EDC Adoption on the Rise

smart doctor The move to conduct clinical trials from paper to Electronic Data Capture (EDC) has accelerated over the past 10 years in an overall effort to increase data quality, regulatory compliance and to reduce cost. This trend has grown because of the need to share real-time data and facilitate strategic decisions to be made during the study based on its progress.

According to a newly released report, the healthcare cloud computing market is expected to grow from $3.73 billion in 2015 to $9.48 billion in 2020. The eClinical solutions market, including cloud-based solutions, is projected to grow 14% by 2020, reaching an estimated $6.52 billion, up from $3 billion in 2014.

Different sources of data present many data management challenges, which is why cloud solutions are quickly gaining popularity. Cloud-based technology brings efficiency and cost-effectiveness in managing clinical data, and works for both pharma companies and their CROs. Utilizing cloud infrastructure scales and streamlines data, improving its quality and allowing for a simple, seamless experience.

According to a recent report by the Industry Standard Research (ISR), in 2013 two providers accounted for more than 50% of EDC service. This year, five EDC providers accounted for over 50% of the market share, which shows that the market for these services is growing. The same report also shows that EDC has become standard practice with approximately 88% of Phase 3 clinical trials initiating use of the technology.

Clinovo’s ClinCapture EDC was featured in this report along with 21 other vendors, selected out of a list of 651 EDC providers.

Try ClinCapture Free EDC

Clinovo - Sign up now


Crowdsourcing Clinical Research & Development (Part 1)

The trend of open collaboration has led to innovation across multiple industries. For decades, big pharma has been known as conservative and slow to change. Today however, there is a growing movement toward open access and crowdsourcing scientific information to accelerate research and development. Patients are no longer just research subjects or data providers, they are problem solvers participating in a global community of stakeholders. Online physician communities are breaking down competitive barriers in the name of patients.

A recent example comes from SERMO- a global social network for medical crowdsourcing, where members helped save a little girl’s life from a deadly e. coli strain that killed her brother two weeks ago. Doctors from around the world worked seamlessly as a team across borders on this case, recommending testing and treatment, and reducing the risk of infection in her community.

Another great example of the power of crowdsourcing comes from Stanford University, where a group of researchers stumbled upon an unexpected discovery- while examining autopsied brain tissue of multiple sclerosis (MS) patients, they noticed high levels of angiotensin enzymes, responsible for hypertension. That is how they came up with the hypothesis that angiotensin inhibitor (Lisinopril) would decrease MS flare-ups, and when they used it on mice with ML-like nerve damage, it reversed their paralysis.In 2012, the FDA approved Lisinopril as the first investigational new drug developed through crowdsourcing.

Experts say this phenomenon has emerged from three main trends that are changing the health research environment: 1) citizen science (non-expert trained individuals conducting science-related activities); 2) crowdsourcing (using web-based technologies to recruit clinical trial participants); and 3) medicine 2.0 (active participation of individuals in their health care particularly using the Internet and social media).

Among the leading health research crowdsourcing platforms are the personal genetics company 23andMe and the health information sharing website PatientsLikeMe. 23andMe has more than a million customers, 80% of which have allowed their DNA samples to be used in research. On a typical week, the company collects more than two million individual survey responses from its online community, contributing to over 230 research studies on the human genome.

PatientsLikeMe is the largest free network where patients with life-changing conditions can track and share their experiences to help each other, as well as researchers, pharmaceutical companies, regulators, providers and non-profits, develop better solutions to their problems. The network has a community of over 220,000 members who share their personal data on more than 2,000 medical conditions.

In addition, patients have also started to organize their own research studies with the help of health social networks and online communities such as Quantified Self, Genomera, and DIYgenomics . Recently, some 200 crowdsourced studies have been added to the PubMed database .

Crowd Data Mining

According to the Oxford Bioinformatics Journal, many recent studies on the adverse effects of drugs are mining crowd data from search logs, Twitter, online patient forums, FDA reports as well as electronic health records (EHRs).

While traditional reporting of drug adverse effects is slow and does not capture all cases, web search patterns can provide early clues to adverse drug interactions. For example, a recent study explored the possibility of two drugs (paroxetine and pravastatin) interacting adversely and causing hyperglycemia. The researchers analyzed 82 million drug queries from 6 million web searches in 2010. Patients opted to share their IP addresses and search activities through a Microsoft add-on browser application. The results confirmed that patients who took both drugs at the same time were more likely to search for hyperglycemia-related terms compared to those who searched for only one of the drugs.

Another study used Google Trends (online tool which gives statistics on search term use) to observe the seasonality of sleep disordered breathing. Using search terms such as “snoring”, “sleep apnea” and “snoring children”, researchers collected queries from a seven-year time span in both the United States and Australia. Using regression analysis, researchers found that peak searches happened in the early spring and early winter season in both countries.

Active Crowdsourcing

As opposed to crowd data mining, active crowdsourcing uses community challenges, labor markets, open forums or games to encourage participation in pharmaceutical clinical trials. Labor market platforms such as the Amazon Mechanical Turk or Crowdflower allow researchers to design and submit crowdsourcing tasks or build entire sophisticated interfaces around a study in order to recruit participants.

A group of scientists recently evaluated the viability of using crowdsourcing for creating large data sets through the public health forums MedHelp and CureTogether. They conducted two different studies- one with 30 professional nurses recruited from oDesk (freelance labor market) and another with 50 crowd workers recruited from MTurk. The results showed that the crowd-labeled data achieved an accuracy of 84% compared to 78% for the expert-labeled data.

Yet another form of active crowd sourcing are scientific games such as “Foldit”- a protein folding game with over half a million registered players, which has dramatically improved protein folding algorithms. Foldit’s goal is to predict the structure of a protein through puzzle-solving scenarios which would aid the creation of designing new proteins to combat disease-related proteins found in HIV/AIDS, cancer or Alzheimer’s, for example.

Infographic (2)



Sign up to ClinCapture Free EDC!


Leveraging Real-Time Reviews: Quality Clinical Trial Data

logoClinovo, ClinOps Toolkit, and Patient Profiles recently hosted an exciting event at the Bay Club in Redwood Shores, the heart of the Silicon Valley biotech hub. Clinical trial and eClinical system experts shared their specific experiences, challenges, and insights on obtaining and managing quality clinical trial data. The panel also discussed new tools and technologies to improve clinical data quality.

The event was organized by Nadia Bracken, Clinical Trial Manager and Founder of the ClinOps Toolkit blog and event series. The discussion was moderated by Peter St Wecker, Senior Manager of Clinical Research at Theravance. The experienced panel included Marc Desgrousilliers (CTO at Clinovo), Barbara Elashoff (CEO at Patient Profiles), Nancy Isaac (Vice President CA, RA, QA at NeoTact), and Vaishali Suraj (Director of Statistics at Auxogyn). Here is a recap of the main lessons learned from the event.


More Clinical Data From More Sources

Marc Desgrousilliers, CTO at Clinovo, pointed out that the life science industry is experiencing a significant growth in the amount of data they’re collecting claiming that “Roche is expecting to double the data they collect every other year”. Clinical data is flowing from more and more sources, including EDC, ePRO, EHR, let alone new devices and smart biosensors. Unfortunately, clinical data is often housed in different databases and current systems are not necessarily talking to each other. Vaishali Suraj explained that Auxogyn works with three different databases. She has to spend a lot of time cleaning up data as well as identifying and solving discrepancies. This is a potential threat to data quality, as it makes clinical data more disparate and less accessible.

Streamline and Organize Clinical Data

Clinical trial professionals have higher expectations of eClinical technology. We expect to visualize collected data in real-time, from anywhere, at any time. “We need to access data at our fingertips” states Vaishali Suraj from Auxogyn. “Data storage and infrastructure is a major challenge for us, as we are working with large data and images”. The challenge for eClinical system vendors is to organize the growing flow of data, and deliver it to clinical study teams in a real-time and an interactive format.

Empower Clinical Trial Professionals

“Our clients want to be empowered to build their own studies” states Marc Desgrousilliers from Clinovo. “If you can use PowerPoint, you should be able to use a simple system to build your own CRFs and create edit-checks, without a huge upfront IT investment”. Cloud technology is the answer: It is scalable, so it can grow or shrink with the data and it is seamlessly accessible from anywhere in real-time. Marc announced: “We’re working on an easy-to-use and pay-as-you-go platform to build and manage clinical studies from the ground up without any programming skills”. 

Look for Patterns to Mitigate Risk

Nancy Isaac from NeoTact explains that as far as safety, we need to be able to look at the data in multiple dimensions and look for patterns. More importantly, “clinical trials are very expensive” states Nancy “but there is only one thing more expensive than good data, and it is bad data.”

Patient Profiles offers an innovative statistical approach and an eClinical system that allows users to visualize patient data in real-time, and automatically identify and flag unusual data. Their web portal allows all team members to visualize clinical data, aggregate graphs and tables, and drill-down in to individual patient profiles. Patient Profiles automatically runs algorithms to mine your clinical data and identify data that is unusual. The rules are inferred from the data without needing to hard code the error checks. This solution goes far beyond the simple programming that identifies an out-of-range lab value. The software summarizes the error rates by patient, site, and variable to identify where to target monitoring throughout the course of a trial. This allows clinical trials professionals to react faster to protect patient’s safety, as well as save both time and costs in data monitoring.


Patient Profiles | patientprofiles.com | info@patientprofiles.com

Clinovo | clinovo.com | contact@clinovo.com

ClinOps Toolkit | clinopstoolkit.com | nadia@clinopstoolkit.com

Subscribe to the Newsletter and join the next meetup! Visit http://opsk.it/connect.

Olivier Roth, Marketing Manager at Clinovo

Your blog eClinical Trends is powered by Clinovo.

Back to Top