Data integrations are complex yet critical to scaling successful AI models. When your organization begins developing AI-based solutions, integrating data is a comprehensive and necessary undertaking. To train AI models, you must find the data, aggregate many sources, load it into your system, and transform it into reliable, usable information to be effective. Rarely are these steps linear time and time again.
Healthcare data further complicates these tasks because it is not standardized across the industry (sometimes not even within health systems), and new apps and solutions use different protocols. That’s why it is essential to have people with healthcare interoperability expertise leading data integration projects. Initially, especially as you juggle budgetary needs, the “build” option seems like a cost-cutting measure. But that can lead to additional problems, such as:
- Unstructured, unconsolidated approaches to connecting patient data
- Fragmented and missing data
- Relying on high-cost specialized resources like data scientists to find, transfer, and clean data
“Ever since I was an engineer working right out of college, we were doing some sort of integration. We were managing the software and writing the integrations. As we grew as an organization, technology matured, and the healthcare industry matured, there wasn’t a lot of value in us having ownership of that portion. It was better to rely on integration experts.”
Cameron Kerber
Vice President, Application Development
Monogram Health
Choosing the right integration partner and solution
Globally, one of the great truths in healthcare is that resources are stretched thin. Building an integration strategy from scratch is time and labor-intensive. Two of the most common approaches to solving for integration needs are using services from the major cloud providers — that will get you some of the basic infrastructure for implementing a lot of your integrations – or, you can move up the stack into thorough software solutions, partnering with experts to complete all aspects of integration.
Don’t forget to first establish a strong foundation for this work by optimizing your people, processes, and technology during the initial planning stages. This will determine your success going forward, as all three are intertwined and reliant on each other.
While every organization has unique requirements, there are three primary goals to keep in mind as you develop AI-based solutions and integrate data.
1. Integrating patient data efficiently and effectively
In many cases, health tech companies have limited integration expertise and may take unconsolidated approaches to connecting patient data. Even if you do have integration expertise, in some cases, you may rely on resources like data scientists, to identify, gather, and clean data. This is an expensive use of vital resources whose capabilities are better used elsewhere – refining the AI model, for example. And, you may end up with fragmented data and poor insights.
“We look to buy wherever we can accelerate features to market, use expertise we don’t have or that provides a solution to scaling that might take us too long.”
Cameron Kerber
Vice President, Application Development
Monogram Health
2. Focusing on scalability
To scale more efficiently, you must develop a consistent, standardized way of acquiring data and creating a pipeline to your solutions. An experienced integration partner allows specialized resources like data scientists to focus on their specific area of expertise – analyzing the data and acting on their findings.
“Rhapsody has the experience and expertise, and we knew we’d be able to scale quickly and be able to accomplish multiple integrations faster – with less duplicative work – if we leveraged that expertise and experience.”
Cameron Kerber
Vice President, Application Development
Monogram Health
3. Improving data and customer onboarding
Successful growth and adoption require the ability to quickly and accurately onboard new data sources and new customers. The right integration partner understands the ins and outs of healthcare data, including security and privacy requirements, and can quickly bring new data into your organization’s system to enhance your algorithms.
By offloading the data integration, your engineers and analysts are empowered to work more efficiently, focusing on differentiation and innovation. Further, with streamlined integration, you can onboard customers faster, increasing the number of people using your AI model.
Questions to address
While AI integration can be a challenge, choosing the right partner and solution is essential for near-term success and long-term return on investment. It is possible to save time and money, enhance revenue and profitability, and reduce risk, while innovating and scaling your AI solution.
When choosing your AI integration partner and solution, answer the following questions:
- Does the vendor have a proven track record in healthcare AI solutions?
- Does the vendor provide reliable technical support and ongoing maintenance?
- Can the solution handle increasing data volumes and users as your organization grows?
- Can the solution adapt to changing clinical needs and regulatory requirements?
- Does the solution adhere to global security and privacy regulations to protect patient data?
- Do the vendor and solution enable data integration via API, HL7, FHIR, and other formats that you or your customers use?
- Are there other organizations like yours who are happy with the vendor and solution?
Finding your answers
Ready to see how Rhapsody supports your needs? To see how we meet you where you are, offering flexibility if you want to take on integration tasks with your team or use ours? Schedule a demo. Contact an integration expert.
Don’t just take our word for it. Here are other customers sharing their results.
