Drug-drug interaction (DDI) alerts are supposed to help clinicians reduce risk of prescribing medications that may result in adverse drug events. The adverse events reflect medications that, when prescribed together, can cause bad events and outcomes for patients. But it is well proven that “alert fatigue” does harm, and in this case, negatively impacts any favorable intents or efficacy of avoiding DDIs.

Alert fatigue, including for DDIs, happens because of the onerous number of alerts considered low-value by clinicians and information overload, causing prescribers to override or ignore alerts as often as 98% of the time. Another reason for limited impact of DDI-related alerts is that, for some specialties, the prescribing of the drug combinations tagged as DDI risks are commonplace and already proven to be good, efficacious medication approaches – cardiology among the most frequent.

The medical staff at Holy Spirit Hospital (Pennsylvania, U.S.A.) studied this issue, and focused on reducing DDI-related alert fatigue while simultaneously making the fewer alerts even more effective. The aim was to increase responsiveness to fewer DDI alerts, and better affect clinical decisions. Specifically the team focused on DDI alerts that clinicians frequently overrode as mere interruptions, or that they did not read for long enough to reflect any effect on their decisions.

Thus, with the focus on less alert fatigue, but higher consideration and processing, three benefits were quantified:

1) Numbers of alerts fired

A team of pharmacists and physicians identified DDI alerts by specialty that physicians/prescribers overrode about 97% of the time. They then determined they could reduce certain DDI alerts for specific specialties without compromising patient safety or outcomes. As a result, the total number of alerts decreased by 11.0% organization-wide, reducing alert fatigue. The reduced total alerts also led to improved time spent considering and responding to the fewer remaining alerts.

2) Alert burden as measured by “think time”

A major metric next became the amount of time clinicians invested in responding to DDI alerts. Calculated as the minutes between alert appearance and closure of the alert window, “think time” had two interpretations:  first, the time for clinicians to process and consider the alert. Second, the time to address the issue without time wasted in further considerations or avoidance.

Results showed fewer alerts yet significantly improved think times. The cumulative change in time was computed organization-wide to equal 26.8 hours per year of reduced waste in prescriber time. While arguably a small number, that is the cumulative effect of alert processing beyond the pre-change condition, ensuring better decision-making and heightened efficacy.

Specifically, think time increased for clinicians within specialties that usually prescribed the drugs labeled as DDI combinations – because they knew best treatment involved the combination of the medications ordered, and that outweighed any risk. Additionally the think time decreased and order changes increased for clinicians in specialties that did not typically prescribe the drugs involved concurrently.

3) Decision changes in prescribers

Increased think time for users who are more familiar with these drug combinations is interpreted that prescribers are weighing the fewer alerts more carefully, actively and attentively than before.

Organizations can overcome alert fatigue and favorably increase alert efficacy when local clinicians and their IT professionals collaborate to fine-tune alert-assisted clinical decision support (CDS) within electronic health records (EMRs or EHRs).

Focused reduction in numbers of alerts, along with targeting maximized efficacy, optimizes relevance and thus improves alert consideration, enhances decision making and saves time. This research confirmed that local, collaborative in-house adaptation of the EHR lead to better resolution of important challenges and achievement of priorities: the keys for helping clinicians help patients.

 

Editor’s note: This research is currently in peer review for publication in an industry journal.

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About the author

Steven H. Shaha, Ph.D., DBA, is a Professor at the Center for Policy & Public Administration, and the Principal Outcomes Consultant for Allscripts. Dr. Shaha received his first doctorate in Research Methods and Applied Statistics from UCLA and has taught and lectured at universities including Harvard, University of Utah, UCLA, Princeton, Cambridge and others. An internationally recognized thought leader, lecturer, consultant and outcomes researcher, Dr. Shaha has provided advisory and consulting work to healthcare organizations including the National Institutes for Health (NIH), and to over 50 non-healthcare corporations including RAND Corp, AT&T, Coca-Cola, Disney, IBM, Johnson & Johnson, Kodak, and Time Warner. Dr. Shaha has presented over 200 professional papers, has over 100 peer-reviewed publications in print, over 35 technical notes and two books. He served on the 15-member team that authored and piloted the Malcolm Baldrige National Quality Award for Health Care, and he contributed to the Baldrige for Education.

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