Chronic conditions are driving the majority of healthcare costs and this burden is only expected to rise. Therefore a shift from reactive to proactive management of chronic diseases is necessary to improve outcomes and reduce costs. However, clinicians have few tools at their disposal that can help determine which patients are at greatest risk for decline.
The rapid rise of available electronic clinical data located in EHRs, patient registries, and from longitudinal electronic health data contains a multitude of clinical measurements taken during routine clinical visits capable of producing more data for retrospective analysis.
This data can provide the opportunity to learn about variability of diseases between individuals, the way a disease manifests, and be able to develop novel approaches for predicting and treating diseases.
The chronic condition scleroderma is especially difficult to treat because patients exhibit highly variable symptoms, complications, and treatment responses. The process for finding an effective treatment for an individual can be frustrating for doctors, be painful, and expensive for patients.
The National Science Foundation (NSF) www.nsf.gov computer scientists are working with an interdisciplinary team of experts at Johns Hopkins to study how collecting data could possibly be beneficial to scleroderma patients.
NSF’s ongoing project called “Smart and Connected Health/Integrative Projects” program awarded $1,407,883 to Johns Hopkins University to enable the research. The Principal Investigator on the project is Suchi Saria saria@gmail.com.
The team is designing statistical algorithms to enable computers to analyze large volumes of medical records and identify subgroups or patients with similar patterns of disease progression. Also, the data from the computer system might let doctors know the specific symptoms and then be able to suggest treatment for an individual patient.
The doctors can then map the course of treatment for new patients, based in part on what the computers reveal about what has happened to other patients with similar symptoms. This type of data analysis can not only help scleroderma patients but clinicians may also be able to use “Big Data” to help treat other patients with chronic diseases such as lupus and rheumatoid arthritis.