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.
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 LighthouseTM 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.
In one of the recent deployments of Incedo LighthouseTM 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 LighthouseTM 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 LighthouseTM 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 LighthouseTM’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 LighthouseTM’s pre-built accelerators, organizations can get to the channel strategy designed to optimize the HCP engagement through each medium while minimizing the investment.
A US-based, bioanalytical CRO firm was using an ad hoc, manual and static rule-based process to identify and generate leads. It urgently required the transition to an intelligence-led, automated lead scoring engine.
Incedo’s Lighthouse enabled an effective lead scoring system that helped the sales and marketing teams identify which prospects were valuable to the sales funnel and were most likely to convert into paying customers.
This company vastly improved its prospect evaluation and targeting using Incedo Lighthouse’s AI/ML enabled prospecting and dashboard solutions. A significant ~20% more prospects reached the proposal stage and resulted in a robust automated solution for the digital team.
COVID-19 pandemic brought with it a complete disruption to the existing normal operating procedures in most of the industries. The unprecedented situation due to the pandemic has struck some of the business functions disproportionately hard. The most impacted functions in the companies however are those where the workforces relied heavily on “on the field” presence for the execution of their work compared to those functions which could easily be converted into a remote working setup.
From the Life Sciences industry standpoint, the drug promotion via Medical Reps (MR) falls into the prior category. Although the industry as a whole has seen rapid adoption of digital solutions across the workstreams in the ongoing decade, their marketing efforts to the Health Care Providers (HCPs) still heavily rely on the Face to Face (F2F) interaction of the Reps with the Physicians.
This status quo however has been challenged by the ongoing COVID pandemic, with the social-distancing norms in place. There are estimates of 92% drop in F2F HCP engagements in April 2020 compared to 6 months ago[1]. It is also estimated that in the new post-pandemic normal, the frequency of F2F engagements will shift as much as by 65% to quarterly/annual rather than the weekly/monthly norms prevalent pre-COVID2. This is indeed a massive blow to the existing Pharma sales and marketing approach and has seen many of the companies rapidly scale up their digital engagement channels to fill the gap. The use of these digital channels for HCP engagement has seen a 2x increase from their pre-pandemic levels.[2]
The current COVID driven environment has several key implications for the Lifesciences organizations in their effort to meaningfully engage with HCPs.
This brings us to an important question of how the Bio-Pharmaceutical companies should navigate the current shock concerning HCP engagement and what lies ahead for them. Pharma Commercial Teams would need a strategic HCP engagement approach that manages the immediate COVID situation as well as builds capabilities for the new digital-driven normal.
As the Bio-pharma companies scramble to optimize their marketing efforts in the current times, they need to formulate a strategy which tackles the problem in phases:
As an immediate measure, Bio-Pharma companies need to evaluate the impact of COVID-19 on HCPs’ practice – Rx, patient counts, geographical impact, etc, and Field Reps access to HCPs. It is imperative that Biopharma companies create a COVID control room, which integrates external trigger impact data with internal data sources to truly assess the impact of COVID situation (and potentially other external triggers and shocks) on their sales & marketing plans.
As the COVID impact is quantified, bio-pharma can synthesize the same to adjust the tactical call plans for their promotional activities. The critical parameters to consider while making changes to the call action models would be:
Once the immediate priorities related to the pandemic are solved, companies can utilize the learnings and key insights from the pandemic times to further advance their digital engagement strategy. The evaluation of what went right and what were the misses in the earlier stage should also be used to formulate a long-term digital and omnichannel engagement strategy. There is also, a lot to learn from Digital-natives who have, highly effectively, leveraged digital channels to driven customer engagement.
Bringing these best-practices from Digital natives together with Bio-pharma context can help accelerate the digital transformation of the industries HCP engagement approach.
Best-practices and Learnings from Digital Natives | Lifesciences Ecosystem Context |
---|---|
Focus on differentiated HCP experience | Physicians have different interaction points, interests, and requirements including clinical content, CMEs, studies, samples, copay coupons, patient counseling material, etc. and hence differentiated experience enables engagement. |
Volume and variety of data | Pharma has access to multi-dimensional physician data in terms of demography, preferences, prescription patterns, patient/payer mix profiles via claims, digital affinity to micro-segment physicians, and uncover preferences, behaviors, and personalized needs. |
HCP/Customer Journey management and personalization | Advanced analytics and ML-based approaches can leverage the available data to predict intents, recommend interventions, and seamlessly deliver them via physician engagement platform and processes. |
Omnichannel execution | Multi-channel interaction provides a foundation platform for delivering these experiences across digital as well as non-digital channels. |
Measure, Learn & Improve | A/B testing driven digital engagement experimentation anchored on performance-driven, yet responsive targeting strategies. |
To accelerate their digital transformation journey, biopharma companies need to inculcate these best practices into their HCP digital marketing capability. An integrated Digital Engagement solution will help biopharma companies create and deliver omnichannel personalized experiences for HCPs, by enabling real-time AI/ML-driven next-best-action recommendations and precision targeting strategies based on their preference and intents.
COVID pandemic is an unprecedented global event, which will radically alter our behaviors, expectations, and interactions. Earlier rules of engagement are now getting irrelevant at a pace that is faster than ever before. To maintain(and grow) their share of voice and engagement with HCPs, bio-pharma organizations can no longer afford to follow the “digital-addon” approach. They have to fundamentally re-design their HCP engagement framework, as a Digital-driven strategy, $to stay relevant, to stay ahead and keep growing.
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 the following forces at work:
Rapid shift in demand of drugs due to the impact of Covid-19 – The pandemic has impacted the demand of drugs for various therapeutic areas differently. There is an unprecedented surge in demand for drugs which are being considered as treatment candidates for Covid-19 e.g Remdesivir (Gilead), Actemra (GNE) and Kevzara (Regeneron). There has also been a significant upsurge in demand for symptomatic medicines like antivirals, pain medications and ICU medicines which are used for managing complications from Covid-19. On the other hand, delays in elective surgeries and non-essential treatments have led to huge drop in Rx for many categories, and a rise of product switching in favor of self administered drugs
Geographical risk due to Covid-19 changing very quickly – After weeks of shutdown, some countries and states are cautiously reopening their economy. As regions open up, there are new emerging hotspots which can modify the density of cases, and hence the downstream impact on key decisions for Pharma Cos like inventory allocation for treatment therapies, supplier management and execution of clinical trials. Given the rapidly evolving dynamics with Covid-19, companies need to ensure that they are using the most updated data and case forecasts for decision making
Pharma forecasts are broken – For an Industry which relies very heavily on forecasting, the historic data on which all forecasting, planning and distribution systems are built on has changed. Many of the previous signals used for forecasting like seasonal patterns, events, channel characteristics and patient behavior might not hold true going forward. There are new behaviors like hoarding, preference for self administered drugs and movement to telehealth which challenge pre-existing assumptions. Forecasters need to factor in this “black swan” scenario into their assumptions, and the geographical risk of cases would be one of the key factors impacting Pharma KPIs
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. We propose a 5 step approach with a Covid-19 Control Room for Pharma companies which composes of the following components.
For example, here is an illustration of Inventory Risk assessment with key insights on Inventory Allocation at county level for a demand archetype with direct Covid impact. An inventory risk assessment and allocation solution would compute the mismatch between demand and supply – measured by the ‘shortfall from required DoH’ to surface alerts. This would ensure that inventory allocation is optimal – precious drugs are sent to the critical locations and hospitals who need it most.
As we have all experienced, every day is a new unprecedented chapter in this outbreak of Covid-19. Strategies leveraging data and tools at our disposal can help Pharma companies win the battle against this pandemic. Companies that execute on these strategies will have a clearer view of what is expected to happen, and hence better prepared to face the challenges which lie ahead.
This Article is part 2 in the series – ‘Managing Pharma Supply Chains in times of Covid-19‘
For more insights on how Pharma companies can Optimize their Supply chains, please click here.
“As the breadth and reliability of RWE increases, so do the opportunities for FDA to make use of this information”, noted Scott Gottlieb, former FDA Commissioner National Academies of Science, Engineering, and Medicine while examining the impact of RWE on medical product development.
FDA has a long history of using RWE to monitor and evaluate the safety of drug products after they are approved. Real world data traditionally come from a variety of sources such as data derived from electronic health records (EHRs), medical claims and billing data, data from product and disease registries, patient-generated data, including from in-home-use settings, and even data from mobile devices that can inform on health status. With such ever-increasing reliability of real world data, FDA published its Framework for FDA’s Real World Evidence program. It laid out a framework for evaluating the potential use of real-world evidence (RWE) to help support the approval of a new indication for a drug already approved under section 505(c) of the FD&C Act or to help support or satisfy drug post-approval study requirements.
Further, the May 2019 draft guidelines, “Submitting Documents Using Real-World Data (RWD) and Real-World Evidence (RWE) to FDA for Drugs and Biologics,” encourage sponsors using RWD to generate RWE as part of a regulatory submission for investigational new drug applications (INDs), new drug applications (NDAs) and biologics license applications (BLAs).
These developments are growing acknowledgement by FDA on the need for Real-World Evidence across RCTs, single arm trials and observational studies, to enhance clinical research and support regulatory decision making.
FDA has made RWD and RWE a “top strategic priority”. Medical Research is at a turning point – with an abundance of real-world data from a wide variety of sources ranging from EHRs to wearables like Fitbits delivering terabytes of data, healthcare practitioners are pushing ahead on delivering personalized care, while acknowledgingthe value of real world data and evidence.
It’s a redefining moment for Lifesciences industry – the professionals across the industry understand that RWD will transform not only clinical development process but also commercialization and reimbursement decisions. However, Life Sciences – Pharma, Biotechs, CROs – are yet to widely adopt RWD insights in an institutional way because many are unsure of the road forward and are facing several roadblocks.
There are challenges such as:
In Lifesciences industry having data has never been a problem, however, the ability to stitch together a robust patient-journey data has been complicated. Once the patient health journey data is complete, it allows researchers to compare interventions and outcomes more meaningfully. With this the objective to fully understand the impact of different clinical options can be fulfilled. More importantly, the insights give researchers an ability to understand patient’s challenges and assess how their medical products perform when the patient need is the highest.
RWD researchers today typically use Electronic Health Record, Health Insurance Claims and Population Health sources of aggregated data for their research. There is a growing desire to leverage several new sources of patient data to make evidence generation more robust, insightful, and richer. Genomic data, biomarkers and digital data i.e. data generated from mobile devices, wearables, health apps or other biometric devices – are topmost in the priority order across organizations and researchers for real world insight generation.
However, organizations usually encounter challenges in stitching together the patient journey across these varied RWD data sets, due to their inherent nuances and complications.
The current nature of RWD initiatives or studies is very bespoke within lifesciences organizations. The typical approach has been to execute each study in a silo, usually dependent upon a few researchers and biostatisticians. Such an approach has led to significant gap in terms of demand for pre and post commercialization real world analytics, and the capabilities of the organization to fulfill the demand.
With hurdles such as lack of limited RWE skilled resource pool, poor knowledge sharing, lack of standard methodologies and next to no reusability, this gap continues to widen.
With the exponentially growing complexity and volume of different emerging RWD data sources,Lifescience and Pharma organization are seen to be insufficient for analysis and evidence generation. For example, with the increasing use of wearables and biometric devices, digitally patient reported data is becoming mainstream for clinical drug trials, while the traditional data platforms are struggling to process this real-time, streaming source of patient health information. Similarly, intelligent insight generation from EHR data is something which is beyond capabilities of current systems, as it requires new-age AI and NLP capabilities to process text data which is missing in traditional systems.
Advanced data and AI/ML driven computing technologies are critical to aggregate consistent and robust patient-journey data from these RWD sources.
The role of real world evidence in drug development and post-commercialization evidence monitoring is really turning the corner. So far, only payers were demanding evidence as part of the access and reimbursement process; but with FDA putting its full weight behind acknowledging it as a critical part of its regulatory framework, it right time for Pharma and CROs to build their real world data capabilities at the institutional level.
Evolving RWE as a core institutional capability and to give it necessary organizational focus, Pharma and CROs has to invest in and build out centralized “RWE center of excellence”, with a federated ecosystem of evidence generation.
Such a “core” Center of Excellence integrated with “federated” evidence generation strategy, is critical to bring the required scale to overall RWE generation and enablement by marrying COE driven standardization with the flexibility to meet growing specific RWE needs across the organization.
Even in stable times, Pharma supply chains are fragile and as complex as they can get. As the Covid-19 pandemic continues to wreak havoc on countries around the world, Pharmaceutical supply chains have come under immense pressure. In this article, we would cover some of the key challenges which Pharma supply chain Executives face on the frontlines, and how Analytics and Data Science can be leveraged to overcome these challenges.
A combination of strategic and operational moves leveraging Analytics and Data Science capabilities will help you get to the critical insights necessary for getting started.
The current crisis has plunged entire countries and the Pharma industry into times of uncertainty. Building a transparent view of the current state, scenario based planning and proactive detection of anomalies are key tools on the frontlines for defense.
By acting intentionally today and using the tools at our disposal, Pharma companies can weather this crisis, emerging stronger and building resilience for the future. And in the process, enhancing and saving many lives around the world.
I have seen patient marketing in HealthCare undergo significant change in a rapidly transforming commercial and digital environment. There are numerous factors disrupting the status quo:
Traditional patient marketing by HealthCare providers presents a number of challenges:
Given the changes in the commercial marketing environment, it is imperative that HealthCare Providers drive personalization in patient targeting – the “right patients” to target with the “right channel outreach” and the “right message”
The native digital firms are at the cutting edge of using advanced AI/ML modelling techniques to drive effective consumer targeting. National retailers’ online loyalty programs are a good example of how these firms leverage data to optimize targeting. They use multi-year history of data (website visits, clicks and conversions along with email opens, clicks and subsequent conversions) in Bayesian machine learning models, to create a propensity score for each product-consumer segment. This is employed for personalized e-marketing and user experience, with estimated marketing ROI improvement of around 20 percentage points.
Some key learnings from these firms include:
In order to leveraging the experience from digital native firms, it is imperative that we bring the “Best Practices and Learnings” to the HealthCare business context:
Patient Acquisition Marketing Personalization: Problem-Solving Framework
A structured problem solving approach is necessary to analyse personalization in patient marketing. A key part of this framework is to link the
propensity prediction and recommendation to patient segmentation and available data (patients with some history within our system vs. new prospects).
In addition, the prioritization must be linked to core business objectives e.g. drive business in “orthopaedic surgery line of service as part of overall expansion and profitability strategy”
A broad outline of the problem solving framework and its constituent parts is shown in the illustration below:
In my experience, the key to driving personalization in patient acquisition is:
There is great diversity across organizations. In order to successfully execute personalized targeting and digital marketing solutions, it is also imperative to define the right operating model that aligns with business objectives, organizational strengths, and the right partner(s).
Here is a brief outline of an execution approach that I have been seen work well:
In conclusion, a data-driven approach that leverages the best in class AI/ML modelling; implemented within the context of our organizational engagement model, and focused on key business metrics is critical to driving successful implementation of patient marketing programs.