Is Your Clinical Trial Software Effective, or Just Efficacious? (Part 2 of 2)

When it comes to your assessing your trial technology, your data managers, study coordinators, Investigators and senior leaders are all study subjects.

In the previous post, I described the difference between efficacy and effectiveness, an increasingly important concept in clinical research and healthcare. After stressing the importance of effectiveness research to health policy planning and patient decision-making, I summarized seven criteria for identifying effectiveness studies. Finally, I asked whether these criteria could be re-purposed beyond a medical intervention to inform how we measure the effectiveness of software systems used to conduct clinical trials.

Is it possible to assess clinical trial software through the lens of effectiveness, as opposed to just efficacy?

I believe that it’s not only possible, but crucial. Why? We all want to reduce the time and cost it takes to deliver safe, effective drugs to those that need them. But if we don’t scrutinize our tools for doing so, we risk letting the status quo impede our progress. When lives are on the line, we can’t afford to let any inefficiency stand.

In this post, I adapt the criteria for effectiveness studies in clinical research into a methodology for evaluating the effectiveness of clinical research software. I limit the scope of adaptation to electronic data capture (EDC) systems, but I suspect that a similar methodology could be developed for CTMS, IVR, eTMF and other complementary technologies. If I open a field of inquiry, or even just broaden one that exists, I’ll consider it time well spent.

Continue reading Is Your Clinical Trial Software Effective, or Just Efficacious? (Part 2 of 2)

The Forecast is Cloudy

GE recently announced it is moving its 9,000 supported applications to the cloud. Nowadays, all of us are bombarded with information about “the cloud”, and it can be hard to wade through the hype and hyperbole to understand the landscape in a way that helps us make decisions about our own organizations.

Enterprise cloud computing is a complex topic, and how you look at it depends on many variables. Below I try to outline one typical scenario. Your inputs, and the weight you give to different factors involved in making the decision will vary, but the general paradigm is useful across a wide variety of organizations.

In the interest of full disclosure, I am CEO of a company that sells cloud-based clinical research solutions (OpenClinica Enterprise, OpenClinica Participate). We adopted a cloud model after going through exercises similar to the ones below. Rather than reflecting bias, it demonstrates our belief that the cloud model offers the greatest combination of value for the greatest number of organizations in the clinical research market.

So… Let’s say you’re a small-to-medium size enterprise, usually defined as having under 1000 staff, and you are considering moving your eClinical software technologies to a public cloud and/or to a Software-as-a-Service (SaaS) provider.

Let’s start with the generic move of in-house (or co-located) servers and applications to public cloud environment. We’ll get to SaaS in a bit.

Economics

For this exercise, we’ll use the handy modelling tools from Intel’s thecloudcalculator.com. And we’ll assume you want to run mission-critical apps, with high levels of redundancy that eliminate single points of failure. We’ll compare setup of your own infrastructure using traditional virtualization to a similar one on cloud, based on certain assumptions:

The results for an internal, or “private” cloud are:

Economics

The public cloud looks as follows:

Economics2

Economics3

Source: http://thecloudcalculator.com

Wow. A 26x difference in cost. Looks pretty compelling, right? But not totally realistic – you’re probably not considering building a highly redundant in-house or co-located data center to host just a couple of apps. Either you already have one in place, or are already deploying everything to the cloud. In the latter case, you don’t need to read further.

In the former case, let’s explore the cost of adding several more applications to your existing infrastructure. What are the marginal costs of adding the same amount of computing capacity (12GB of memory, 164GB storage) on top of an existing infrastructure? We can use the same calculator to compute the delta between the total cost of a private cloud with 190GB of memory and 836GB of storage. But here it gets much trickier.

According to the calculator, our 190GB cloud costs $379,324 – the same as the 12GB cloud in the first example! Moreover, adding another 12GB of capacity pushes the cost up to $513,435, a difference of $134,111. However, if we change our assumptions and start with a 150GB cloud, then add 12GB of capacity, the marginal cost is $0.

What we’re seeing is how the IT overhead costs of running your own private cloud infrastructure tend to grow in a discrete, rather than continuous, manner, and the cost of going from one tier to the next is usually very expensive.

Our calculator makes a bunch of assumptions about the size of each server, and at what point you need to add more hardware, personnel, cooling, etc. The exact number where these thresholds lie will vary for each organization, and the numbers in the example above were picked specifically to illustrate the discrete nature of IT capacity. But the principle is correct.

Large cloud providers, on the other hand, mask the step-wise and sunk capital costs from customers by only charging for each incremental unit of computing actually in use. Because these providers operate at a huge scale, they are able to always ensure excess supply and they can amortize their fixed and step-wise costs over a large number of customers.

The examples above show that the actual costs of a public cloud deployment are likely to be significantly lower than those of building or adding to a comparable private cloud. While there’s no guarantee that your public cloud cost will be less than in-house or colocated, market prices for cloud computing continue to become more competitive as the industry scales up.

What is certain however, is that flexibility of the public cloud model eliminates the need for long-term IT capital budget planning and ensures that a project won’t be subject to delays due to hardware procurement pipelines or data center capacity issues. In most cases it can also reduce burden on IT personnel.

Qualitative Advantages

The central promise of the cloud is a fundamental difference in the ability to run at scale. You can deploy a world class, massively scaled infrastructure even for your first proof-of-concept without risking millions of dollars on equipment and personnel. When Amazon launched the S3 cloud service in 2006, its headline was “Amazon S3 enables any developer to leverage Amazon’s own benefits of massive scale with no up-front investment or performance compromises”.

It is a materially different approach to IT that enables tremendous flexibility, velocity, and transparency, without sacrificing reliability or scalability. As Lance Weaver, Chief Technology Officer for Cloud at GE Corporate identifies, “People will naturally gravitate to high value, frictionless services”. The global scale, pay as you go pricing models, and instantaneous elasticity offered by major public cloud providers is unlike anything in the technology field since the dawn of the Internet. If GE can’t match the speed, security, and flexibility of leading public cloud providers, how can you?

What You Give Up

At some level, when moving to the cloud you do give up a measure of direct control. Your company’s employees no longer have their hands directly on the raw iron powering your applications. However, the increased responsiveness, speed, and agility enabled by the cloud model gives you far more practical control that the largely theoretical advantages of such hands-on ownership. In a competitive world, we outsource generation of electrical power, banking, delivery of clean, potable water, and access to global communications networks like the Internet. Increasingly, arguments for the cloud look similar, with the added benefits of continuous, rapid improvements and falling prices.

Encryption technologies and local backup options make it possible to protect and archive your data in a way that gives you and your stakeholders increased peace-of-mind, so make sure these are incorporated into your strategy.

Risk Reduction

The model above is based on the broad economics of the cloud. However, there are other, more intangible requirements that must be met before a change can be made. You’ll want to carefully evaluate a solution to ensure it has the features you need and is fit for purpose, that the provider you choose gives you the transparency into the security, reliability, and quality of their infrastructure and processes. Make sure that data ownership and level of access is clear and meets your requirements. Ensure you have procedures and controls in place for security, change control, and transparency/compliance. These would be required controls for an in-house IT or private cloud as well. One benefit of public cloud providers in this area is that many of them offer capabilities that are certified or audited against recognized standards, such as ISO 27001, SSAE16, ISAE 3402, and even FISMA. Some will also sign HIPAA Business Associate Agreements (BAAs) as part of their service. Adherence to these standards may be part of the entry-level offering, though sometimes it is only available as part of a higher-end package. Be sure to research and select a solution that meets your needs.

External Factors

No matter who you are, you are beholden to other stakeholders in some way. Here are a couple areas to ensure you pay attention to:

  • Regulation – Related to risk reduction, you want to have controls in place that adhere to relevant policies and regulations. In clinical research, frameworks such as ICH Good Clinical Practice and their principles of Computer System Validation (CSV) are widely accepted, well understood, and contain nothing that is a barrier to deploying a well-designed cloud with the appropriate due diligence. You may also have to consider national health data regulations such as HIPAA or EU privacy protections. Consider if data is de-identified or not, and at what level, to map out the landscape of requirements you’ll have to deal with.
  • Data Storage – A given project or group may be told that the sponsor, payer, institution, or regulatory authority requires in-house or in-country storage of data. Sometimes this is explicitly part of a policy or guideline, but just as often it is more of a perceived requirement, because “that’s the way we’ve always done it”. If there is wiggle room, think about if it is worth fighting to be the exception (more and more often, the answer is yes). Gauge stakeholders such as your IT department, who nowadays are often overburdened and happy to “outsource” the next project, provided good controls and practices are in place.
  • Culture – a famous saying, attributed to management guru Peter Drucker, is that “Culture eats strategy for breakfast, every time”. Putting the necessary support in place for change in your organization and with external stakeholders is important. The embrace of cloud at GE and in the broader economy helps. Hopefully this article helps :-). And starting small (something inherently more possible with the cloud) can help you demonstrate value and convince others when it’s time to scale.

SaaS

SaaS (Software-as-a-Service) is closely tied to cloud, and often confused with it. It is inherently cloud-based but the provider manages the details all the way up to the level of the application. SaaS solutions are typically sold with little or no up-front costs and a monthly or yearly subscription based on usage or tier of service.

SaaS-IaaS-PaaS

Source: http://venturebeat.com/2011/11/14/cloud-iaas-paas-saas/

When you subscribe to a SaaS application, your solution provider handles the cloud stuff, and you get:

  • a URL
  • login credentials
  • the ability to do work right away

Which leads to a scenario like the following:

A few years ago, you typically had to balance this advantage (lack of IT headaches and delays) against the lack of a comprehensive feature set. As relatively new entrants to the market, SaaS platforms didn’t yet have all the coverage of legacy systems that had been around for years, or in some cases decades. However, the landscape has changed. The SaaS provider is focused on making their solution work great on just one, uniform environment, so they can focus more of their resources on rapidly building and deploying high-quality features and a high-quality user experience. The result is that there is far more parity. Most SaaS solutions have caught up and are outpacing legacy technologies in pace of improvements to user experience, reliability, and features. Legacy providers have to spend more and more resources dealing with a complex tangle of variations in technology stack, network configuration, and IT administration at each customer site.

 

Furthermore, the modern SaaS provider can reduce, rather than increase, vendor lock-in. Technology market forces demand that interoperability be designed into solutions from the ground up. Popular SaaS frameworks such as microservice APIs mean your data and content are likely to be far more accessible, both to users and other software systems, than when locked in a legacy relational database.

The SaaS provider has the ability to focus on solving the business problems of its customers, and increasingly-powerful cloud infrastructure and DevOps technologies to automate the rest in the background in a way that just works. These advantages get passed that along to the customer in continuous product improvements and the flexibility to scale up and down as you need to, without major capital commitments.

Conclusion

YMMV, but cloud & SaaS are powerful phenomena changing the way we live and work. In a competitive environment, they can help you move faster and lower costs, by making IT headaches and delays a thing of the past.

 

2015 Future of Open Source Survey Results

Open source software has emerged as the driving force of technology innovation, from cloud and big data to social media and mobile. The Future of Open Source Survey is an annual assessment of open source industry trends that drives broad industry discussion around key issues for new and established software-related organizations and the open source community.

The results from the 2015 Future of Open Source Survey reflect the increasing adoption of open source and highlight the abundance of organizations participating in the open source community. Open source continues to speed innovation, disrupt industries, and improve productivity; however, a reported lack of formal company policies and processes around its consumption points to a need for OSS management and security practices to catch up with this growth in investment and use.

Check out the slides below for survey results.

Reacting to #ResearchKit

OpenClinica_AppleRK Apple, Inc. has a remarkable ability to capture the world’s attention when announcing “the next big thing.” They have honed their well-known Reality Distortion Field skills for over 30 years. As the largest company in the world, and bellwether of the technology industry, Apple’s announcements are immediately recounted, opined, lionized, and criticized all across the Internet—sometimes with very limited real information on the new product itself. Of course, it helps to have their unmatched track record in actually delivering the next big thing.

ResearchKit has grabbed such attention. Maybe not as much as The Watch, but amongst the minority of us who pay attention to such things. And the reactions have been typically polarized—it’s either an “ethics quagmire” or “Apple fixing the world.”

But reality rarely presents an either-or proposition. I’ve written before on the need to use technology in simple, scalable ways to engage more participants in research and capture more data. Every form of engaging with patients and conducting research is fraught with potential for bias, bad data, and ethical dilemmas. Properly controlling these factors is difficult, and the current handling of these factors lead many to conclude that clinical research is overly “bloated and highly controlled”. There’s truth to that, but the fundamental need for good controls is real. As technology enables us to engage in new ways, how we implement such controls is likely to transform, perhaps unrecognizably so.

I don’t think Apple—or anybody—has these problems fully solved yet. And I expect we’re going to a see a vigorous debate in coming years between #bigdata and #cleandata that I hope will lead us to more of both. But ResearchKit, or at least the announcement thereof, is a game changer. Whether or not ResearchKit in its present form becomes a widely adopted platform, the impact was felt overnight: “11,000 iPhone owners signed up for a heart health study using Apple’s newly-announced ResearchKit in the first 24 hours… To get 10,000 people enrolled in a medical study normally, it would take a year and 50 medical centers around the country”. ResearchKit builds on momentum towards patient-centricity established in the last five years within pharma, NIH, online patient communities, mHealth, and health care, and uses Apple’s consumer clout to bring it to the attention of the average person on the street.

So let’s break down what we know about ResearchKit. Since this is a blog about OpenClinica, we’ll also share early thoughts on how we see OpenClinica, ResearchKit, and OpenClinica Participate fitting together.

It’s Open Source. Great move! We’ll learn more about what this means when the code is released next month.

The technical paper indicates it is a front-end software framework for research, and that they expect it to expand over time as modules are contributed by researchers. Through use of both platforms’ APIs, OpenClinica could serve as a powerful backend and ‘brain’ to ResearchKit.

It’s not clear if data goes through Apple’s servers on its way to a final destination. I also haven’t seen anything from Apple mentioning if it will be portable to other non-iOS platforms (which represent 80% of mobile device market share), though its open source nature would suggest that will be possible.

Surveys. Analogous in many ways to the forms module in OpenClinica Participate, it is a pre-built user interface for question and answer surveys. As somebody who’s worked in this realm for years, I know that this can mean a lot of things. What specific features are supported, how flexible is it, how easy is the build process? Perhaps most important, can it be ported to other mobile app platforms, or to the web?

Informed Consent. The need for fundamental ethical controls for for research conduct and data use are just as important in the virtual world as they are in the brick-and-mortar realm, and Informed Consent is a cornerstone. I’m glad to see ResearchKit taking this on; I don’t expect they have it 100% figured out, but their work with Sage Bionetworks, who has released an open toolkit on Participant Centered Consent, is a great sign.

Active Tasks. Maybe the most exciting component, here’s where ResearchKit takes advantage of the powerful sensors and hardware in the device and provides a way to build interactive tests and activities. In this way, I expect ResearchKit will be a great complementary/alternative frontend to OpenClinica Participate when specialized tests tied to specific, highly-calibrated devices are required.

In general, the promise is big: that technology will lower barriers in a way that leads to fundamental advances in our understanding of human health and breakthrough treatments. That we’ll go from data collected once every three months to once every second, and we’ll encounter–and solve–problems of selection bias, identity management, privacy, and more along the way. And that, according to John Wilbanks at Sage, “there’s coming a day when you’re not going to have an excuse to have a tiny cohort just because you chose not the use digital technologies to engage people.”

The Future of Open Source

It was a privilege for OpenClinica to help with the “Future of Open Source” survey recently completed by Michael Skok of North Bridge Ventures, Black Duck and Forrester. The survey polled users and other stakeholders across the entire spectrum of OSS.

Recently published results from the survey substantiate the idea that open source is ‘eating the software world’s lunch’ (to borrow a phrase from Michael). OSS powers innovation, increases security, and enables a virtuous cycle of proliferation and participation across major sectors of our economy. This is even true in healthcare and life sciences, and we are seeing these trends within OpenClinica community. People are adopting OpenClinica and other open source research technologies because of the quality, flexibility, and security they provide, not just to save a buck or two.

What I find particularly significant in the results is the increased recognition of quality as a key driver of adopting open source. 8 out of 10 survey respondents indicate quality as a factor for increased OSS adoption. This has vaulted from the #5 factor in the 2011 survey to #1. In research, quality and integrity of data are paramount. OpenClinica’s active (and vocal!) community’s constant scrutiny of the code, and continuous improvements demonstrate the power of the open source model in producing quality software. Furthermore, working in a regulated environment means you need to do more than just have quality technology. You also must provide documented evidence of its quality and know how to implement it reliably. The transparent development practices of open source are huge contributors to achieving the quality and reliability that clinical trials platforms require. Knowing that feature requests and bug reports are all publicly reported, tracked, and commentable means nothing can hide under the rug. A public source code respository provides a history of all changes to a piece of code. And of course, it greatly helps that many of the key tools and infrastructure that power open source projects are open source themselves.

That’s just one set of factors driving us to a more open, participatory future:

“As a result of all this, Open Source is enjoying a grassroots-led proliferation that starts with a growing number of new developers and extends through the vendors and enterprises to the applications and services, industries and verticals, reaching more people and things in our everyday lives than ever before… there are now over 1 million open source projects with over 100 billion lines of code and 10 million people contributing.”

One thing I predict we’ll see a lot more of in the next year, especially for OpenClinica and life sciences as a whole, is greater interaction between projects and communities. OSS reduces traditional barriers and lets more people ‘get their hands dirty’ with tools and technologies. As OSS tools, libraries, and apps proliferate, innovation will increasingly come from the mashups of these projects.

Follow the survey findings and updates @FutureofOSS and #FutureOSS  

Using Patient-centered Technology to Improve Recruitment and Retention

Sponsors of clinical research must increasingly focus on improving patient engagement in order to meet many of today’s research challenges. Promising disruptions are already under way that could define new models for patient recruitment and retention.

In a time when drug development success is becoming scarcer and more expensive, the industry is looking everywhere it can for new, innovative approaches to improving health. Meeting recruitment goals is one of the biggest challenges for traditional clinical research. Less than one-third of people who come in for a screening end up completing a clinical trial.1 Thinking in a more patient-centric manner can help is in recruiting patients. A fundamental idea behind patient-centered research is to “amplify the patient’s role in the research process.”2 Employing new ways to engage patients and physicians while increasing their level of knowledge and trust can improve the sponsor’s ability to meet recruitment goals.

One often overlooked factor for study participation and retention is convenience. Raising the level of convenience for both the investigator and participant can eliminate a huge obstacle to non-participation or non-completion. There are many ways to incorporate increased levels of patient and physician convenience into trial design and execution, particularly using Internet-based technologies. For instance, social media can be an effective recruiting tool and an important way to build trust with targeted populations. Disease-specific online communities are becoming more and more prominent for chronic diseases. Matchmaking tools act as mediators that draw together researchers and participants. “Traditional” social media offers a less targeted, but no less effective, way to engage patients and investigators.

In general, the four key determinants of a person’s likelihood to participate in a trial are prior participation in research, existing relationships with researchers, involvement of trusted leaders, and trust in the organization. Keys to recruiting success in social media should keep these determinants in mind, and engage communities in a thoughtful, ethical way while respecting the norms of the community you are targeting.

Participant retention post-recruitment can be improved by strengthening the connections between participants and researchers, and enhancing communication structures to support these relationships.3 Capturing Patient Reported Outcomes electronically (ePRO), through the web or mobile devices, offers a way to interact with the participant in a meaningful way while also capturing critical data. For instance, offering the ePRO user risk scores and health recommendations based on their data, or using gamification techniques to increase protocol adherence, can enhance the traditional ePRO experience by offering direct, immediate value to the user. Enabling a “Bring Your Own Device” (BYOD) strategy can increase convenience for populations who already own their own smartphones or tablets. Of course, the study design and applicable regulatory considerations should drive when and how these techniques are used.

Increased focus on the patient experience is not a phenomenon unique to research, but something that is rapidly permeating healthcare systems. These rapid changes can enhance research engagement. There is enormous potential to capture far more robust data and have better follow up than ever before as widespread infrastructure is put in place for coordinated team-based care, home-based continuous monitoring, and wireless data reporting from medical devices. The (still elusive) promise of using the Electronic Health Record system in research to identify participants and capture clean, accurate trial data is more critical than ever before. As medical practices become more electronic and less paper-driven, investigators and staff should be engaged by providing them trial-specific information at the points in their workflow when they can best make use of it. Conversely, requiring them to go outside the workflows and systems they use in routine practice creates complexity and hassle that can deter research participation. A new level of integration between research and health data systems, based on standards (which exist) and open interfaces (which are coming, as part of Meaningful Use), will be necessary to make good on this potential.

As difficult research questions drive increased complexity in trial designs, many feel that the answer is to use technology in simple, scalable ways to engage more participants in research and capture more data. Dr. Russ Altman, a physician and Stanford professor recently told the New York Times, “There’s a growing sense in the field of informatics that we’ll take lots of data in exchange for perfectly controlled data. You can deal with the noise if the signal is strong enough.”4

References

1. Getz, Ken, The Gift of Participation: A Guide to Making Informed Decisions About Volunteering for a Clinical Trial, 2007, p40.

2. Pignone, Michael, MD, MPH, Challenges to Implementing Patient-Centered Research, Ann Intern Med. 18 September 2012;157(6):450-451

3. Nicholas Anderson, Caleb Bragg, Andrea Hartzler, Kelly Edwards, Participant-centric initiatives: Tools to facilitate engagement in research, Applied & Translational Genomics, Volume 1, 1 December 2012, Pages 25-29, ISSN 2212-0661, 10.1016/j.atg.2012.07.001.

4. http://www.nytimes.com/2013/01/15/health/mining-electronic-records-for-revealing-health-data.html?ref=health?src=dayp&_r=3&

Does EDC Help or Hurt Site Relations?

Getting reluctant clinical research sites to embrace technology such as electronic data capture (EDC) software can be difficult. This is a recipe for troubled relationships between the sponsor/CRO and sites. However, just as it is possible for a poor EDC implementation to erode sponsor-site relations, it is also possible for the EDC software to help cultivate improved relationships. Take a look at the new whitepaper, “Improving Site Relationships through EDC” to learn about some important considerations when thinking about site relations in the context of EDC.

The Evolution of Electronic Data Capture

OpenClinica was recently featured in an article in Genetic Engineering and Biotechnology News titled “Commandeering Data with EDC Systems,” written by Dr. James Netterwald. The article briefly recounts the early days of clinical trial Electronic Data Capture (EDC). But how far have we come? Dr. Netterwald’s title (perhaps unintentionally) conjures up images of struggle and strife, which may be perhaps more a more apropos description of the journey of Electronic Data Capture than it may first appear.

As an industry, it’s taken us a good 20 years to get to where we are, and to be plain, it’s been a slow start. (In my own defense, I, and my company Akaza Research, have only been a party to the industry for the last 5 of those 20 years.) Climbing the evolutionary ladder from shipping laptops to sites to keying data into electronic case report forms is certainly progress by any measure. However, while the days of mailing tapes and disks are over, the days of real electronic data capture are yet to come. Today, most experts agree that somewhere between only one-half and two-thirds of all new clinical trials use EDC software, an of this only a very small fraction are “e-source,” defined as collecting data in electronic form at its source as opposed to keying it in from some other source. In some ways it is ironic that cutting-edge biopharmaceutical technologies are developed themselves with technologies that are, relatively speaking, much further down the technology food chain.

Notwithstanding, there are some enterprising few who have pushed the pace towards true EDC. Spaulding Clinical, a large phase 1 unit in West Bend, Wisconsin has developed a system that automatically captures ECG data from their facility’s patients and directly populates the clinical trial database with these data. A patient wears the ECG device and the data are transmitted wirelessly to the EDC system. However, this slick and highly productive solution was not developed by either the ECG vendor or the EDC vendor. It was developed by hand by one of Spaulding’s own software developers.

Why isn’t this type of solution more commonplace in clinical trials? What prevents the industry from making the most of today’s information technology? With the strong incentives currently in place to make research more efficient, our field could certainly benefit from some more forward thinking.

– Ben Baumann

OpenClinica Community Surpasses 10,000 Members …and oh yeah, what is this open source thing?

Heartfelt thanks to everyone who has supported the OpenClinica project over its relatively brief history. Our community now stands at over 10,000 registered members, representing a 3-fold increase in size over the past two years alone. With members in over 70 countries across six continents, open source is now a central part of the clinical trials software landscape. This is a major accomplishment that we should all be proud of.

While 10,000 may sound like a lot of people, there are still many within the clinical trials industry who do not understand the key concepts of open source. Other software categories have a high prevalence of open source offerings. For instance, when you look at database products (like MySQL, Postgres) and operating systems (like Linux, Android, BSD) there are numerous open source options. Open source is even widely prevalent in the EMR/EHR space, with OpenVista, and over 20 others to choose from.

As OpenClinica ushers the benefits of open source into the clinical trials space, it is instructive to periodically revisit the fundamentals of what exactly open source is.

What is open source?

Open source is a type of free software license–free as in “freedom,” not “beer.”[1] It is not “freeware” and it is not “shareware.” More specifically, open source provides users with[2]:

  • The freedom to run a program, for any purpose
  • The freedom to study how a program works and adapt it to a person’s needs. (Access to the source code is a precondition for this.)
  • The freedom to redistribute copies so that you can help your neighbor.
  • The freedom to improve a program and release improvements to the public, so the whole community benefits. (Access to source code is a precondition for this.)

There are numerous open source software licenses based on the above tenants and roughly 60 open source licenses have been approved by the non-profit Open Source Initiative. The OpenClinica Community Edition is distributed under the LGPL open source license.

Open source as a development model

The software development models around open source projects are typically characterized by transparency and collaboration within the community. Opening the product up to the community, allowing anyone to see the good with the bad, helps to quickly uncover problems and identify areas for improvement. Most open source projects will publicly maintain a project roadmap and defect tracking system. Release cycles of active open source projects tend to be early and often.

The result of such openness and transparency is software that is often more reliable and better performing than proprietary, closed alternatives.

What is professional open source?

A symbiotic relationship exists in a health professional open source model between the Community, Company, and Customer.

Some people may think of open source projects as purely volunteer efforts. That is definitely not the case! While governance models vary from project to project, commercial enterprises have helped make open source consumable by ordinary people and businesses. For example, through its OpenClinica Enterprise Edition, Akaza Research provides support and regulatory assurances that help to minimize business risk and ensure success for organizations wishing to use OpenClinica in mission critical settings. Organizations can turn to Akaza to rapidly develop in-house expertise, obtain hosting and expert professional services, and ensure their OpenClinica systems and users are productive and satisfied.

A pervasive trend in software

Open source is everywhere[3]. From the Firefox web browser to the most popular websites, everyone who uses the World Wide Web uses open source. As web-based technology, OpenClinica and the OpenClinica community are direct beneficiaries of numerous other open source projects. Those within the clinical trials space who recognize the significance of open source will be a step ahead of their colleagues.

– Ben Baumann, Co-Founder, Akaza Research, LLC

Want to be an OpenClinica Community Member? Members get free access to OpenClinica software downloads, Issue Tracker, email forums, and the OpenClinica Case Report Form (CRF) Library. Register at http://www.openclinica.org/register.php.

Footnotes:
[1] See Open Source Software Definition, http://www.opensource.org/docs/osd
[2]From the Free Software Foundation: http://www.fsf.org/licensing/essays/free-sw.html
[3]The SourceForge repository of open source codebases counts over 230,000 OSS projects.

Selling open source without mentioning open source

I am a regular reader of  “The Open Road” blog by Matt Assay on news.com. In one of his latest posts, “Getting open-source criticism wrong”, he does a great job of making the case that commercial open source software is about ease of adoption, flexibility, and choice.

It struck a chord because my sales team and I spend a great amount of time and effort explaining to prospective customers that we offer the same level of quality, stability, performance, service, and support as a proprietary vendor. In many cases we must meet a higher threshold than those vendors, because we do not have the lock-in of a commercial software license to compel customers to come back to us for repeat business. Our track record of successful long-term customer relationship is evidence we meet this threshold.

In certain sales situations, for the sake of simplicity and clarity, we have to focus only on these apples-to-apples characteristics, and do not have the opportunity to educate on the economic and technical advantages of OSS as much as we would like. It’s great to know that our open source clinical data management software technology and service offerings can stand successfully on these merits. However, as many readers of this blog already know, open source offers an additional set of critical benefits: “the ability to adopt software rapidly and at low cost, the flexibility to develop and extend their systems as they choose, and the ability to reduce risk by obtaining paid commercial-grade [or better] support”. As more decision makers are coming to understand, it is following this path, rather than the adoption of pricey, monolithic proprietary software, that leads to better outcomes and greater ROI.