Putting AI to work in healthcare: turning data into actions

Chris McCann Christopher McCann | 6th December 2018

There’s no shortage of statistics, surveys and industry hype that have espoused the benefits of artificial intelligence (AI) within healthcare. AI already plays a significant role in our daily lives. Apple Siri and Amazon Alexa’s ability to decipher speech, no matter the accent, is because of AI. Netflix and Amazon make movie and product recommendations to us based on AI models. When we search on Google, the most relevant results are identified by AI models.

Today AI delivers immediate benefit in automating defined and repeatable tasks when there is large amounts of data to consider. This makes it particularly appealing within healthcare, where both new volumes of data are generated daily and there are many repeated, well-defined and routine tasks conducted. AI currently can replicate tasks that are “human possible” but would be time consuming, repetitive and potentially prone to human error.

While safe and effective AI in healthcare is still in its infancy, we’re starting to see the industry make great strides. Here are just a few ways that AI is already helping to streamline workflows and improve healthcare processes.

Converting data into actionable information

The human body generates an immense amount of internal data. Collecting vital signs, lab test results, written notes and imaging data from patients is a key part of making good clinical decisions during care. Combined with other potentially relevant lifelong health data such a visits, diagnoses, prescriptions, self-reported symptoms and multiplied over vast populations make this data of dizzying proportions. In 2015 in the U.S., there were 991 million physician office visits. There are simply not enough doctors in the world to analyze all of the information being generated as quickly as it could make a difference.

When we meet with health systems, the number one area of interest for executives is data. Everyone wants data to support and develop intuition. But there is little point in collecting data if it cannot be used to improve healthcare or healthcare delivery. However, AI is capable of analyzing vast datasets to derive actionable insights that professionals can use to make earlier and more informed decisions.

Some of the signals and patterns within these datasets are either too subtle for the human eye, or need to be characterised across a vast scale of data to be truly representative and valuable. Societally, we still have little understanding of how these patterns relate to future outcomes because we have simply been incapable of collecting this data or analyzing it at scale up to now. This is something we work on every day.

With enough training data and labels, deep learning allows for recognition of patterns that would be incomprehensible to humans, but AI allows for massive computational capacity to learn patterns across many tens of thousands of patients. This will let doctors react faster and address a health event before it becomes critical.

Enter the Medical Internet of Things (MIoT)

Previously, only in the ICU was it possible to continuously monitor patients. ICU monitors, with their many leads and wires, were infeasible in general medical and surgical units and other lower-acuity environments. In these environments, vital sign collection was restricted to every four or so hours, with deterioration often unnoticed until the next check. Risk stratification was only possible using general EMR or other infrequent data. That means the risk stratification can only ever be as good and timely as the data that’s collected manually.

This has now changed. With today’s connectivity options, wireless monitoring devices that fit comfortably on a patient’s arm can now deliver ICU-caliber health data continuously to care teams in a way that is practical for lower acuity environments.

However, while there are only a small number of patients in ICU, there are exponentially more in low acuity areas, such as general medical and surgical units. Thus, simply generating data is unlikely to solve the problem -- there are not enough doctors in the hospital to handle this amount of data.

Again, this is where AI becomes a powerful ally. AI provides instant and continuous evaluation of health data to identify the patients who may be deteriorating. This allows better utilization of our healthcare professionals, who can instead focus their time on patients who need them the most.

Devices like Current’s are a key aid to physicians and care teams. Detecting the health deterioration of a patient in the hospital faster has the potential to greatly reduce the number of patients that are discharged prematurely, only to be readmitted soon after for the same condition. Hospital readmissions are currently a $40 billion problem in the United States.

Proactive versus reactive care: AI in the home

As healthcare resources become more strained and our population ages and grows sicker, providers are seeking new models that allow for the delivery of more health and care at home. Hospitals are becoming quaternary treatment centers with shorter lengths of stay, and every health system wants to minimize readmissions and costly unplanned ER visits.

To enable this we must be able to identify which patients can safely be treated as outpatients, will develop sepsis, and or require treatment or not. The development of AI models to predict these outcomes, with extremely high sensitivity and specificity, will completely reshape our healthcare system.

Mobile monitoring devices let patients enjoy the comfort and familiarity of their own home instead of a hospital bed - yet feel safe knowing there’s someone keeping an eye on them Previously, once patients left the hospital, it was almost impossible to monitor their health. Many won’t call their doctor until well after the earliest symptoms have started and their condition has worsened.

With mobile monitoring, vital signs and other health data is passively collected from the patient and sent to the cloud where AI models can alert appropriate healthcare professionals if the person starts to become unwell. This ability to manage throughput -- to separate the signal from the noise -- is the power of AI. It lets healthcare professionals focus on the patients that need their attention the most, and helps those that don’t need healthcare attention feel safer and more secure at home. Many patients simply require the reassurance that everything is OK, and is going to be OK.

The early-detection capabilities enabled by AI’s analysis of health data that’s continuously captured from these remote monitoring devices lets healthcare professionals shift from reactive to proactive care. The best way to reduce healthcare costs is to treat at a lower acuity, lower cost point. That means treating patients before they turn up at the ER and before they require a readmission.

For the foreseeable future, AI will have a significant role in improving healthcare throughput, helping better deploy our limited healthcare resources to the patients who most need attention and delivering it at an earlier point. However, while the promises of AI and other emerging technologies are high, a computer has no emotion. This is the power of computation, but also its weakness. Healthcare professionals are an essential part of healthcare delivery -- they are the ones that turn information into action. Often, a decision required is just as much about what not to do, and much information is subjective and can only be elicited through real human contact.

It is currently impossible for AI to replace a human’s intuition and ability to see outside the box and discern an outcome. However, tools like AI will help fundamentally reshape how we deliver healthcare and will improve the lives of patients, doctors and other healthcare professionals in a way that is economically sustainable.

At the moment, while already enhancing patient care and improving clinical processes, the industry has only begun to scratch the surface.

If you’d like to schedule a demonstration, we’d love to hear from you.

Chris McCann
Christopher McCann
Chris is the CEO & Co-Founder of Current. A Computer Science graduate, Chris founded Current as a medical student.

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