Automate Your Collection of Lab Reference Ranges

Data managers invest a lot of time and attention documenting lab processes, and for good reasons. Regulatory compliance demands it. Also, ensuring the validity and clinical significance of lab results is critical to assessing safety and efficacy. But while necessary, this process is often inefficient and error-prone.

In an ideal clinical study, every lab sample would, within minutes of collection, find its way to a central lab whose equipment was forever up-to-date, whose validations were always fresh, and whose inner workings were transparent to the data manager. But clinical trials aren’t conducted in an ideal world. More often than not, data managers and local lab managers share an ongoing responsibility to document equipment features and report on results collected on a variety of instruments, all calibrated differently. The challenges associated with this process are familiar. Equipment changes. Validations expire. And one lab’s “normal” may be another lab’s “low.”

The task of keeping labs up to date for many data managers is akin to keeping dozens of centrifuges spinning at the same rate, all at the same time. Collecting lab reference ranges from one lab for one analyte may be straightforward, but when the process is multiplied across dozens of analytes and sometimes hundreds of sites, your study can be exposed to significant time delays and human error. Success in this task, like most, hinges on clear expectations and guidance. Here is where good data managers shine. By providing sites with explicit instructions, a deadline, and tools to boost completeness and accuracy, data managers can make the collection of reference ranges a lot less painful and time-consuming.

Anatomy of a Lab Reference Range

Ranges are always defined by either:

  • a standard applied to all labs contributing data to a study (“textbook ranges”), or
  • the individual lab

Often, the difference between the two is minimal, so adopting the textbook range can save time and administrative burden. For measures that are critical to analysis, though, using a textbook range may not be suitable. In that case, each local lab manager (or the site coordinator representing that lab) must communicate to the study’s data manager their “in house” range for all analytes measured in the study. In both cases, a range is not complete unless it specifies

  • the name of analyte
  • the unit of measure
  • the lower limit of normal, by gender and age
  • the upper limit of normal, by gender and age

Even for one analyte, the normal range for a 25-year-old female may differ from that of a 50-year-old female, or a 25-year-old male. Consequently, specifying a range for an analyte often means specifying a number of “sub-ranges” that, taken together, associate a normal range for every possible patient. For example:

In the course of providing comprehensive ranges for dozens of analytes, it’s easy for a lab representative to overlook (or duplicate) an age or gender inadvertently. A well designed, dynamic form for capturing these requirements can help ensure exactly one range is provided for any given individual.

Anatomy of a Lab Reference Range Collection Form

Just as a value without a unit of measure is meaningless, so too is a local lab range that is not tied to a particular lab. Along with their ranges for each study analyte, labs should also provide a set of identifying information. The data manager, as part of her request to provide the ranges and lab information, should also specify the study for which the ranges are being collected. A complete lab reference range collection form includes all of these components.

Specified by the data manager

  • the name of the sponsor and study (avoid abbreviations or paraphrases)
  • which analytes are included in the study, and therefore require ranges from the lab
  • where the lab representative must send the completed file
  • a deadline for completing the file

Entered by the lab representative

  • the name, address, and applicable ID numbers (e.g. CLF, or core laboratory facility, number) of their lab
  • the name of the Principal Investigator for the site and study it serves
  • the effective date of the ranges to be provided
  • the LLN and ULN for each analyte, by gender and age

Tools You Can Use

For users of OpenClinica, we’ve designed a form template that can be used as a reference range collection form, which includes the components listed above. Try it here! Would you like to use a customized version of this form in your study? Contact your client support lead. For those not using OpenClinica, we’ve built an Excel workbook. Download it for free here.

Click either image above to test this form.

Click either image above to download the Excel version.

Staying Current

Regardless of how labs communicate their reference ranges, it’s essential that the communication is ongoing. Changes in equipment or clinical guidelines often occasion changes to upper and lower limits of normal. That’s why an effective date must be documented for all ranges. Good data managers encourage sites to communicate any such changes promptly. Great data managers give them the reminders, and tools, to do so.

We welcome your input on the workbook above, just as we do on our data management metrics calculator. Please let us know what you find most valuable.

What DIA Stands For, To OpenClinica

What is it about the annual Drug Information Association meeting that energizes those of us working to improve the eclinical experience? Sure, it’s a terrific opportunity to showcase our products and services to research teams that could benefit from them (read: business development). But it’s more than that. “Just make the sale” is no credo for this industry. We serve those who serve patients, tirelessly working to enhance their lives. It’s impossible not to feel privileged by that responsibility, and the DIA conference is a chance for OpenClinica to demonstrate once again our resolve in meeting it. Every summer, we’re reminded to step back and prove to peers that our business aligns with the all-important goal of making trials as effective as they can be, so that safe and effective medicines get to the right patient at the right time. That means distilling the complex processes behind data capture into a story from which every DIA attendee, from data manager to CRA, can draw inspiration.

Back in February, I suggested an outline for that story: “making the complex easy”. I’m pleased to report that that narrative is gathering momentum. Our upcoming release combines power and ease-of-use in a manner that we believe is unprecedented. It will enable data managers, researchers and study participants to do more in less time, while rediscovering that sense of joy a well-designed web experience offers. It’s our way of keeping trials from turning into ordeals.

So yes, as a business, we want to grow by meeting more research teams and sharing the OpenClinica story with them. The annual DIA conference helps us achieve that. But by bringing together the most accomplished teams in drug development, the conference does more. It’s a place to improve our understanding of the challenges research teams face, and stay accountable to the ideals that led to our founding and all of our growth since then. If you’re attending DIA in Chicago, I hope you’ll find time to visit us at booth #1748, so we can show you just how energized those ideals are keeping us.

A Preview of our DIA Plans

Talks from drug development luminaries. Exhibits that combine “luxury apartment” with “miniature theme park.” And a city that offers some of the world’s best modern architecture.

Those descriptions don’t do justice to what DIA 2017 has in store, but they do fit the experience. As any veteran attendee can attest, there’s an outsized splendor to the conference. But it isn’t splendor for splendor’s sake. Half of it is celebration for the advances the industry has made in bringing life-changing therapies to market. The other half is a rallying cry to bring even more.

 

OpenClinica will be there, to join the celebration and the rallying cry. Attendees can find us at booth 1748, near the central break lounge. And we plan on using our patch of exhibit space to the fullest.

Our goal is simple: we want to thrill everyone there. We’ll do that by giving visitors to our booth an up-close look at the new OpenClinica, featuring a collaborative study designer, forms with beauty and brains, rich, visual reporting, and more. We’ve come to call this “re-engineering the e-clinical experience,” because it’s our hope that software in this industry can shed its reputation as a “utility” and gain one as the way researchers want to work (and the way participants want to contribute).

We think this new experience is as thrilling as, say, journeying through the human brain, or conducting zoological research in an alternate universe. Almost as thrilling, anyway. So we’re bringing along an Oculus Rift to help make our point. Visitors to our booth will get a chance to wear the goggles, grab the controllers, and immerse themselves in some fantastic worlds. Best of all, we’ll raffle the system off to one lucky winner.

But the most valuable offering comes from the attendees. We get to hear directly from them on what’s working, what’s not, and what needs changing in the world of eclinical. We’re confident we’ve addressed many of those needs with our upcoming release, but as company devoted to continuous innovation, we’re never finished learning and iterating on our successes.

Will we see you there? If so, be sure to schedule a visit to our booth, and brace yourself for the e-clinical experience you’ve always imagined.

 

A Prescription for Data Management Health

“Three days to enter data, five days to answer queries.”

The rule couldn’t be any clearer. You’ve told your sites at the IM and reminded them in each newsletter. You know you won’t get 100% compliance, and that’s fine. You’re reasonable.

But this is getting out of control.

As a data manager, you’ll always live with missing forms, blank fields and open queries. It’s a chronic condition that gives rise to acute episodes around interim and final locks. You’ve learned to manage it, even thrive with it, but you know there’s got to be a more effective treatment regimen.

Good news. While there’s no panacea, I’d like to offer a tool you can begin using today, regardless of your systems or processes, to spur your sites onto improved data entry, query resolution, and even enrollment. But as with any treatment, we need to consider directions, precautions and potential side effects.

First, though, some background. If you use EDC and IxRS to facilitate data collection and enrollment, you’ve probably made it a habit to pull their stable of available reports at some regular interval. (If not — if you’re relying solely on the summary statistics and visualizations available on these systems’ dashboards — consider getting acquainted with the detailed exports. This post will explain why.) These reports are almost always available in some Excel-readable format. Chances are you’ve become practiced at applying some formulas to the data inside. (If not, here’s a tutorial on getting started.) The calculations you make are vital in assessing which sites are leading the pack in subject recruitment and data management tasks, including the timely entry of data or resolution of queries. You and your fellow study leaders depend on this information to refine projections, meet lock deadlines, and offer assistance to those sites behind the curve on key operational metrics. But do you share this information with sites?

Yes! As interim locks approach, I always email out the number of total open queries and missing forms, along with encouragement to tidy these issues up. If that’s your response, you’ve already adopted a best practice. But there’s more you can do.

Provided you do so with the right context and tone, you can and in many cases should communicate to each site exactly how they compare to their peers on several key metrics, from average open query age to subjects screened per month. When you supply this information, you recognize the site’s invaluable contributions, feed their natural and justified curiosity, and tap their desire to maximize their performance.

This practice involves three major challenges. The first challenge is calculating useful, “apples-to-apples,” site metrics from the raw data found in your EDC and IxRS reports. The second is distributing this information to each site in a systematic way.  The third is couching this information in a message that conveys gratitude and support. But each can be met.

Making the calculations

Here, I can offer some great news for users of OpenClinica, and a valuable tool for everyone. OpenClinica now supports a suite of configurable reporting dashboards, providing data managers and those they authorize (including sites) with clear, real-time visualizations of their study data. If you’re currently using OpenClinica, contact us and we’ll gladly share more details.

To help you get started now, regardless of your EDC or IxRS, we’ve created a workbook that performs dozens of calculations for each of your sites based on reports common to nearly every system. It’s free, and guides you step-by-step through converting raw exports into powerful analytics.

Distributing the information

Once you’ve created a table of performance metrics by site, you have the beginnings of a “mail merge.” You simply need to add a column specifying the email address of the individual responsible for data entry for each site.

The steps for executing a mail merge differ from email client to email client. However, some starter documentation is available here:

Setting the context

So far, we’ve touched on the technology of quantitative performance reporting. But what about the art? It’s crucial that sites understand that your intent isn’t to chastise, but to inform and encourage. The metrics you calculate are just one piece of a broader discussion, which would include particularities that simply aren’t reflected in a spreadsheet, such as patient availability and staff experience. A site whose “screened per month” measure ranks in the bottom quartile may have had to overcome incredible hurdles to enroll their six or seven subjects. Meanwhile, they may be adding valuable thought leadership.

To establish the right tone, you might consider adopting a message template like this one:

Hello Site <<site_id>>,

The Data Safety Monitoring Committee will meet two weeks from today, so it’s important we enter all data for visits that occurred on or before March 31st by this Friday, and close all queries by next Wednesday. We can’t thank you enough for your diligence in screening qualified patients and entering data. As you well know, your efforts here support not just our study, but the patients themselves.

It’s been an incredibly busy month, and we recognize it’s not always possible to enter data within five days of events. We realize some queries take weeks to close. And we know your first priority remains and should remain your patients, whether they’re participating in this study or not. Your accomplishments are all the more impressive in light of these facts.

We believe you deserve insight into the contributions you’re making to our study. That’s why we’re initiating a weekly, custom report to share your site’s progress with you. We understand you may be curious about how your “numbers” stack up against those of those of other sites, so we’ve included some comparative measures in this report. Also, to help you navigate data management, we’ve listed out your missing forms and open queries as of the report date shown. (Please note that you may have closed one or more queries or submitted one or more forms in time between report generation and your receipt of this email. The numbers below are not real-time.)

Thank you again for all you do in service to our study and your patients!

Site <<<site_id>>> By the Numbers
Report date: <<<date>>>
Screened : << screened>>
Failed : << failed>>
Randomized : << rand>>
SF Rate (Failed / Failed + Randomized) : << sfrate>>
Months Activated : << mons>>
Screened/Month : << srate>>
Screen Rate Country Rank : << srankc>>
Screen Rate Global Rank : << srankg>>
Randomized/Month : << rrate>>
Randomization Rate Country Rank : << rrankc>>
Randomization Rate Global Rank : << rrankg>>
Days Since Last Screening : << dsls>>
Days Since Last Randomization : << dslr>>
Open Queries : << oq>>
Queries Per Subj Screened : << qrate>>
Queries/Subject Country Rank : << qrankc>>
Queries/Subject Global Rank : << qrankg>>
Average Age of Open Queries : << avgqage>>
Age of Oldest Query (Days) : << oldestq>>
Query List : << qlist>>
Missing Pages : << mpgs>>
Missing Pages Per Subject Screened : << mrate>>
Missing Pgs Per Subject Country Rank : << mrankc>>
Missing Pgs Per Subject Global Rank : << mrankg>>
Average Age of Missing Pages (Days) : << avgmpgage>>
Age of Oldest Missing Page : << oldestmpg>>
Missing Page List : << mpgslist>>

Some final precautions

How often you provide a report like the one above, and what you include in it, are at your discretion. Fast-moving infectious disease trials may warrant a weekly report. Large, endpoint-drive cardiac studies may benefit from just one report per month. Also, carefully consider the cultural differences that exist among sites in various countries. There may be no acceptable way of communicating comparative metrics in some.

There’s power in your metadata. You should consult it frequently on your own, weekly if not daily. You can use the workbook above to do that and nothing more. But we have an obligation to patients worldwide to conduct trials in the most efficient manner compatible with the highest data quality. Bringing some gentle pressure to bear on sites is one method of achieving that. If you adopt some version of the practice described in this post, please let us know your experience with a comment or email.

Around the World in Three Data Integrations

Big data has been a recurring topic in medical research news for years now. It’s a topic that deserves our attention. Big data’s potential to revolutionize fields like genomics and to advance precision medicine generally is stunning. Today, though, a lot of the press is speculation. Robustly effective designer drugs for cancer, based on the patient’s genetic markers, remain an ideal that is likely decades away.

But if we adopt a broader conception of big data–one that includes the massive infrastructure supporting social media, the Internet of Things, and (potentially) interoperable health record platforms–real world applications are not hard to find.

March 2015: Researchers at Stanford University recruit 11,000 subjects into a cardiovascular study in 24 hours using Apple’s open source ResearchKit app.

September 2015: “By outfitting trial participants with wearables, companies are beginning to amass precise information and gather round-the-clock data in hopes of streamlining trials and better understanding whether a drug is working… So far, there are at least 299 such clinical trials using wearables, according to the National Institutes of Health’s records.” – Bloomberg News

July 2016: The National Cancer Institute introduces OpenGeneMed, “a portable, flexible and customizable informatics hub for the coordination of next-generation sequencing studies in support of precision medicine trials.”


One facet of these examples stands out. For all their diversity, projects that rely on big data rely just as much on collaboration.
Moving from genomes to biomarkers to disease risk models and personalized treatment requires more than one big dataset: it requires the integration of data from multiple systems that are secure, geographically separated, and disparately schematized.

Ten years ago, the ability to handle this task might have been seen as a leading-edge, if not commonly leveraged, feature of clinical technology. Today, software that cannot facilitate integration is doomed to obsolescence.

eCitizen of the Data World

What does this requirement mean for EDC? Simply put, those of us building data capture solutions need to look far beyond the “coordinator keying in vitals” use case. (Our solution for that use case had better already be rapid, reliable and easier to execute than ever, considering the burdens placed on trial sites in 2017.) With “insight by integration” at the forefront of research strategies, we technologists had better think of our system as a world traveler: one familiar with the laws in multiple countries, authorized to enter and leave those countries, and fully knowledgeable of their languages and customs. In the world of data management, this means the ability to pass authentication to enter a source database, map the data to a target, and leave the source while maintaining data provenance.

As a long-standing promoter of open, standards-based interoperability, OpenClinica represents this “world traveler.” The native language of OpenClinica’s EDC is the Clinical Data Interchange Standards Consortium Operational Data Model (CDISC ODM). This fact alone makes the OpenClinica data model an ideal cosmopolitan, instantly conversant with research peers around the globe. But holding fast to one standard is not sufficient. We need to be willing to learn new languages. By offering a well-documented web services API, OpenClinica makes it easy for its users to leverage RESTful web services, together with OAuth protocol version 2.0, to systematically:

  • extract data from almost any third-party source (e.g. labs and imaging centers),
  • associate each element of that data to the relevant Case Report Form (CRF) field.

APIs and authentication protocols offer the most direct route to turnkey integration. But it’s not enough to be powerful in the pursuit of data integration. A system has to be flexible, too, when tapping data sources that aren’t available to an API. For OpenClinica, this means providing a host of configurable tools to data managers and data entry personnel.

  • OpenClinica’s Jobs feature allows for custom imports from local files. A Job may be scheduled to run at any frequency, so that users responsible for data entry based on a regularly updated flat file (e.g. a CSV on their hard drive) may provide that data without keying in each element. A Job well-defined and set up just once improves accuracy and saves hours of research time.
  • An Import Data feature makes ad hoc batch uploads easy, as well. Users simply generate a XML file based on OpenClinica-supplied Object Identifiers (OIDs) to map data from the import file to the EDC.
  • OpenClinica supports a variety of Single Sign On (SSO) protocols, reducing repetitive authorization while maintaining security. OpenClinica is also an early and already experienced adopter of SMART on FHIR, a set of open specifications to integrate its core EDC with Electronic Medical Records (ER) and other health IT systems.

A Look at Our Passport

So far, I’ve outlined a set of capabilities required of any EDC in 2017, and claimed that OpenClinica meets them all. But where’s the evidence? In the second half of this post, I’m going put three of our partners in the spotlight. For each, OpenClinica was able to play a pivotal role in bringing together multiple data sources.

The Dutch Translational Research IT (TraIT) project, an initiative from the Center for Translational Molecular Medicine (CTMM),  “enables integration and querying of information across the four major domains of translational research: clinical, imaging, biobanking and experimental (any-omics).” While multiple systems power that integration, OpenClinica is the central hub. TraIT continues to host and support https://www.openclinica.nl, having joined together 10 trials on the platform in October of 2011. By March of 2015, adoption had grown to include 852 users at 157 sites conducting 136 studies, and by October of 2016, that usage had grown to more than 2,800 researchers and 250 research projects.

Among the selection criteria used to evaluate and ultimately select OpenClinica as a partner, TraIT specifically cited:

  • “links to other data storage and analysis tools within the TraIT platform, allowing researchers to integrate and analyse case report data, imaging data, experimental data and bio banking information,” and…
  • the “possibility to integrate with Trusted Third Party which handles proper (de-)identification of participant data within OpenClinica and other tools/services used in TraIT.”

It is worth noting that, in addition to an infrastructure that allows database integration, TraIT relies equally on OpenClinica’s open source model to build custom integrations. “The advantage of the Open Source model compared to a proprietary model, is that multiple independent contributors can review the source code, making enhancements which are then added to the version available to the entire OpenClinica community.”

Usage by the broader community helps ensure the innovation’s longevity and continued evolution. TraIT leverages these tools (such as the OC Data Importer) to help their sites import vast quantities of data in bulk fashion, eliminating transcription errors and delays.

The 100,000 Genomes Project, led and funded by Genomics England, is another example of a large-scale effort to combine clinical and genomic data. The 100,000 Genomes Project is sequencing 100,000 genomes in order to:

  • better diagnose rare disease,
  • understand its causes, and
  • set a direction for research on effective treatment

Whole genome sequencing (WGS) offers the best hope for determining which genetic mutations give rise to particular phenotypes, including disease states. WGS yields the syntactical equivalent of the three billion nucleotide base pairs that make up just one strand of one individual’s DNA, so a research program involving even one such sequencing has already entered the territory of “big data.” While highly specialized systems are responsible for sequencing itself, and yet others for the analysis of the output, an equally essential tool for this research is a system that can manage the clinical data and biospecimen tracking of subjects visiting one of several geographically dispersed clinical centers. Here, too, OpenClinica serves as the hub. Researchers at 13 NHS Genomic Medical Centers are using OpenClinica to register participants, capture clinical information, and ensure that blood samples stay matched with their de-identified contributors.

Project leaders have made public a 10-page guide to researchers on this process, one whose brevity and clarity speaks to how easy OpenClinica makes it. Due the dedication of the researchers, collaboration of participants and the fitness of the technology, the project is on track for completion in 2017.

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Click image to enlarge

PECARN, the Pediatric Emergency Care Applied Research Network, is the first federally-funded pediatric emergency medicine research network in the United States. To date, PECARN has conducted 24 studies that have already changed how clinicians are preventing and managing acute illness and injury in children.

As part of their mission to advance clinical practice, PECARN has taken a lead role in the implementation and study of clinical decision support tools. For all the potential benefit offered by these tools, questions remain about their adoption and effectiveness. Do physicians and nurses generally follow evidence-based recommendations for treatment or diagnostic procedures? When they do, are outcomes improved?

To help answer these questions, PECARN study leaders conducted a nonrandomized trial with concurrent controls at thirteen emergency departments between November 2011 and June 2014. These thirteen departments were consolidated into ten research sites. At eight of these sites, clinicians creating an EHR record for any patient <18 years old with minor blunt head trauma were automatically presented with a custom template. This template solicited additional data about the injury before providing recommendations on CT use and risk estimates of clinically important traumatic brain injuries. (CT imaging of the brain is associated with a non-negligible risk of tumor formation in those who undergo the procedure, especially children. At the same time, early detection of ciTBI–i.e. injuries leading to death or requiring neurosurgery, intubation for more than 24 hours, or hospital admission for two or more nights–is critical for effective intervention. The recommendations provided by the EHR template were intended to limit CT use to those patients who met established predictive criteria for significant ciTBI risk.)

The clinicians work in their EHR, together with subsequent cranial imaging and TBI-related outcomes, all generated data that would require aggregation to determine (1) how frequently care providers heeded recommendations surrounding CT use, and (2) whether the predictive rules for ciTBI risk were valid. That aggregation fell to OpenClinica. By accepting reports generated by each site’s EHR to automatically create study subjects, and by integrating with the source of imaging data at each site, OpenClinica enabled a true e-source study that left clinical workflows unaffected. Not one of the 28,669 subjects created in the study database required manual data entry.

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Click image to enlarge

 

 

 

 

 

 

Images courtesy of Jeff Yearley, BA, Manager of Clinical Data Management, Data Coordinating Center, University of Utah. Click here to download the slides containing the images above.

The moral? Big data isn’t just found: it’s made, through the coordinated efforts of both people and systems that travel light and fast. You’re contributing to big data during more and more of your waking hours these days. If you want to help shape it through technology, get ready to cooperate… and pack your digital bags.

OC Participate Delivers Better Data, Faster, Again

Some topics in clinical trials bear repeat attention. With patient-centricity claiming more and more of the spotlight in both research and care (rightfully so), we think patient-reported outcomes is one of them.

In our
last post, we described some of the most common obstacles to getting quality data from PRO measures. Patients, especially those are very sick, don’t want to hand-write dry medical diary entries. They don’t want to learn yet another electronic device, download and manage an app, or have to recall yet one more password. And who can blame them? Trial participants are the heroes of the research story, and when it comes to the collaborative process of data gathering, they deserve a hero’s welcome.

That’s why we developed OpenClinica Participate. We’re gratified by the success our clients have found leveraging this innovative ePRO solution, but we’re not surprised. When you prioritize a trial subject’s convenience and obsess over making things simple, you simply get better results. Here’s another example. Let’s call it:

Out with the old, in with the new

OpenClinica teamed with Danish CRO, Signifikans, to implement OpenClinica Participate for a leading Denmark-based bioscience technology company developing an innovative treatment to alleviate colorectal disorders, a common side effect of numerous medicines affecting millions of people at any time. The study’s objective was to investigate exercise induced intestinal permeability, immune markers, and bowel habits in 18-40 year old healthy volunteers. Participants were given two strains of bifidobacterium, an anaerobic bacteria that resides in the intestinal tract. The study involved 48 participants throughout Sweden, and each patient was required to provide 65 daily diaries in addition to 5 in-person visits over the course of the study.

The Old Way

In a similar prior study, the sponsor collected paper diaries from 700 participants. Each participant provided their (hopefully) completed and accurate paper diary to their site coordinator during the in-person study visits. The site coordinators then delivered the completed paper diaries to a data coordinating center. Coordinating center staff then scanned the diaries into a document management system and an overseas data entry vendor used a double data entry workflow to populate a database. Completed diaries were scanned and uploaded in batches for data entry. Phew!

On average, four months elapsed between the point of data capture and the first day of availability of that data to the sponsor. Monitoring participant compliance was also a challenge in this study, as it was
impossible to discern when each patient actually completed their daily diary. The expenditures associated with this process for data entry tallied over $213 per patient diary, or $4.97 per diary page.

Overall, this process was cumbersome, expensive, and logistically complex. The sponsor was planning a similar new study, and this time around was determined to find a way to

  • get faster access to the study data,
  • improve data reliability, and
  • reduce costs

The New Way

The sponsor enlisted local specialty CRO, Signifikans, to help it identify and implement a better approach. Signifikans recognized that with OpenClinica Participate, the sponsor could have immediate access to patient data, and that this data would automatically sit right alongside data captured from other sources during the study. No EDC integration was necessary.

Signifikans also took the lead on configuring the study in OpenClinica. (Our “make it simple” credo guides how we design tools for data managers, as well. That’s why we have invested so much in our forms engine, a topic for another post.) While building the study, Signifikans was able to easily demo prototypes to the sponsor along the way, iterating rapidly through edits and changes. Data capture forms were developed in the Swedish language, and the study was configured to send email reminders to patients to help ensure diaries were completed on time. The reminders contained a secure, uniquely-identified link the participant could click to go right to their diary, eliminating the need for participants to remember usernames and passwords.

Results

As soon as the study went live, the sponsor was able to monitor precisely when data were captured something that was not possible with the old paper-based method. They observed, for example, that all five participants enrolled in the study’s first week each completed their diary card daily, per the protocol. The sponsor’s confidence in patient compliance and data quality surged; so much so, that they implemented an increase in the amount of data being collected this way. Scaling that quickly would have been impossible with paper diaries and slow transcription processes.

“Participate was very low friction: set-up was quick and efficient, and patients really seemed to embrace the technology.”

– Andreas Habicht, CEO, Signifikans Aps

The OpenClinica solution delivered a unified study database out-of-the-box, with patient-reported data sitting alongside clinician-reported data and accessible via the same interface. Having everything in one audit log made it easy to follow the patient’s trajectory through the study. Signifikans was able to use the same tools to configure and manage both ePRO and non-ePRO aspects of the study, resulting in a faster time-to-launch, and facilitating mid-study changes.

In addition to enhanced data quality and faster access to data, the cost of data capture per diary with OpenClinica Participate resulted in cost savings of over 80%.


Comparison: Paper vs. Participate
(click to enlarge)

Keep an eye out for more ePRO success stories on this blog. Our next post will delve into a different topic, but, as with this one, you can be sure it will feature better results through a better eClinical experience.

My Resolution for 2017

The traditional month for making resolutions is over, but since the first day of the year, I’ve been hard at work on mine. I shared it with my team members early in January, and now I want to make it public.

My resolution for 2017 is to make life easier.

Not necessarily for me–though I’d take it–but for clients and collaborators of OpenClinica. The pace of clinical research will only accelerate, so eclinical software (and its users) will need to keep up or get left behind. While we can always make improvements in processing power and data storage, the big gains will come from empowering our users to do more in less time and to rapidly make smart decisions. In the world of electronic data capture, that means giving data managers powerful tools to get studies started, to seamlessly integrate randomization, EDC, and ePRO, and to gain insight into their queried and missing forms.

As a team, we’ve adopted “making the complex easy” as our theme for 2017. As you’ll see in the posts below, we’re poised to do just that. We have new clients that will challenge us to break new ground. We have new team members bringing that rare combination of domain expertise and raw passion. And we have a growing appreciation of the role ePRO will play in identifying therapies that aren’t only efficacious, but effective. So if my life doesn’t get any easier this year, it’s still bound to be exciting.

Our newest clients represent the future of research and care

New client relationships are exciting in any business. But they don’t get much more gratifying than the ones we’ve been fortunate to make. Looking back over the last six months of new client relationships, I’ve been struck with the emergence of some inspiring trends. A focus on personalized medicine. The willingness to take on looming problems in population health. The application of advanced computing and “big data” to challenges old and new. It’s been a potent reminder of the impact that our work here at OpenClinica makes possible.

Here is just a sample of the new partners that are energizing us:

The biotechnologists at miR Diagnostics specialize in the development of prognostic testing in cancer treatment. Their mission is to provide people diagnosed with cancer a deeper understanding their disease, and to help them make the safest, most informed treatment decisions possible. Using state of the art research, miR Diagnostics has developed cutting-edge, prognostic cancer testing systems that provide insight into tumor progression previously impossible to ascertain.

With Tools4Patient, too, medicine is personal. Tools4Patient develops companion diagnostics which contribute to the design of new treatment paradigms to improve outcomes and enhance quality of life for patients. Their first commercialized tool, Placebell, increases the sensitivity and power of clinical research results through improved Individual Placebo Response characterization.

From microbial pathogenesis to gene therapy, The Research Institute at Nationwide Children’s is claiming new frontiers on behalf of children from around the world. Add to that incredible mission a suite of stunning computational resources, and we knew we needed to work together.

Mercy Research, a centralized, multi-faceted research group within the Mercy health system, conducts more than 700 clinical studies at any given time. They’ve developed more than 40 innovating products and are now building one of the foremost teams for healthcare analytics. Suffice it to say, we’re proud to play our role in this enormous enterprise.

Malaria is responsible for more childhood mortality than any other single infectious agent. At Sanaria, through collaborations with renowned institutions like University of Tübingen, Germany, research is taking on a big aim: eradication through vaccination.  

Biolux Research develops technologies that enhance clinical outcomes and dramatically reduce treatment timelines in dentistry, implantology and orthodontics in a safe, effective and non-invasive approach. Learn how they’ve already succeeded with OrthoPulse®.

Again, these are only examples. In the space of a blog post, I can’t do justice to these missions, those of the new clients I didn’t mention, and the many we’ve been helping to advance for years. But I will be reporting our progress in making all these ventures like these as successful as they can be.

 

New eCRFs (Colleague Revelation Forms)

OpenClinica has welcomed several new team members over the last few months. We’ve collected some eCRFs (Collegue Revelation Forms) to introduce them!

Name: Paul Bowen
Title: Product Owner
Responsibility: I bridge the gap between our stakeholders and our engineering team to ensure that we build the features into OpenClinica that are needed most.
Background: Prior to joining OpenClinica, I spent ten years at Quintiles/Outcome Sciences developing an EDC platform for late phase studies. I also spent one year at Clinical Ink working on a patient engagement app.
What I love about research: I like being a part of something that is making people’s lives better in a significant way.
What I love about technology: Technology provides us with many new options to improve the way we do research. Working to figure out the best solutions is a fun part of this job.
What I love outside of work: When the weather is nice, my girlfriend and I like taking long walks with our dog. When it’s not as nice, we like watching sci-fi TV and movies.


Name:
 Bryan Farrow
Title: eClinical Catalyst
Responsibility: I’m the link between the data management community and OpenClinica. It’s my job to distribute news about our products and services to data managers who’ve been looking for solutions to industry-wide problems like integration and ease of use. Just as importantly, I bring unmet challenges back to our incredible team of developers and customer success professionals so that we can find a solution.
Background: Prior to joining OpenClinica, I spent five years at DrugDev, learning how just how much technology can affect the duration, cost and experience of running a trial. Prior to that, I was responsible for physician and patient communications at Boston Children’s Hospital.
What I love about research: The journey and the destination. Asking questions, devising ways to get an answer, and analyzing evidence are thrilling. With clinical research, we get to do all that with the aim of improving lives.
What I love about technology: For me, technology comes down to problem-solving. If you’re curious and persistent, you can always find a better way. And it’s so gratifying when you do.
What I love outside of work: My family above all. I love being a father to my two kids and a partner to my incredible wife. But when I need some “me time”, I usually reach for a book or journal dealing with philosophy. That was my major in college, and I’m still enamored with the power of logic and the gravity of big ethical and political questions.

Name: Brittany Stark
Title: Project Manager
Responsibility: I direct client projects involving the implementation of clinical trials using OpenClinica software. I oversee the planning, build, testing, and delivery of client projects on time, within scope and budget.
Background: Prior to joining OpenClinica, I spent 4 years in the Cancer Clinical Trials Office at Beth Israel Deaconess Medical Center, working with phase 0-IV clinical trials in oncology. During this time, I gained experience as a Clinical Research Coordinator, Regulatory Affairs Specialist and later Clinical Trials Staff Educator. Prior to this, I spent over 4 years working in academia at the University of Kentucky (in a Human Behavioral Pharmacology and Clinical Psychology Lab) and later at the University of Maine (teaching SPSS as part of a Research Methods and Designs Course Lab).
What I love about research: The potential to advance our understanding of the world we live in and change lives for the better.

Name: Chris White
Title: Customer Success Team Lead
Responsibility: I direct client projects involving the implementation of clinical trials using OpenClinica software. I oversee the planning, build, testing, and delivery of client projects on time, within scope and budget.
Background: Prior to joining OpenClinica, I spent two years in the consulting industry working with many different types of software. Prior to that, I spent 14 years creating positive customer experiences with two successful start-ups, helping to build their client focused operations.
What I love about research: There is still part of me that is the seven-year-old Calvin (from Calvin and Hobbes), always asking questions, wanting to know and understand the wide world around me.