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Other new capabilities include electronic sign-in for instructor-lead courses, eliminating paper-based systems, and providing a layer of authentication to monitor attendance and streamline recordkeeping. The new User Group format allows administrators to organize and manage employee qualifications, career goals, and other role-based activities. They can then suggest knowledge criteria and curricula based on employee qualifications.  

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It is widely recognized that the pharmaceutical industry faces serious financial challenges. Large numbers of blockbuster drugs are losing patent protection and going generic. The pipeline of new drugs is too sparse to fill the gap and generate a platform for future growth. Moreover, many of the new products are biologics with much narrower target patient populations and comparatively higher prices relative to traditional pharmaceuticals. 

As the number of such specialty pharmacy products has grown, payers in the United States have become increasingly concerned about cost, demanding higher levels of evidence about the economic value of biopharmaceutical products from manufacturers. At the same time, the health care system is aggressively moving toward a consumer-directed care model in which the consumer (and the cost of treatment) is given much more weight in the treatment decision. Adapting to these changes requires a wholesale rethinking of drug development and marketing strategy on the part of today's biopharmaceutical companies. 

Faced with this scenario, the pharmaceutical industry has moved to accelerate drug development timelines and to design studies containing health economic and patient-reported outcomes in order to provide payers with the evidence of the economic value of their products that payers are demanding. One important aspect of demonstrating value is to develop products that provide treatments for conditions that previously had no treatments, or which are much more effective or safer than existing products. 

Assessing unmet need can begin very early in the clinical development process—as early as Phase I or II—and provide valuable information that informs early strategic decisions about potential indications to pursue. For example, suppose a company has a compound in development that has the potential to treat a variety of mental illnesses. Very early in the clinical development process the company could examine sources such as medical and pharmaceutical claims data from large insurers to determine the relative sizes of the markets for major depression, obsessive compulsive disorders, social phobias, etc. 

Moreover, it would be possible to determine from these data the various treatments that are currently being used for these conditions; measures of unmet need such as medication switching or discontinuation; and rates of mental health hospitalization. These same data could reveal the medical specialties of the physicians treating the patients, as well as their geographic locations. They could also provide profiles of the patients in terms of their medical co-morbidities, concomitant medications, and health care utilization. 			

Although companies are increasingly building health economics into their early clinical development processes, many still focus on the clinical efficacy and safety criteria needed to obtain market approval. 

These companies then typically develop decision analytic models in which clinical results from such trials are combined with health economics and outcomes data obtained from database studies or meta-analyses of the literature. Models such as these are useful to help payers assess the cost-effectiveness and budget impact of new treatments relative to existing therapies. However, a significant limitation of such models is that they do not tie together the health economic, outcomes, and safety data for the same individuals—rather, they bring together information from several different sources to determine the expected impacts of covering the product. 

Moreover, most clinical trials that incorporate health economic and patient-reported outcomes are powered to detect statistically significant differences in the primary clinical endpoints but seldom to detect differences in health economic or patient-reported outcomes. This is because the wider variation in health economic and patient-reported outcomes requires substantially larger sample sizes to enable hypothesis testing at comparable levels of statistical significance. Unfortunately, cost-effectiveness conclusions based upon such trials may be misleading because there is no guarantee that sample sizes are large enough to ensure that randomization can adequately balance the treatment cohorts in terms of the health economic outcomes. 

Partly because of the substantial cost associated with conducting trials powered to detect health economic and patient-reported outcomes, and partly because payers are demanding real-world data on clinical effectiveness and safety, there is a growing emphasis on postlaunch surveillance studies. 

Like clinical trials, such prospective real-world studies represent substantial research efforts that must be carefully designed to maximize the probability of answering the study's research question. For example, such studies could be used to collect data on health-related quality of life among patients receiving different treatments for a particular condition in actual clinical practice. Similarly, real-world prospective studies can obtain information from patients about their reasons for medication refill decisions as well as from physicians about factors influencing their prescribing decisions. 

Given the significant investment required to execute clinical trials and prospective studies, it is instructive to consider how secondary use of post-launch health data can contribute to the planning, design, and implementation of clinical trials and real-world prospective studies to increase the likelihood of "success." Such data have a number of desirable properties, including large sample size; the ability to observe the use of drugs in real-world settings (in particular, patients with multiple co-morbidites being treated with concomitant drugs); and the ability to observe treatment patterns in actual clinical practice. 			

The use of health care data can influence the planning and execution of clinical trials in many ways, including study design, development of the research protocol/clinical research form (CRF), and physician and site recruitment. Use of health care data may slow the development of the study design and protocol somewhat, but will generally lead to an improved design that will accelerate CRF development. It may also aid in more efficient recruitment of investigators and patients, help determine the sample size needed to detect statistically significant differences in health economic and patient-reported outcomes, and has a variety of other uses, including determining the length of follow-up needed to detect certain endpoints, such as hospitalization risk. Overall, such evidence-based trial design should increase the likelihood of timely recruitment and successful trial completion. 

Most medical claims databases contain geographic codes (e.g., three-digit zip codes) that make it feasible to calculate prevalence rates of particular diseases or the use of particular drugs in a given geography. This is helpful in identifying and prioritizing geographic areas that are ripe for patient recruitment. For example, many companies have clinical trial investigator lists. Overlaying the geographic locations of the investigator list with the geographic concentrations of patients helps to identify areas (and, therefore, the investigators) where patient recruitment will be most efficient. 

In some large payer organizations such as certain large health plan settings, it is possible to identify particular physicians treating patients of interest without ever identifying individual patients. With appropriate Institutional Review Board (IRB) approvals, participating physicians can then recruit patients for studies and obtain patient consent at the time of recruitment. This is an efficient mechanism for identifying and recruiting patients, and ultimately results in improved identification of physicians and sites, improved quality of investigators, and reduced time for recruitment and training. 

Medical claims databases also contain a great deal of information on real-world patterns of health care utilization and costs for patient cohorts of interest. Data on rates of hospitalization and the variation in total health care costs can be valuable for calculating sample sizes needed to detect statistically significant differences in health care outcomes. 			

Clinical trials and prospective studies that focus on the collection of health economic data present a number of unique challenges. Generally, researchers assume that 20% of patients account for 80% of health care utilization and cost (hence, "the 80/20 rule"). This "skewness" requires that health economic outcomes studies must often be substantially larger than studies focusing on efficacy and safety outcomes. The relatively small numbers of patients with very high expenditures need to be balanced across treatment cohorts in order to avoid biasing conclusions about cost effectiveness. 

Given this, it is important to estimate the variance in health care expenditures among a population that closely resembles the patient population that will be entered into the clinical trial in order to properly power the trial to detect differences in economic outcomes across treatment cohorts. This often can be accomplished using retrospective claims data from large national health plans or other sources. 

In addition, many features of a study protocol can be tested—including evaluation of inclusion and exclusion diagnoses, inclusion and exclusion drugs, wash out periods, length of follow-up, etc. Of course, it is not always possible to model all features of a study protocol in this fashion. Some features, such as blood pressure or body mass index, might not be present in administrative claims databases. Nevertheless, the "simulation" of costs associated with patients similar to those that are planned to be studied can provide useful guidance on the required sample sizes necessary to have the statistical power to detect differences in health economic endpoints across treatment groups. 

For studies conducted within large national health plans or other payer organizations, the medical claims offer another important advantage: The claims capture tremendous amounts of detail on patterns of health care utilization that does not need to be collected via survey from patients (who have notoriously poor recall). This enables the survey question to focus more selectively on data not otherwise captured, such as health care not reimbursed by insurance (e.g., over-the-counter pharmacy utilization), health-related quality of life, and other patient reported outcomes. 

In turn, data collection becomes more efficient and less costly, thereby enabling a given clinical trials budget to be spread over a greater number of patients—improving the power of the study to detect statistically significant differences in health economic or patient reported outcomes across treatment cohorts. With patient consent (to comply with regulations such as the Health Insurance Portability and Accountability Act), it even becomes possible to "follow" the patients in the claims for extended periods after the trial has ended—enabling long-term health economic and safety outcomes to be assessed. 

Table 1 illustrates these concepts with an empirical example. Suppose we want to evaluate the feasibility of a clinical trial protocol for osteoarthritis (OA). Using a database of de-identified medical claims, we first identify all patients in the database, age 40 or older, with a diagnosis code for OA. This yields an initial sample size of 27,015. 

Patient Records for Forecasting a Protocols Potential				

Next, we exclude patients currently being treated with certain OA drugs, reducing the sample to 21,791 (or 80.7% of the original). Further restricting the sample to patients on a particular class of drug and excluding patients exceeding a maximum dose causes a major drop in sample size to 3923 patients (18% of the original patient pool). Finally, excluding patients with hypersensitivity to drugs, a history of certain co-morbidities, and a history of substance abuse yields a final potential sample of 3281 patients. 

It is useful for clinical development teams to understand that only about 12% of potential patients (3281/27,015) meet the contemplated study criteria. The impact of changes in inclusion or exclusion criteria on estimated sample size can be assessed for parameters such as gender; age cutpoints; primary and co-morbid diagnoses (using ICD-9 codes); drug therapy, dose or duration; and use of particular diagnostic tests or procedural interventions. 

Linking Investigator Site Records for Added Value			

Linkage of physician identifiers to other databases provides even more value (see Figures 1–3). In the context of a large health plan or provider group, it is possible to go beyond the de-identified patient data and have physicians (or the health plan) recruit patients for studies. These studies must follow standard IRB and patient informed consent processes to ensure compliance with regulations governing clinical research and treatment of protected health information. 

Identifying Investigators by Primary Specialty				

Health economic data and analysis can (and should) play a substantial role in the clinical development of biopharmaceutical products. Although there is substantial variation by country, payers around the world are demanding evidence of economic value, safety, and effectiveness to support coverage and benefit design decisions. The applications' evidence range widely: 					

the use of data analysis very early in the clinical development process to inform strategic decisions about the indications to be pursued, determining the sample size needed to detect statistically significant differences in health economic outcomes

the use of claims data to assess and improve the feasibility of clinical trial protocols

the actual identification of physicians and investigator sites.

Tapping into Investigator Experience						

Such evidence-based trial design should become a routine part of the clinical trial planning process. 

As a result of changes in the marketplace for biopharmaceutical products, payers and regulators are requiring companies to provide much more information about the economic value and safety of their products in actual clinical practice. Phase IV is the first opportunity to observe a drug's use in routine clinical practice. As a consequence, Phase IV clinical studies provide the first chance to observe the impact of a product upon medication adherence and switching patterns, use of health care services, and drug safety in large numbers of patients who often have other co-morbidities and who are being treated with concomitant medications. 

A major challenge with Phase IV studies is the need to control for the myriad other factors, besides drug treatment, that can influence patient behaviors and outcomes. In the hierarchy of evidence, most observers would agree that large, simple Phase IV trials specifically designed to measure health economics and outcomes research endpoints would probably do the best job of this. But because such trials are expensive to undertake, nonrandomized study designs are more common. Such studies can provide reliable evidence, provided that they are properly designed and analyzed using appropriate multivariate statistical methods. 

In general, the more robust the data collection effort, the better the job that such methods can do in controlling for confounding factors, such as unobserved disease severity, which may be the real reason behind variation in patient outcomes. Health economics has much to offer along the entire clinical development continuum—from strategic decision-making early in clinical development to the generation of evidence after the product is on the market. 

, PhD, is vice president, global health economics and outcomes, i3 Innovus. 

, MD, MPH, is therapeutic area director, primary care, with Roche Laboratories Inc., Nutley, NJ.

Feature Article

Increased cost concerns set the stage for health data to play a major role in drug development.

Health Economics & Analysis in Clinical Development

In 2004, President Bush presented a formidable challenge to the health care industry when he called for widespread adoption of electronic health records (EHRs) in the United States and the creation of a National Health Information Network (NHIN) by 2014. Since that time, health care organizations, community health professionals, and technology vendors have turned their attention to creating the strategies, protocols, and IT infrastructures necessary to make this vision a reality. As part of this effort, a growing number of health care organizations are choosing to participate in regional health information organizations (RHIOs), umbrella organizations for local data exchange networks and gateways, which many view as an essential element in the formation of a NHIN. 

In recent years, we have also seen increased documentation regarding the potential for electronic health record networks to improve continuity of patient care, reduce medical errors, and lower health care costs. Adoption of electronic medical records (EMRs) in both hospitals and private practice is rising steadily, with 50% of U.S. hospitals using electronic medical/health record systems.

As exciting, but often less reported, are the potential benefits that electronic health records—aggregated through RHIOs and, ultimately, the NHIN—can deliver to the life sciences industry. There are significant crossover benefits that take aim at three of the industry's most vexing challenges: more effective and efficient identification and recruitment of clinical trial participants, the ability to simultaneously capture clinical trial data from the electronic health record to decrease the costs of clinical trials, and faster identification of adverse events postmarket. 

With the cost of developing a new prescription drug creeping toward $1 billion and the process often spanning more than a decade, pharmaceutical manufacturers are continually searching for ways to extend efficiencies at all stages of the development continuum, allowing them to bring safe products to market faster and at a lower cost. 

Clinical trials—which average $124 million per drug candidate after accounting for drug failure rates

  and whose costs are rising faster than preclinical research and development activities—are a prime target for scrutiny. Rising clinical trial costs can be attributed to several factors, including new challenges related to trial candidate identification and recruitment. The number of patients required for a trial by U.S. drug regulators, for instance, has almost doubled in the past decade. 

Clinical trial patient recruitment is also an increasingly time-consuming process. One study, which looked at 4000 clinical trials over five years, discovered that nearly half of the time spent on the trial process involved patient, site, and investigator recruitment.

  On average, difficulties in patient enrollment delay 81% of all clinical trials from one to six months, costing pharmaceutical companies as much as $8 million each day.

  This figure does not take into account the human costs of such delays in terms of the inevitable morbidity and mortality when promising new drugs are delayed in reaching the market. 

RHIOs and the patient information they collect are poised to play an important role in the pharmaceutical industry's quest to bring products to market faster, more cost effectively, and at a lower risk. Specifically, RHIOs, which have the potential to aggregate large amounts of anonymized patient information, offer an ideal tool for cost-effective identification and recruitment of clinical trial participants. 

In theory, life sciences organizations or contract research organizations (CROs) could interface with multiple RHIOs, and ultimately the NHIN, to quickly screen large numbers of anonymized electronic records for potential trial candidates using any number of factors, including age, sex, co-morbidities, lab results, etc. In this way, they could approach physician groups that they know treat significant numbers of patients matching a specific trial candidate profile. This method could be particularly useful when recruiting for trials for drugs intended to treat rare conditions, as the sponsor could search for specific criteria. The end result could be faster and more cost-effective trial participant identification and recruitment, ultimately accelerating time to market. 

While the approach outlined above would enable more effective targeting of physician practices that treat viable candidates, another approach being advanced would take information on clinical trials directly to potential candidates. It would involve the creation of a patient opt-in mechanism, in which individuals would grant permission to have life sciences organizations access their health information when launching a clinical trial and contact them directly. This approach could go a long way toward empowering patients to take charge of key health decisions, as well as streamline the participant recruitment process. 

To optimize the success of this approach, physicians would also need to be notified, in tandem, when a trial sponsor contacts their patients. Studies indicate that patients are most likely to participate in trials when information is provided in a health care setting, as opposed to delivered via advertising and direct mail. 

To gain access to RHIO or NHIN information, life sciences organizations would pay a transaction or subscription fee, which would be more than offset by the savings gained via faster and more effective recruitment. This revenue would also be an important factor in ensuring the self-sufficiency of RHIOs, which is required for their ultimate success. 

Today, initiatives at individual investigator sites are revealing glimpses into the potential of EHR data to transform clinical trial recruitment. For example, researchers from the University of Cincinnati College of Medicine studied the potential of a clinical trial alert (CTA) system to increase physician participation in and expedite recruitment of patients for a clinical trial. After one year of traditional recruitment to a clinical trial, the researchers used their EHR-based CTA system at The Cleveland Clinic to recruit patients. When a patient's records met selected trial criteria, the CTA alerted the physician about the ongoing trial.

The study revealed that the group using CTA intervention experienced a 10-fold increase in the number of referrals generated by physicians: 5.7 per month before intervention to 59.5 per month after. In addition, the researchers found that the CTA-enabled group recruited more than twice as many trial participants per month (6 participants) than the group using traditional recruitment methods (2.9 participants).

By combining EHRs with data mining tools and CTA systems, investigator sites have the potential to quickly query the EHR database to determine the number of potential candidates for a specific study and to assess the viability of candidates for a specific trial. For example, during a routine exam, a physician could open a patient's EHR and reference the information documented in it to determine if the patient meets eligibility requirement for a particular clinical trial based on the record and the trial requirements as presented through the CTA. The physician could then either select or dismiss a candidate. If a patient matches the criteria for any study, the physician could immediately collect the additional information required for the trial, record the data in the EHR, and send an electronic notification to a study coordinator for follow-up. At that point, screening for the trial is complete, and  within a matter of minutes. 

Once a patient is enrolled in a trial, researchers could then incorporate data captured from a specific study as part of routine clinical care and automatically populate study data elements into case report forms (CRFs) from other components of the EHR database. By implementing rules and alert features and documentation checks, organizations are also able to ensure data collection compliance and accuracy.

  Automating these core processes can help accelerate clinical trials by streamlining patient enrollment as well as documentation. 

Accelerating identification of adverse events

An average of 17,200 adverse events (AEs) and 800 serious adverse events (SAEs) are reported annually to the U.S. Food and Drug Administration (FDA) for newly approved drugs.

   Rapid identification and reporting of AEs to the FDA are important for several reasons. First and foremost, they minimize risk to public health. They also reduce corporate legal and reputational risk. As important, diligent reporting by the pharmaceutical industry—in essence, self-policing—is the best defense against the potential of additional regulation or more protracted drug approval processes. 

Pharmaceutical manufacturers recognize the benefits of improving their ability to more rapidly identify and interpret AEs. One has only to look to high-profile drug recalls in recent years to get a sense of the potential financial cost. For example, the FDA recalled the drug Fen-Phen in 1998 after reports of heart valve disease and pulmonary hypertension primarily in women who had taken this drug. In April 2005, 

 magazine reported that more than 50,000 product liability lawsuits had been filed in relation to this recall and that the total cost to the manufacturer at the time of publication was estimated to be as high as $14 billion. 

Anonymized patient data provided through RHIOs holds the potential to help life sciences organizations in their efforts to more rapidly identify AEs postmarket—protecting public health and reducing corporate risk. In theory, pharmaceutical companies could interface with RHIOs and mine data from anonymized records of patients taking a new drug, with the goal of quickly identifying patterns and reporting adverse reactions that might result from drug and disease interactions or combined drug reactions. 

To effectively and quickly identify potential AEs from large numbers of patient records, pharmaceutical organizations require powerful data mining and analytical tools that enable them to track and integrate events from multiple sources, including the Internet, patients, physicians, and regulatory agencies such as the FDA and the World Health Organization. In essence, they require a powerful lens that allows them to rapidly analyze patterns in AEs that may indicate emerging drug safety risks pre- and postmarket, enabling faster response times and streamlined compliance. 

As important, organizations require signal detection capabilities to focus on the cases of most interest. Ideally, drug safety teams should have the ability to establish thresholds for automatic safety signal detection, receive alerts when thresholds are reached or exceeded, and assess their case information using data mining tools for statistical analysis. 

RHIOs and the NHIN hold the potential to provide considerable benefits to pharmaceutical organizations, including improving the effectiveness and efficiency of clinical trial recruitment, accelerating the collection of trial information, and identifying AEs. The ability to execute on this vision will require a robust and stable network of RHIOs. Progress on this front is visible but challenges remain. 

The health care and life sciences industries are optimistic about the potential of electronic health data to transform the delivery of health care as well as the drug development process, including spurring advances in the quest for personalized and translational treatments and therapies. The critical technology components needed to enable meaningful exchange of information between the health care and life sciences industry exist today—as do the data mining and analytical tools needed to interpret data and drive incisive action. By combining these core technologies with proper planning and vision, RHIOs and other EHRs offer one of the brightest hopes for the future of the health care and life sciences industries and their quest to save and enhance the quality of lives. 

 is vice president of Global Health Industries with Oracle, 500 Oracle Parkway, Redwood Shores, CA 94065, 

 is senior director of Life Sciences Industry Strategy and Marketing with Oracle, email: 

*To whom all correspondence should be addressed.

1. D. Garets and M. Davis, Electronic Medical Records vs. Electronic Health Records: Yes, There is a Difference, HIMSS Analytics, LLC, January 26, 2006. 

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

3. CMR International R&D Briefing: Benchmarking for Efficient Drug Development (2000). 

5. P.J. Embi, A. Jain, J. Clark, S. Bizjack, R. Hornung, C.M. Harris, "Effect of a Clinical Trial Alert System on Physician Participation in Trial Recruitment," 

6. National Center for Research Resources: Ensuring the Inclusion of Clinical Research in the National Health Information Network, Integrating Electronic Health Records and Clinical Trials: An Examination of Pragmatic Issues, May 9, 2006. Available at  http://

www.esi-bethesda.com/ncrrworkshops/clinicalResearch/pdf/MichaelKahnPaper.pdf

8. Society for Clinical Data Management, Phoenix Data Systems, The Gordian Knot: Integrating Global EHR and Global Clinical Trials, Oct. 8, 2006. Available at http://

www.phoenixdatasystems.net/news/documents/SCDM_IntegrateEHRandClinicalTrials.pdf

EHR has the potential to help solve long-time clinical trial recruitment, cost, and safety challenges.

Electronic Health Records: A Magic Pill?

Continued expansion of human subject research around the world is generating calls for a more efficient and effective oversight system to evaluate research protocols and monitor trial operations. This task largely is the responsibility of Institutional Review Boards (IRBs) at research organizations and in communities where clinical research takes place. 

For several years now, research sponsors at government agencies as well as pharma companies have been turning more to central IRBs to provide more timely and coordinated research review of multicenter trials, many involving complex study designs and large numbers of subjects. 

This trend has raised an outcry from academic research centers that consider local review critical to understanding the values and attitudes of the local population participating in a study, a particularly important issue for conducting research in foreign countries. At the same time, many local IRBs lack the resources, staff, and expertise needed to evaluate and monitor the growing volume of trials. 

The benefits and weaknesses of both local and central IRBs was the subject of debate at a National Conference on Alternative IRB Models in Washington, DC, last November. The conference, which followed the annual meeting of PRIM&R (Public Responsibility in Medicine and Research), attracted 400 participants eager to discuss the changing roles of IRBs in biomedical research. The starting point was a report from a smaller workshop in November 2005 that had been proposed by the Secretary's Advisory Committee on Human Research Protections (SACHRP) to explore alternative models to local IRB review. 

Following that workshop, FDA issued a guidance last March (2006) to clarify that reliance on central IRBs to oversee multicenter trials does not violate agency regulations. The guidance explains that FDA regulations permit an IRB at a different location from the actual research site to review the study, so long as the central board understands the local context and is sensitive to community values. Conversely, separate research review at multiple sites can result in "duplication of effort, delays, and increased expenses" says the document, adding that "greater reliance on a centralized IRB review process, in appropriate circumstances, could reduce IRB burdens and delays." 

This document and other guidance under development by the Office of Human Research Protections (OHRP) reflect continued tension between the need for IRBs to represent the interests of the local community where research is taking place, and the need for more efficient and coordinated oversight of research at multiple sites. 

Central IRBs, which may be commercial operations or established by public research organizations such as the National Cancer Institute, often have greater resources, access to experts on cutting-edge science, and large information systems. 

Cancer researchers are some of the most vocal proponents of central IRB review, particularly for "smart studies" that test candidate therapies in patients most likely to respond and that need to enroll patients at many sites across the nation and the world, explained Lowell Schnipper, MD, of Harvard Medical School and Beth Israel Deaconess Medical Center. Central IRBs may reduce costs by eliminating duplicative reviews and ensuring consistency in consent forms, protocols, and results across all sites. 

Pharma companies have long used central IRBs to obtain efficient review of protocols and research programs, which often involve many study sites, including doctors offices and clinics. Even with the recent increase in industry-sponsored trials at academic institutions, industry sponsors want academic researchers to use a central IRB to oversee the total study, a demand that increasingly dismays many IRB operators. 

In fact, officials at research organizations around the country fear that a wholesale shift to central IRBs will undermine the current infrastructure designed to ensure that human subject research fits community standards and values. Richard Bianco, associate vice president for research and regulatory affairs at the University of Minnesota, advised attendees at the IRB conference to "slow down" the rush to outsource research oversight. He maintained that the local system for subject protection "is not significantly flawed," but suffers primarily from a lack of resources and support. 

What is really needed is more investment in building the local compliance infrastructure and more help for investigators in developing sound protocols that IRBs can review more efficiently. Bianco blamed delays in local IRB review on poor applications, lengthy intellectual property negotiations, and difficult conflict-of-interest issues. Such problems can be addressed, he proposed, by making the institutional review process more efficient, instead of outsourcing entire categories of research. 

Robert Levine of Yale University's Interdisciplinary Center for Bioethics agreed with Bianco's assessment of the situation, but not with his remedy. A key problem is that local IRBs no longer can attract prominent academics as they once did. Leading medical school professors don't want to serve on IRBs, Levine observed, because they're too busy running trials, don't want to discipline colleagues, and don't want to spend hours in meetings and reviewing adverse event reports. 

Levine predicted that the shift to central IRBs will accelerate, but acknowledged that "something of immeasurable value will be lost" in the process. There is a need for a local "institutional presence" to remind people of the importance of ethical research, Levine observed, noting that it is hard to transmit a local board's knowledge of a local population and cultural norms to a central IRB. 

The leading remedy for this type of town–gown conflict is to clarify responsibilities and articulate which oversight activities are best performed by local and by central organizations under a shared review process. In situations where local context is particularly important for subject protection, a central IRB would collaborate with a local board. But for multicenter trials, most other oversight activities—protocol review, evaluation of study risks, review of safety information—are best achieved centrally, commented Jean-Louis Saillot of Schering-Plough Research Institute. 

A key issue is liability for patient harm if there are problems in a clinical trial. Local IRBs are concerned that they are legally responsible for research performed at that institution and thus are obliged to review planned research programs. Any joint oversight model requires written contractual agreements up front, noted Angela Bowen, head of Western IRB. OHRP Director Bernard Schwetz explained that if there is a clear agreement regarding study oversight between a central and local IRB, OHRP will pay attention to this if a problem erupts. But he acknowledged that "the liability issue for IRBs is huge" and that it is difficult to draw the line between central vs. local responsibilities. 

The other big issue is the potential loss of resources for local IRBs, Schwetz notes. If research institutes shift more oversight responsibility to outside organizations, local boards stand to lose badly needed funding, making it even more difficult for them to maintain the infrastructure needed to provide efficient and effective oversight. One proposal is that sponsors provide some funding for local board operations out of the savings achieved by shifting review responsibilities to central IRBs. 

Whatever model is adopted, Schwetz commented, there is a need to ensure human research protection at every research organization. He objected to suggestions that OHRP has lost confidence in the ability of local IRBs to monitor research or that the agency wants to push researchers to use central IRBs. OHRP's goal is to "match the IRB model to different needs," he said, and to ensure that research sponsors that choose to use alternative oversight models understand the rules and their responsibilities. 

OHRP and sponsors of the November conference are preparing a report to clarify the key issues for both research institutions and sponsors to consider in seeking the most appropriate oversight approach. 

View from Washington

Regulators and sponsors encourage alternative review models to fit a growing research enterprise.

Central vs. Local: Rethinking IRBs

According to clinical investigators, it's ugly out there! Investigators and site staff consistently report that managing site operations is difficult—particularly maintaining positive cash flow and profitability. Lead generation has intensified as competition for new study grants has increased. Study protocols have become more complicated and demanding. Patient recruitment and retention challenges have escalated. And the burden of regulatory compliance has become onerous and extremely frustrating. 

Indeed, in recent interviews with clinical investigators, regulatory compliance is one of the top burdens that sites face today. Many argue that compliance requirements place unreasonable demands on study staff time and focus, and may, ultimately, undermine study conduct performance. 

"It's not clear if sponsors have even noticed the connection between mounting compliance paperwork and the low morale and steady exodus of good investigators from clinical research," lamented one New Jersey–based investigator. 

The results of a recent study by the Tufts Center for the Study of Drug Development are consistent with these observations. Turnover among clinical investigators is at an all time high; and the majority of those dropping out of clinical research are experienced, community-based investigators—not novice investigators, as is commonly believed. 

Interviews with clinical investigators reveal that there is much to gripe about. Top site concerns offer research sponsors insight into areas where intervention and assistance might improve study conduct performance. 

One of the largest problems relates to the way Investigational New Drug (IND) Safety Reports are handled. Food and Drug Administration (FDA) regulations require that all investigators and institutional review boards (IRBs) involved in a clinical study be informed about any associated severe adverse event (SAE) that occurs anywhere in the world. 

"That's a lot of reports," noted Jim Maloy, MD, executive vice president with Drug Research and Analysis in Montgomery, AL. "One global cardiac study for which we enrolled one patient had 400 IND Safety Reports submitted to the IRB." 

"And the information isn't particularly useful. If a study is blinded, recipients have no way of knowing if the SAE was connected to the actual study drug, a comparator drug, or a placebo," added Maloy. "The reports also don't indicate how many patients experienced the SAE. I have yet to see a local IRB change a protocol or ask for a change in the consent form based on [IND] Safety Reports, and I've been involved in a thousand studies," he said. If an SAE is severe enough to warrant such changes, they're generally made before an IND Safety Report gets issued. 

"The problem is compounded if a central IRB is involved," said Jed Ervin, MD, founder and president of the Center for Pharmaceutical Research in Kansas City, MO. "Central IRBs receive dozens of copies of each IND Safety Report, reviewed and signed by individual investigators." 

"But more time consuming and bothersome for sites," added Ervin, "is the requirement that they go back through the entire consenting process, inclusive of IRB approval, whenever an amendment is made to the study protocol." For a single study, at least two or three amendments are not unusual. "Reconsenting annoys patients and creates an ethical problem if they're asked to retroactively agree to changes once the study has ended—a disturbingly common occurrence," he explained. 

"Financial disclosure forms represent another big headache, especially for private research operations that provide administrative support to subinvestigators at disparate locations around town," said Maloy. "Federal regulations require that the forms be signed by all investigators at the beginning and end of a study and a year after study completion. Yet conflicts of interest are almost solely a problem in academia where the results of government-sponsored research get commercialized." 

"In terms of time and paperwork, I believe that regulatory compliance is most burdensome when a hospital uses its own local IRB," said Staci Gola, RN, BSN, CCRP, director of operations at Advanced Clinical Research (ACR) in Salt Lake City, UT. "Study approvals by local IRBs take an average of two months. And this is not helped by the fact that all regulatory forms, with the exception of the 1572 [Statement of Investigator Form], are different in type and format." 

"The maintenance of study records represents another major burden for sites," said Gola. "Since the regulatory guidelines are so ambiguous, the investigator ends up keeping records indefinitely. ACR has records in storage that have been there more than 20 years. Replacing boxes in which records are stored every 10 to 15 years is a monumental job and storage fees run thousands of dollars annually," she added. 

Research and development costs, cycle times, and the scope and complexity of clinical trials are all increasing. At the same time, the volume of global clinical trial activity is rising, with an increasing proportion of active clinical projects entering the more demanding later stages of drug development. An unstable clinical investigator community does not serve growing sponsor demand for strong and sustainable study conduct performance while ensuring patient safety. 

Clinical investigators believe that relieving some of the regulatory burden is a critical place to begin improving stability. Although many investigators view efforts to ease the regulatory burden as futile at this point, some remain more hopeful. 

Investigators want sponsors to play a more active role in addressing several key areas. Among their suggestions: 					

Take the time to closely evaluate the regulatory requirements placed on investigators and study staff. Identify those areas that are the most paper and labor intensive and look into streamlining them and/or working with FDA to streamline those that require agency assistance.

Modify the 1572 Form so principal investigators can include financial disclosure information for themselves and for all sub-investigators. Have investigators complete traditional disclosure forms only if there is an actual conflict.

When the protocol gets amended, require that patients reconsent only to those specific amendment changes.

Spend more time doing up-front study planning so that investigators can deal with regulatory issues in a proactive, stepwise way rather than an emergent, reactive fashion.

Encourage institutions to use central IRBs to reduce variation in paper-work and to assist in streamlining document-routing activity.

Create standardized, universal IRB forms and have sponsors include preprinted general information common to all sites in submission packets.

Send IND Safety Reports to a designated data safety monitoring board (DSMB) that is responsible for issuing periodic summaries to IRBs.

Have the sponsor or contract research organization (CRO) submit a single document to central IRBs for each IND Safety Report that is reviewed and signed by multiple investigators.

Assess, recognize, and remunerate the "hidden" cost of records archiving during the budgeting process.

Establish and encourage mechanisms to receive continuous feedback from principal investigators and study staff in order to identify and tackle operating concerns.

Many experienced investigators are seeing a more difficult operating environment and high levels of turnover. They are looking to sponsors for assistance.  

"The regulatory climate has become so oppressive in the recent past that two out of three principal investigators who have worked with us for 20-some years said they don't want the responsibility anymore," warned Maloy. "They don't have time for it. They're busy seeing patients in their practice." 

"It would seem completely sensible for sponsors to do all they can to make regulatory compliance less nightmarish an experience for their study sites," concluded an investigator based in New Jersey. "It's in their best interest, isn't it?" 

,MS, MBA, is a Senior Research Fellow at the Tufts CSDD and Chairman of CISCRP, both in Boston, MA, email: 

Clinical Trial Insights

With today's clinical investigators awash in regulatory burdens, it's high time sponsors provided a lifeboat.

Drowning in the Sea of Regulatory Compliance

Partnership puts pulmonary data in the hands of researchers when and where they need it

 (Lenexa, KS, and Buckingham, UK) to deliver expedited access to pulmonary function data collected during clinical trials. The system works by closely integrating Nextrials' Prism clinical trial management system with the Vitalograph Spirotrac Centralized Spirometry System.  

As a result, biotechnology and medical device researchers can quickly evaluate pulmonary lung function, potentially improving patient safety. For researchers, real-time access to study data also translates into enhanced workflow and efficiency. Ultimately, this results in reduced costs and quicker time to market for new therapies. As a result of the collaboration, sponsors have access to a single product that provides all relevant data including pulmonary function information, in real time across multiple test sites and multiple studies.  

eShowcase: Nextrials and Vitalograph

The use of Electronic Data Capture (EDC) systems for the capture of clinical trial data is increasing.

  The estimates of the extent of adoption are varied and confounded by the definition of what constitutes EDC (e.g., electronic diary data is included in some of the estimates). The true percentage is somewhat irrelevant, as EDC is clearly replacing paper for the majority of trials. 

The speculated benefits of EDC are many, including improved quality, faster access to data, reduced effort, and faster database lock. So if this is the case, why has it taken more than 20 years for Remote Data Entry (RDE)/EDC to gain acceptance? Whatever the reasons, be they technical, process, and/or cost related, the uptake has been slow and there are still drawbacks—the primary one being re-entry of data by the site staff. Others include delays in data accessibility, albeit shorter delays, and the continued need for source data verification, which is on the critical path to database lock with EDC. 

The global emergence of the availability of patient care data electronically, albeit a slow emergence in the United States, presents the opportunity to address all of these issues by transferring data electronically from the electronic source. 

In a nonscientific survey conducted on the 

 Web site, 71% of 407 respondents felt that electronic health records (EHR) and EDC would merge within the next five years—a very short time frame when one considers the comparatively slow adoption of EDC. Of those that disagreed, it is reasonable to speculate that the primary reason was not a belief that it will not happen but rather that it will not happen within five years. 

There have been successful eSource pilots (e.g., CDISC's Starbrite study at Duke and Siemens' implementation at the Technical University of Munich). CDISC is coordinating a demonstration of EHR/EDC interoperability at the HIMSS show this month. The eClinical Forum and PhRMA have joined forces to produce a white paper on "The Future Vision of EHRs as eSource for Clinical Research."

  ExL Pharma has organized two EHR/EDC meetings. So the momentum is there. 

The purpose of this article is not to describe the perfect solution nor to lay out a road map for EHR/EDC adoption. Its intent is to set out a list of "points to consider" before embarking on the path to data capture nirvana. The scope is multicenter pharmaceutical-sponsored global trials. 

Before discussing the use of EHR data for clinical trials, it is important to define what is meant by EHR in this context. There are a number of terms used interchangeably to describe systems that computerize patient health care data. The terms Electronic Health Record (EHR), Electronic Medical Record (EMR), Electronic Patient Record (EPR), Clinical Patient Record (CPR), and Lifetime Clinical Record (LCR) are all terms that are used by various individuals and organizations at times to mean the same thing and at other times to mean different things. 

The most widely used term today in the context of the use of data for clinical research is EHR, but the same term is often used to describe a series of interlocking solutions that are tied to a series of care delivery activities (including nonclinical ones such as billing) and that track a patient across multiple institutions such as hospitals and physician practices and even across different health systems.

  It is not necessary to wait for such systems to be widespread before electronic data can be automatically transferred to clinical research systems used by the biopharmaceutical industry. 

Also, these "electronic" systems do not necessarily contain data in a format that can be used for electronic transfer of data. An EMR can be comprised of scanned documents with nothing more than a retrieval capability based on patient name or some other identifier. This may still qualify as an EHR, but would not contain source data and would not enable the automatic transfer of data for clinical trials. 

In the remainder of this article, the term EHR will be used to mean source data captured in a format that enables it to be electronically transferred in a structured way to clinical research systems, with the qualification that some manipulation and mapping may be required during the transfer. The automated transfer of data from EHRs to an EDC system will be referred to as EHR/EDC. 

The care of patients is the number one priority for health care providers, be they hospitals or physician practices. Therefore, research requirements are going to be secondary. The implications of this simple truth are manifold. 

When choosing new information systems, the decision makers are going to be focused on solutions that optimize the management of patient care. Features to facilitate clinical research are going to rank low on the selection criteria, if they appear at all. The corollary of this is that Healthcare Information Technology (HIT) vendors will therefore focus on functionality to support health care provision and not clinical research—so features to support clinical trials such as the ability to collect and store trial specific data not required for routine care will have low priority on their development plans. A particular implication is that HIT vendors will be reluctant to invest in the effort to evaluate 21 CFR 11 compliance and rectify any shortcomings. This does not mean that vendors will not develop functionality to enable clinical trials, but any scalable implementation of EHR/EDC will have to be able to cope with systems that are not optimized for clinical trials. 

Health care providers will not replace their existing health care information systems purely to facilitate research. Therefore, any EHR/EDC solution must work with a large variety of systems, with varying degrees of sophistication. 

Because of the focus on patient care, clinical trial processes and procedures must cause minimal disruption to the existing processes and procedures in place for standard clinical practice. One implication of this: Any EHR/EDC solution that requires health care data for patients enrolled in trials to be captured and managed differently than data for patients not enrolled in trials is unlikely to be successful. Staff do not have the time or inclination to collect the same data for two patients differently, based on trial participation. Thus, it is most likely that solutions that automate the capture of trial data will receive data directly from the EHR rather than from an intermediate source that populates both the EHR and EDC system. 

The adoption of new data standards to make data collection and transfer in clinical trials more efficient is unlikely to be acceptable to health care providers. Historically, there has been little reason to share data outside of an institution, so the data structures have been developed to optimize the internal operations of each institution. The collaboration between HL7 and CDISC and the work of Duke Clinical Research Institute to standardize the collection of cardiovascular data across both health care and research are two examples of positive steps forward. However, there has to be a realization that data will have to be mapped in some way for each institution—although this mapping can be used across multiple trials. An example of how this is achieved is shown in Figure 1. 

Data recorded for individual patient care does not have to be highly structured nor coded for subsequent integration for summarization purposes. There are exceptions (e.g., disease coding for billing purposes), but for some data types the data is not collected in ways that directly enable transfer of trial data. Today's EHR systems do offer the ability to collect data in a structured and coded way, but often the data comes from dictated notes and is stored in an unstructured way that is not amenable to automated extraction. Therefore, to increase the percentage of readily usable data, it will be necessary for the users of the system to record the data in ways that, although possible, are not currently utilized routinely. 

An example of this is medical history data. If recorded by a physician, it will most likely be in a dictated, unstructured record. Related to this is the requirement of regularly scheduled visits for clinical trials—a concept that does not exist, at least not in the same disciplined way, within EHRs. Therefore, methods have to be developed that capture only the data that is required for particular visits. 

IT staff within Health Care Providers (HCP) are focused on systems that support the provision of health care and the associated administration. With EDC studies, they are minimally involved, assisting with any initial evaluation of the suitability and requirements of the site. EHR/EDC is perceived as being disruptive, taking them away from their primary tasks. Therefore, solutions must minimize the site's IT department's implementation and maintenance effort, and it is essential to involve the appropriate IT staff in any discussions, as a motivated investigator will not be able to participate without the buy-in of the IT staff. 

All of the above relate to the relative priority of health care and research. The final points to consider are security and data privacy. With EDC, no data is transferred electronically from HCP systems and there is no connection between such systems and those used by the pharmaceutical company. However with EHR/EDC there is a concern about the security risk, as systems have to be opened up to allow for the transfer of data. This concern should be addressable through appropriate security measures, and the automated transfer of data outside of the HCP's firewall is done today for applications such as point of admission insurance coverage verification. 

A related concern is data privacy and the need to ensure that only data that is required for the clinical trial is transferred to the pharmaceutical company. This implies that data manipulation and mapping needs to be done within the firewall or at a trusted third party. The privacy issue is further complicated by regional, national, and state differences. 

Overall, the implementation of an EHR/EDC solution at a healthcare provider is technically feasible today but needs to be carefully considered and requires in-depth discussions with multiple stakeholders at the HCP, including investigators, the IT department, data privacy officers, and, most importantly, the administration. Given the novelty of automated transfer of data, the administration will be concerned on many different fronts, and any potential implementation needs to be discussed in detail with them long before any anticipated trial start date. 

Points to consider: Pharmaceutical industry

Although the majority of issues in implementing EHR/EDC will focus on the site, there are considerations that focus on the use of such an approach by the pharmaceutical industry. 

As described earlier, implementation at a particular site is going to be time consuming, and therefore it is very likely that multicenter studies in which such an approach is used will also involve sites that use "traditional" EDC. Therefore, to minimize disruption, the data should flow into the company's existing EDC system so that existing edit checks and discrepancy management can be used. 

The alternative is for the data to be checked and discrepancies managed in some other system—a major disruption. One implication of this is that the industry is unlikely to be interested in receiving data from EDC systems used at HCPs unless it flows directly into the sponsor's EDC system. Also, this approach is very likely to lose the advantage to be had from eSource, as the data in the HCP's EDC system will most likely have been transferred from the EHR, either automatically or, more likely, manually. 

It is very likely that any prospective clinical trial will require data to be collected that is not part of the data routinely collected as part of patient care, and this data will most likely not be entered into the EHR. Therefore, in order for the "hybrid" approach described earlier to be viable, it is important that trial data that is not available in the EHR can be merged with data automatically transferred within the electronic case report form (eCRF). Otherwise, a different database would have to be developed for sites using EHR/EDC. 

In order to maximize the benefits of automated transfer of source data, careful consideration should be given to changes to the data once it has been transferred. Changes made at the source should be propagated to the sponsor's system, ideally automatically, along with the necessary audit trail information. Also, site staff should not be able to make changes to eCRF data if the data has been transferred from an EHR. This capability may not be possible with all implementations, so processes need to be put in place—similar to those used with central lab data—to ensure that changes are only made at the source. 

The transfer of data automatically to an EDC system is going to require investigation with the vendor and may require additional configuration and changes to the system. If this is the case, it may be possible to do proof-of-concept studies with workarounds to ensure that the investment in changes is justified. Also, it is likely that as the automated transfer of EHR data becomes adopted, EDC vendors will need to adapt their systems to optimize such transfers. 

Another consideration is regulatory acceptance. CDISC's eSDI group, which was commissioned by the FDA and included FDA representation, has issued a document that describes the requirements for using eSource in regulated clinical trials. It also describes five scenarios that, if implemented appropriately, could meet those requirements.

 As the direct use of EHR data is still very much in its infancy, it is advisable to have specific discussions with the FDA and other regulatory bodies before using the approach in a clinical trial that might be used to support a submission. 

Implementing the infrastructure to enable the automated transfer of EHR data is not a small undertaking and may require investment in sites at which a sponsor will run multiple trials. It will certainly require time-consuming discussions with multiple stakeholders at the site. 

The electronic transfer of EHR data for clinical trials is expected to be the future of data capture at some point in time and offers significant advantages over today's EDC systems, where manual data entry is the primary input method. There are multiple challenges, but these challenges are well characterized and all surmountable. Before beginning implementation there are many points to consider, but it is indeed possible today. 

1. D. Borfitz, "Conspiring Forces Behind EDC Adoption," CenterWatch, 10 (2) (2003). 

2. J. Paul, R. Seib, T. Prescott, "The Internet and Clinical Trials: Background, Online Resources, Examples and Issues," 

3. "The Future Vision of EHRs as eSource for Clinical Research" (Release 1, 14 September 2006). Available at: http://

www.eclinicalforum.com/frameknowledge.htm

4. T. Handler, R. Holtmeier, J. Metzger, M. Overhage, S. Taylor, C. Underwood, "HIMSS Electronic Health Record Definitional Model Version 1.0," 6 November 2003. Available at: http://

www.himss.org/content/files/EHRattributes.pdf

5. Electronic Source Data Interchange (eSDI) Group, "Leveraging the CDISC Standards to Facilitate the use of Electronic Source Data within Clinical Trials," 20 November 2006. Available at: http://

 is general manager, clinical trials business, with Siemens Medical Systems, 51 Valley Stream Parkway, MS E56, Malvern, PA 19355, (610) 219-5140, email: 

A review of important factors to consider before implementing the solution that will transform EDC.

EHR & EDC: Tomorrow's Technology Today

Web-based dashboard places clinical trial designers squarely in the driver's seat 

 (Oakbrook Terrace, IL) has released version 2.0 of its clinical trial operations planning software to help biopharm companies and CROs plan, budget, and optimize the operational design of clinical trials.  

The heart of this latest version is a Web-based "dashboard" that offers a view of monthly budgets, accruals forecasts, study metrics, cycle-time dates, costs, and FTE/resource requirements. Also new is support for multiple outsourcing strategies, full-service outsourcing, hybrid studies, staff augmentation, and internal studies.  

Other adornments to this version include the ability to compare cost and timelines between internal studies and outsourced ones, as well as assign study tasks to multiple service providers by function or country. Collaborative planning is also now possible between departments, business units, and strategic partners.  

ClearTrial 2.0 is a J2EE-compliant, native Web-based application running over an Oracle database.   

Web-based dashboard places clinical trial designers squarely in the driver's seat

eShowcase: ClearTrial 2.0

The pharmaceutical industry initially seemed to have escaped more lightly than might have been expected from the catastrophic Phase I clinical trial with TGN 1412 in London nearly a year ago. 

UK report on last year's TGN 1412 trial provides industry with blueprint for improved early clinical studies.

Applied Clinical Trials-02-01-2007