Digital Health in Pharma

Brian Torres

Principal, Herspiegel Consulting

Technology has rapidly changed the pharmaceutical industry and while some companies are evolving, many find themselves struggling to evolve.

Technology has changed customer expectations across all industries. Traditionally consumers had separate expectations for consumer goods and healthcare, however the pervasiveness and simplicity of consumer technology has led to a shift in what consumers expect of healthcare.

These evolving expectations can be grouped into three categories:

Customer Service

a. Content can immediately be found through Google

b. Questions can be answered through voice (e.g., Alexa/Siri/Google Assistant) or through text (e.g., chatbots)

Product Availability & Delivery Speed

Products should be easy to order and arrive immediately (think 2-day shipping with Amazon Prime)

Personalized Insights

Content should proactively be presented to customers based on their interests and needs (i.e., the Netflix recommendation algorithm)

While these concepts seem simple, the technology behind them is quite sophisticated. Here’s a quick look behind the curtain:

Customer Service

For Alexa or Siri to work, software must interpret your speech, recognize the request in native language, and serve up the best possible content. This poses at least two problems:

a. Traditional search algorithms are built on phrases (i.e. Chocolate Chip Cookie Recipe) and not native speech (i.e. How do I bake chocolate cookies)

b. On-screen search provides multiple results (blend of paid search and organic content) but voice responses only provide the top answer. That means content must be incredibly precise to meet user needs and new feedback mechanisms are required

Product Availability & Delivery Speed

Free 2-day shipping requires a robust distribution network capable of handling serialization, RFID, automation and complex algorithms guiding shipments from distribution centers to customers’ doorsteps

Personalized Insights

Netflix uses artificial intelligence and machine learning to mine massive datasets and serve up the right content to the right person at the right time. Netflix has been at this a long time and held a 2006 hackathon in with a $1M prize for any person able to build an algorithm that was 10% better than their current version. It took until 2009 but a team finally accomplished that milestone and made content easier to search (SOURCE)

Pharma is underdelivering in these areas and customers are demanding change


Pharma’s customer service model is centered around field-based and call center response:

a. Field-based: Sales reps have historically called on doctors but with increased competition and information becoming more widely available through digital channels, the list of no-see doctors is rapidly expanding.

b. Virtual / Call-Center: Patients, Physicians, and Payers can contact a pharma company’s call center to speak with a live representative regarding key questions. Unfortunately, this channel falls short because call center representatives are provided a script from which they cannot deviate, topics/answers are restricted to what the pharma company provided during the last training session (generally only held 1-2x / year) and representatives generally work across many products at once and can quickly lose familiarity with lower-volume products (particularly in the rare disease space)


Depending on the pharmaceutical product's complexity, it is either available within a brick & mortar pharmacy or ordered through a specialty / mail-order pharmacy. For many patients, this distribution model works perfectly fine, but what about very sick patients? Or newly diagnosed patients with complex diseases? These are likely the patients who need products most but are also most hindered by this model.

a. Very Sick Patients: Homebound patients face challenges in driving to the pharmacy to pick up products; they must rely on caregivers or research still patchy add-on delivery services.

b. Rare Disease Patients: Rare diseases are typically low volume products and not always stocked in pharmacy. These products use a pull-demand model where a product request initiates shipment from a Distribution hub to the pharmacy and may take a few days to arrive. This requires patients to wait for their medication and often drive to the pharmacy multiple times.

c. Newly Diagnosed Specialty Pharmacy Patients: Entire journal articles are dedicated to the horror stories encountered by oncology patients attempting to fill their first script but being blocked by PBM inefficiencies and shipping delays from specialty pharmacies. These stories range from a few stressful days to multi-month delays resulting in potentially avoidable dissatisfied patients.(EXAMPLE)


Pharmaceutical insights are generally driven by laborious (and costly) market research studies. Processing the research, updating materials, and pushing live generally only occurs 1-2x / yr (timed to the POA cycle). However, as stated above, patients are becoming more accustomed to personalized, fresh content and are starting to expect it from pharmaceutical manufacturers as well.

Pharmaceutical companies understand these concerns and are actively trying to evolve

Customer Service

Progressive pharma companies are beginning to interact with customers in real-time

Field-based: companies are piloting on-demand models where physicians can “order a visit” with the appropriate field-facing staff. These requested visits can be live or virtual conversations. The savviest of companies are even beginning to capture analytics linking HCP type, detail topics, conference attendance, and online presence with the questions being asked through this portal.

Virtual / Call-Center: For the first time, companies are starting to utilize online chatbots to engage with customers in real-time. This change requires companies to capture the natively-asked questions, build databases of the information, and continuously improve the available answer pools. However, companies are still early in the journey, as MLR processes generally lack the agility required to support this.

Product Availability & Delivery Speed

In an effort to provide product and care more efficiently, companies are evaluating novel operating & partnership models:

Product: The product-plus model is emerging as a new way to bundle ancillaries, devices, services, and drugs/biologics in a subscription-based model. Under this framework, a customer can select desired subscription frequency, content, and delivery options, much like an Amazon Subscribe-and-Save order. Bundling creates efficiencies which result in lower prices, while a set subscription with automatic delivery ensures a seamless end-to-end experience for the patient.

Care: Pharmaceutical manufacturers are partnering with novel providers to offer virtual care to patients through Telemedicine (think FaceTime with your doctor) and Asynchronous Medicine (videos sent directly from your doctor explaining results / offering care. You have the ability to digitally interact but are not online at the same time).

Personalized Insights

Pharma companies are beginning to build data warehouses, mine that information using artificial intelligence/machine learning, and generate insights to help provide patients with relevant content at key points in their care journey. However, it generally takes companies 3-5 years for big data to become useful and make the algorithms precise/accurate enough for usable insights. One great example of an innovative company using data to build personalized insights is Flatiron Health, which accesses billions of oncology data points to supply Real World Evidence. This data is so powerful that it could be used in regulatory filings to supplement/supplant clinical trials.

However, as companies begin to evolve they are realizing how ill-equipped they are to do so


Most companies are structured in a brand-centric model. While this model is useful for traditional marketing campaigns, it is narrowly focused, slow (i.e., because it is built around the POA cycle), and fosters the “do as we’ve always done model”. When companies operating in this fashion begin evolving or partnering with more agile technology companies, the inefficiencies become brutally obvious. For companies to adapt their structure and improve agility, they must have leadership’s buy-in, clearly define the long-term vision, infuse organizational design thinking / process-improvement methodologies, and be willing to embrace the uncertain.


Digital-centric thinking requires a very different skillset from a traditional marketing role. Digital focuses on continuously understanding customers through data, analytics, and technology, whereas traditional marketing uses pulse-checks such as market research and static brand maps. These skillsets tend to be mutually exclusive and gaps on either side cause issues. Digital-centric employees struggle with convincing commercial leadership that their new way of thinking will have a stronger ROI than the traditional approach, in part because it may take longer to see the first returns from a shift from a traditional to a more to digital strategy. Traditional marketers struggle to grasp the big picture and full potential of this new technology; thus, they tend to force concepts into their annual operating plans and trivialize their impact.


Launching a new product generally has an established set process that is well-understood by the organization. However, organizations generally don’t have plans built that are flexible / diverse enough to accommodate a Connected Technology launch or larger organization shift into this space. Here is one example of this point:

Regulatory Pathways: US pharmaceutical products go through CDER or CBER for approval but connected devices may be better off going through a CDRH review (for Rx or OTC)

The pharmaceutical revolution is just beginning, and exciting new technologies will continue to change the way companies do business. As new competitors emerge (Amazon, Verily, Apple, etc.) pharma will be forced to evolve or lose share. For companies to be successful in the future, they must start now. However, finding the right partner to guide them through this change is challenging. Herspiegel Consulting is an expert within the Digital Health and Connected Technology space. We have worked with numerous companies across the Diabetes, Oncology, Cardiovascular, Neurology, and Rare Disease spaces. Here are a few questions we’ve helped our clients answer:

  1. What are my competitors doing in digital health and how do I fit in?

  2. How much should I be investing? What do I need to support that investment? How can I scale up to meet that support?

  3. How do I justify and convince leadership that this is a whorthwhile investment?

  4. What processes do I need to successfully launch this project?

  5. How to manage increased oversight and an unclear reporting to get the job done?

  6. What does best in class look like in digital?

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