How to Leverage Machine Learning to Predict Telehealth Usage as Part of Pharma Commercialization Strategies

Telehealth has emerged as a vital channel for health services delivery, especially in the post-COVID-19 era. However, gaining a nuanced understanding of patient telehealth use remains a complex challenge for both healthcare providers and pharmaceutical companies.

As manufacturers develop strategies for this new model and understand how telehealth could help patients with certain disease states find care faster, a new powerful solution fueled by data insights has emerged to help brands drive predictive modeling and more. Machine learning (ML) offers a powerful tool to predict telehealth usage and integrate these insights into their commercialization strategies. Simply put, new ML models can accurately predict propensity to utilize telehealth over in-office visits, and patient segments more likely to use telehealth. This information is invaluable for tailoring marketing and outreach efforts to patients or even physicians.

However, how it works and the value it can bring are things we must consider today, especially as brands move toward more direct-to-patient models. Let’s dig in.

Understanding Telehealth Trends

Telehealth is an offering that can encompass a broad range of services, from virtual consultations to remote patient monitoring. It’s been proven to be an effective way to see patients across a variety of clinical states – from primary care to dermatology – and even, in some cases, emergency room visits.

And it’s here to stay. Major pharmaceutical companies have recently announced their own direct-to-patient care models, leveraging telehealth as a way to ease the barriers for patient care, decrease time to therapy and increase adherence across a number of therapeutics – all while improving patient experiences relative to traditional channels.

Telehealth adoption has been accelerated by technological advancements and changing patient preferences. For pharmaceutical companies, understanding these trends is crucial for effective market penetration and patient engagement. So, how does ML fit?

The Role of Machine Learning to Predict Telehealth Usage

Telehealth requires us to reinvent how we target patients for outreach. Don’t try to put a square peg into a round hole; traditional patient finding or healthcare provider (HCP) segmentation fails to account for the nuances of this unique channel. Leveraging existing data sets (e.g., social determinants of health) in new ways and utilizing the power of ML can help ensure successful telehealth programs.

The first step in predicting telehealth usage is gathering relevant data, including historical telehealth usage, social determinants of health and patient health records. Integrating these data sources provides a comprehensive, longitudinal view of patient behavior and preferences. Patient confidentiality is paramount, and companies that have rich, deidentified data sets will be in the best position to leverage ML.

Next, data science teams must develop predictive algorithms, which involves selecting appropriate ML models such as regression analysis, decision trees or neural networks. These models are trained on historical data to identify factors influencing telehealth usage, such as age, geographic location and medical history.

Once trained, the models can recognize patterns and trends in telehealth usage. For instance, these models can identify peak usage times, preferred telehealth services and patient segments more likely to use telehealth. This information is invaluable for tailoring marketing and outreach efforts to patients and even physicians. 

Then predictive analytics comes into play, which involves using the trained ML models to forecast future telehealth usage by assigning distinct propensity scores to different audiences. Pharma companies can then take these predictions to optimize resource allocation, plan marketing campaigns, develop targeted patient or clinician engagement strategies and more.

Predicting Telehealth Comes to Life

In concept, this may seem relatively simple, but it’s a lot of work. Fortunately, we’ve seen it done, and it can bring real value to a manufacturer as well as patients in need.

Our team recently designed similar models for two brands considering telehealth for patients: One model is patient proactivity, and the other is what we call visit accessibility.

The patient proactivity model was based on a patient’s internal willingness to choose telehealth services and used patient-level features, such as region, rural versus urban location type, age and overall number of visits, to identify cohorts of patients who are likely to choose telehealth services over in-office visits.

The visit accessibility model focused instead on a patient’s external access to telehealth services. It used individual visit-level features such as HCP specialty, network telehealth usage and prescription association to identify visits that are telehealth-accessible to patients.

By combining these two metrics with clustering algorithms, our team was able to generate different telehealth-use personas for several distinct therapeutic areas to help predict how many patients may utilize telehealth services in the future for different medications.

That’s the power of ML in healthcare.

Integrating ML Insights into Commercialization Strategies

By predicting telehealth usage, pharma companies can more effectively and accurately segment the market. Understanding which patient groups are more likely to use telehealth allows for personalized marketing and communication strategies, enhancing patient engagement and satisfaction.

Predictive insights can enable pharma companies to allocate resources more efficiently. For example, knowing the peak times for telehealth usage can help in scheduling virtual consultations and ensuring adequate staffing levels.

Additionally, ML insights can help inform product development by identifying unmet needs and preferences in telehealth services. What products may be better suited to telehealth offerings than others?

Challenges to Consider

Although the potential of ML in predicting telehealth usage is immense, there are challenges to consider. Data privacy and security are paramount given the sensitive nature of health information. Additionally, the accuracy of ML predictions depends on the quality and completeness of the data used. Continuous monitoring and updating of ML models is necessary to maintain effectiveness while ensuring fit-for-purpose real-world data is chosen for the specific use cases and therapeutic areas. 

What’s Next?

We firmly believe that ML coupled with unique data sets offers pharmaceutical companies a strategic advantage in predicting telehealth usage; integrating these insights into their commercialization strategies is vital to a brand’s launch and success in market.

ML strategies can help companies enhance patient engagement, optimize resource allocation and stay ahead in the competitive healthcare landscape. As telehealth continues to grow, the ability to predict and respond to usage trends will be a key differentiator for forward-thinking brands. And in the end, patients will win.