Finding Your Alikes. Discovering the Future of Health Care

The simultaneous availability of sophisticated AI and machine learning are required to complete the picture of making clustering analysis and insights possible. It is the convergence of both those realities – unprecedented data access and the ability to put people into the right groups, sub-groups, and sub-sub-groups based on that information – that’s behind the creation of Alike.
Finding Your Alikes.  Discovering the Future of Health Care
Written by
Alike Team
Published on
May 27, 2021

There is no single path to bringing the benefits and insights of healthcare technology to the world. There are, in fact, two paths. The first is what’s called “population health” – looking at very large groups of people to improve care and recognize gaps.   This is a critical set of activities for the world. The second is “personal health” – identifying small clusters of individuals who share overlapping similarities – are  truly “Alike” each other – so that care can be improved, and insights generated based on those commonalities.  

This is equally essential, and is where our focus lies. To illuminate the difference between the two,  let’s use a real estate example.  If you want to know how much 54 Elm Street in Pleasant Bluffs is worth, broad data about macro trends in home pricing are not going to help at all.  But finding local houses with a high degree of similarities – age, condition, number of bedrooms, size of property, taxes -  will be invaluable. In medicine, we need to be able to filter and cluster in the same hyper-targeted way.  It’s vital because research is increasingly finding that clustering people based on their similarities – for example, a subgroup of diabetes plus hypertension plus vision problems – will lead to better diagnosis and treatment.  As a paper in Frontiers in Physiology puts it:

Patient similarity…has the potential to transform systems medicine by offering predictive models of a patient’s outcome.

It’s intuitive:  we are more than one condition, we are the sum of them. To produce that organizing capability requires a combination of data access and technology - both at scale.  You need to know a lot about a lot of people, and be able to instantly assess and structure it. A major access breakthrough arrived on April 5th, when the bi-partisan 21st Century Cures Act went into effect.  This legislation guaranteed all patients free and instant access to their medical records.  That includes their history, their labs and more – a total of eight categories of data. But the vast amounts of data that are becoming available - thanks to the Cures Act, and similar initiatives around the world – are not useful on their own.

The simultaneous availability of sophisticated AI and machine learning are required to complete the picture, making clustering analysis and insights possible. It is the convergence of both those realities – unprecedented data access and the ability to put people into the right groups, sub-groups, and sub-sub-groups based on that information – that’s behind the creation of Alike.

Alike’s founders come from the two disciplines behind this convergence: One of us a physician, the other an experienced technology leader.  We started Alike because we believe that when you put Alikes together, magical things will happen. Varda likes to tell this story to demonstrate how her clinical experience led her to the vision of Alike:  

For many years as the Head of a Medical Informatics and Research Institute I developed decision support systems for doctors and organizations, but as a practicing physician I understood that the improvement in healthcare should (and will!) come from the patients. To this day, we have not developed enough tools for patients to understand their clinical data, to act upon insights, and to connect to other patients going through the same thing.  Alike allows the patients to understand their medical situations through their EMRs, and engage with others who can share insights about their conditions.


Here’s how the Alike community operates. First, our members share their medical records with us – it’s quick and easy – and we then generate a SimScore (“Sim” for similarity) based on the entirety of an individual’s medical situation.  The SimScore then triggers our proprietary AI to find others who are, as we like to say, as “medically alike” as possible. You’re a unique individual, that’s true, but that doesn’t mean there aren’t others whose clinical proximity is such that you have a lot to learn from each other.  (In other words, you’re Alike and not alone.)

Once you’re in a community cluster of your Alikes, the real value begins.  Think of it in these multiple dimensions.

• Your personal health record will be beautifully presented.  Streamlined and organized by Alike’s ability to translate the complex into the easily digested.  That’s of great value, because if you looked at the electronic record that comes from your doctor, you wouldn’t be able to make any experience to give others the benefit of your experience. 

• You’ll be in a safe space where everyone is vetted and there for the same reason.  To share their situations, diagnoses and treatments; even those with the finest physicians will learn something – that’s the power of the access to experiences that can only be found in a common cluster.

Alike is both deeply personal and totally private.  Your data is de-identified, so even we don’t know who you are.

Looking ahead, the power of our technology will be able to draw from the insights only available in clusters and use them to predict the course of illness.  Bringing people together with the same SimScore, including those who have more advanced symptoms, is like looking into the future with great clarity.

It’s an enormously optimistic moment, as advances in both personal health and population health will serve patients and researchers in ways of parallel significance.

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