Leveraging of open EMR architecture for clinical trial accrual.

Leveraging of Open EMR Architecture for Clinical Trial Accrual Lawrence B. Afrin, M.D.H, James C. Oates, M.D.I, Caroline K. Boyd, B.S.', and Mark S. Daniels, M.S.' H Division of Hematology/Oncology,IDivision of Rheumatology, and ' Center for Computing and Information Technology Medical University of South Carolina (MUSC), Charleston, South Carolina Abstract. Accrual to clinical trials is a major bottleneck in scientific progress in clinical medicine. Many methods for identifying potential subjects and improving accrual have been pursued; few have succeeded, and none have proven generally reproducible or scalable. We leveraged the open architecture of the core clinical data repository of our electronic medical record system to prototype a solution for this problem in a manner consistent with contemporary regulations and research ethics. We piloted the solution with a local investigator-initiated trial for which candidate identification was expected to be difficult. Key results in the eleven months of experience to date include automated screening of 7,296,708 lab results from 69,288 patients, detection of 1,768 screening tests of interest, identification of 70 potential candidates who met all further automated criteria, and accrual of three candidates to the trial. Hypotheses for this disappointing impact on accrual, and directions for future research, are discussed. Introduction. Accrual to clinical trials has been and continues to be a major bottleneck in scientific progress in clinical medicine. In oncology, for example, fewer than 3% of potentially eligible patients enroll in trials.1 This situation is particularly frustrating given that the current acceleration in biomedical discoveries is driving an increasing need for clinical trials. Many methods for identifying potential subjects and improving accrual have been pursued. Most methods have focused on heightening the awareness of investigators, referring clinicians, and/or patients and the public. That commercial advertising has some effect is verified by the existence of the advertising industry, but the cost of sufficient commercial advertising is often prohibitive. Other methods of heightening awareness include paper and electronic flyers distributed by trial centers’ internal mail systems, community and trial center bulletin board postings, contacts with patient support groups and advocacy organizations (e.g., the Susan G. Komen Breast Cancer Foundation2 and the American Diabetes Association3), listings in trial registries (e.g., PDQ4), web sites (e.g., CenterWatch5, clinicaltrials.gov6, Yahoo clinical trials7), spam (mass e- mailings), and pocket computer-based trial databases and eligibility checkers.8 Although no controlled studies of methods of identifying potential subjects and improving accrual have been performed, it is generally acknowledged that few of the methods that have been employed have been appreciated by investigators to have had a significant impact, and none have proven generally reproducible or scalable, thus explaining why accrual remains the bottleneck described above. Identifying potential subjects can be particularly frustrating in trials with especially stringent eligibility criteria or trials investigating uncommon diseases. In this regard use of the web for trial promotion appears to have had a significant impact on accrual for an occasional trial,9 but the lack of general improvement in trial accrual to date despite the now widespread use of the web by the public10-11 attests to the general lack of accrual impact by existing web-based trial promotion activity. An alternative approach to identifying potential subjects is mass screenings. Where such screenings require human involvement (e.g., examination of patients by clinicians in the exam room or at a community event), resource limitations often decidedly constrain the “mass” part of “mass screening.” However, often a trial’s key eligibility criterion is a data element that has been recorded about a subject as a byproduct of an interaction with the subject totally unrelated to any trial activity. For example, a blood pressure routinely recorded at an annual check-up may identify the patient as a candidate for a hypertension trial. Modern information systems make it theoretically possible to mass-screen any given data element at comparatively little cost, but in practice there have been challenges to such mass-screenings in the technical and ethical arenas, to which are now added regulatory challenges such as the privacy provisions of the Health Insurance Portability and Accountability Act of 1996 (HIPAA) which are now being implemented nationwide.12 AMIA 2003 Symposium Proceedings − Page 16 Although a few vendors are now beginning to develop data warehousing and other functionality useful in clinical research, most clinical information systems today in use today are commercial offerings which provide no technical functionality for mass-screenings of specific data elements, and the database schemas and programming hooks that would be needed to locally develop mass-screening functionality are either unavailable or reserved out of proprietary interests by the vendors of most commercial clinical information systems. There also are significant political/ethical considerations. Current clinical and research ethics clearly proscribe a third party making an investigator aware of a potential subject without the subject’s advance consent for release of his identity to the investigator, and blanket consents (e.g., “I consent that any tissue or data obtained from me during my encounter may be used for research purposes and that our researchers are allowed access to my tissue and data”) are generally seen as invalid, too. We leveraged the open architecture of the core of our electronic medical record (EMR) system, and we worked with our institutional review board, to prototype a solution for these barriers to automated massscreenings. There has been limited experience in the literature to date in automated mass-screenings of electronic clinical data for purposes of identifying potential trial subjects13. Our work adds to this experience. Materials and Methods. MUSC uses the Oacis system (Dinmar, U.S., Inc., San Francisco, California) as its core clinical data repository and primary clinical data viewing application. MUSC’s current implementation of Oacis include a “back end” Sybase database serving as the repository, which contains demographic and encounter data, diagnostic test results, provider notes, and other clinical data on more than half a million patients. The repository has been continuously collecting data since 1993 from an expanding array of best-of-breed enterprise and departmental systems, including the Cerner PathNet laboratory information system. An MUSC rheumatologist (author JCO) opened a trial for lupus nephritis patients. Eligible patients are required to have certain suggestive diagnostic laboratory test results (e.g. (...truncated)