Computerworld - Targeting alerts in electronic medical systems can greatly reduce unnecessary tests that yield false positives and help physicians pay closer attention to the messages they get, according to a new study.
The study, conducted by managed care organization Kaiser Permanente, involved a randomized trial of 788,000 patients at eight clinics. It found that an electronic alert sent to physicians the moment they ordered a blood test for elderly patients reduced unnecessary use of tests that often produce false-positives for the elderly. The alert was simple: it told physicians that the test was not reliable for that age group.
The study was among the first to look at the effectiveness of sending electronic alerts for a specific condition in a specific patient population.
Electronic medical alerts, or electronic messages, come standard with electronic medical records (EMRs) software. But alerts can overwhelm physicians, who then ignore them in much the same way that pop-up advertisements are quickly dismissed online. As hospitals add IT systems and software, new layers of automated messages are often added to older systems that already offered alerts.
For example, a hospital's legacy pharmacy electronic order entry system likely came with a drug allergy alert system. That tool is often replicated in today's EMRs.
While early EMRs typically had several hundred alerts that could be deactivated manually, newer technology can deliver thousands of alerts for non-specific conditions.
The more those alerts can be targeted, the less likely they are to be ignored, according to Dr. Ted Palen, a clinician researcher at Kaiser Permanente's Institute for Health Research in Denver, Colo. and the study's lead author. "What we've seen in electronic medical records is [that] the provider many times clicks through alerts and just wants to get rid of them."
Kaiser Permanente's study focused on a blood test called the D-dimer, which is used to detect clots. In reality, the test is only detects chronic inflammation, an indicator that a patient may have a clot and therefore would warrant further testing. While the D-dimer test is very effective in spotting the possibility of clots in younger patients, the older a patient gets, the more likely they are to suffer from chronic inflammatory conditions, such as arthritis, which can trigger false positives.
"The issue is that if you're ordering a test that doesn't give you good information, why order it?" Palen said. "We didn't embark on this to show how we can save on testing costs. We want to advise clinicians how to follow good evidence-based medicine guidelines."
Evidence-based medicine involves best practices that use evidence gained from the scientific method for medical decision-making. One of the main reasons the federal government is pushing the rollout of EMRs in healthcare facilities is to promote the use of evidence-based medicine.
Instead of giving the D-Dimer test for clots, physicians who suspect an elderly patient may have a blood clot would instead order an ultrasound or CT scan. While a D-dimer blood test only costs about $35, results can take up to two hours, potentially affecting patient treatment, Palen said.
"What we wanted to see is if we targeted an alert to a specific condition, i.e. blood clots, for a specific group of a patients, i.e. older patients, can it change physician behavior?" he said. "The other message of this study is ... how best do we engineer or configure our electronic medical system to give the alert at the right point in time and deliver the right message.
"We saw we could influence physician behavior and have it remain consistent over time," he said.
Lucas Mearian covers storage, disaster recovery and business continuity, financial services infrastructure and health care IT for Computerworld. Follow Lucas on Twitter at 
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. His e-mail address is lmearian@computerworld.com.
Read more about Healthcare IT in Computerworld's Healthcare IT Topic Center.
With the promise of big data (solving the unsolvable problems, informing better decision making, creating new products and services, discovering patterns and acting on them, etc.) on the horizon, what has really changed? Does this mean that everything we know and do with not-so-big data should be tossed?
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