RENCI-Duke project aims to use data to improve medical treatment decisions

CHAPEL HILL, NC, July 22, 2011–A R21 NIH exploratory/developmental research grant from the Agency for Healthcare Research and Quality (AHRQ) will enable RENCI and Duke University to develop a system that aggregates and visualizes historical medical data so doctors can use it to help them make the best possible treatment decisions for their patients.

AHRQ, a division of the U.S. Department of Health and Human Services, will provide $300,000 over two years to RENCI and Duke University Health System to develop VisualDecisionLinc, a software prototype that integrates historical patient data and comparative data from similar patients, all derived from electronic medical records (EMRs), into a decision support tool. The VisualDecisionLinc system hypothesizes that doctors will make better treatment decisions if they can quickly access and easily analyze data about similar patients and the effectiveness of various treatments.

The system uses data from the MindLinc EMR system developed at Duke University Medical Center. MindLinc-EMR is a widely used behavioral health EMR system containing data from more than 2.1 million patient encounters, making it the largest data warehouse of anonymous psychiatry data in the U.S. The AHRQ-funded work will build on an ongoing RENCI-Duke project and will focus on three key initiatives:

  • Developing the best processes for selecting comparative populations. The researchers will use demographic information, case histories and diagnoses to help clinicians select comparative populations from the EMR that are most relevant to their patients.
  • Creating a visual user interface to help in selecting the best treatment choices. Clinicians need to be able to find the important information in their datasets quickly and to view data in a way that is easy to analyze. Visualization and visual analytics techniques will be used to aggregate, view and interact with large volumes of patient data, and to help clinicians understand their data quickly.
  • Evaluating the effectiveness of VisualDecisionLinc in preparation for a larger-scale research implementation.

“Our premise is straightforward,” said Ketan Mane, senior research informatics developer at RENCI, who is creating VisualDecisionLinc with Chris Bizon a RENCI senior research scientist, Phil Owen, RENCI IT developer, and Charles Schmitt, RENCI’s director of informatics. “The EMRs include massive amounts of patient data on diagnoses, medication, and treatment outcomes, but doctors don’t have time to analyze pages and pages of data in a spreadsheet format. We want to use information technology to resolve this information overload problem, while at the same time gathering insights about data characteristics for better decision support.”

The focus of the RENCI-Duke research project is to provide EMR data to clinicians in ways that are useful—for example, summaries of patients with similar medical profiles– and in a visual format that is easy to understand, said Mane, “so that the data can be used to support clinical decision-making at the point of care.”

The RENCI team will work with Dr. Kenneth Gersing, a psychiatrist and medical director of clinical informatics in the psychiatry department at Duke University, Dr. Ricardo Pietroban, vice chair of the department of surgery at Duke, and Bruce Burchett, an assistant professor of psychiatry at Duke. Drs. Ranga Krishnan and John Rush, dean and vice dean of clinical sciences at the Duke-NUS Graduate Medical School in Singapore, will serve as advisors to the project.

The Duke team linked with RENCI two years ago to assist in an ongoing effort to use electronic medical records to improve medical decision-making.

“The goal is to use EMRs to make the best treatment decisions possible and to improve patient outcomes,” said Gersing, who led the development of MindLinc-EMR. “If we can treat patients more effectively, it means fewer clinician visits, a better quality of life, and lower healthcare costs.”

For more information:

Feature story: Evidence-based medicine

Mindlinc Website