In this revolutionary era of personalized medicine and a new intersection of technology and patient care, healthcare practice is transforming from reactive to proactive, predictive, preventive, and participatory. With so many advancements made in gene, genetic pathway, and biomarker discovery, the hurdle to establishing personalized medicine is no longer generating data, but analyzing and interpreting it, and data storage, analysis, and mining are critical components of this shift.
The explosion of data is growing faster than the ability to convert that information into intelligence and improved decision-making at the point of care. And with the decreasing cost of sequencing genes, massive amounts of genomic information will be added to that data base in the future. More sophisticated analytical tools can help research clinicians compare and share a deluge of medical data while reducing their reliance on computer spreadsheets, paper forms, and proprietary data systems that manually process information.
University of Pittsburgh Medical Center (UPMC), recently ranked No. 10 on U.S. News and World Report's Honor Roll of America's Best Hospitals, announced in October 2012 a 5-year, $100 million investment in a sophisticated enterprise analytics effort that will foster personalized medicine. Joining with technology partners Oracle, IBM, Informatica, and dbMotion, UPMC aims to create a best-in-class data warehouse that brings together clinical, financial, administrative, genomic, and other information that at this time is difficult to integrate and analyze. Advanced analytic and predictive modeling applications for medical and financial decision-making are expected to produce better patient results, enhanced research abilities, continual quality improvements, and reduced costs.
After methodically studying best practices in analytics both inside and outside of health care, the end goal is to develop new models of affordable, effective, patient-focused health care. With the help of its technology partners, UPMC over the next few years will install the hardware and software needed to generate a comprehensive data warehouse that will bring together data from more than 200 sources of information, including labs and pharmacies. Clinicians, researchers, and administrators will have secure, immediate access to data and analytic tools that fit their particular needs.
Officials at UPMC say its big data strategy will boost clinical and financial analytics, which will complement the data physicians already have in an electronic health record. The goal is to help physicians, for example, take an electronic health record and flag patients at risk for kidney failure based on subtle changes in lab results, or predict the most effective and least toxic treatment plan for an individual breast cancer patient based on genetic and clinical information. In the case of breast cancer, much of this work will be done through analyzing groups of patients so researchers and physicians can follow the reaction to treatments and health status over time.
Personalized medicine is medical care tailored to a patient's detailed genetic and demographic profile. While the majority of personalized medicine focuses on finding genetic markers that trigger certain diseases or biological processes, personalized medicine is also seeing innovation in the areas of data analysis and novel drug therapy models. Genomic medicine is the largest and most prominent area of personalized medicine. Genomic medicine looks for genetic markers and biological mechanisms in patients and diseases that allow doctors to prevent and treat them more effectively. The FDA has already approved medications that target genetic forms of breast cancer and melanoma, and there are potentially many more.
IBM is also addressing the issue of data storage and data analysis by collaborating with researchers in Italy to develop a platform that will deliver customized action options for cancer patients. The platform will work by storing scores of genetic and demographic information, as well as different treatment possibilities and their results. This platform will make it easier for doctors to personalize medicine for their patients, and researchers feel that with the amount of data now available, these types of platforms could be built for almost any disease. A similar collaboration between IBM and the Memorial Sloan-Kettering Cancer Center in New York is using the IBM Watson technology to build a diagnostic support tool. Vanderbilt University is also actively combining publicly available data sources with de-identified patient medical records and genomic data to create a massive database.
Every day, we gain new knowledge about the role of genetic factors in common adult diseases including osteoporosis, osteoarthritis, various cancers, migraine headaches, obesity, Alzheimer's, arthritis, diabetes, multiple sclerosis, schizophrenia, and hearing loss. Just to take one example, Alzheimer's disease alone affects 4 million Americans at a cost of $152 billion per year. The numbers multiply when considering the impact of improving the lives of just 10 percent of these patients by better understanding how they should be treated, how their version of the disease is different from other versions, and how to avoid side effects of medicines for the personalized characteristics of their disease.
Laura Kovacs is a freelance writer based in Philadelphia.
For Additional Reading
- Wegener, D, et al. Towards an environment for data mining based analysis processes in bioinformatics & personalized medicine. IEEE Explore Digital Library. Available at: http://ieeexplore.ieee.org. Last accessed Oct. 5, 2012.
- Kintz, Stephen. Personalized medicine: How genetic testing, data mining and mice are changing medical care. Patexia. Available at: www.patexia.com. Last accessed Oct. 5, 2012.
- GenoSpace, LLC. Personalized Medicine - No Longer Star Trek, It's Real. Thomson Reuters. Available at: http://thomsonreuters.com/content/news_ideas/articles/science/731474. Last accessed Nov. 2, 2012.
- Frist, William H. Personalized Medicine: Innovation to Clinical Execution. Institute of medicine. http://iom.edu/Global/Perspectives/2012/PersonalizedMedicine.aspx. Last accessed Nov. 2, 2012.
- Lewis, Nicole. Pittsburgh Healthcare System Invests $100M In Big Data. InformationWeek. Available at: www.informationweek.com. Last accessed Nov. 2, 2012.