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Innovation in Life Sciences Delivers New Breakthroughs

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


David Nickelson, PsyD, JD

Director, Health Transformation Strategy & Behavior Change

Life sciences CIO

Innovation in medication adherence addresses a $300 billion problem

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

Why do patients skip taking medications? 24% cite forgetfulness,l 20% worry about side effects, 17% cite high drug costs, 14% believe the drug is ineffective.

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 find key stakeholders and opinion leaders

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).

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.

Smart health records accelerate clinical trial recruitment

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 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.  

Improving adherence in clinical trials.

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.


Laboratories of the future free up investigator time

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).

More time for experimental design

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). 

Machine learning plays a key role in lab automation

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).


Conclusions and recommendations

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:

Improve medication adherence

Consider brand-neutral platforms to optimize patient engagement or to increase medication adherence using smartphone apps and Bluetooth-enabled pill bottles. While these inventions are simple in concept, they deliver much-needed innovation to a problem that burdens the healthcare system with billions of dollars in unnecessary costs. Platforms such as Medisafe, have gathered more than 2.5 billion data points that pharma companies can use to gain insight into product and market usage trends.

Find the right opinion leaders

Business leaders in life sciences organizations are starting to do a better job at identifying and segmenting potential key opinion leaders (KOLs) by leveraging big data, advanced analytics and algorithms. By using more advanced techniques, business leaders ultimately engage with the right people to maximize the strength of their organization’s reputation. Graphical tools make it easier to visualize the strength and quality of relevant connections across the community of HCPs.

Recruit qualified patients for clinical studies

Clinical leaders can improve patient recruitment and adherence, especially in rare disease studies, with modern, innovative techniques for running clinical trials that lead with patient needs. Patient-centric strategies should do everything possible to remove obstacles to trial participation and adherence. Clinical leaders might also consider virtual trials that draw on mobile devices, sensors and wearable technology to make it easy for subjects to participate from home.

Consider lab automation

The market for laboratory automation is more mature than autonomous vehicles or manufacturing/supply chain robotics. New ways to power laboratory instruments, especially in increasingly regulated labs, offer numerous benefits for R&D managers and laboratory IT directors with responsibilities for LotF, laboratory modernization and biologics. Hence, business and technology leaders in life sciences should start to evaluate innovative laboratory automation solutions.



  1. 2018 CIO Agenda: Life Science Industry Insights, by Stephen Davies, Gartner; 2 October 2017.
  2.  Demonstrating Double-Digit Impact on Medication Adherence in Hypertension Patients, Medisafe, March 2018.
  3.  Adherence and health care costs, by Aurel Iuga and Maura McGuire, US Library of Medicine, National Institutes of Health, February 2014. 
  4.  High Level Expert Forum - How to Feed the World in 2050. Office of the Director, Agricultural Development Economics Division Economic and Social Development Department. Forum held in Viale delle Terme di Caracalla, Rome, Italy