This is part one of a two-part series on AI and the patient journey, using clinical examples to highlight exactly where in physician workflows AI can augment and serve as a tool for enhanced patient care.
Physicians are charged with the noble responsibility of improving the lives of their patients. From the oncology ward to the interventional suite, the role of the doctor in healing patients is sacrosanct. Towards their mission of providing patients with better health outcomes, physicians are required to constantly adopt novel treatment methodologies, better drugs and nascent patient management tools.
At its core, the field of medicine is a lifelong commitment towards innovation and care. Innovation for healthcare ought to be engineered with the patient in mind. If an invention in medicine disturbs patient care, it is useless for healthcare providers. If a product enters a physician’s workspace that doesn’t improve workflow, its utility is questionable at best.
With artificial intelligence permeating the patient-physician relationship, it is important to highlight how an AI platform can impact the patient. In what manner will this algorithm or device improve the lives of patients? Herein, we explore clinical scenarios that will depict the profound impact of AI as it works behind the scenes for patients around the world.
Mark is a 55-year-old male with a history of diabetes, hypertension and hyperlipidemia who comes to the emergency room with a chief complaint of shortness of breath. He informs the nurse that he has been smoking since the age of 25. He denies any chest pain, but informs the ED physician that he feels weak and dizzy and has a rapid heartbeat.
The medical doctor is managing a high quantity of patients in the ED and is 9 hours into his shift. During his consultation, a colleague enters the triage room and asks for his opinion on an adjacent patient. The physician orders a host of tests, including blood work and a CTPA to rule out a pulmonary embolism.
Within 55 minutes, the patient is taken to the CT scanner. A backlog of patients in the ED delays his transfer to radiology. The ED and trauma reading room is managing an extensive list of “STAT” images, and Mark’s CTPA is read 104 minutes after image acquisition. The radiologist calls the ED physician caring for Mark, who just started his shift and is catching up on acute patients in the trauma bay. He informs the ED that Mark has a pulmonary embolism with suspected heart strain. The ED physician knows Mark needs to be admitted, and consults with the on-call interventional radiology team to determine the best course of action. IR informs the ED doctor that interventional cardiology handles PE patients with right strain. Needing to get back to his patients, the ED physician asks the nurse to page interventional cardiology. Twenty-eight minutes go by. Interventional cardiology reviews the scans with the radiologist and admits Mark under their service for appropriate clinical management.
With AI constantly running the background, patients with a variety of acute pathologies are managed swiftly, and Mark is able to get to the CT scanner within 40 minutes. Although the reading room is managing a swath of “STAT” images, Mark’s CTPA jumps to the top of the list, and the trauma radiologist is alerted to a potential critical finding. Within minutes, Mark’s CTPA is read as positive for a pulmonary embolism with right heart strain. The ED physician, diagnostic radiologist, and on-call interventional cardiologist are plugged into the an AI care coordination platform, which enables them to view Mark’s images immediately, discuss the best course of action and quickly view his incoming lab values. Mark is promptly admitted under the care of interventional cardiology.
AI can streamline patient management, thereby alleviating ED burdens by reducing ED lengths of stay and getting patients the care they need promptly. Equally important, the methodology through which AI reduces the clinical burden of a hospital does so in a manner that is felt by the patient. The improvement of the patient experience as they are moved through the labyrinth of the hospital is an added benefit of AI in medicine, ultimately serving the goal of all medical practitioners: improving patient outcomes.