As the pandemic accelerated healthcare’s digital transformation, it also accentuated the importance of data. A survey of 2,500 tech professionals across the U.S., UK, Germany, China, and India conducted by Novartis between May and June 2020, found that 89% of respondents believe data science will be crucial to the development of healthcare solutions and services moving forward, as reported by pharmaphorum. That includes how life sciences companies use data to deliver better experiences to patients and HCPs, but it’s an area that remains a struggle for this industry.
“About 90% of the large organizations in healthcare today find it hard to use the data they have been capturing over years due to the discrepancy it surfaces—from tracking to standardization to measurement strategy,” explains Priyanka Prakash, Director, Marketing Analytics, Ogilvy Health. “Adding to the existing complexity, data providers can’t share, and analytics folks can’t track all the information due to the regulatory compliance that exists within healthcare. If an organization wants to be data driven, then investing in their data and analytics workforce is crucial, including forming a team responsible for how data is defined, how it flows, at what point to integrate the external data sources, and how well the captured data fits into the company’s data infrastructure.”
The importance of the right data team should not be ignored, even as technological advances in artificial intelligence, natural language processing, and machine learning make it easier to get insights such as next-best-action, expert identification, and sentiment analysis.
“While medical analytics can generate faster decisions by tethering actionable insights to the outcomes of these systems, these approaches can bear more fruit with an augmented intelligence, or human-in-the-loop, perspective,” says Matt Lewis, MPA, Global Chief Medical Analytics and Innovation Officer, MEDiSTRAVA. “From congress coverage to literature monitoring, patient engagement to scientific platform development, many components of commercialization are realizing the benefit of integrating the best of both man and machine. Long recognized as an aspirational competency of a future-focused organization, digital fluency, with capability for advanced analytics/AI, is now a must-have in today’s life sciences firms.”
Improving Your Data Strategy
Besides bringing in the right people, organizations can make other changes to ensure they are getting the most out of their data. That starts by planning with the end in mind by identifying your desired outcomes and measuring indicators and progress towards those outcomes, rather than just measuring whatever you can along the way, explains Jenny Herritz, Principal, SVA Life Sciences.
“For example, if a company wants to position their drug as a first-line therapy for a particular indication, they will need to take a step back and leverage a broader perspective of how the organization engages with the patient and provider communities,” Herritz adds. “This goes beyond the commercial organization to include interactions and information sourced from medical affairs, marketing, and patient services, as well as supporting areas such as finance. Often, one of the most overlooked sources of insight comes directly from the field through those enabled to have a scientific dialog with key opinion leaders in the community. When used strategically, the information collected can provide a level of insight far greater and more timely than traditional, backwards-looking metrics.”
Something else that can help is implementing a comprehensive omnichannel model, which can prevent data from being siloed off between different departments.
“Omnichannel success requires a comprehensive suite of pivotal data sets and a data-driven technology model that provides real-time visibility into the impact of sales calls and marketing efforts,” says Bill O’Bryon, Managing Director, Digital, EVERSANA ENGAGE. “The right approach routes actionable data and deep insights from all channels, providing optimal coordination of customized touch points to create a seamless brand experience for providers, patients, caregivers, and payers. With a sophisticated omnichannel model, companies can even predict which patient personas are most likely to discontinue or switch their medication, map them to HCP practices with similar patients, and then offer corrective messaging to providers that halts potential discontinuations before they occur.”
Patients and Privacy
While data is typically seen as abundant, that’s somewhat changed due to the degradation of third-party cookies and an increased focus on privacy protection. David Shronk, Chief Commercial Officer, Health Union, says life sciences companies will no longer have access to the full breadth of data and analytics that powered digital campaigns five years ago.
“Instead of digital media campaigns from life sciences companies being data-first, the focus will increasingly be on addressing consumers’ needs and facilitating campaigns via voices and perspectives people can relate to,” Shronk explains. “This has set the stage for an influx of life sciences companies investing in content and influencer marketing, and determining the most effective ways to measure and analyze those campaigns.”
However, Stephen Flaherty, PhD MBA, Assistant Professor of Healthcare Administration at Stonehill College and Data Scientist/Outcomes Researcher at Harvard Pilgrim Health Care, still sees the need for organizations to be able to collect more detailed individual-level data on social determinants of health (SDoH)—even though this can be complicated by privacy concerns and lack of trust. To get around the privacy issues and obtain this important information regarding what factors most impact a person’s health and their utilization of healthcare, Dr. Flaherty offers one possible solution.
“Organizations could employ relatively simple surveys with only 10 or so questions to capture key SDoH information on their customers/members,” he says. “The individual-level data collected can help drive delivery of the right programs or information, tailored to an individual’s language and cultural needs, to each customer or member.”
Furthermore, combining data and AI/ML can help ensure the life sciences industry is better serving patients in need of help. For example, healthcare informatics company Vidence, was able to identify patients at risk of non-compliance who were missing therapy or not keeping appointments for their cancer care out of fear of visiting hospitals/outpatient care facilities during the pandemic.
“Analyzing thousands of patients, we were able to build an algorithm with AI/ML to develop a predictive model of patients most likely to miss therapy,” explains Gregory Gallo, SVP, Vidence. “From there, care teams were engaged to utilize telehealth, in-person, and treatment modification options to reduce the number of patients becoming non-compliant in their treatment, ultimately maintaining or improving outcomes. Findings like this also surfaced the continued need for care coordination and communication so that patients, HCPs, resource teams, and new technology tools collaborate for optimized patient care. While applications like these were battle tested during the pandemic, they should become standard of care in ‘normal’ times.”
