6 AI Healthcare Solutions for Remote Patient Monitoring
It’s no secret that big tech companies like Amazon (AMZN), Microsoft (MSFT), and Alphabet (GOOG), the parent company of Google, are investing in digital healthcare. The market opportunity is pretty enticing when you consider that the U.S. alone spent $3.65 trillion on healthcare just last year. Google made the latest headline-grabbing move when it announced that it would buy wearables-maker Fitbit (FIT) in a deal valued at $2.1 billion. Analysts have noted that the acquisition is part of the company’s overall strategy to build an ambient intelligent system where Google is omnipresent. Another motive behind the purchase – pending regulatory approvals – is that Fitbit gives Google access to a treasure trove of healthcare data that it can feed to its London-based AI lab DeepMind or its life sciences subsidiary Verily, which is already collaborating on at least one AI healthcare device for remote patient monitoring.
That project, in partnership with medical device manufacturer iRhythm (IRTC), is looking at ways to improve the collection and analysis of data for patients with atrial fibrillation, an irregular heartbeat that can cause serious health problems. The ultimate goal is to train algorithms that can take the data from iRhythm’s remote monitoring devices in order to predict heart conditions before they even occur. Now think what Google could end up doing with fitness monitoring devices on nearly 30 million people.
While iRhythm may be one of the biggest companies out there today using AI for remote patient monitoring of a medical condition, it isn’t alone. There are plenty of AI healthcare startups raising money to develop hardware or software for various monitoring solutions. The amount of money being poured into these and other AI healthcare companies is simply mind-boggling, based on the latest data from research firm CB Insights:
Below we take a look at 6 AI startups focused on remote patient monitoring platforms that have taken funding within the last year or so.
Computer Vision for Remote Patient Monitoring in Clinical Trials
One of the companies responsible for the big jump in funding to AI healthcare startups in the third quarter is London-based Babylon Health following a $550 million round in August. Its AI chatbot provides health information based on a Q&A with patients. New York-based AiCure has developed a very different mobile AI healthcare assistant in IMA. Founded in 2010, AiCure has raised a total of $51.75 million, including a $24.5 million Series C earlier this month (not to mention another $7 million in grants from the National Institutes of Health). Its platform enables clinicians and researchers to monitor a patient’s treatment progress by measuring changes in facial expression using facial recognition and computer vision algorithms. (At least that sounds more benign than the facial recognition tech from Faception that claims to spot terrorists based on their scowling faces.)
The company, in particular, markets the technology for use in clinical trials to help boost participant engagement and adherence, ensuring that patients are taking medications according to prescribed protocols. A 2017 study, for example, found IMA was 25% better than Nurse Ratched in ensuring schizophrenic patients in a clinical study took their meds.
Remote Heart Monitoring Using AI and Sound
Companies like iRhythm and French AI startup Cardiologs rely on machine-learning algorithms to analyze traditional electrocardiogram (ECG) readings. Founded in 2013, Berkeley, California-based Eko’s algorithms move to a slightly different beat. The startup has raised $27.8 million in disclosed funding, with a $20 million Series B in September representing the latest infusion of cash. Eko has designed two stethoscope-like devices. The CORE delivers 40X sound amplification compared to a traditional stethoscope, while the DUO combines sound plus ECG recording. The latter also comes in an at-home device that patients can use to monitor their vital signs.
The secret sauce is in the software, which consists of sophisticated algorithms that analyze heart health through a single lead ECG, as well as the heartbeat using a deep neural network model trained on multiple datasets of heart sounds. The sound analysis can pick up the vibration of tissue caused by turbulent blood flow, a murmur that is hard for the human ear to detect because it overlaps with the cardiac beat. Both devices have FDA clearance, while the algorithms are HIPAA-compliant but awaiting regulatory approval.
Update 11/09/2020: Eko has raised $65 million in Series C funding to expand its new telehealth platform and algorithm to more clinics, and launch an at-home monitoring program for cardiopulmonary patients. This brings the company’s total funding to $95.5 million to date.
Turning Wearables Data Into Healthcare Action Using AI
Perhaps the folks at Verily should add San Francisco-based Myia Labs to their growing list of partnerships. Founded in 2017, the startup has raised $16.75 million since last year, including a $10 million Series A in September. Myia uses the streams of personal and health data from wearable devices like Apple Watch and Fitbit to monitor chronically ill patients and then applies machine learning and other artificial intelligence techniques to predict relapses before they happen.
AI-Powered Wearable for Remote Patient Monitoring
Scottish startup Current Health, founded in 2015, has built both the hardware and software to provide real-time insights into a patient’s health using a wearable armband. The company has raised $11.7 million in disclosed funding. Its algorithms continuously analyze a number of vital signs, including pulse rate, oxygen saturation, respiration rate, temperature, and movement, from its own device, but the platform can also capture additional metrics.
Earlier this year, Current received Class II clearance from the FDA for hospital care. Its system is already used in the UK, where healthcare providers use the device to remotely monitor patients after they’ve been discharged from the hospital. The data has enabled health officials to reprioritize
who lives and who dies when to conduct home visits, resulting in a 22% drop in home visits, as well as fewer hospital re-admissions and emergency department visits.
Remote Patient Monitoring for Respiratory Conditions
A startup called Feebris in the UK appears to be doing something quite similar to Myia Labs, using signal processing algorithms and machine learning algorithms to extract clinical insights and even diagnoses. Founded in 2017, the company raised about $1.4 million in a Seed round in May. However, Feebris is focused on the most vulnerable populations – children and the elderly – to detect complex respiratory conditions away from a clinical setting.
For example, Feebris is currently running a pilot in India to help healthcare workers diagnose respiratory conditions for 10,000 children in the field. Feebris is also working with 1,000 elderly patients in the UK to develop AI-powered remote monitoring for asthma and related conditions.
Smart Patch for Remote Patient Monitoring
Speaking of India: Bangalore-based ten3T, which was founded in 2014, has raised an undisclosed amount of money, including a Seed round in April. It has developed the Cicer patch for remote patient monitoring. The device collects pertinent vital signs like pulse, respiration, and blood pressure. Its algorithms decipher that data in real-time, sending alerts when it detects an imminent problem but the system is also capable of detecting more subtle changes over time. Since 75% of life-threatening events happen outside the ICU, there’s a lot of low-hanging fruit.
We’ve been writing quite a bit lately about how AI will transform healthcare, especially in the diagnostics arena. Using machine learning and other types of algorithms to monitor patient health remotely is an integral part of diagnostics, so it’s no surprise that most of what these startups are doing is actually trying to detect, diagnose, and prevent disease or illness. Expect to see more startups emerge in this category, especially as the FDA and other regulators show an increasing willingness to approve these devices and algorithms for clinical use.
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