Have your subjects record electronic information in blue ink with Health Decisions' writing instrument

 (Chapel Hill, NC) is far from the only company that will have some pens at their booth at the next conference. But no other pen will be able to practically double as a PDA. 

Tools for Clinical Trials Professionals  			

Its Smart Pen is a distinct difference to all of the other EDC products available. Most require subjects to either enter their data electronically or fill out a form, which is then entered into a database for them. The Smart Pen lets patients fill out a form, and then have the pen itself transmit the recorded data via a cradle to the database.  			

How? Each specialized form used as a CRF is light gray: that's actually a tiny grid of boxes. The pen records which boxes are touched by the pen strokes with a miniature camera, and then turns that into handwriting analysis. Dock the pen in its recharger/cradle to send the information to your database. Any irregularities in the data are immediately shown to the site, which can correct them against the paper copy. The pen also creates an exact copy of the CRF to be saved online. 

Health Decisions is a full-service CRO, and offers other methods of collecting information that aren't as space age as the Smart Pen. 

One little acronym converts your Oracle Clinical database of trial information for FDA submission

 (Cambridge, MA) new product, OC2SDS, may sound like an drug discovered by an array, but its usefulness extends for every type of drug or device being tested for FDA approval. 

OC2SDS converts data from Oracle Clinical files into SAS Version 5 transport files—the software's name is an acronym describing the process. Version 5 meets the latest standards of the Clinical Data Interchange Standards Consortium.  

Converting a simple trial is relatively easy—the true test comes from the complicated trials. OC2SDS comes with utilities to map source views to a single target domain, create new mapping views, convert items, create new domains, and map oracle values to controlled CDISC terminology. 

When the magic is done, users will be left with a perfectly acceptable xml metadata file, exactly as FDA requested. Sponsors' attention can be focused on analyzing and interpreting trial data, instead of wondering how that data will  be sent to one of the Offices of New Drug Evaluation. 

 (Wil-mington, NC), which provides dozens of services to help a compound become a drug, and a drug come to market. 

A new program in ICTS's suite makes sure the all-important Q-word— quality—gets the respect it deserves

(Raleigh, NC) was founded in 2003 to maximize the patient enrollment process for CROs, sites, and sponsors. One of the ways it is accomplishing this is through its new Integrated e-Q™ program.   						

ICTS offers platforms to help develop a clinical trial's patient recruitment in a host of ways. Media planning, call center planning, education, compliance, community resources, investigator relations, and even concept design and creative services are available from ICTS. 

Integrated e-Q, then, is designed to get the most from each of these elements of patent recruitment. e-Q, a Web-based program, makes sure that each aspect of your project is going as planned, from the physician referrals to the SMO work to the retention and compliance programs. It also ensures that other members of the team are as up-to-date as you are with "My Pages," which lists tasks. 

ICTS already uses the quality assurance suite in-house, and is now offering it to third parties. Making sure all work is of high quality is a sure way to reduce time and costs of a trial.  

Integrated Clinical Trial Services, (919) 388-3964, 

Articles

eShowcase

Have your subjects record electronic information in blue ink with Health Decisions' writing instrument.

Drug and medical device developers face an important fork in the road concerning data management for clinical trials. For years they have dropped information gathered by case report forms (CRFs) into data "silos"—database applications designed for one trial only. Their focus has been strictly on completing the trial's analysis so they can submit their new products for FDA approval as quickly as possible. There has been little concern for the future usefulness of the raw data.  			

After less than a decade of such purely tactical data management, the typical clinical data repository appears as disorganized as most people's attics. However, life sciences companies have begun to realize the enormous, even strategic, value of clinical data integrated across trials. When properly collated, this collection of raw data becomes an asset worth billions in cost savings and additional revenues. Yet before one can turn this data into dollars, someone must go through the attic and "organize the boxes." This means placing each trial's data into an integrated "clinical warehouse" designed to eliminate the trial boundary separating valuable pools of data, so it can be innovatively accessed and mined.  

Though database technology makes building such a repository possible, it is still an effort expensive enough to require a careful business case. This article outlines that business case, sketching the major action steps to creating the integrated clinical data warehouse (iCDW). More importantly, it details the large rewards that make such an investment unquestionably worthwhile.  			

For life sciences companies, data management plays a central role in converting the expense of clinical trials into large revenue streams. Once drug and device developers have identified a new product, they must still gain regulatory approval. The FDA grants its coveted approval only when a company can soundly argue that data from multiple clinical trials proves their new product is both safe and effective. The cost of the necessary series of clinical trials has grown steadily; bringing a new drug to approval now requires between $350 and $800 million. But that drug is often worth billions in revenue when it is finally released to the market.   						

Figure 1 depicts the clinical data "value chain" that begins with the clinic trial capturing raw data collected on CRFs. To turn the CRF data into a regulatory approval filing, the life sciences company must execute a "statistical analysis plan" (SAP) where biostatisticians assemble the raw numbers into analytical data sets and feed them to statistical programs, which generate hopefully compelling evidence of efficacy and acceptable side effects. The product developers submit the resulting analyses and supporting raw data to the FDA along with the design of the SAP, so that the agency can validate the developer's claim. Life sciences companies must repeat this process several times as the potential product progresses its way through multiple Phase I, II, and III clinical trials, and again for Phase IV trials whenever they want to re-label existing products for additional uses or to meet regulatory requirements for postmarketing studies.   

 Figure 1. Clinical data value chain and X-trial integration benefits. 

As we will see, the iCDW will have several impacts upon this value chain, beginning with increased speed of execution for the SAP. Efficiencies gained in the SAP save time and expense, but, more importantly, the iCDW works "upstream" to abbreviate trial designs. Protocols involving integrated data require smaller patient populations, causing the trials to require less calendar time. Ultimately, combining data from multiple trials can increase its statistical power. That allows firms to reveal and win approval for additional applications of their products, eliminating the need for additional clinical trials designed solely to gain FDA approval for such expanded indications. 

Second generation clinical data warehouses

Before detailing the value of cross-trial integration, let us first demonstrate that it is possible. The standard clinical data handling tools today are a mainstream database, such as Oracle, and an analytical engine, typically SAS. With structures and routines custom-developed for a given trial, this combination very capably transforms a single trial's data (once captured) into the analytical datasets and aggregated results required for product approval. However, a "first generation" approach stovepipes the data from CRF straight to the submission report. Although the trials may be co-located in something called a "clinical warehouse," the data structures and programming for each trial are uniquely tailored to the particularities of the trial's design. Stovepiped designs can be reused only if the next trial is identical to this first. Yet for most companies, even trials within the same therapeutic area often experience 20% to 50% variation in their CRF elements alone, as the science underlying their designs evolves.

(Variability of 20% to 50% between trials were the targets adopted by the CRF Reference Library team at a leading medical device company at which the author consulted as a clinical data warehouse architect. The target arose from an analysis of CRF elements shared by six recent trials in the same product line. These planning targets lay within the same range as those adopted by a major pharmaceutical firm, where the author also consulted.) 			

Working with a clinical data warehouse designed for trial integration and re-use obviates the trial-by-trial development of data schemas, table joins, aggregation logic, and output formats—all inordinately expensive and time consuming to continually re-invent and validate. Understandably, building a second generation iCDW is not just a software challenge, but also requires efforts on several fronts. First, a company will need to provide some codification of its research data. Leading pharma and device companies have already started developing reference libraries that guide their researchers toward standard phrasing and recording for the CRF items they use repeatedly in their trial protocols. We see similar industry initiatives such as  MedDRA for clinically validated medical terminology. Recent CDISC formats now standardize clinical data interchanges and will soon be the lingua franca for submissions to the FDA.

Secondly, iCDWs will require a codification of the context of observations within the study's design. Luckily, other industries have already pioneered the notion of "universal data models" that express in a generally accepted and reusable fashion the interrelation of business components with their applications. Life sciences can achieve this also, because with few exceptions every trial involves the same links between such notions as protocol, patient, treatment group, and visit. Indeed, data modelers in the pharma industry have begun publishing suggested schemas, hoping to obviate senseless variation in basic data designs between trials.

With this backdrop of standardization, the only missing foundational component for the iCDW is a repository for trial data flexible enough to accommodate the inevitable variation in CRF elements. Luckily, data modelers have also devised the means for abstracting a research observation for universal storage. At the cost of some additional processing, mostly upon retrieval, the iCDW converts each item in a CRF into a "name-value pair" stored independently in the database, as depicted in Figure 2. 

Figure 2. Abstracting CRF columns into iCDW rows.  

As a result of this data abstraction, what were columns in a first generation repository become rows in the second generation iCDW. Formerly "short and fat" CRF tables become "tall and skinny." Observations that were once stored in the DEMOGRAPHICS table for Trial A and PHYSICAL CHARACTERISTICS table for Trial B are now both stored in a single table called simply OBSERVATIONS. The name of the source trial and CRF is stored alongside as an attribute of each observation, supporting trial-specific retrieval. However, the CRF attribute can be ignored or more accurately re-assigned upon cross-trial retrieval so that results can be collated in any manner that makes sense. Astonishingly, this architectural solution achieves cross-trial integration of CRF data almost as an afterthought.

The enormous benefits offered by the iCDW begin with a single trial by accelerating the execution of the statistical analysis plan. The biostatisticians within the company who are entrusted with the SAP are typically adroit at statistical inference, but extremely vexed by the drudgery of marshalling together the many tables that comprise a trial's raw data. Within the iCDW, the elements that make trials unique have been abstracted so that their variation affects only the value of a few attributes stored with each observation. The physical column in which all observations reside now remains the same from trial to trial. Granted, the firm must develop a de-abstraction routine that swings the desired observation out from the tall and skinny table into the desired short and wide analysis dataset, but this routine is re-usable, a crucial note. 			

Once given a spreadsheet-like interface to make its usage straightforward for biostatisticians (or the IS team supporting them), the de-abstraction routine needs only to rerun for subsequent trials, obviating the inordinate development or time-consuming revalidation work required for each trial by the first generation approach. One medical device manufacturer estimated such a reusable clinical data management platform would reduce the "data gymnastics" required for each trial by 75%, and that the SAP for a typical trial would consequently complete in one week rather than one month. In a marketplace where the top 40 drugs average $500 million dollars per quarter, streamlining the SAPs for just the final analysis of these compounds would yield between $50 and $350 million.

 With vendor-supplied iCDW implementations running under $5 million, the projected savings over multiple trials suggests that even midsize R&D companies and clinical research organizations can expect a 10-fold return on investment with an iCDW when applied to trial-specific analysis alone (summary of initial list price quotes for software and implementation services received from four major vendors in August 2003). 

Yet these rewards pale in comparison to the cost savings, reduced time-to-market, and improved R&D impacts that arise once the iCDW can be used to combine clinical data across trials. Consider how cross-trial integration can make products more competitive. Any given clinical trial is very focused by design upon the safety and efficacy of a single product. By combining data across trials, biostatisticians can mine for correlations between efficacy and product characteristics outside the narrow focus of a single compound or device, revealing new advantages to existing products and identifying subpopulations that could be more effectively served. Such insights will point the way to improved product formulations and designs.  

As a hypothetical example from the world of medical devices, a recent paper in a medical journal suggested that strut diameters among cardiac stents are directly related to the reoccurrence of vascular blockage or "restenosis."

 A stent maker equipped with an iCDW could readily assemble data from five of its past stent trials, each of which focused on a different strut dimension. Quickly, it could statistically prove that thinner struts yield better outcomes, and simultaneously point out that it offers stents with some of the thinnest struts on the market. This type of compelling argument is exactly what marketing executives want and expect from their research departments. 

As a second hypothetical, consider a Fortune 100 drug maker that had gained approval of a compound for reducing ocular edema. The drug was approved for prescription by ophthalmologists, of which there are approximately 80,000 in the United States, generating a market slightly under $100 million per year. Considered separately, the data of each trial only hinted at improved vision among diabetics as one of the secondary benefits of the drug. By assembling the data of multiple prior trials, a sample population of diabetics was created with enough statistical power to infer with greater than 95% confidence that the compound did in fact improve visual acuity in diabetics. Upon resubmittal of the compound to the FDA for an expanded indication, this compound was approved for prescription by general practitioners, of which there are several hundred thousand in the United States alone. The relatively simple act of combining data across trials enabled a five-fold increase in the number of practitioners that could prescribe the medication, expanding the market for the drug to where it generated several billion dollars of revenue over a five-year period.

If cross-trial data integration is so valuable, it is natural to ask whether it can be achieved by manual means rather than requiring the implementation of a second generation CDW. Collating data across trials that have not been formatted for integration turns out to be an inordinately labor-intensive task. The device manufacturer cited earlier discovered that the manual collation of only four trials required three person-months as the biostatisticians carefully knitted together 600 items scattered among 50 data tables with missing or incomplete metadata. Not only could the professional time consumed by this painstaking effort have been better invested in designing new trials rather than rehashing old ones, the results were not as compelling as they could have been because six other trials worth of data were excluded because the budget for the exercise was exhausted.  

The three-prong endeavor of implementing the iCDW—reference library, standardized data model, and abstract CRF item storage—pre-processes much of the integration work for such cross-trial analyses. The task is reduced to simply selecting which elements the warehouse should combine in one output column versus separating into individual fields of the analysis dataset. Furthermore, the data mining empowered by an iCDW will allow life sciences companies to improve their trial designs. By increasing the statistical power of the data, biostatisticians will be better able to predict such things as the concomitant medications that will impact a given class of compounds and the physical characteristics linked to more frequent follow-up events. Such insights will allow protocol designers to better target their inclusion and exclusion criteria, as well as adapt their visit schedules, thus avoiding contingencies that prolong and undermine the quality of research. 

The final benefit of iCDWs that we will consider may be the most important of all: Bayesian inference. It will allow companies to utilize the recent innovation in biostatistics, which promises to regularly reduce trial size and duration within a given therapeutic area. Whereas old-school, frequentist statisticians believe that each trial must observe for itself outcomes across the entire domain reported for each treatment group defined, Bayesian statisticians argue that inferences made in other trials with other patients can be carefully applied to a new study's data analysis. Based upon the theories of conditional probability promulgated by the 19th century mathematician Thomas Bayes, Bayesian biostatisticians employ prior knowledge to increase the predictive power of a given set of trial data. This advantage is rapidly gaining adherents and acceptance, as Figure 3 implies, including a steady stream of presentations among the advisory councils of the FDA.

Figure 3. Growing occurrence of Bayesian inference topics in medical journals.						

Applying the concept to life sciences, consider the occurrence of anemia in the subjects of an antiviral study. The goal is to state with 99% confidence that this adverse event is not causally linked to the drug being studied. A frequentist would design the trial to gather observations across a wide range of dosages, leading to a large number of subjects. The Bayesian design, however, would require a much smaller population because its analysis would include the probability that a 500-mg daily dose is safe, based upon good scientific reasoning supported by the ADME and pharmacodynamics documented in previous trials.  

The controversy over this technique centers upon appropriateness of the prior knowledge that gets "pipelined" into a current study. Use of Bayesian inference must include a careful disposition on how the raw data from previous trials was identified and screened to remove significant "covariates." To make it work, the analysis team must be able to start with data of as many prior trials as possible stored in a repository that does not impede their efforts with trial boundaries. When it works, the trial requires fewer data points to statistically prove a product's safety and efficacy, thus allowing smaller trials to achieve more.

Re-basing entire research programs on smaller trials improves the economics of clinical research, and even the life sciences company's revenue stream. Clinical trials entail high per-subject cost—$2,500 per patient in drug trials and $10,000 per patient for devices are commonly cited figures. If even one Phase III trial can be brought down from 5,000 patients to 3,000 patients, the Bayesian inference enabled by the iCDW would save the sponsor over $5 to $25 million on its first use. 

Yet far more dramatic is the impact that reduced patient populations will have on an organization's revenue stream. For obvious reasons, life sciences companies pursue their trials using physicians and medical centers they already know well, a practice that imposes upon them a finite capacity for completing a study, often expressed in a maximum number of patients per week. By using Bayesian inference to reduce the number of subjects within each trial, a company will shorten the time required to process the necessary number of patients through its existing research network. Combining this effect of Bayesian inference with the accelerated execution of the SAP and other benefits explored earlier across the multiple study phases required to approve a new product, drug and device developers investing in an iCDW can shorten a new product introduction by a year or more. With new blockbuster drugs averaging revenues of  $500 million per quarter, the first-year value of improved time-to-market offered by this single technology alone translates to  $2 billion of additional sales per major product supported.  

This article detailed multiple means by which cross-trial data integration can reduce the cost, size, and number of trials required to gain FDA approval for a new product. We explored how this streamlining of the clinical research process generates cost savings and revenue enhancements that will grow beyond the $100 million level for even a midrange life sciences company. Once a company has re-engineered its clinical data repository so that it can accommodate multiple trials of widely varying designs, the financial benefits will far outweigh the expense required to build the iCDW. In the highly competitive environment that recent achievements in basic science have created for drug and medical device developers, a tool costing only tens of millions of dollars to deploy that enables them to reduce the drug and device approval process by months or years will seem an easy investment to make. 			

1. Formats for "submission data model" and discussions of XML format for such data interchanges, available at 

2. R. Hughes, "Towards a Universal Data Model for Pharmaceutical Clinical Trial Data," 

, vols. 1, 2 (New York: John Wiley & Sons, 2001). 

4. P. Nadkarni, The EAV/CR Model of Data Representation, accessed at 

ycmi.med.yale.edu/nadkarni/eav_cr_contents.htm

, "Exploring Performance Issues for a Clinical Database Organized Using an Entity-Attribute-Value Representation," 

Journal of the American Medical Informatics Association

6. "Lipitor, Zocor Top 2Q Sales Charts," 

, "Intracoronary Stenting and Angiographic Results: Strut Thickness Effect on Restenosis Outcome (ISAR-STEREO-2) trial," 

Journal of American College of Cardiologists

, "Maculopathy in Patients with Diabetes Mellitus Type 1 and Type 2," 

, 84, 871-876 (August 2000), available at bjo.bmjjournals.com/cgi/contentfull/84/8/871.

9. "Panel Examines Moving CDER's Thinking Forward to 'Desired State,'" 

, 10 (3) (August 2004), Center for Drug Evaluation and Research, accessed at 

10. R. Weiss, "Bayesian Sample Size Calculations for Hypothesis Testing," 

Journal of the Royal Statistical Society (Series D): The Statistician

11. S. Senn, "Consensus and Controversy in Pharmaceutical Statistics," 

 MA, PMP, is managing architect with Ceregenics, Inc., a data warehouse consulting association in Denver, CO, (720) 951-2100, 

 and is a founding member of the World Wide Institute of Software Architects. 

Feature Article

The benefits offered by the iCDW begin with a single trial by accelerating the statistical analysis plan.

Multi-Trial Data Integration

At first glance, ensuring the efficient and effective transfer of clinical trial data between key stakeholders seems a matter just for the information technology (IT) department. Surely, given the ubiquitous presence of IT in clinical trials, data transfer between systems and companies is already seamless and fully optimized—or is it? Unfortunately, assuming that your systems can communicate with those run by third parties can prove to be an expensive mistake, resulting in longer and more expensive clinical trials.  

 Table 1. Work and groups involved in typical clinical study			

Over the last few years, the application of technology has dramatically improved the conduct and management of clinical trials. Clinical data management systems (CDMS), clinical trial management systems (CTMS), electronic data capture (EDC), electronic patient-reported outcomes (ePRO), drug supply management (DSM), and interactive voice response systems (IVRS) help contain costs and shorten clinical development. These systems often affect common processes and use overlapping information. Yet they are generally used in isolation. As the number of IT clinical systems increases, there is a growing need to share data (Figure 1). By integrating technologies and processes, clinical trial sponsors can eliminate redundant tasks and accelerate the flow of critical information to key stakeholders.   

Figure 1. Common data sets shared between clinical systems.			

Despite clear benefits, biopharma companies lack a complete set of standards for data and its transmission. As a result, clinical trials typically use a variety of software and solutions to create, maintain, and share data. Furthermore, the same information is kept in multiple sources and formats. There are organizations addressing this key issue, such as CDISC (Clinical Data Interchange Standards Consortium) and Health Level 7 (HL/7), although it will probably be some time before a comprehensive set of standards are finalized. In the interim, some sponsors and vendors have developed internal data models to fill the gaps. Against this background, this article underscores the value of integration and interoperability. In particular, it highlights the importance of using flexible, standardized, robust systems that allow universal integrations and illustrates how considering integration can streamline business processes. 

We need to begin by defining three key terms: 

Data exchange refers to moving data from one system to another. In most cases, data exchanges are not automated; people export, map, and import the data. For example, a human operator usually loads laboratory data in a sponsor's CDMS. Even though many clinical data managers strive to establish standards with labs, the mapping and loading of values into the back-end clinical database usually involves considerable work.

Interoperability is "the ability of two or more systems (computers, communication devices, networks, software, and other information technology components) to interact with one another and exchange data according to a prescribed method in order to achieve predictable results" (ISO ITC-215). Achieving interoperability involves managing systems, procedures, and corporate culture in a way that maximizes the exchange and re-use of information, both internally and externally.

Integration brings together systems, processes, and people. Integration leads to interoperability by making systems work together synergistically.

To illustrate one of the problems that can arise from poor integration, consider a project manager with access to online patient enrollment reports from the IVRS. Since investigators use IVRS to randomize patients, the enrollment figures are always up to date. But at the corporate level, the company tracks enrollment information in a CTMS to maintain high level and critical information for the entire clinical program. The clinical team is responsible for keeping the CTMS current. At the same time, the project manager's team has many other responsibilities. Since they already have the IVRS enrollment data, there is little incentive to do "extra" data entry. This means that the CTMS information is not in sync with real-time IVRS figures. Additionally, while entering or reconciling the information from many reports, they may make data entry errors and create discrepancies between systems. 

But what does better integration mean to the pharma industry? Trials are getting very complex. This complexity contributes to the rapidly increasing cost of clinical trial programs. Between 1995 and 2000, for example, the cost of researching, developing, and launching a drug rose by nearly 50%. Over the same time, however, the cumulative success rate fell from 14% to 8%.

 Currently, clinical studies account for more than half of the total cost of bringing a new drug to market (U.S. $467 million of $802 million; 2000 prices).

This complexity also means that data is created and shared among many different parties. For example, a typical trial could include: the sponsor, different CROs for Europe and the United States, a data safety monitoring board, a central lab, as well as vendors of ECG, EDC, and IVR for randomization, drug supply management, and ePRO data collection. All of these parties need site information to begin the trial. Typically, the sponsor holds site information inside a CTMS. But sharing the site information means that the sponsor needs to provide the data in different formats to all these parties. 

Each of these study participants then needs to import, or in the worst case, rekey this information into their systems. Additionally, the originator of the data must remember to update all required parties using their method of notification for any additions or changes. Failure to do so could affect the supply of study medication, cause delays in patient enrollment or lead to  other problems for the study. 

Once all the sites are ready, patients are enrolled and randomized. For this example, investigators use EDC for screening and IVR for randomization. The patient is screened at the site and the eCRF is completed. The monitor then updates the CTMS at the CRO, typically by keying information. Then the CRO must send this information to the sponsor so it can be loaded or rekeyed into the sponsor CTMS system. The investigator then enters screening, demography, and other information into the IVR system to randomize the patient. Later, the randomization and dose information is rekeyed into the eCRF. Meanwhile, the investigator performs many other procedures that must be tracked by the CRO and sponsor to ensure accurate and timely payments are made to the site. 

Again, the monitor needs to track this information in the CRO's CTMS, once again typically by keying information. Then the CRO needs to send this information to the sponsor so it can be loaded or rekeyed into the sponsor's CTMS system. The rekeying of information often leads to many data reconciliation errors, which must be queried or manually resolved. Such a lack of integrated data exchange and processes generally results in frustration at study sites, not to mention additional time, resources, and costs. The ultimate goal is to enter data in the most appropriate place and have it propagated or shared with all parties that need the information without the need for manual re-entry. To accomplish this, standardized data structures and transfer methods are critical. 

A conservative estimate suggests that the biopharma industry spends more than $150 million per year on data exchange. These costs include the manual work required to import and export data between applications, as well as associated data reconciliation. Such manual manipulation of data is very slow and inefficient. They very often lead to delays in regulatory submissions, which are extremely costly in terms of lost sales opportunities. The biopharmaceutical industry needs to work smarter to do more, do it faster, and do it with fewer resources. 

Analyzing processes and defining an optimal workflow is the key to working smarter. Some data workflows can be designed once on an enterprise level, such as CTMS or drug supply management system. Other optimal workflows are designed on a trial-by-trial basis, such as EDC or ePRO integrations. In the above trial, processes encompass enterprise and trial-level integrations. The key goal is a process that will reduce workload, reduce data error rates, and save time and money overall.  Table 1 summarizes the work and groups involved for three steps in a clinical process. 

By using efficient data workflow, all parties are up to date and receive correct information throughout the trial. An optimized workflow also reduces the workload and frustrations felt by study participants by reducing queries and duplication of data entry. Today, the whole picture may not be available, but an increasing number of pieces are falling into place.  

As the above discussion suggests, successful integration relies on flexible, standards-based technology. Unfortunately, not all system providers and vendors offer this approach. Relatively few systems offer the ability to deliver real-time data exchanges across multiple platforms and databases. Some providers offer solutions that are integrated with their own in-house systems, but that will not necessarily work with their clients or third parties. Given the problems that can arise from poor integration, executives need to consider which potential partner is most likely to offer seamless integration. There are some companies  that have developed proprietary technology based on today's standards to meets this need. 			

Nevertheless, there is more to seamless integration than IT alone. Experience is critical. To derive the most benefit, clients and vendors need to consider data and process integration while the study is being planned. The client needs to be open with the solution provider, allowing the latter's staff to communicate with the internal IT staff. These discussions can become rather technical, and it is likely that a suitable outsource partner will offer a team specializing in data integration that is able to apply best practices. Further, it is essential that the sponsor and all outsource providers meet to plan the details of any integration project, including both the systems and data that will be involved and the users that will be affected. Such meetings need to occur early to allow everyone to understand the issues.  

These are predominately short-term, "tactical" issues. However, successful integration depends equally on a long-term "strategic" approach that understands and characterizes the sponsor company's business processes and workflow. Over the years, experience has shown that processes and workflow are sometimes unclear to clients. Sometimes it is because systems had developed over a long time; sometimes it is due to changes imposed by mergers and acquisitions. Further, emerging technologies are often adopted and implemented without standards. It is important to analyze existing processes to identify opportunities to streamline internal procedures. For example, a company may recognize that it holds the same data in several disparate places and that it always needs to rekey these items many times. In turn, this realization should lead the company to design a better workflow to reduce the redundant efforts. 

Services and processes can, of course, always be improved—and integrated clinical trials are no exception. Organizations like CDISC and HL/7 will continue to help define standards for the electronic acquisition, exchange, submission, and archiving of data from clinical trials. Furthermore, over the next five to 15 years eCRFs and other data sources will become fully integrated with electronic patient records. This will make it much easier for clinicians and patients to participate in clinical studies. In the meantime, by ensuring that a company offers flexible, robust systems that allow universal integration and the expertise to analyze workflow and processes, executives can help contain rising costs and ensure that clinical studies finish on schedule and on budget. 

1. J.A. DiMasi, R.W. Hansen, H.G. Grabowski, "The Price of Innovation: New Estimates of Drug Development Costs," 

2. A. Singh, J. Gilbert, P. Henske, "Rebuilding Big Pharma's Business Model (A#2003800191)" 

3. D.B. Stein, M. MacDonald, B. Byrom, "Integrating eClinical Systems," 

 BSc, is director, strategic product development, ClinPhone, Inc., 650 Dundee Road, Northbrook, IL 60062 (847) 714-9709, fax (847) 714-9753, email: 

Estimates suggest that biopharmaceutical companies spend more than $150 million per year on data exchange. The industry needs to work smarter, faster, and with fewer resources.

Raising the Standards for Clinical Integration

				Patients and patient advocacy groups have long demanded more easily understood information about their diseases and about clinical trials. Given these demands, it seems reasonable to provide trial-related information in an audio-visual format. Indeed, Brady has already reported the use of multimedia informed consent programs in several leading research centers.

Investigators are under increasing pressure to recruit subjects for clinical trials and may face difficulties in providing each subject with an adequate amount of detailed information. This is why tools such as interactive informed consent (iIC) might benefit investigators and subjects alike. It is understood that investigators have credibility and medical expertise in the given clinical area that cannot be replaced by any tool.

 With this in mind, iIC has been introduced as a supplement to the paper-based informed consent (IC) process. The investigator still has a personal consultation with the subject after the latter has viewed the multimedia program.  			

The IC process is a critical factor in subject recruitment and retention. However, the text-based IC documents currently used in clinical trials are rife with shortcomings. To overcome these shortcomings (which often negatively impact recruitment and retention), some researchers now offer interactive multimedia presentations as part of the IC process. The effectiveness of a iIC program was evaluated in this feasibility case study, which involved subjects at seven German sites.  

This article will discuss the methods used to evaluate this innovative technology and describe the results of the pilot trial.  			

Novartis conducted a pilot study to evaluate the feasibility of adopting the iIC processes. The primary objective was to ascertain how well the various site staff would embrace the new technology. Additional goals included standardization of the consent information subjects would receive and full acceptance by ethics committees (ECs) for using this innovative approach to IC. Technology-related business objectives of this pilot program included evaluations of the usability of the content, the effectiveness of the software in delivering the interactive video presentations, and the reliability of the involved hardware components (i.e., Tablet PCs).   						

Clinical trial outcome-related objectives were the reduction of screen failure rates and thus accelerated enrollment. Effects on patient retention were unlikely to have been observed due to a short trial duration of only two weeks. Similarly, the pilot trial was not designed to provide an estimate of the impact of the use of this technology on costs. 

The Phase II clinical trial selected for this feasibility study employed a complex drug administration scheme. Therefore, the subjects required intense education about the treatment protocol. This was a perfect selection criterion for using iIC in this pilot trial. The trial was conducted at 12 dermatological clinics in Germany. Seven of the sites used the iIC to supplement the paper-based IC, and five sites used a standard paper-based IC only. Subjects at all sites also received a face-to-face consultation with a member of the research team.   

Figure 3. Full screen of the Vital Consent(tm) application displaying male 3D character. Session management details are displayed on the top of the screen. Underneath are the main sections (library and presentation). Below the content window are the navigation buttons and tick boxes for autoplay mode. On-screen text is presented in the subjects local language.						

Novartis selected the Vital Consent™ application from Keris, Inc. as the e-learning tool for this iIC pilot trial. The same application was used to present the e-learning modules and also track the progress of each subject through the recruitment process in real time. The vendor created the e-learning modules with input from Novartis and full consideration of 21 CFR Part 50 requirements for adequate IC.

 By incorporating photographs of the clinical site study team, the presentation is personalized and the viewer becomes familiar with team members. The strengthening of the subject-researcher relationship is considered a powerful tool for promoting subject retention.

Subjects watching the iIC presentations used Tablet PCs (Motion 1300™, Figure 1a) equipped with headphones to listen to the audio without being disturbed. The Tablet PCs combined optimal mobility with ideal user friendliness. Viewers were able to use the Tablet PCs offline—mostly in the waiting room—and did not require a keyboard or a PC mouse.

 Figure 1a. Tablet PC with touchscreen stylus and headphones provided to subjects.   			

After the subject completed the iIC session, site personnel set the Tablet PC into an Internet-enabled docking station (Figure 1b), which permitted an external keyboard to improve ease-of-use for site staff. Once linked to the Internet, the Vital Consent™ software transmitted tracking data to the vendor's central server. This feature allowed individual sites or the sponsor to retrieve numerous real-time reports from the Vital Consent™Report Center (Figure 2).

Figure 1b. Tablet PC linked to docking station and keyboard.

Figure 2. Screen shot of a real-time recruitment report derived from VitalConsent(tm) Report Center.

In selecting the iIC technology to be used in the pilot trial, certain requirements had to be met, including: 

The vendor must have the capability to create multimedia content for clinical trials and extensive experience in this role.

Quality control measures must be in place.

In-depth knowledge of pertinent regulations must be demonstrated (e.g., HIPAA, 21 CFR Parts 11 and 50).

The video content was separated into a library section and a study-specific section that included all elements of the paper IC document. The reference library contained optional clips that covered information on disease management, background information on clinical trials, and other relevant topics. A representative screen shot is displayed in Figure 3. 

Near the end of the video session, subjects were asked to complete a series of true/false questions to verify their comprehension. In addition, e-questionnaires were built into the application for both subjects and site personnel to rate the usability of the Vital Consent™ technology and its associated tools in the pilot trial. 

There were numerous activities involved in the implementation of the iIC technology—all focused on supporting the site personnel. These activities included face-to-face training from the vendor to CRAs and investigators, detailed user manuals, and a responsive help desk available during local working hours. 			

Implementation of iIC at the clinical sites depended on obtaining EC approval at each of the seven involved research sites. The ECs at these sites unambiguously accepted the iIC content, thereby successfully fulfilling one of the operational objectives of this study. As neither ECs nor investigators requested site-specific changes to the iIC presentations, the content was highly standardized. Throughout the content development process, Novartis and the iIC vendor were mindful not to use any kind of promotional-like visual material. 

About 51 patients used Tablet PCs offering iIC as a supplement to the paper IC. Eight additional patients did not complete the iIC, mainly for administrative reasons. As long as patients had not signed the paper IC, they were considered prescreened. Out of the pool of prescreened patients, 58 out of 59 (98%) signed the paper IC and thus became screened patients. In this group, a single patient dropped out of screening. 			

For the five sites using paper IC only, no data were collected for prescreened patients on a regular basis. In this group, two patients dropped out of screening. The screen failure rate is shown in Table 1.

A trend towards a screen failure reduction could be detected for sites using iIC, but the pilot trial was not empowered to apply statistical analysis. The small difference between the two groups of sites has been impacted by the overall low screen failure rate of 5%, which was lower than in other comparable trials.  

The usefulness of the involved technology was assessed at least in part by results from questionnaires. Of the prescreened patients, 48 out of 51 (94%) completed at least some of the eight questions. All patients (100%) reported an interest in participating in another study using iIC. The usefulness of iIC and involved hardware was rated quite high, as indicated by scores for quality of the content (100% rated "very good" or "good"), completeness of content (95% rated "complete"), appropriate duration of the presentations (67% rated the duration as "just about right"), usefulness of navigation tools, language used, and the Tablet PC itself (91% rated "very high" or "high"). An overwhelming majority (91%) stated that they would feel comfortable receiving medical information via computer in the future. Very few subjects reported minor problems, such as typos in the on-screen text. For many patients, of all the hardware and software components used, the video components worked out best (55%).  			

Twelve different investigators from seven sites using iIC completed at least some of the nine questions. The majority of investigators considered iIC added value to the informed consent process. Of these, 90% reported that their personal discussions with the patient took as much or less time compared to the normal text-based consent. Investigators (35%) stated that patients were better informed, and 25% reported that patients demonstrated increased compliance to the study protocol. The quality of the iIC video presentations was well-perceived (60% of investigators rated the quality as "high"). Some sites reported on transmission problems for video session data, which partly seemed to be related to the absence of dedicated high-performance Internet connections. The user-friendliness of the hardware, therefore, might need some improvement. Nevertheless, 80% of the investigators stated they would be interested in participating in another study using iIC. 

Involved trial teams were asked to provide feedback via a standardized questionnaire as well. Indeed, the introduction of iIC was not considered to present an additional burden, and the adoption was considered easy. 

Overall, the pilot trial provided positive results for clinical objectives as well as for technology-related objectives.  

Technical enhancements could include the generation of an on-screen text consent form that is signed via the Tablet PC's embedded keyboard. The subject could provide a 21 CFR Part 11-compliant e-signature, and a PDF format of the text IC form—including the signature—could be printed out one of the printers at the site. The patient could then receive a signed copy immediately while another copy was placed in the medical record.  							

Customized iIC presentations could be used in future trials as well. One example of this is collecting gender information. Once the gender of the subject has been captured in the session management screen, gender-specific versions of the presentation are delivered (e.g., female patients would be offered sections on contraceptive precautions during the trial). 

By introducing animated slideshows, one might satisfy e-learning requirements for rapidly changing content. 

This pilot study dealt with using iIC for subjects with atopic dermatitis. Disease-related video presentations have been reported to enhance the IC process for subjects with other diseases as well, such as glaucoma.

 Clearly, it is the iIC process itself, not the specific disease, which makes it an effective adjunct to traditional consent sessions. Published studies report that alternative methods for obtaining IC such as multimedia systems are considered to provide substantial benefit to subjects.

The current paper-based IC process is highly regulated. Using iIC can be considered a breakthrough in improving IC without impacting the current paper-based system. iIC can overcome the weaknesses in the existing paper-based processes, such as subjects' limited comprehension and a lack of standardized information across sites. An additional advantage of the applied e-learning tool is the documentation of the comprehension by the subject. If required, tracking information could be retrieved to document which presentations a subject had viewed or repeated. 

Subjects appear to have neither qualms nor boundaries towards using this kind of technology. Respondents in this pilot study stated they would use computers in the future for retrieving health care-related information. Similarly, Eilenberg 

 reported on patients using interactive Web-based IC with high satisfaction rates. 

It is well known that pharmaceutical companies invest heavily in recruitment activities.

 Although iIC should not be considered strictly a recruitment tool, one would expect significant improvements in screen failure rates and retention rates in large-scale trials—providing the sample size is sufficient to detect these effects. 

The selected pilot trial was not powered to provide robust data on screen failure rates or even retention rates. However, additional prescreening data could be collected without additional effort, and a trend towards reducing screen failures rates was determined in this trial. 

As all objectives of this feasibility study were fulfilled, the trial was considered a success. The standardization of the IC information and delivery was achieved. All sites received an identical video presentation, with the exception of a brief site-specific welcome clip. Subjects seemed to be highly satisfied with using iIC video presentations as a source of valid information. Because of this, cooperation of the subjects with the research sites improved, and investigators and site personnel provided very positive feedback about the experience. 

Ethical guidelines prohibit clinical trial sponsors from influencing the relationship between subject and investigator. Nevertheless, the use of iIC was accepted very well by ECs and required no alterations to the proposed content. The usefulness of the content as well as of the software application was rated very highly. All parties involved in this trial confirmed the added value of using iIC in this study. 

Additional pilot trials would be required to collect more data on screen failure rates, enrollment and retention rates, and potential associated cost savings. It would be interesting to expand the scope of future iIC pilot trials to include pediatric patients who cannot be expected to legally provide consent, but who could provide their assent instead. In addition, visually impaired patients could benefit from iIC video presentations as they would not have to receive a text-based document as the source of information only. Moreover, certain technology-related extensions (e.g., e-signature of the IC document) could be evaluated. This could help to further improve the IC process for the good of subjects, sites, and the sponsor as well. 

1. J.S. Brady, "Multimedia Delivery Can Enhance the Consent Process," 

2. F. Kermani and P. Bonacossa, "New Ways to Recruit Trial Subjects," 

Patients and patient advocacy groups have long demanded more easily understood information about their diseases and about clinical trials. Given these demands, it seems reasonable to provide trial-related information in an audio-visual format. Indeed, Brady has already reported the use of multimedia informed consent programs in several leading research centers.

Supplements-02-02-2005

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