Speaker
Amrita Mukhopadhyay, MD, MS
Eugene Braunwald, MD Assistant Professor of Cardiology
The Leon H. Charney Division of Cardiology Department of Medicine
Division of Healthcare Delivery Science Department of Population Health
NYU School of Medicine
NYU Langone Health
Keywords
Heart Failure; Electronic Health Record; Prescribing
Key Points
- Heart failure is a major public health issue and a leading cause of hospitalization, affecting over 6 million Americans. Mineralocorticoid antagonists (MRA) are a potentially life-saving treatment but are under-prescribed in patients with heart failure with reduced ejection fraction (HFrEF). Closing this treatment gap could save over 20,000 lives in the U.S annually.
- Electronic Health Record (EHR) tools could be a low-cost, scalable way to improve prescribing. However, there’s wide variability in EHR tool development and design. The optimal delivery and timing of EHR tools is unknown.
- EHR tools fall into 2 categories: alerts and messages. Alerts apply to a single patient at a time and pop up during a clinical encounter; messages apply to multiple patients at once and are seen between encounters. The BETTER CARE-HF team designed both in accordance with Cognitive Load Theory and Nudge theory, applying the concepts of positioning, the split attention effect, default option, the transient information effect, and social influence.
- They hypothesized that A) among patients with HFrEF who are evaluated by a cardiologist in the outpatient setting, an alert or a message will improve prescribing of MRA as compared to usual care, and B) the alert would be more effective than the message.
- The researchers approached the pilot study as a “qualitative phase,” in which they would solicit feedback from participants and refine the intervention. They made several modifications to the EHR alerts and messages in response, and noted that guiding frameworks and pilot-testing were critical to designing an electronic intervention.
- The pilot study was followed by a pragmatic trial that took place in over 60 practices in the NYU Langone Health System. Patients were cluster-randomized to an alert arm, message arm, or usual care. The primary outcome was new MRA prescription during the study period.
- In the alert arm, nearly 30% of MRA-eligible patients were newly prescribed MRA – a highly statistically significant increase. The alerts were effective across all practice settings but were especially effective in high-volume settings.
- In the message arm, 15.6% of MRA-eligible patients were newly prescribed MRA. Compared to 11.7% in the usual care arm, this was still a statistically significant increase, but was less effective than the alerts. Looked at another way, the number of MRA-eligible patients needed to result in one prescription was 25.6 in the message arm, compared to 5.6 in the alert arm.
- An automated, EHR-embedded, tailored, and selective alert delivered at the time of the visit more than doubled prescribing of MRA as compared to usual care. Well-designed EHR tools could save lives.
- Despite EHR tool effectiveness, busy physicians may still be hesitant. Too many tools can cause fatigue and burnout; concerns about workload and time costs can hinder uptake. Conversely, EHR tools that save time and reduce cognitive load may be more beneficial in busy practices. A post-trial survey indicated that cardiologist perceptions were generally favorable towards the BETTER CARE-HF tools, with some notable differences when asked about workflow.
- The research team is conducting a multi-center trial to assess the effectiveness of the alert at other institutions, specifically across 3 high-volume health systems around the country. They are actively seeking other institutions to join the trial and encouraged attendees to reach out if interested.
Discussion Themes
The research team started by compiling EHR data on the current gap in care at NYU Langone. Having that real-time data helped the health system, and the physicians were a part of it, recognize that the intervention was necessary – despite their predisposition that they were delivering high-quality care.
This intervention was targeted to a specific population (cardiologists at NYU Langone) and a specific treatment (MRA) for a specific condition (HFrEF). In a different setting or if there was a different treatment involved, implementation may need to be adjusted.
Dr. Mukhopadhyay noted that folks who saw how the intervention worked were often surprised by how rarely the alert was triggered. She suspects that the selective nature of the intervention helped drive the intervention’s effectiveness by preventing burnout.
Working with a single IRB that understood the intention behind a learning health system helped standardize regulatory expectations across sites and facilitated onboarding.