David Nickelson, PsyD, JD
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Data from a connected economy is funneling a wealth of monetization opportunities for business leaders across the life sciences industry Data that reveals how consumers use a company’s products, and the context in which such products are consumed, creates new business opportunities by monetizing what we call “consumption data.” In many cases, consumption insight provides opportunities to offer advice for how product users can save money (auto insurance), lose weight (FitBit), prevent costly repairs (smart appliances) or get better gas mileage (connected cars). The connected economy is similarly inspiring CIOs in life sciences organizations to monetize data and to use its resultant insight to run their organizations more competitively. When asked to prioritize technology investments, business intelligence and analytics ranked first in a survey of CIOs across the life sciences sector (See Figure 1).¹
Life sciences CIO Life Sciences CIOs echo the priorities of top performers in other sectors when it comes to investing in business intelligence and analytics for competitive differentiation.
Source: Adapted from Gartner 2018 CIO Survey
Pharma marketers often deploy branded solutions for medication adherence. However, an agnostic, cross-brand platform, driven by consumers, offers pharmas an alternate solution versus one that might appear biased. Alternatively, pharmaceutical organizations such as Merck KGaA, Takeda and Gilead use a cross-brand platform (called Medisafe) with a registered base of four million patient and caregiver users, who have collectively recorded over two billion successful medication doses on their iOS and Android smartphones and tablets.² The platform’s consumption analytics, which are regularly shared with pharmaceuticals, detail the story behind the 200 reasons patients don’t follow their prescribed drug treatment plans (a $300 billion problem in the US alone where 50 percent of cardiovascular disease patients don’t adhere to their prescribed medications).³
Source: NIH study of 10,000 patients
An AI-enabled interface delivers a tailored contextual experience to each user, with content, resources, videos, coupons and motivational messages—based on the patient’s regimen, condition and specific circumstance. Many patients opt for coaching through their Apple Watch. A Bluetooth-connected pill bottle (via the cap) is also offered to simplify the reminder process. Pharmaceutical firms also use the platform to deliver co-pay information and education in disease management and drugs. (See Figure 2.) Tools for instantly surveying patients are also available.
Noble, a provider of prefilled syringes and respiratory devices, uses a similar platform called HealthPrize to reward patients with positive educational experiences and videos for verifying prescriptions and reporting compliance. Patients received added incentives and rewards for reaching key milestones that measure health improvements. Healthprize founder, Katrina Firlik, (a former Yale Medical School professor and neurosurgeon) says “every constituent benefits when adherence increases: hospitals, doctors, health plans, employees, pharmacies, pharmacy benefit managers and pharmaceutical companies.” HealthPrize’s architecture, which supports web and mobile platforms, is designed around financial incentives, education and reminders, using principles of behavioral economics, gaming and consumer marketing.
"Though outcomes vary depending on the illness, life sciences and healthcare givers agree that even the smallest improvement in medication adherence would save billions of dollars."
Advanced analytics, natural-language processing and algorithms are being used more and more to examine healthcare professional (HCP) data, map entire disease populations and to find important stakeholders, such as key opinion leaders (KOLs). Top pharmaceutical companies, for example, are using analytics to help them find points of leverage within customer communities. With disease mapping and KOL identification solutions from providers (such as Voxx Analytics, Veeva, IQVIA and SteepRock), leading pharma organizations use social network analysis (SNA) mathematical models to garner insight into relationships surrounding HCPs and to graphically illustrate important network influences. Once a specific effort is scoped, technology is used to harvest and cleanse relevant information from publications, social media and other affiliations. HCPs can then be linked and analyzed (based on the strength of their connections) using graphical tools. SNA analysis uses algorithms to source top influencers and key collaborators in the disease community, letting medical affairs teams assess actual reach and potential KOL impact (See Figure 3).
Several providers offer KOL data and identification services to help medical affairs teams quickly find the right Key Opinion Leaders (KOLs). Providers such as Austere Analytics have a Rising Star service to help pharmas be the first to establish relationships with those that show high probability of being future KOLs. A rising star is evaluated by its network position. If a physician is connected to several influencers through the course of medical school, residencyand a fellowship, for example, her position within a network is much more likely to make her a rising star than simply reviewing her publications might lead you to believe. Voxx describes the rising star paradox as a moment when a client looks at a rising star list and either says “I don’t know any of these people” or “I know all of these people.” The best rising star shortlist is somewhere in between those two.
It can take years, even decades, to get new life sciences products to market. As much as 80 percent of the problem can be attributed to the challenges of recruiting qualified patients for clinical trials. The Pulmonary Fibrosis Foundation was able to find the right patients significantly faster than traditional centers by using a mobile app and real-world data from electronic health record (EHR) systems. The foundation built a smart health record for each prospective participant which notified patients for trial participation if its algorithms detected a >90% match with its acceptance criteria. High rates of success were also insured by drawing on patient-powered research networks (PPRNs), which contain patients and/or caregivers motivated to play a role in ongoing research for a specific disease state or medical condition. The Pulmonary Fibrosis Foundation enrolled 10 patients in three weeks for a type of study that typically recruits five patients in a year. The process helped enroll 10 patients in three weeks for an idiopathic pulmonary fibrosis study (versus the more typical rate of five patients in a year). The foundation took a patient-centric approach, making a concerted effort to make the needs of participants the priority, including the removal of typical obstacles to trial participation and adherence. For example, mobile devices, sensors and wearable technology allowed the trial subjects to perform trial activities from home.
AbbVie, Roche and Takeda have deployed similar technology to support central nervous systems (CNS) studies with an artificial intelligence (AI) platform for mobile devices (research shows 20 to 30 percent of failed clinical trials point to non-adherence; see Figure 5). The software-based solution draws upon the participant’s phone camera to automatically confirm medication ingestion (no manual video review is required). The technology (from AiCure) visually identifies the drug and the participant based on facial recognition and the act of ingestion. No other requisite hardware is required. The solution doesn’t require any change to the manufacturing process of the medication or its packaging. By using the technology in an outpatient environment, sponsors and investigators increase adherence and improve statistical power in multisite clinical trials.
According to the United Nations Population Division,4 food production needs to increase by at least 70 percent over the next 40 years to keep up with the world’s forecasting population growth. Although our natural resources are limited, the potential for advances in agricultural production are not limited according to executives at Dow AgroSciences, a subsidiary of The Dow Chemical Company, which develops solutions in order to innovate farming. To address the challenge, Dow executives are investing in laboratories of the future (LotF).
Today, it takes a considerable amount of time to reprogram laboratory automation to run new protocols. Hence, instrument utilization rates often fall under 15 percent resulting in significant delays between hypothesis and confirmation testing. Scientists at several leading pharmaceutical and biotechnology firms scientists address the problem with a solution from Synthace (called Antha), which translates protocols across various hardware vendors to rapidly reconfigure instruments. This frees up investigator time to focus on experimental design versus robotic programming (also making more time for rapid testing and reporting).
Antha’s operating system, based on an open-source-based language, lets investigators visually design, execute and analyze biological workflows without the need for code. Added flexibility enables more degrees of freedom when creating complex multifactorial design of experiments (DOEs) workflows. Built on Microsoft Azure and Google Cloud, the system leverages active learning and knowledge graphs to continually update and design experimental systems. User clients improve their models by opting into data share elements to let the solution conduct machine learning over cross-industry protocols and methodologies. Synthace’s strategy responds to the industry’s ballooning biologics and cell therapy pipelines, providing a solution at the intersection of laboratory software, bioengineering, cloud-based labs, research informatics and advanced computing (see Figure 6). Scientists use a drag and drop visual workflow editor to configure experiments, which can be replicated by others by drawing upon digitally represented, cloud-stored methods.
Generating valuable insight from data inside and outside the organization to create new business value and to optimize processes, is a strategic goal of life sciences organizations across the industry. According to Gartner’s 2018 CIO survey, business and technology leaders in life sciences organizations would like to move beyond rearview analysis into more forward-looking intelligence with advanced capabilities provided by predictive and prescriptive analytics. When it comes to supply chain, patient engagement, clinical trial patient recruitment and biologic research, we believe investments in advanced analytics offer life sciences organizations the opportunity to compete and operate more effectively, for example:
David Nickelson, PsyD, JD
Director, Health Transformation Strategy & Behavior Change
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