I was recently interviewed for an article about the use of analytics in medical research at colleges and universities. It’s not surprising this topic is gaining attention. As a result of Meaningful Use, the ongoing digitization of medical records has created unprecedented opportunities for university researchers to make breakthroughs in preventative medicine, drug efficacy and safety, and better patient outcomes.
Research at institutions of higher education is critical to advances in medicine, and SAS has been a valuable tool in those efforts for decades. For example, Duke University’s Cancer Institute is studying patient side effects from colon cancer treatment. Using mobile device-enabled software to enter data, physicians can ask colon cancer patients about the side effects and symptoms they experience and record their responses. This direct patient interaction provides robust data, which can supplement the information from large patient data registries. With analytics from SAS, they can analyze all their data to better manage and anticipate adverse patient effects caused by different cancer treatments.
As more and more medical data is digitized, more challenging questions can be asked. Clinicians and researchers will want to go beyond summary statistics and traditional analytics and use more sophisticated analyses. As the data volume explodes and the complexity of analyses grows, new methods and architectures will need to be applied to solve complex problems in a timely manner. SAS is leading the industry with technology for High Performance Analytics (HPA), including cost-effective ways to implement that technology.
HPA is essential for the most data intensive analyses. For example, true drug safety surveillance involves calculating signals on many drugs and on many adverse events or categories of events. For example, if you need to perform a separate analysis for 100 different drugs (plus combinations of brands and generics) with 100 different adverse event terms (plus high-level classifications) each along with multiple covariates, you would need to run an analysis overnight to arrive at reliable, complete results. HPA can reduce that to minutes through multiple approaches such as parallel processing, in-database and in-memory analytics.
In addition to the challenges of performance, the growing access to electronic healthcare data reveals a huge data quality challenge. Terminologies are commonly not standardized and unstructured data (text) appears everywhere and remains a largely untapped, valuable resource. For instance, pathology information typed into free fields can be analyzed using text mining and natural language processing to surface more data about the pathologists observations about a patient. By combining extracted pathology concepts with EMR data, we can have an even more comprehensive view of patterns in disease, treatment and prevention.
Ultimately, we want doctors to know what treatment is likely to have the most benefit, and fewest adverse effects, based on a patient’s symptoms, demographic information, medications, etc. The data can reveal what has been most effective for similar patients, and doctors can have that information at their fingertips. Instead of just relying on memory or published best practices, doctors could access their own data to make more knowledgeable treatment decisions. This would help eliminate more unnecessary treatments, which would not just reduce risk, but costs, as well.
The growth of EMR and other medical data also benefits pharmaceutical companies. Clinical trials data is very thorough, but could not approach the scope and volume of observational data in the field. Doctor and hospital reports could reveal that a specific demographic is experiencing adverse effects to a medication, or that a drug is less effective with a certain population.
EMR and unstructured data, analytics and HPA, and dedicated researchers in universities and the private sector hold the key to a new age of preventative medicine and drug efficacy. I’ve presented a couple examples, but I would love to hear from readers about the possibilities.