Developing a new drug today takes anywhere from 10 to 12 years and costs between $1 billion and $2 billion. One source of these ever-expanding costs is the data compliance issues due to siloed information. The current standard method for verifying this is a process of preclinical and clinical trials.

In fact, clinical trials have become so costly that a portion of smaller pharma companies developing drugs create a roadmap for either floating on the stock market or looking for merger or acquisition opportunities, if they are unable to raise the exorbitant amounts of capital necessary to complete the commercialization process. The list of pain points in clinical trials is long, and includes lengthy timelines; difficulty in recruiting and retaining participants; insufficient clinical trial workforce; administrative barriers; inefficient data review and analysis; and the complexity of maintaining and monitoring safety.

Much of this cost is passed onto the end-user. Name-brand drugs, while accounting for less than 10% of prescriptions, account for more than three quarters of prescription costs. Clinical trials account for an estimated 20% - or $200m - of overall drug development costs. And there are four areas that account heavily for clinical trials’ ever-expanding budgets: recruitment, retention, reimbursement and review.

The links in the chain: Explore blockchain context
Image: World Economic Forum

Blockchain technology offers a novel way to solve each of these areas, while allowing privacy settings for data sharing in order to shorten the time necessary for trials. A concrete example of the benefits this can bring is allowing access to information in parallel, meaning that as patient data is being collected by physicians, researchers can review it at the same time as the marketing department and executives at the pharma company. In other words, each department can work contemporaneously on a drug in the development pipeline. Currently, there is no way to share such disparate information in real time.

Let’s walk through a few generic clinical trials to see how blockchain can save time and money. (It’s worth pointing out here that the estimated average cost-per-day of a delayed clinical trial is $300,000, as based on Tufts and ACRPNet studies.)


Let’s say researchers in the US have identified a strong drug candidate, completed all of the pre-clinical (animal) trials and are now transitioning to human trials. This requires a robust variety of patients and the collection of gigabytes of information: their electronic health records and genomic information (and metagenomic analysis for highly complex interactions) as well as various samples from the trial participants.

Let’s also say that there are 20 institutions who will each be responsible for recruiting five patients for this trial. Today, each of these researchers has to rely on their regional patient population to fulfil study requirements. Finding such patients takes an average of 18-24 months. Researchers and pharma executives have estimated that building a rapid onboarding chain for clinical trials could save six months on average - that’s $54 million.

Fundamentally, there is a lack of geographic data coordination, meaning patients in rural Nebraska or in small towns, for example, are excluded from trial consideration. It’s important to make sure the right patients are selected, and having a rating system would allow participants to feel empowered while ensuring feedback flows both ways for ongoing clinical trials.

There’s a huge amount of patient health information that has to be reviewed by researchers to accept patients into the trial, which lengthens the recruitment process. Blockchain could broadcast a message on behalf of the trial that identifies the required patient condition and status, and matches them automatically, allowing researchers to make the administrative decision to accept patients in days, not months. Building a bridge between researchers and an immense storage of patient health information would create efficiencies that could save pharma companies an average of 25% of their time over a ten-year drug development. That’s 912 days, or $273 million.


Supportive considerations are key for high retention rates, which vary depending on the patient population. For example, paediatric participants have parents, primary care physicians and other supportive clinical staff to offer advice and explain the benefits. However, older patients have different needs. Some trials require them to relocate closer to the trial centre for long periods of time. The primary consideration is about quality of life as well as data. If the patient can enroll, have a local clinic administer the required measurements using advanced technologies such as the Internet of Things (IoT) and tele-consultations, instead of having to move away from their support network, retention rates could rise by as much as 15%.

So far, we have 100 patients in 20 different regions who each have unique considerations for retention. With an average dropout rate of 30% across clinical trials and 85% of clinical trials failing to maintain enough participants, time and money is wasted collecting incomplete and often unusable data. The recruitment and participation of additional patients, where needed, increases trial costs and duration. A patient-centric decentralized system, allowing a clinical trial to be conducted in several locations simultaneously to a uniform standard, would help increase retention rates on the front end and during the trial by increasing patient satisfaction in addition to retention rates.


The stickiest of all issues, reimbursement is where blockchain shines. Creating immutable and transparent records allows for clear pricing for all information exchanged. Currently, clinics and patients are paid directly by pharma companies, who cover the majority portion, and by government grants. This causes some conflict of interest on all sides, which may lead to a bias in patient selection and recruitment.

However, blockchain can fix this with a ratings system that matches someone’s holistic health profile with a suitable trial and monitors the doctors who are selecting these patients. Ratings and match-percentages would help to identify partners and would increase accountability for all involved. Furthermore, artificial intelligence can increase efficiency and identify patients based on clinical trial criteria, allowing all stakeholders to participate in the clinical trial pathway at the ideal phase for their condition.

Phase 1 screens for safety to determine safety ranges of drug dosages in a small (20-80) patient population. One key challenge here is the review of preclinical data as well as risk assessment criteria.

Phase 2 establishes a baseline for efficacy of the drug in a large (200-300) patient population. One key challenge here is identifying less common side effects. Having a diverse patient population, coordinated through blockchain utilization, can streamline phase 2 selection.

Phase 3 finalizes the safety and efficacy profile of the drug with a very large (1,000-3,000) patient population. This includes monitoring side effects and comparing the drug to current standards of treatment.

Note: Phase 4 occurs after the new drug has been marketed and is to monitor efficacy in the general population.


Researchers then review the received data only after the trial is completed. On average, there 30-40% of data omission, meaning 60% of the data collected is potentially statistically significant for the trial. This adds up to a cost loss of at least 30% of the trial cost, where the average cost per trial phase is $180 million. Since there are three phases of clinical trials, this could mean up to $162 million in savings.

Recruitment through fast, automatic identification of appropriate participants from a pre-constructed database could save 25% of the total drug development time, in turn saving up to $273 million. Retention in trials will be enhanced by the ability to carry out the trial at a location convenient to the individual participant through standardized procedure reinforced by new technologies. The centralized database logging participant information and trial data would allow continual review of data throughout the trial, enabling identification of significant and insignificant data as it is collected, as opposed to retrospectively, as is current procedure. Finally, trust between stakeholders is augmented by transparent and immutable records, removing patient bias and ensuring swift and fair reimbursement.

It is important to note that while the application of blockchain technology will not solve all the problems in a broken drug development system, it will greatly alleviate many of the costs of clinical trials. Aggregation of anonymized patient data onto a common platform through the blockchain will expedite the recruitment, reimbursement and review processes, while adoption of Internet of Things technology and tele-consultations will increase retention rates. By reducing inefficiencies in the nearly $2 billion drug development process, the implementation of blockchain technologies would culminate in a significant decrease in trial duration and overall cost of drug development.