IBM announced it has developed a unique biomedical analytics platform for personalized medicine that could enable doctors to better advise on the best course of medical treatment. This could lead to smarter and more personalized healthcare in a wide-range of areas, including cancer management, hypertension, and AIDS care.
Scientists from IBM Research are collaborating with the Fondazione IRCCS Istituto Nazionale dei Tumori, a major research and treatment cancer center in Italy, on the new decision support solution. This new analytics platform is being tested by the Institute’s physicians to personalize treatment based on automated interpretation of pathology guidelines and intelligence from a number of past clinical cases, documented in the hospital information system.
Selecting the most effective treatment can depend on a number of characteristics including age, weight, family history, current state of the disease and general health. As a result, more informed and personalized decisions are needed to provide accurate and safe care.
IBM’s latest healthcare analytics solution, Clinical Genomics (Cli-G), can integrate and analyze all available clinical knowledge and guidelines, and correlate it with available patient data to create evidence that supports a specific course of treatment for each patient. Developed at IBM Research - Haifa, the new prototype works by investigating the patient’s personal makeup and disease profile, and combines this with insight from the analysis of past cases and clinical guidelines. The solution may provide physicians and administrators with a better picture of the patient-care process and reduce costs by helping clinicians choose more effective treatment options.
“Making decisions in today’s complex environment requires computerized methods that can analyze the vast amounts of patient information available to ease clinical decision-making,” notes Dr. Marco A. Pierotti, Scientific Director at the Istituto Nazionale dei Tumori. “By providing our physicians with vital input on what worked best for patients with similar clinical characteristics, we can help improve treatment effectiveness and the final patient outcome.”
Founded in 1925, the Fondazione IRCCS Istituto Nazionale dei Tumori in Milan is recognized as a scientific research and treatment institution in the field of pre-clinical and clinical oncology. The Institute’s special status as a research center enables it to transfer research results directly to clinical care. The Institute initiated this collaboration with IBM to enhance patient care through better use of innovative IT solutions. Once physicians make a diagnosis, they will receive personalized insights for their patients, based on medical information, automated interpretation of pathology clinical guidelines, and intelligence from a number of past clinical cases, documented in the hospital information system.
In addition to supporting decision-making about treatment, it can provide administrators at Fondazione IRCCS Istituto Nazionale dei Tumori with an aggregated view of patient care, enabling them to evaluate performances and using this knowledge to streamline processes for maximum safety. For example, hospital administrators can drill down into the data to better understand what the guidelines were for insights, what succeeded, and whether treatment quality has improved.
“Our clinical genomics solution may enable care-givers to personalize treatment and increase its chances of success,” explains Haim Nelken, senior manager of integration technologies at IBM Research - Haifa. “The solution is designed to provide physicians with recommendations that go beyond the results of clinical trials. It may allow them to go deeper into the data and more accurately follow the reasoning that led to choices previously made on the basis of subjective memory, intuition, or clinical trial results.”
Any patient data securely collected from hospitals and health organizations is ‘de-identified’ or made anonymous through the removal of personal identifying details. The IBM system does not need to know which individuals the information came from in order to draw conclusions. It works by identifying similar cases based on age, sex, symptoms, diagnosis, or other related factors.