What can cross-form do for you? (Part 3 of 3)

In the examples given here and here, our cross-form logic depended on data with a known location. In the first case, we knew exactly which event, form, and item to turn to in order to retrieve participate sex and date of birth. In the second case, each of our event dates marked the start of a unique, one-time event, so “finding their address” within the database was a straightforward process.

Now where did I leave that item value?

But what happens when we need to reference data with an indeterminate location, supposing that it even exists? In these cases, we may need to walk around a remote neighborhood, comparing building shapes and sizes, before we find what we’re looking for.

Consider a study that requires drug cessation if a certain adverse event recurs within 90 days. For an Alzheimer’s study, that adverse event may be detection of ARIA (amyloid-related imaging abnormalities) on an MRI scan. Suppose that a second presentation of ARIA within 90 days of the first means that the participant must discontinue study drug. What better occasion could there be for “checking the records” than while reporting a new ARIA? Checking the records here means:

  • retrieving the start dates of any previous AE whose report indicated ARIA
  • calculating the days’ difference between the most recent of the dates above with the new ARIA presentation date
  • showing an alert if that difference is less than 91 days

It’s hardly complex, but a busy CRC working with dozens of participants in multiple trials may forget to follow the process. Cross-form logic, on the other hand, never forgets. The screenshots below depict the result of a third AE report for a single participant. Of the two previous reports, the first indicated detection of ARIA on 1-Nov-2018.  Because the newest ARIA, on 24-Jan-2019, falls within 90 days of the prior one, the form displays instructions to discontinue study drug. 

 

The AE log for this participant shows two reports. The first, documenting an AE on 1-Nov-2018, indicates ARIA.
Here the CRC is reporting a new presentation of ARIA, on 24-Jan-2019. That date is fewer than 91 days after the previous ARIA of 1-Nov-2018. As a result, the form displays the relevant instructions from the protocol.

Few questions are too complex for cross-form logic to answer and act upon. If you can state a rule in logical or mathematical terms, you can most likely implement it using a straightforward expression, no matter how many other forms you need to reference. The OpenDataKit library of XPath functions offers a wealth of tools you can combine to create smart, versatile forms that collaborate with researchers.  So don’t let your innovation stop with drug development or study design: carry it through to your forms!

What can cross-form do for you? (Part 2 of 3)

In the previous post, we presented a cross-form example of clinical data collected in one event factoring into the normal lab range for a subsequent event. But clinical data aren’t the only factors that drive decisions. When an event occurred may determine when it should happen next. Dosing visits provide a common example. Depending on the protocol, dosing might occur at precise intervals (e.g. exactly 21 days between doses) or within windows (e.g. at least 7 days and no more than 10 days from the previous dose). Your EDC system should be able to enforce either type of scheduling, by reading not only the dates entered into forms, but dates found in form and event metadata.

In the example illustrated below, the form makes calculations between the start of a current event (“Dosing Visit 2”) and the start of the previous visit (“Dosing Visit 1”). According to this imaginary protocol, no fewer than 7 and no more than 10 days may elapse between these two visits.

  • If dosing visit 2 occurs within this range, the form guides the site-based user on how to prepare the dose.
  • If dosing visit 2 has a start date fewer than 7 days after dosing visit 1, the form displays instructions not to proceed, and provides the earliest and latest start dates for the visit.
  • Finally, if dosing visit 2 has a start date greater than 10 days after dosing visit 1, the form displays instructions to submit a protocol deviation note.

All of these calculations and feedback take place instantaneously.

For this participant, Dosing Visit 2 is within window.
For this participant, Dosing Visit 2 has been scheduled for too early a date.
For this participant, Dosing Visit 2, were it to occur, would happen beyond the 10 day maximum from Dosing Visit 1.

Up next: check for recurring Adverse Events

What can cross-form do for you? (Part 1 of 3)

Your study database has just locked. To celebrate, you decide to treat your two in-house monitors to dinner in the city. You’d like to offer your colleagues a choice of three restaurants. Take a moment and imagine which three restaurants you’d choose. Got it? Now suppose you recall that one of your monitors follows a gluten-free diet. Does that change your selection? If all of your initial picks specialized in wheat pasta, it ought to.

What’s good for dinner plans is essential for study conduct, when data quality and safety are on the line. An unremarkable value for height in one participant ought to trigger a query for another; for example, for a teen and a six-year-old enrolled in a pediatric trial. To evaluate the input in one field based on data in another, data managers rely on cross-field edit checks. If the fields are part of distinct forms, that evaluation is known as a cross-form edit check.

At OpenClinica, we extend this capability. We give data managers a tool to make their forms responsive to any element they choose from their database, from the participant’s most recently recorded blood pressure to the start date of the last dosing visit. Using easy-to-understand syntax in the form definition, data managers may reference any item in the database to trigger dynamic edit checks, make calculations, show/hide relevant information, and even change the content and logic of the active form. Your forms can now “know it all” to present a fully contextualized data capture experience.

We developed this feature to help our users:

  • capture more consistent, higher quality data,
  • drive protocol compliance,
  • highlight potential safety issues, and
  • mitigate the risk of unnecessary study procedures.

The capability goes far beyond cross-form edit checks. This is total study intelligence. Below is the first of three case studies we’ll present this month that illustrate the difference. If your study has requirements that resemble these, don’t hesitate to contact us for a more in-depth guided tour.

Case #1: Age- and Sex-Dependent Normal Lab Range

One person’s high blood pressure is another person’s normal. Why? The primary reasons are differences in age and sex. When it comes to lab results, “normal” may also be a function of the specific lab conducting the analysis. But not all studies rely on lab-specific ranges. For clarity’s sake, let’s imagine a study that will evaluate lab values against standards that are lab-independent. Known as “textbook ranges”, these ranges define the upper and lower bound of normal based solely on patient-specific factors. Below is a table indicating the lower and upper limits of normal for DHEA-S in adult males and females:

Chances are that a form associated with a screening event has already captured a participant’s sex and date of birth. A subsequent event may include a lab form. It’s inefficient to ask for the participant’s sex and age on this lab form. Sex has already been documented, after all, and age requires a calculation that compares the specimen collection date with the participant’s date of birth. Asking a CRC to make that calculation opens the door to error, especially if collection occurred close to the participant’s birthday. But age and sex information is required to determine whether an entered value is above or below the limits of normal.

Enter cross-form logic, which handily “pulls in” the required data from an external form. In the current case, an expression within the form compares the specimen collection date with the externally-supplied date of birth, to calculate participant age. That age, together with the externally-supplied sex and lower and upper limits indicated within the lab form, are all it takes to instantly evaluate the lab value against the appropriate range for the participant. Results of that evaluation may trigger or hide additional fields, get piped into question or response text for a separate item, or simply provide instructions, as shown in this video.


 

Up next: comparing event dates to ensure that dosing occurs within window.

HTML Tips to Enhance Your eCRF

In some cases, the display of your OpenClinica eCRF may not be exactly what you had in mind. You may want to highlight key words or phrases, create a bullet point list, or insert a URL or image. Using HTML tags, you can make some simple manipulations to change the look and feel of your case report forms and make them more inviting for data entry.

Using HTML tags to enhance your eCRF

The HTML tags described in this document can be used in the following columns in the CRF Excel template:

  • Items Tab: LEFT_ITEM_TEXT
  • Items Tab: RIGHT_ITEM_TEXT
  • Items Tab: HEADER
  • Items Tab: SUBHEADER
  • Sections Tab: INSTRUCTIONS

What are HTML tags?

HTML, or Hyper Text Markup Language, is a markup language that is commonly used for web page development. HTML is written using “tags” that surround text or elements. These tags typically come in pairs, with a start tag and an end tag:

<start tag>Text to format</end tag>

To insert an HTML tag, simply surround the text you want to format with the desired tag. Below are the HTML tags that work in OpenClinica:

Table

You can download this HTML Tags Knowledge Article to help you to get started.

Inserting URLs and Images

HTML also allows you to insert a URL or Image into your CRF, which may be used to provide users with additional information or references.

Insert a URL

A URL may be inserted into a CRF in order to provide a link to further instructions or protocol information. To insert a URL into your CRF, use the following format:

Inserting images - using HTML tags to optimize your eCRF

Simply replace the areas highlighted in yellow with (a) your URL (inside the quotation marks) and (b) the hyperlinked text that you want to display to the user.

The following example will prompt the user to “Click Here!” and will open the OpenClinica website in a new browser tab:

<a href=”https://www.openclinica.com” target=”_blank”>Click Here!</a>

Inserting an image - using HTML tags to optimize your eCRF

Insert an Image

Similarly, HTML can be used to insert an image into your CRF. You might consider using an image to display a pain scale (or other reference image), or even to display your company’s logo.

Inserting an image - using HTML tags in OpenClinica

To insert an image into your CRF, use the following format:

<img src=”images/ImageName”>

Again, simply replace the highlighted text with your image name. You can use PNG, JPG, or GIF image extensions. You can control the height and width of the image using the following format:

<img src=”images/ImageName” width=“n” height=“n”>

The highlighted n corresponds to the desired width and height of the image in pixels.

The following example will insert an image (image1.png) with a width of 300 and a height of 150:

<img src=”images/image1.png” width=”300″ height=”150″>

You can download this Images & URLs Example CRF to help you practice.

The examples included in the above CRF Excel template will insert an image that already exists in the images directory of your OpenClinica application. To insert a custom image, community users will need to place the image in the following directory of the OpenClinica application:

tomcatwebappsOpenClinicaimages

OpenClinica Enterprise customers can request an image be placed on the application server by reaching out to the OpenClinica Enterprise Support team via the Issue Tracker.

Have you used HTML in your CRFs? Let us know if you have any other suggestions or tips!


IMPORTANT NOTES:

 The RESPONSE_OPTIONS_TEXT field is not included in the list above, as HTML tags are currently not supported for response options.

 The QUESTION_NUMBER field will display the text properly, but has been known to cause issues when extracting data. Therefore, HTML should not be used in this column.

EDC Scandinavia uses OpenClinica for BYOD ePRO

Krister Kristianson, PhD.
EDC Scandinavia AB, Stockholm

RESTful web services with OpenClinicaIn a recent study involving several hundreds patients, we decided to offer patients the ability to collect their diary data using their own smart phones instead of the traditional paper diary. The patients who decided to participate in the study downloaded the app to their smart phone or could use their desktop to access the application.

The apps were developed for iPhones and Androids with a reminder function that notified them when to report their symptoms. The data was then transferred to OpenClinica using the RESTful web services immediately upon entry. The patients ID and pin code were tested before data was added to the database to avoid any illegitimate entries.

About 80% of the patients decided to use the electronic diary – 65% using iPhones and 35% using various Android devices. They could also download the app to iPads or other tablets and if they preferred, they could use the application on desktops.

Outcomes:

  • Paper CRFsOf the patients who used the traditional method of reporting diary data on paper, 2.5 times more patients failed to report at scheduled time points compared to the patients using the app.
  • The app recorded the date and time automatically. When using paper, you can never be sure that the diary has not been completed at the time listed.
  • The addition of simple edit checks mitigated data entry errors, greatly contributing to the increased quality of the data.
  • It further reduced the manpower needed to manually enter data on to the eCRF and enabled us to monitor the patients in real-time and contact them if anything went wrong.

Although the patient population was relatively young, in this part of the world, even elderly patients are likely to use smart phones or desktops and would to be willing to use electronic data capture (EDC) for reporting diary data. The easy configuration of web services in OpenClinica and the ability to query data upon arrival made it an easy task to set up and validate the study.

OpenClinica and TraIT: A Dutch National Research Infrastructure

Is it possible to set up an IT infrastructure for translational research for an entire country? The Dutch Translational Research IT (TraIT) project (http://www.ctmm-trait.nl/) believes it is. Admittedly, The Netherlands is not exactly the same size as China or the US, but nevertheless already 26 partners from industry and academia to collaborate in this consortium to organize, deploy, and manage a nationwide IT infrastructure for data and workflow management targeted specifically at the needs of translational research. It includes the Dutch university medical centers, notable companies like Philips, and charities such as Dutch heart and cancer foundations.

At its outset, TraIT worked closely with selected translational research projects and scientists, piloting potential solutions with real research data from these projects. This work resulted in the selection and implementation of central TraIT services for clinical data gathering (OpenClinica) and image archival and retrieval (NBIA). TraIT has now reached a very interesting phase where the first IT solutions put in place in each of the four major domains of translational research (clinical, imaging, biobanking and experimental (any-omics)) are starting to flourish.

Word is quickly spreading about the TraIT infrastructure available. In particular, OpenClinica is taking off very well with amazingly little advertisement: currently the TraIT installation (OpenClinica.nl) contains 47 studies with 256 users representing 77 institutions, and is still growing rapidly as can be seen in the following graph:

Uptake of OpenClinica in TraIT

Based on the input obtained from users, we decided to augment some of OpenClinica’s capabilities, in particular for data loading. Obviously, the improvements are made available to the community whenever possible. For further enhancements, TraIT and OpenClinica, LLC entered into a partnership focusing (initially) on improvements in the areas of role-based security, linking to external (imaging) data archives, and data import/export. Hopefully, the fruits of this collaboration will soon be available to the entire OpenClinica community.

The next challenge faced by the TraIT project is integrating the clinical data from OpenClinica with molecular profiling data, which is needed to address the key question in translational research: how to correlate the variation in disease phenotype to variations in underlying biology. Another open source solution has been selected for this purpose: tranSMART, a translational workbench and data integration environment supported by several major Pharma and research consortia. The open source tools selected promise to be a powerful combination: data collected in OpenClinica can be further shared and analyzed in the tranSMART environment.

View slides from a presentation on TraIT delivered at the 2013 OpenClinica Global Conference.

Jan-Willem Boiten
Project Manager
TraIT at the Center for Translational Molecular Medicine (CTMM)

Synchronizing OpenClinica Instances: Another Option for Using OpenClinica in Disconnected Settings

While tablet software maker Mi-Co is showcasing an integration of their Mi-Forms tablet-based forms software with OpenClinica that can be used in “offline” settings, elsewhere within the OpenClinica community, Raymond Omollo and Michael Ochieng have developed a separate option for using OpenClinica in settings without internet connectivity. Their method synchronizes multiple locally deployed instances of OpenClinica with a central OpenClinica database. Michael and Raymond recently presented their work at the OC13 conference. You can access their presentation slides here to see how they address key issues such as synchronization, back-ups, encryption, and user training.

Synchronization Flow Chart
Synchronization Flow Chart

While working for Drugs for Neglected Diseases initiative (DNDi), Michael and Raymond devised this approach for a WHO study of Buruli ulcers in West Africa (Ghana and Benin). The study, which is ongoing as of the date of this post, is a randomized controlled trial comparing the efficacy of 8 weeks treatment with clarithromycin and rifampicin versus streptomycin and rifampicin. It involves 430 subjects across 5 sites. The participating sites have limited or unstable internet connectivity, so a solution is needed that provided timely, auditable, and quality data entry given this constraint. A positive byproduct is enhancing the capacity of these disconnected sites to utilize EDC.

As they say, necessity is the mother of invention. And open source makes it easier for people to believe that what is necessary can in fact be accomplished. Kudos to Raymond and Michael for devising a solution that works for them. Perhaps it may work for others as well.  If you’d like to access the source code and documentation for their work, you can download these from the OpenClinica Tools and Tips page (scroll to bottom).  You can reach Raymond and Michael on the OpenClinica Developers mailing list: developers@openclinica.org.

– Ben Bauman

More About DNDi

Headquarted in Geneva, DNDi is a global organization that develops safe, effective, and affordable treatments for neglected diseases. The neglected diseases that DNDi tackles afflict many of the world’s poorest people (Malaria, Leishmaniasis, Chagas disease, Sleeping Sickness, Paediatric HIV, Filaria). DNDi’s goal to develop 11 to 13 new treatments by 2018. More at www.dndi.org.

OpenClinica to be Used for European Commission Project “beta-JUDO”

As part of the European Union 7th Framework for Health, the “Beta-JUDO” project will involve hundreds of juveniles with obesity and type 2 diabetes. Pharmacology-based treatment strategies are limited for this growing patient group and the aim of the project is to identify novel strategies reducing insulin hyper secretion, which has so far not been considered a target for intervention in young obese individuals. The project will also involve beta-cell biology, brown adipocyte imaging, transcript and protein profiling, genetics, epidemiology and bioinformatics. A number of European institutions with expertise in these areas are thus involved. A kick-off for the project was recently held at Uppsala University, where about 30 expert scientists form several European countries attended. The project will continue until 2016 and has a total budget of €8 million.

OpenClinica will be used for data collection and data management across several European countries. The project is led by Professor Peter Bergsten Department of Medical Cell Biology at Uppsala University and two firms, Scandinavian CRO and e-Source Technology EMR, will be responsible for the management of the clinical part of the project.

We are proud to be involved in this prestigious project and to be able to demonstrate the abilities of OpenClinica and its powerful web services, which will facilitate the integration of external data from many sources across Europe.

– Dr. Krister Kristianson
e-Source Technology EMR

OpenClinica in an Academic Environment

Here at the Women & Children’s Health Research Institute, Edmonton, Alberta, we have been using OpenClinica since version 1. In that time the product has evolved significantly, providing more functionality and tighter focus on support for regulatory clinical trials. Our objective in this institute is to support our researchers regardless of the type of study they are undertaking. However, in the last two years, we have not been involved in any studies that needed to conform to regulatory standards. How then does OpenClinica perform in a purely academic environment?

Because OpenClinica is designed to support rigorous clinical trials it has many features that are of value to academic researchers. In our environment we stress the following features to our researchers, many of whom are used to managing their data in Excel or Access:

  • OpenClinica is hosted within a secure data centre provided by the University’s Faculty of Medicine and the servers are supported by the Faculty’s IT team.
  • Access to data is controlled through individual logins and user roles. The system is web based, using 128 bit encryption between the browser and the server. Studies implemented in OpenClinica are compliant with provincial privacy requirements.
  • Data entry rules and validations allow input to be validated at the point of entry. This greatly reduces the opportunity for user error, reducing the need for double data entry and reducing the cost of the data entry and cleaning effort.
  • CRF versioning enables changes to the data entry tools during the course of a study whilst maintaining the integrity of existing data.
  • Collaborative, multi-site studies are well supported in OpenClinica.
  • Discrepancy management and monitoring workflow allow annotation of the data and facilitate quality management whilst the study is ongoing. This reduces the cost of end of study data cleaning. It also minimizes the interval between end of study and analysis leading to reduced time to publication.

Many of the studies we have implemented in OpenClinica have been unregulated clinical trials, and these are clearly a good fit for the product. Many though have not fitted into this category but OpenClinica has still proved to be good for the job. The following two examples demonstrate our approach to some research projects that were not clinical trials.

Example 1 – Retrospective Chart Review (Double Data Collection)

We were approached by an investigator who asked us to perform double data entry for a project involving a retrospective review of 50 patient charts. However, when detailed requirements were established it became apparent that the charts had been independently reviewed by two separate investigators (double data collection). As a result traditional double data entry was not appropriate as the data entry staff were not qualified to adjudicate between data collected by two medically qualified reviewers.  What was actually required was single data entry and comparison of two separate sets of forms.

For this study we entered the two sets of data into two separate sites in OpenClinica. This allowed the data to be easily subset (into two separate libraries) once we’d imported it into SAS. We then wrote a SAS macro which used PROC COMPARE to compare the two libraries, and generate a difference report.

The final stage of the process was for the Principle Investigator to review the difference report and adjudicate the discrepancies. In over 90% of cases the discrepancies were due to different styles of documentation between the two investigators and were not significant. However approximately 10% of the differences required the subject’s chart to be checked before the discrepancy could be resolved. As the discrepancies were resolved the PI annotated the report and a final round of data entry was performed to apply the corrections in one of the OpenClinica sites. This site was designated the ‘primary’ data and was extracted for analysis.

The combination of OpenClinica and SAS performed well for a chart review where traditional double data entry was not practical. Discrepancies between the two separate reviews were identified by data management staff, which resulted in significant time savings for the two investigators.

Scenario 2 – Research Data Warehouse

Staff at Edmonton’s Pediatric Centre for Weight and Health (PCWH) required a database to collect physical examination, demographic, exercise and diet information from their patients and their patients care givers. The intention was to proactively build a research data warehouse from which future studies could perform retrospective analysis. Budgetary constraints meant that bespoke database development was out of the question, so we suggested they try collecting their data with OpenClinica. After four hours of training and with support from the informatics team the PCWH research coordinator built CRFs reflecting the content of every data collection form used in the clinic.

In order to facilitate our data extraction and analysis needs, a SAS database was created to reflect the contents of OpenClinica, but in a more analysis friendly form. Data is extracted overnight using a Java application that we developed to simplify and automate data extraction into SAS.

As the project evolved, the research teams’ understanding of their data improved and modifications were made to OpenClinica CRFs. As a result, data structures increased in complexity and additional SAS code was written in order to consolidate data structures for the end user.

It also became apparent that the database was required to manage complex relationships between the clinic’s patients and their care givers. For instance:

  • Data was required for all the care givers who attended a clinic visit with the patient
  • Care givers could be parents, other family members, foster parents, etc.
  • Un-related patients could have common care givers. A woman could be the mother of patient A, aunt of patient B and foster mother to patient C.

To handle these relationships, the team created separate numbering conventions for the subjects and their care givers. When a care giver attends the clinic for the first time, they are entered as a new subject. OpenClinica’s ‘secondary identifier’ field is used to enter a comma separated list of ‘relationship codes’ representing patients to whom the individual is a care giver and also their relationship to that patient. The data in this field is used by SAS to create a relationships table that defines all the caregivers for a patient and the relationship. It allows the researcher to subset the data based on these relationships and facilitates research involving complex multi-family structures.

Inventive use of subject numbers and the secondary identifier field in OpenClinica allowed the PCWH to model complex relationships that OpenClinica isn’t primarily designed to handle. The SAS data warehouse facilitates rapid querying of retrospective clinic data by investigators and their trainees, facilitating studies that would otherwise have been performed by chart reviews.

Conclusion

OpenClinica performs well in an academic environment where low cost, flexibility and study implementation speed are critical factors. We are able to extend OpenClinica with additional tools (notably SAS based data manipulations) in order to provide a tailored solution.

It should also be noted that as is the case with any research a deep understanding of the study and its data collection requirements is critical to the success of the project.

Rick Watts B.Sc, FICR, CSci

Team Lead, Clinical Research Informatics Core

Women & Children’s Health Research Institute

University of Alberta

Rick.watts@ualberta.ca

The Open Source Effect: Akaza Research Provides Insight into Rapid Growth of OpenClinica

OpenClinica has seen a surge in usage over the past year, according to recent survey conducted by Akaza Research.

“Our annual survey of the OpenClinica community showed strong expansion in all key measurements of system usage,” said Cal Collins, Chief Executive Officer at Akaza. “In the past year we have seen doubling in the number of OpenClinica users and subjects, and a nearly 10-fold increase in regulatory submissions.”

The company reports that a reported 168,989 subjects have been involved in OpenClinica-powered clinical trials, a 224 percent increase from the prior year. In tandem, the company identified a 246 percent increase in the number of OpenClinica software users. The figure measures users working at the sponsor or CRO level and does not include users at clinical trial sites.

“Since these figures are based on a voluntary survey of the OpenClinica community, they are likely underestimates,” said Collins. “While it can be difficult to precisely measure the usage of freely distributed open source software, they provide a clear indication of the growth in OpenClinica adoption around the world,” he added.

The Professional Open Source Model

OpenClinica stands in stark contrast against the landscape of other EDC products that are provided under a closed source license. Akaza Research’s “professional open source” business model makes OpenClinica available in two editions. The OpenClinica Community Edition is freely available to use and modify, and may be downloaded form www.openclinica.org. The OpenClinica Enterprise Edition is a certified build of the open source technology commercially supported by Akaza Research. In many respects, the company’s business model is similar to that of RedHat (Linux), MySQL (database software), and other open source companies.

The OpenClinica rapidly growing open source community currently comprises over 10,500 users and developers, many of whom help review and adapt the open source software. Roughly 33 percent of OpenClinica users are located in North America, 30 percent in Europe, 14 percent in Asia, 9 percent in Africa, 7 percent in South America, and 7 percent in Australia. OpenClinica community members drive much of the product’s evolution, and in recent years have helped to usher the technology into a wide variety of clinical trial settings.

Worldwide Acceptance in Regulated Trials

The composition of the OpenClinica community is changing over time, with an increasing number of OpenClinica users representing commercial clinical trials. Currently, 55 percent of the OpenClinica community members identifies themselves as working in industry, with the remainder in academic or government settings.

According to Collins, “the robust overall growth is highlighted by an increasing proportion of OpenClinica users representing pharmaceutical, biotech, device, and other companies. We saw a 975 percent increase in OpenClinica-powered trials used in regulatory submissions in the past year, and in the next 12 months OpenClinica adopters expect to increase this number by another 200 percent. This is consistent with our OpenClinica Enterprise Edition customer growth, where a majority of new customers are from industry.”

For more information about OpenClinica see the OpenClinica website.