Author: Smile Digital Health
August 5, 2022
Things to Know When Deciding on Publically or Commercially Available Healthcare Data Models or Ontologies
In order for healthcare data to be shared among data models and ontologies, information systems need to securely communicate with each other. These systems became more commercially available in the 1990s and 2000s, but the majority were unable to support the integration of healthcare data and weren’t available as full fledged, independent products. Because of this, many healthcare Chief Information Officers (CIOs) have questions surrounding how to determine new technology initiatives for existing data models and ontologies, including how to accelerate data modeling for new technology initiatives.
Spotting this gap in information, we recommend CIOs to access Gartner® , “Quick Answer: What Healthcare Data Models or Ontologies Are Publically or Commercially Available?”, by Mark Beyer, published May 17, 2022. This report states that “healthcare CIOs and data and analytics leaders often inquire about data models and ontologies to accelerate data modeling for new technology initiatives. This Quick Answer helps CIOs understand the limitations and trade-offs of available options prior to implementation.
Finding the Right Data Modeling Tool
In the past there were many variables for an organization to consider when updating their technologies. From the added costs of computers, printers, fax machines, toner, paper and regular repairs, to waiting for the system download disks to be mailed out, there was a significant amount of time and money involved.
Today, upgrading an organization’s technology systems is costly for a new reason. Instead of adding fees for printer and fax machine maintenance and repairs, there are added fees for online subscriptions and paying for permission to get data. The cost of seamlessly integrating different programs and systems, however, becomes more beneficial in the long run.
Investing in new technology can have a high price tag, especially for healthcare organizations who need to consider their partner expenses on top of their own. After all, an upgraded system is only useful if compatible with labs and other clinics the organization works with.
Implementing a HL7® FHIR® (Fast Healthcare Interoperability Resources) solution includes an added level of cost and prospects above and beyond those needed to maintain current systems. Recently, more policies and data standards have been introduced as a way to encourage healthcare providers to bring their organizations into the present. Some examples include, the introduction of electronic health records (EHRs) and updated medical equipment used to simplify a patient’s care.
Healthcare organizations should benefit from the technological advancements that have happened in recent decades. However, there are still fundamental improvements needed to implement these digital technologies in a way that markedly improves clinical decision making and patient experience.
There are different options and features among the multiple healthcare data models available—most of which are included with purchased technology. However, there are some data standards, models and ontologies that are publicly available. Whether or not a resource is obtained at a cost, they could advance capabilities and possibilities among healthcare providers.
There are three categories of technology purchases, as outlined by Gartner, that also include data models or ontologies:
- “Healthcare industry clouds (which increase efficiency while decreasing costs)
- Horizontal enterprise technology providers (such as EDW, MTM and data quality vendors)
- Healthcare-specific applications like health information exchange or population health management)"
Within healthcare, there are four categories in which data models and ontologies are made available:
- “Cross-Industry Enterprise Data Management and Analytics Technology: Industry data models and ontologies are sometimes included when purchasing enterprise data management and analytics tools, such as data quality, ETL, or BI and analytics applications.
- Industry Clouds: In recent years, industry clouds in healthcare have rapidly increased their premodeled content.
- Healthcare-Specific Technology Vendors: In spaces like analytics and interoperability, healthcare vendors are making their application models extensible for use in client systems. These solutions are increasingly implementations of shared industry standards, most especially Fast Healthcare Interoperability Resources (FHIR®) and Observational Medical Outcomes Partnership (OMOP).
- Public Standards: Some organizations directly utilize publicly available standards documentation, such as the FHIR (Resource Description Framework [RDF]).”
In our opinion, finding the right data model or ontology, especially on a commercial level, takes a lot of resources. A key factor to choosing one is understanding your organization’s needs and seeing if it will work harmoniously with the systems being used by external partners.
What We Know about Healthcare Data Modeling
As the shift to open standards becomes more commonplace, many CIOs and Chief Data Analytics Officers (CDAOs) are still looking for improvements to the systems they purchased over 20 years ago. This means that there’s some catching up to do; after all, a lot has changed since the last time many of these organizations updated their systems.
Initial models did well. Their internal data modeling was user-friendly and provided insight on the necessary concepts, as well as available extras. With the multitude of available options, seeking an external source to decide on a healthcare data model is a good way to start the process. It’s important to know the best option for your organization’s needs and asking experts the right questions is only the first step.
Each data model or ontology will contain different variants that may conflict with the other, so taking the time to understand the options is essential, which is where Gartner comes into play. By gathering insight, making inquiries, building and implementing successful strategies as well as connecting with their partners, Gartner works to ensure that their solutions are effective.
Lately, there’s been an increase in healthcare organizations switching to open standards like FHIR—which allows for automated clinical decision support, while ensuring that data from EHRs are available, discoverable and understandable. Application servers like Smile CDR use FHIR based data architecture to utilize and store data while allowing for individualized configurations that fit an organization’s needs. Building the data fabric that works for our clients—payers and providers—gives them the tools to allow for interoperability, thus putting them at the forefront of the healthcare community.
Using Smile allows for organizations to create, evaluate and enrich the data integrated to their platform. We provide the tools that healthcare organizations need to advance their care and treatment options alongside a network of participating peers. Pulling away from the siloed and inaccessible way healthcare information is stored and opting for an interoperable solution is the direction in which healthcare is headed.
When considering storing your organization’s healthcare data, managing that information and ensuring it remains secure is only the beginning. The information also needs to be categorized, managed and made available to those authorized to access it. Using a solution that supports all elements of a FHIR-based standard gives your organization those benefits. Smile CDR is designed to scale rapidly and support large data sets used to collect large amounts of information and can be a direct recipient of data from automated systems for user applications designed to meet an organization’s needs.
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