IBM and Cleveland Clinic to develop a translational medicine platform to support genetic research

Sep 29 2004

IBM and The Cleveland Clinic today announced an agreement to develop a translational medicine platform at The Cleveland Clinic that will use information from electronic medical records to support basic and genetic research.

The goal is to deliver better, more personalized patient care by more easily incorporating research discoveries at the patient bedside.

"Using data effectively means treating patients effectively," said C. Martin Harris, M.D., MBA, chief information officer at The Cleveland Clinic. "By developing an infrastructure to integrate clinical care with the Clinic's genetic research initiative, we can achieve the notion of personalized medical treatment. This is about translating ideas into action to improve lives."

Under the collaboration, The Cleveland Clinic will be one of the first organizations to use IBM's new clinical genomics solution. The IBM Healthcare and Life Sciences Clinical Genomics Solution incorporates IBM technology, industry expertise, and best practices, and along with applications available from the network of IBM Business Partners not only to help accelerate the adoption of genomics in the clinical environment but also to help facilitate compliance with regulatory and patient privacy requirements and enable the sharing of research data in a security enhanced environment. The solution promotes flexibility, based on open-industry standards, so that the program can grow and adapt.

"The Healthcare industry has been invigorated with the promise of personalized medicine since the Human Genome Project," said Carol Kovac, general manager, IBM Healthcare and Life Sciences. "With the push now for more healthcare providers to capture patient data electronically, we are beginning to fulfill that promise and build the technological infrastructure to support information-based medicine."

In the first project of this strategic initiative, The Cleveland Clinic and IBM will build an information infrastructure that provides the Clinic with unprecedented access to medical and genetic information involving patients with abdominal aortic aneurysms in a confidential, anonymous fashion. The infrastructure provides methods to identify the cause of aneurysms and to predict those patients who will respond to various treatment options.

Currently, The Cleveland Clinic has the largest vascular surgery electronic medical record database in the world, in addition to expertise in the treatment of abdominal aortic aneurysms. This combination makes The Cleveland Clinic an ideal entity to collaborate within the project, which is part of IBM's strategic initiatives in the area of information-based medicine, specifically clinical genomics. By giving physicians and researchers access to clinical research data, the platform will aid in identifying potential triggers and ultimately more effective treatment solutions for patients with abdominal aortic aneurysms.

"Abdominal aortic aneurysms typically produce no symptoms until rupture is imminent, and the odds of surviving a rupture are less than 50 percent, even in those patients who reach the hospital," said Kenneth Ouriel, M.D., chairman of the Division of Surgery and the Department of Vascular Surgery at The Cleveland Clinic. "We would like to change this statistic by better understanding how to diagnose and treat our patients with the disease. By working with IBM on this project, we can use technology to perform more efficient and focused research that will bring us closer to understanding the underlying variables associated with this disease."

The project will enable The Cleveland Clinic to facilitate research, discovery and patient care. Eventually, The Cleveland Clinic will expand this new practice of using electronic medical records to study disease and create new knowledge and better treatments for its patients across all medical specialties. Key objectives include providing clinicians with valuable information for diagnosing and treating disease, creating an integrated information environment to enhance clinical research in genetics, and building a foundation for statistical analysis to identify correlations across disparate patient information.