A predictive analytics tool uses EMR data to trend whether patients are getting better or worse by using more than 30 different variables, such as cardiac rhythms, labs, vital signs, nursing assessments, and shift data.
Back in October, we saw a vividly frightening example of how EHRs are no substitute for nurse-physician communication, when nursing notes entered into the medical record for an Ebola patient were either overlooked or ignored.
Relying too much on technology certainly can be dangerous, but Virginia-based Riverside Health System is showing that technology can also be powerful when it's combined with interdisciplinary care.
For the past year, Riverside has been using a predictive tool from PeraHealth that uses EMR data to trend a patient's condition over time, using more than 30 different variables, such as cardiac rhythms, labs, vital signs, mental status, fall risk, and pressure sore risk. The data automatically flows into the tool from EMRs, requiring no additional input, and a score is calculated with each data point.
"Anytime there's new information that flows in, it creates the calculation," Susan Tanner, MSN, RN-BC, Riverside's System Director Clinical Informatics, says of the tool.
Tanner says a challenge of EMRs is their disparate data fields. For instance, nursing assessments aren't necessarily presented with labs. Also, looking at individual nursing assessments at individual points in time doesn't tell you much.
The predictive tool combines several variables over time to trend whether the patient is getting better or worse.
The most heavily weighted of those variables are nursing assessments and shift data, Tanner says, and watching those trends could allow caregivers to find subtle changes in conditions that may otherwise be missed.
Alexandra Wilson Pecci is an editor for HealthLeaders.