Why we created a next-gen Health Data Supermodel – Part Four

Healthcare organizations have invested millions in data infrastructure, but is it delivering the value you expected? In the final part of our How We Created a Next-gen Health Data Supermodel series, Orion Health CEO Brad Porter takes on the critical question of why.

Building on the technical insights shared by VP of Engineering Reece Robinson in the first three parts, Brad explores how the right Health Intelligence Platform can unlock untapped value and why we built the next-gen Health Data Supermodel.

Why invite people to spend more money when they’ve already invested seven or eight figures into their current data infrastructure? 

I believe patients, providers and health data curators can expect far more from our health data infrastructure tools, but too few of us understand what more could look like, and whether it’s even technologically possible. It’s time for that to change.  

Astonishingly, despite the often-huge costs of data lakes, lakehouses and data platforms – we are sometimes talking in the hundreds of millions – the current crop of solutions do not let you extract the full value from your data. One tool is good for reporting and analytics, and another for machine learning.  But until now, none have catered to health enterprise-wide use cases, and some just simply don’t understand health and health-related data.

That’s why we built the Orchestral Health Intelligence Platform. It’s a standards-agnostic, open data fabric that maps raw data into a health data supermodel supporting enterprise-wide use cases.  

What does that mean to your average health system c-suite executive?  

At a micro level it builds you solid, trusted data foundations, and a solution that is informed by, but not wedded to, any and every data standard. At a macro level, it sets up every single datapoint in your organization to be fully understood by every application, at every level, at the same time. 

It covers reporting and analytics, the integration of machine learning and artificial intelligence, and acts as an interoperable platform to feed downstream applications. It’s an incredibly exciting technology. I’ve heard people call it a purple unicorn.  

As an open, standards-agnostic data fabric powered by a health data supermodel, it consolidates large tech stacks towards full interoperability from ingestion. From source data systems to integration with other applications, as well as exciting stuff like AI and analytics. By accommodating all and any data types, standards or formats, it eradicates the need for subsequent investment or upgrades. Now you have something that ticks every box and with it, we can start to fix healthcare’s seemingly insurmountable problems.   

Everyone’s claiming to pull analytics and drive AI using health data right now. What’s the difference between their analytics and our analytics?  

The first part of the answer is cleanliness and openness of data, efficiencies, depth and speed of insight gathering, and the ability for disparate systems to integrate. The second part of the answer is that there is way more value to your health data than just analytics. Once we’ve properly brought your data together, then as well as AI – you can expect to use it to support interoperability and integration, and the rest of the highest value use cases in healthcare. Early disease detection, payer forecasting, resource allocation and supply chain, risk stratification, precision medicine. Once a platform can do it all, that same platform will enable your data team to double their output by reducing labor-intensive data housekeeping tasks.  

I like the way Reece explained the Health Intelligence Platform like a sponge that can expand around any tech stack. Its DNA is in health data, but it can also connect and integrate with non-health data, such as a government’s social housing system or pollution records, to mine incredibly rich insights into social determinants of health or value-based care. It is designed to break down the barriers to safe, secure, and open data use.  

Openness is a key part of what we’re doing. Perhaps because we’re New Zealanders, fairness and honesty are in our DNA.  

Of course, data security and privacy must predicate openness and necessitate our platform’s focus on security, de-identification, and anonymization tooling.

Something else Kiwis run on is resourcefulness. It comes from being stuck with limited resources at the bottom of the world. We have a habit of asking ourselves – what’s the secondary use case for this? Whether it’s fencing wire or data architecture – what else could we do with it?  

That’s the approach we take to our products. We started building them thirty years ago with a specific use case in mind. And we’ve come this far, we’ve got all the data together; now we’re thinking what else can we actually do with it?  

The use case applications for this new product are phenomenally exciting, and obviously, it’s hard to overstate the pressing need and incredible opportunities for properly connected healthcare data.

The companies that do adopt open, standards-agnostic health data fabrics mapped to health data supermodels will be the ones that are fully equipped and ideally positioned to thrive as healthcare catches up with other industries that have undergone data-driven transformations. There’s been a lot of talk about what the future for healthcare looks like. Now the tools exist, and it’s time to act.  

On the technology side, it’s up to us – industry leaders and providers – to lead the way not with talk or demands, but with real, concrete solutions that solve the fundamental challenges we face. 

Did you miss the  How We Created a Next-gen Health Data Supermodel series? Catch up here.