Can NLP Improve Patient Engagement?
February 26, 2021, 3 Minute Read
One of the major public complaints about the Pharmaceutical industry is that client economic incentives are often not aligned to the product user base. I assert that innovation in Patient Engagement is the requirement to achieve value-based outcomes (VBO); one such technology that can help extract datapoints is Natural Language Processing (NLP). In the next few years, value-based outcomes will continue to be a central focus in Pharma; the practical implementation of quality outcome measures will become the standard way to price products. In reduction, implementing value-based outcomes require extensive collection of patient outcome data to make price-to-value determinations. Ultimately, the number one driver of the ability to respond to pricing regulation, medical affairs, and market access innovation is indeed Patient Engagement, or in general industry parlance, “customer centricity”.
What if I told you the transition to Patient Centricity is happening faster than ever in certain global markets?
In simplified-payor countries such as China and EMA-regulated countries, especially for mass-market drugs, Patient Centricity is being integrated to all aspects of business including drug development, regulatory approvals, and commercial operations. During COVID, think of how often “track and trace” became the norm. Think of how, in China, user apps drove the knowledge of when, how, and where disease was being spread. Imagine an app-driven passbook that also tracks what treatments patients received and what longitudinal response data can be. Imagine if brand marketing, mobile diagnosis and prescription, and patient interaction all became streamlined. Several public payors, telemedicine companies, and pharmaceutical companies (e.g. Roman) who are digitizing their experiences will be highly advantaged.
The COVID-19 pandemic is teaching us that there is so much data that patients are willing to give us. To access such data, Pharma will be in dialogue with patients through forums, social media, portals, apps, and traditional provider avenues. Regulators are also responding to next generation drug pricing challenges, communications barriers, and patient needs including health disparities and access strategy. Vaccine demand, product sentiment, social-media voluntary adverse event reporting, and related quality of life success metrics (e.g. return to office, restaurants, gyms) are being offered to cloud data repositories. Natural Language Process can help you extract the related data points needed to conduct significant collection of Brand Interaction, Real World Evidence (RWE), Social Determinants of Health (SDoH), Health Economics and Outcomes Research (HEOR), and more. Pharmaceutical companies should partner with Lymba to use this unique opportunity as a bell-weather value-based outcomes laboratory.
To engage Lymba, we need a clear remit across a Pharmaceutical’s business functions such as patient advocacy and experience, Brand Leadership, RWE-IT, HEOR, Social Determinants of Health, or Medical Affairs. If you have a COVID-19 therapy or vaccine, we will work with you on a priority basis. Our goal should be to hear the patient voice and collect data values in patient communities of interest, assert behavioral responses, extract messaging cues, and engage in consistent patient touchpoint messaging – powered by machine learning and NLP. It is not just regulatory bodies and public health resources that are responsible – Pharma has a huge opportunity to study the future of value-based outcomes and put together a cohesive patient-centric experience roadmap from launch and beyond.
Have any questions or want to keep the conversation going?