of IT to provide canned solutions in a dynamic business environment very hard.

Self-service intelligence is therefore imperative for organizations to enable their business users to take their critical decisions faster every day leveraging the true power of data.

Enablers of self-service AI platform – Incedo Lighthouse^TM

Incedo Lighthouse^TM is a next-generation, AI-powered Decision Automation platform targeted to support business executives and decision-makers with actionable insights generation and their consumption in daily workflows. Key features of the platform include:

Specific workflow for each user role: Incedo Lighthouse^TM is able to cater to different sets of users, such as business executives, business analysts, data scientists and data engineers. The platform supports unique workflows for each of the roles thereby addressing specific needs:
- Business Analysts: Define the KPIs as business logic formulations from the raw data, also define the inherent relationships present within various KPIs as a tree structure
- Data Scientists: Develop, train, test, implement, monitor and retrain the ML models specific to the use cases on the platform in an end-to-end model management
- Data Engineers: Identify the data quality issues and define-apply remediation across various dimensions of quality, feature extraction and serving using online analytical processing as a connected process on the platform
- Business Executives: Consume the actionable insights (anomalies, root causes) auto-generated by the platform, define action recommendations, test the actions via controlled experiments and push confirmed actions into implementation
Autonomous data and model pipelines: One of the common pain points of the business users is the slow speed of data to insight delivery and further on to action recommendation, which may take even weeks at times for simple questions asked by a CXO. To address this, the process of insights generation from raw big data and then onto the action recommendation via controlled experimentation has been made autonomous in Incedo Lighthouse^TM using combined data and model pipelines that are configurable in the hands of the business users.
Integrable with external systems: Incedo Lighthouse^TM can be easily integrated with multiple Systems of Record (e.g. various DBs and cloud sources) and Systems of Execution (e.g. SFDC), based on client data source mapping.
Functional UX: The design of Incedo Lighthouse^TM is intuitive and easy to use. The workflows are structured and designed in a way that makes it commonsensical for users to click and navigate to the right features to supply inputs (e.g. drafting a KPI tree, publishing the trees, training the models, etc.) and consume the outputs (e.g. anomalies, customer cohorts, experimentation results, etc.). Visualization platforms such as Tableau and PowerBI are natively integrated with Incedo Lighthouse^TM thereby making it a one-stop shop for insights and actions.

Incedo Lighthouse^TM as self-serve AI at a Pharmaceutical Clinical Research Organization (CRO)

In a recent deployment of Incedo Lighthouse^TM, the key user base is the Commercial and Business Development team of a Pharma CRO. The client, being a CRO, had drug manufacturers as its customers. The client’s pain point revolved around the low conversion rates leading to the loss of revenue and added inefficiencies in the targeting process. A key reason behind this was the wrong prioritization of leads that have lower conversion propensity and/or have lower total lifetime value. This was mainly due to judgment-driven, ad-hoc and simplistic, static, rule-based identification of leads for the Business Development Associates (BDA) to work on.

Specific challenges that came in the way of application of data science for lead generation and targeting were:

The raw data related to the prospects – using which the features are to be developed for the predictive lead generation modeling – were lying in different silos inside the client’s tech infrastructure. This led to inertia to develop high-accuracy, predictive lead generation models in the absence of a common platform to bring the data and models together.
Even in a few exceptional cases, where the data was stitched together by hand and predictive models built, the team found it difficult to keep the models updated in the absence of integrated data and model pipelines working in tandem.

To overcome these challenges, the Incedo Lighthouse^TM platform was deployed that allowed them to:

Combine all the data sources’ information into a Customer-360-degree view, enabling the BDAs to look at a bigger picture effortlessly. This was achieved by pointing the readily available connectors within the Incedo Lighthouse^TM platform to the right data sources, and establishing data ELT pipelines that are scheduled to run in tandem with the data refresh frequency (typically weekly). This allowed the client’s business analysts to efficiently stitch together various data elements, that were earlier lying in silos, in a self-serve model and include custom considerations that are region and product specific during the data engineering stage.
Develop and deploy AI/ML predictive models for conversion propensity using Data Science Workbench which is part of the Incedo Lighthouse^TM platform, after developing the data engineering pipelines that create ‘single-version-of-the-truth data’ every single time raw data is refreshed. This is done by leveraging the pre-built model accelerators for predictive modeling, helping the BDAs sort those prospects in the descending order of their conversion propensity, thereby maximizing the return on the time invested in developing them. The Data Science Workbench also helped with the operationalization of various ML models built in the process, while connecting model outputs to various KPI Trees and powering other custom visualizations.
Deliver key insights in a targeted and attention-driving manner to enable BDAs to make most of the information in a short span of time. This is achieved through well-designed dashboards to rank-order the leads based on the model reported conversion propensity, time-based priority and various other custom filters (e.g. geographies, areas of expertise). The intuitive drill-downs were encoded using the region-specific KPI Trees to enable them to know the exact account portfolios of their business that were lagging behind. These KPI Trees were designed by the client’s business analysts within the platform’s self-serve KPI Tree Builder, saving multiple iterations with the IT teams. The KPI Trees allowed the BDAs to double click on their individual targets, understand the deviations from actuality, and review the comments from earlier BDAs who may have been involved, to decide the next best actions for each lead.

The deployment of Incedo Lighthouse^TM not only brought about real improvements in target conversions, but also helped transform the workflow for the BDAs by leveraging Data and AI.

HCP engagement in the new era

The engagement strategies for pharma representatives to connect with HCPs were already in a state of transformation. Covid-19 has only accelerated this process. With fewer HCPs now preferring in-person meetings and with the advent of new technologies, there has been a steady rise in the use of various digital channels like email, social, virtual connects etc. Statistics show that the volume of emails sent to HCPs increased by almost 300% in the last year and the average interaction duration in virtual meetings has increased manifold. All of these developments have compelled the drug companies to reimagine their engagement strategies, maintain a healthy relationship with the HCPs and use the right channels for making the required impact on the HCPs.

Personalization – The need of the hour:

With different technologies at their disposal and HCP preferences differing, pharma companies have realized that they have to change their marketing and engagement tactics to meet the engagement needs of each doctor. Each HCP’s expectation from the interaction is different and the education required for each of them is largely dictated by the patient cohort the HCPs serve. The interest points of HCPs differ and their response to channels differs. So does the response of HCPs to various incentives offered by pharma companies. For example, in one of the recent analyses executed using the Incedo Lighthouse^TM Platform, we found that the Pediatricians (PD) respond to nutritional rebates much more than their Non-PD counterparts.

Personalization, and sometimes hyper-personalization, therefore is the central theme of customer engagement across domains. HCPs now prefer to be connected to a digital platform of their choice e.g. mobile, email, social, call activity, etc. This behavior may differ across various HCP segments e.g. across therapeutic areas, affiliation, years of experience, and geography, apart from the patient cohorts they serve.

With response data now available to the drug companies, it is possible to derive insights on the HCP preference by various cuts such as segment, sub-segment, geography, etc. The HCP preference and behavior patterns shed light on how receptive they are to digital engagement. This data analysis is leveraged by organizations to analyze the context and content for a digital interaction to derive Next Best Action by answering critical questions related to the message and channel strategies.

Helping Pharmaceuticals understand High Impact Channels using Incedo Lighthouse^TM

In one of the recent deployments of Incedo Lighthouse^TM at a pharma organization, the client wanted to understand the most impactful channels for engaging with the HCPs. This was also driven by the CMO agenda to understand the profitable marketing channels to get a bang for the buck. Understanding the segments and sub-segments of HCPs from the visualization and segmentation powered by the Incedo Lighthouse^TM platform, ML models were built at different therapeutic areas. The marketing investment was translated to input variables specific to the channel and the impact was measured on the overall sales. Using the Data Science workbench of the Incedo Lighthouse^TM platform, different linear and non-linear ML models were built. The insights from the models were used to derive the contribution of different channels on both the baseline and promotional sales.

Using this, the ROI of channels was determined by various cuts. For example, at a broad level, every marketing dollar spent gave an extra 40 cents as a return. Also, it was understood that among all the channels, digital channels were underinvested and the HCPs responded best to them.

Using the KPI Tree and Cohort Analyzer functionality of the platform, one could see which HCPs were under-reached and had responded well and which were overreached and didn’t respond on certain channels. Using the deep drill-downs, one could go to the affiliation/hospital levels and identify the next action and specific ways to reach them.

Lastly, Incedo Lighthouse^TM’s advanced visualization capabilities help generate response curves of each channel with further drill-down capabilities. These can be instrumental in simulating the performance of HCP cohorts and channels and identifying the breakeven dollar spend. Laying optimization algorithms on top of it, leveraged from Incedo Lighthouse^TM’s pre-built accelerators, organizations can get to the channel strategy designed to optimize the HCP engagement through each medium while minimizing the investment.

There’s never been a greater need than now to reimagine the impact that patient support programs can deliver as therapies become more specialized and complex. It brings a new emphasis on ensuring patients start their medications, reducing treatment discontinuation and elevating patient experience across the journey, embracing whole-person care. In this webinar, industry leaders will discuss the fundamental shifts manufacturers can make leveraging AI and Experience-driven interventions across a patient’s journey to predict and apply data-driven insights and recommended next-best-actions.

Key Takeaways

Market forces driving change for Patient Support & Engagement Programs
AI-enabled Patient Support Analytics approach that can generate deep insights into patient engagement, access support needs, and process efficiency
An optimal approach to utilizing advanced analytics and machine learning drives personalized engagement models and proactively predicts “Next Best Actions”
Gain a thorough understanding of the Caregivers’ changing role attributed to the increase in disease complexity and the specific challenges and needs

About the speaker

The COVID -19 pandemic continues to disrupt the Pharma industry. As uncertainty around the pandemic lingers and refuses to go away, Pharma leaders are facing extraordinary challenges due to demand and supply side shocks, causing supply chain disruptions.

Unprecedented problems can still be solved with conventional solutions. With the right tools, Data Science can provide much needed clarity, direction and guidance on what is happening now, and what is expected to happen ahead.

This webinar covers how an AI powered COVID Control Room can help Pharma Supply Chain teams:

About the speaker

Afrozy is a Data Science leader with global experience in partnering with Fortune 500 clients to define strategies, execute projects and deliver significant KPI movements leveraging Analytics & AI.

She lives in the San Francisco Bay area and enjoys hiking and writing, along with her passion for all things Data.

The amount of patient data is exploding, with studies suggesting the amount of health data growth increasing by over by 378% per year since 2016 (according to data from a study by Dell/EMC). The scale of this health data explosion means finding the meaningful nuggets of information, and spotting opportunities is harder than ever.

Data Science and Analytics are widely recognized as an important tool for making sense of this data, and quickly identifying opportunities. But with all of the disruption and outside pressures on Life Sciences companies, the costs, time, and resources required to develop analytics capabilities, find qualified staff, managing a data science infrastructure, and review volumes of results can exceed what they can or are able to commit. With a Data Science platform, you can take advantage of these advanced capabilities, while managing within your budget.

To leverage Data science for Real world evidence, here is a webinar with focus on how an AI/ML-enabled approach can drive impact and value for RWD Studies, delivering patient insights at scale.

Following are the topics covered:

About the speaker

With more than 25 years of robust experience in service operations, dedicated project-based team organizations, induction of newer technologies along with required certifications and data security, Pamela is well adept in designing and spearheading digital transformations in diverse process areas or business units for the clients. She follows an innovative, analytical, adaptive and integrative approach to develop and deliver consistently improving outcomes for the clients that reflect in their capabilities and performance. She believes in clients to be partners for growth irrespective of their size and scope. Pamela heads the business operations and automation business at Incedo.

Learn how we’re helping Life Science Enterprises accelerate their digital transformation.

Common questions they encounter include:

Payer contracting is a key function for any life sciences organization to ensure their drugs have the desired Market Access against its competition. Across the next 5 years, the gross contracted sales market expects to grow from $ 120B USD to $ 170B USD. This makes it critical for Pharma contracting team to change focus from being reactive, and shift gears on Payer Contracting, defining market access strategies and seek opportunities to make the Payer Contracting decision making accurate and efficient.

Now, the stages across Payer Contracting is long and complex with multiple questions that needs to be answered by Contracting Team, and other stakeholders – to decide if they should contract , or not? Moreover, if they decide to contract- is it going to be financially profitable? ( i.e. desired GTN, positive ROC etc.) Alternatively, is it going to be a strategic contract for the Contracting Team?

By answering, each of the above set of qualitative and quantitative questions- a contracting Team can take a decision, whether to contract, or not to contract with the Payer for the particular brand, in question. The answer to the qualitative questions rests completely on the subject matter expertise of the contracting team, but the answer to each of these analytical questions- can be provided using Reporting, Analytics and AI/ML features and state of the art- decision engine and decision science platforms. Implementation of RPA, BI (Business Intelligence) & AI features across the Payer Contracting Life Cycle can improve the overall efficiency of the team from a decision-making standpoint (optimize rebate payout, improve ROC, impact pull through), but also lead to improved accuracy and speed of the overall process.

In this point of view, we will do a deep dive into the current Payer- Pharma Contracting space, the key intrinsic, and macro challenges pharma organizations have in this space (i.e. Loss of Exclusivity, PBM consolidation). Finally lay down opportunities for Pharma, as to how AI, RPA, and Analytics can change the future of Payer Contracting Strategy for a Pharma organization.

About the Author

Rebate Payout is one of the biggest sources of cash outflow for any Pharma organization. With an ever changing market dynamics and pressure from Payers, PBMs and States , it is critical to have a closer look into the rebate payout process so that any leakage of revenue due to inefficiencies in the Rebate Validation and Reconciliation Process can be reduced. Even a 10% improvement in reducing leakage can lead to an overall cost savings of billions of dollars for the Pharma Industry.

In the POV sections, we will try to understand the Pharma Rebate Payout process in more detail and highlight opportunities for Pharma as to how AI, RPA, and Analytics can be utilized to optimize their Rebate Adjudication Process and in turn lead to cost savings for the industry.

Key Area of Impact: Pharma- Commercial

About the Author

A leading biotechnology company that discovers, develops, manufactures, and commercializes medicines to treat patients with severe or life threatening medical conditions wanted to optimize the estimates for print material which were time consuming and often error prone. In order to help mitigate the chances of stockouts, minimal holding stocks, and wastage, Incedo developed a ML based forecast model, which benefitted the client through:

Recruitment Fraud Alert

What is Contract Pull Through?

Why does pharma need to focus on Contract Pull Through?

Use case deep dive: Portfolio purchasing summary of an account

How to generate the pull through business insights from data?

Conclusion: CPT – Top of mind for strategic and financial objectives

Enablers of self-service AI platform – Incedo Lighthouse^TM

Incedo Lighthouse^TM as self-serve AI at a Pharmaceutical Clinical Research Organization (CRO)

HCP engagement in the new era

Personalization – The need of the hour:

Helping Pharmaceuticals understand High Impact Channels using Incedo Lighthouse^TM

Key Takeaways

About the speaker

Nicholas Hart

Ashish Gupta

About the speaker

Afrozy Ara

About the speaker

Ashish Gupta

About the Author

Debjit Ghosh

About the Author

Debjit Ghosh

What is Contract Pull Through?

Why does pharma need to focus on Contract Pull Through?

Use case deep dive: Portfolio purchasing summary of an account

How to generate the pull through business insights from data?

Conclusion: CPT – Top of mind for strategic and financial objectives

Enablers of self-service AI platform – Incedo LighthouseTM

Incedo LighthouseTM as self-serve AI at a Pharmaceutical Clinical Research Organization (CRO)

HCP engagement in the new era

Personalization – The need of the hour:

Helping Pharmaceuticals understand High Impact Channels using Incedo LighthouseTM

Key Takeaways

About the speaker

Nicholas Hart

Ashish Gupta

About the speaker

Afrozy Ara

About the speaker

Ashish Gupta

About the Author

Debjit Ghosh

About the Author

Debjit Ghosh

Enablers of self-service AI platform – Incedo Lighthouse^TM

Incedo Lighthouse^TM as self-serve AI at a Pharmaceutical Clinical Research Organization (CRO)

Helping Pharmaceuticals understand High Impact Channels using Incedo Lighthouse^TM