MGH ACLS

The MGH ACLS app was developed by Harvard-trained physicians, in collaboration with the American Heart Association (AHA), to help fellow physicians, physician assistants, nurse practitioners, and emergency medical technicians (EMT) deliver the highest level of advanced cardiac life support (ACLS) at the point-of-care. It began as a project to assist clinicians within Massachusetts General Hospital (MGH), a Harvard Medical School teaching hospital—and then expanded to have a national scope after significant positive impact was demonstrated. Importantly, real-time feedback from our clinician users continuously drives improvements in app design, features, and function so that you will have the best experience providing life-saving care at the bedside.

MGH ACLS app is the only one to have all content vetted by both the American Heart Association (AHA) science team (with license to use ACLS content) and practicing Harvard-affiliated physicians. It also features the latest 2020 release of AHA recommendations for ACLS.

We owe it to our patients to use the best digital health tools to give them the highest chance of surviving acute life-threatening cardiac illnesses. To this end, we have developed a free, intuitive, and rigorously vetted mobile app to assist healthcare clinicians— including in all stages of training—navigate ACLS at the bedside.

Features:

- Intuitive design to rapidly access 5 ACLS algorithms (i.e. cardiac arrest, cardiac arrest COVID-19, tachycardia with pulse, bradycardia with pulse, and post cardiac arrest care)

- Includes all ACLS content including drug therapy and dosing, reversible causes, etc.

- Easy-to-read timers and ability to log rounds of CPR, epinephrine, and defibrillations

- Button within cardiac arrest algorithm that allows for rapid transition to post cardiac arrest care pathway once patient achieves ROSC

- All content rigorously vetted by AHA science team and practicing Harvard-affiliated physicians

- Regularly updated with the most up-to-date ACLS content

We continue to iterate based on real-time clinician feedback, so that you will have the best experience providing life-saving care at the bedside.