Bill Adams and Yannis Paschalidis

Some things in life, like sunrises and sunsets, are predictable. Others, like traffic or the weather, are much harder to predict. Health outcomes tend to fall somewhere in the middle, but Bill Adams, MD, a Boston Medical Center pediatrician, and Yannis Paschalidis, PhD, a Boston University data scientist, are attempting to make more health outcomes predictable with an algorithm that utilizes electronic medical record data.

Their system, which is funded by a five-year grant from the National Science Foundation, utilizes anonymous data from BMC patients, which is stored in a database called I2B2. This data dates back to 2000 and includes diagnoses, procedures, admissions, length of stay, and basic demographic data. Adams had been using this data to study what works and what doesn’t work in terms of health outcomes for urban patients and teamed up with Paschalidis and a team of graduate students to develop and test algorithms that can identify opportunities to alter health outcomes, some of which might be missed by researchers. Currently the team is working on an algorithm to predict whether individual patients with a history of heart disease will be hospitalized within a year. In testing this algorithm, they have been able to predict approximately 80 percent of hospitalizations.

“The health care system is not very efficient,” says Paschalidis. “We spend lots of money on diseases that can be prevented. With an algorithm that can predict re-hospitalization of high-risk patients, doctors can pay more attention to those patients, and potentially prevent the predicted hospitalization. Hospitalization is very expensive, but other health care costs are modest, so predicting re-hospitalization and preventing it is better for both patients in terms of health and hospitals in terms of costs. In fact, the National Institutes of Health found that $30 billion is spent in the U.S. every year on preventable hospitalizations, so we have the opportunity to save huge amounts of money.”

The goal is to have algorithms that will run in the background when providers see patients and alert providers when something in a patient’s record suggests that he or she is at risk of a negative health outcome. It would prompt doctors to intervene during visits and case managers (if applicable) to intervene outside of doctor visits.

“While some risk factors, like smoking, are obvious to doctors, there are some, such as trends in laboratory data, that are harder to see,” says Adams. “The challenge in medicine isn’t to know that someone is high risk, but to know what type of risk they have and how to intervene. Our algorithm is a useful tool to help doctors intervene early before negative outcomes happen, instead of waiting for them to happen and then starting treatment.”

The heart disease algorithm primarily uses outpatient data to make predictions, because most long-term health outcomes are treated in outpatient settings. Currently the algorithm pulls in data from Logician, and STK, the hospital’s registration system, but soon will begin adding information from eMERGE now that the new electronic medical record system is live across the hospital. While the new system won’t change the type of data that the algorithm uses, it will allow the researchers to share their work with people across the country who also use Epic electronic medical records. It will also allow researchers to better integrate inpatient and outpatient data.

The software built on the algorithm is not in use yet, but Adams and Paschalidis expect to finish testing by the end of the year and start running focus group tests with BMC doctors next year. These groups will allow Adams and Paschalidis to refine the type of information their algorithm uncovers and how the data is presented to providers, to create a valuable tool. They also plan to expand the use of the algorithm to include diabetes patients and are in talks with the Department of Surgery to predict re-hospitalization of patients who have undergone surgery – this is an important metric for Medicare quality measures. Eventually the goal is to create an algorithm that can provide recommendations for intervention, which will involve further partnerships between providers and computer scientists.

“This is a cutting-edge approach,” says Adams. “There is general interest across the country in trying to use electronic data to predict health outcomes, but the BMC/BU team is special in that it combines the clinical expertise of the medical campus with expertise in engineering and computer science from BU. In addition, this type of project is not something generally undertaken in safety net hospitals, which makes our algorithm unique. Our goal is to make this data into something meaningful and useful for the direct care of our patients, and while we’re not there yet, I believe we will be soon.”

(The story appeared at the May 15, 2015, Volume 4, Issue 6, Boston Medical Center Brief.)