I once wanted to be a doctor. And then I watched one work.
I was pre-med when I had the opportunity to shadow a physician in a hospital setting. I showed up that morning excited to observe the practice of medicine—a respectable and honored practice—in action. As the day went on, I spent more time watching the doctor stare at his computer screen, clicking through data and notes, than observing him interact with patients. It was clearly making him miserable, and he let me go that evening with a solemn warning, “Medicine’s not what it used to be.”
With the Rise of AI, What IP Disputes in Healthcare Are Likely to Emerge?
Munck Wilson Mandala Partner Greg Howison shared his perspective on some of the legal ramifications around AI, IP, connected devices and the data they generate, in response to emailed questions.
It’s not. It’s been reshaped and contorted by an overload of data.
Data collection has been a healthcare industry priority for nearly two decades, and one of the principal goals of digital health. While data generated by these tools makes its way into the EHR, most of it isn’t easy for clinicians to use. For them, there exists a trove of data to go through, including clinical notes, labs, tests, medication history, and data from remote patient monitoring devices. There should be enough information to paint a pretty clear picture of an individual’s health, but the EHR doesn’t present information in a way that supplies the doctor with crucial information needed to treat their patient.
Most of the patient data health systems have is not usable by their clinicians and therefore not benefiting patients. As much as 97% of healthcare data isn’t being used, according to some estimates, and as many as 900,000 patient data points on a single critical care bed go to waste every hour.
Digital health solutions are supposed to augment and enhance the ability of clinicians to deliver high-quality care, but many are contributing to the influx of patient data and manufacturing an impossible digital environment for clinicians. There is a latent expectation that clinicians should have the time, space, and mental capacity to put all of this data into action. They don’t.
A Personalized Approach to Medication Nonadherence
At the Abarca Forward conference earlier this year, George Van Antwerp, managing director at Deloitte, discussed how social determinants of health and a personalized member experience can improve medication adherence and health outcomes.
It’s true that more data is better, but putting the onus on clinicians to make sense of it all without giving them the proper tools has been one of healthcare’s biggest blunders. Placing the burden of responsibility upon clinicians to analyze and weave together every disparate data point forces them to continue treading water in an ocean of data and documentation – and the sea level is rising every day. It’s fueling fatigue and burnout.
What clinician has time to look at what that ocean of data is trying to tell them and factor it into patient treatment? That shouldn’t be their job. But it’s a role perfectly suited for AI.
Putting health data to use
AI visionaries have illustrated possible futures where our healthcare system is powered by generative AI—where AI assistants can refill our prescriptions, connect us to the right doctors at the right time, and help people age at home. After a year dominated by AI hype, it may feel like this future is imminent.
It’s not. We’re on the path, but so far, the output of most generative AI solutions in healthcare has been more data and even more information-laden documentation. We won’t realize a fully automated healthcare system until healthcare organizations are able to make the most of the data they already have. That future – one where AI can synthesize all of the data points that make up the picture of a person’s health and present it to clinicians in a way that makes sense – is just as exciting, and much closer.
In that future, a person might be admitted to the hospital for a respiratory condition. Before their doctor even walks into the exam room, they will be able to view a summary within the EHR of the patient they are about to see. That summary will be generated by the patient’s entire medical record—past clinical notes, labs, imaging, medication history. The doctor doesn’t have to tread water in an ocean of data and documentation. The data, harnessed by AI, now empowers doctors and acts as their lifeboat.
In a few moments, the doctor has a comprehensive understanding of the health of the patient they’re about to see. They may learn that the patient had a stent put in a few months ago and hasn’t refilled their blood thinner. They’ll discover the patient hasn’t been particularly active, and that they have a genetic predisposition to congestive heart failure. They don’t have to rifle through a digital tome of data, potentially costing them valuable hours of time, to save that patient’s life. They can make the right diagnosis and provide a personalized treatment plan in minutes.
Today, clinicians have to spend an inordinate and impractical amount of time combing through the EHR just to find the information they need to provide that level of care. All of that back-end administrative work should be the function of technology.
It’s technology’s job to collect, analyze, and summarize. Most technologies in hospital settings only accomplish the first. Digital health should be hyper-focused on making the lives of clinicians easier by doing all three. It’s how we’ll eventually create the AI-enabled healthcare system of the future. But in the short-term, we can be certain it will create a healthcare system clinicians deserve to work in.
Photo: ipopba, Getty Images
Eli Ben-Joseph is the co-founder and CEO of Regard, the leading AI clinical platform. The concept for Regard was born out of Stanford while Eli was a graduate student, alongside his co-founders Nate Wilson and Thomas Moulia. Eli received his bachelors of science in bioengineering and biology from MIT and a master of science in computer science and management from Stanford University. Prior to Regard, he worked at the MIT Media Lab on special projects.
