The journey to making medical information a strategic asset

Co-written with Laurent Malivert

We’ve covered what the MI journey is and the challenges and opportunities it presents in our previous article. But how can pharma companies turn their MI components into tangible, strategic assets?

Consider technology first

The primary way to often address a challenge is by adding something to the mix—by relying on a new tool or technology. In many industries, technology has proven to be an accelerator, enabling people to innovate or simply achieve more. Selecting and using new technology can also modify a company’s culture, as employees must adapt to competition, new user preferences and innovative tools.

Of the various levels of technological advancements available in the market to leverage for MI, pharma should invest in fundamental elements before scaling up. Companies should define, select and adopt a technology platform that suits their level of technological savviness. This essential step is known as a technological intervention, which can be broken down into three phases.

Inquiry management: The simplest technology platform should at least receive and manage medical queries through traditional channels—such as telephony systems—and triage them through established and exclusive routes to meet the influx of MI inquiries. The platform conveys the answer back to the requester in a verbal or written form through digital channels like web or mobile apps, or in-person channels such as call centers.

A more complex omnichannel engagement and communication approach will use various digital channels, ranging from chatbots and intelligent search engines to customer relationship management (CRM) systems and MSL interactions. Having widely adopted a CRM software connected to the platform, MSLs will ensure inquiry intake collection is consistent across the organization, and this facility can be supplemented by translational software. Local and regional presence—as per company requirements—may be strategically positioned to address localized scientific needs while also covering outside customers. Proper communication and coordination with field medical teams will provide a seamless customer experience. Additionally, the content can be personalized to customers through artificial intelligence or machine learning optimization algorithms, such as an auto-tagging content tool. Data visualizations from scientific publications can be simplified to focus on the relevant information, minimize the time to interpret key data and organically increase audience engagement. New ways of communicating outside of written content can be leveraged through videos, podcasts and infographics, highlighting why the format to share with customers is important.

Content curation: All inquiries, details on the requester and responses must be appropriately stored in a database or central repository, while patient-specific information—which could be pulled for audit trails—should be tracked through inquiry logs. Designing an effective centralized repository is often the absolute requirement to fully automate the MI process and ensure sound regulatory procedures. This can be done with the support of many cloud databases such as SharePoint, Microsoft Teams, or Amazon Web Services.

The responses are validated by controlling mechanisms such as document ownership and standard operations procedures or through quality assurance processes. Since authorities often focus on process clarity, training and execution—specifically considering quality assurance and pharmacovigilance—integrating a content management software is instrumental for response identification.

More than 10,000 scientific journals globally and collectively published over 3 million articles in 2018 alone. Publication search optimization tools can support the content creation and data extraction from this increasing amount of literature.

Measurement: The impact of MI can be measured through quantitative metrics such as response turnaround time, the number of inbound and outbound calls and abandonment rates. Qualitative feedback can also be provided as part of these results.

The overall performance of the MI process can be further unlocked through integrated analytics that extract insights from topic clustering (frequency of occurrence of words in the data, with business rules allowing it to be known when associated with specific words). Unstructured analytics can play a role in assessing feedback, responsiveness, or quality of insights gauged from customer satisfaction and feedback, response speed, quality of insights and responses, and the use of new engagement tools. Finally, the impact on publications can be measured through journals, authors or article metrics to further enhance the quality of the content.

Whether a company’s technological interventions are sourced internally or externally, organizations must answer the following questions regarding their future:

• Is the provider really saving costs to the company?
• Can a service provide the same quality required by
• MI compared to internal capabilities?
• Can third parties respond to enquiries faster?
• Does outsourcing help decrease the amount of work of the internal MI team, or does it create more burden?
• How can you measure the performance of a service provider? 

Don’t forget the human component of MI

Technology is not the only answer—at least not yet.

MI structural and operational models are all different and continue to evolve. There is no silver bullet, since the types of models used across the industry depend on the size and resources of the company. MI departments are tactical by habit, usually following routine processes developed in the past that are still considered efficient by internal stakeholders. However, change is coming, as companies embed MI into their hierarchical organigrams as part of efficiency models, which is achieved through integrating MI with operations, evidence generation, MSLs and other internal groups. This is often more observed with small pharma, as their MI resources remain limited. Interestingly, although this burdens employees, it’s also an efficient way to centralize information, feedback and relevant insights to the wider organization. The downside, though, is with their reduced time spent on MI, employees have a more limited impact than if they were part of a fully dedicated in-house MI team responsible for all activities. Some form of centralization is required to implement high-quality, efficient and transparent cross-functional areas.

Overall, three key MI models seem to prevail within the industry:

Some manufacturers, with a strong bias towards global organization, will prefer a globally centralized environment that is highly dependent on technology and doesn’t need team members in every country working on MI. These manufacturers often prefer to appoint a dedicated central global team to create and disseminate the content while ensuring the entire organization is aware of any vital company insight. Even translation can be managed through technology with a local representative assuring quality checks; for example, a Chinese-based or native speaking employee who would validate responses to requests received from China.

The other end of the spectrum is a locally decentralized approach, with country managers reporting back to the global team. While this is often considered an efficient approach because local affiliates have the most control on operations, it can also be inefficient, since countries develop their own siloed content without the benefit of sharing with a central team and other global affiliates.  

Finally, a hybrid model features local and global teams supporting and collaborating with each other. For example, the global team create the core content, while local teams provide translational and validating services and share country-specific documents with global. Constant feedback helps grow the content library by harmonizing content across countries and supporting fragile affiliates with additional resources while feeding back any critical information to relevant teams.

These models may evolve as healthcare professionals, patients and potentially payers continually force pharma to change their approach to MI, and this evolution can be sequentially optimized to suit all types of pharma organizations. Given this demand, how can pharma efficiently prepare for the future when new ways of delivering information are on the way? 

What does the future of MI look like?

Moving forward, could technology directly support the general public with MI? For example, what if Siri- and Alexa-enabled devices become authorized to deliver MI to physicians or patients as a mainstream support tool? The current tactical role of searching for and providing answers to customers could be delivered by both machines and MI employees. Although MI will remain highly regulated to ensure accurate data is provided to relevant healthcare professionals and patients, some technology companies such as Tempus, a Chicago-based technology firm, are going in that direction, raising billions of dollars to improve patient care.

At the 2021 J.P. Morgan Healthcare Conference, Tempus displayed a small cube to answer questions that oncologists may have on their patients and their indication. The device, it turns out, can be asked to review a patient’s medical case while researching the database for additional publicly-available information, such as peer-reviewed articles. Tempus sources their own clinical, genetic and imaging real-world data in the U.S. as a service, which could be leveraged as an internal, real-world evidence capability and add an extra layer of information to doctors. This information is provided without any treatment decisions made on the physicians’ behalf, only acting as a passive practical channel, such as today’s MI.

There’s no doubt that technology will continually support our society in the future; however, if companies don’t evolve their MI teams from being tactical to strategic, they will miss the vital insights needed to truly change the game.

“Alexa, how will we access Medical Information in the next few years?”