The healthcare industry has undergone major changes over the years. Now, clinicians have more work to do than ever. They manage large volume of medical imaging data ranging from X-ray reports to MRIs, including CT scans.
Having access to robust imaging data can empower clinicians to understanding their patients’ health problems on a deeper level. But, it’s so unfortunate that most of the data remain unused or even underused.
You can bridge this gap via true data interoperability. This includes enabling artificial intelligence models to combine patients’ medical histories with imaging insights to deliver smarter, faster, and more accurate diagnoses.
To gain a deeper understanding of how artificial intelligence enhances diagnostics, read this guide to medical imaging analysis.
Importance of Interoperability For Medical Imaging and Artificial Intelligence
Why is it essential to connect imaging data with patients’ medical records and artificial intelligence models? The reason is simple. In the absence of such a connection, imaging data will be a siloed asset. Artificial intelligence, on the other hand, will be nothing but a novelty.
Merging imaging data with AI and patient data will enable the following:
- Full patient context: You will have access to robust patient information, including medical histories, lab values, and demographics, entered into the imaging analytics system.
- Robust AI model inputs: With this, you will achieve fewer false positives, solid predictions, and actionable outputs.
- Improved efficiency: Clinicians can become more efficient when they have the whole picture. Merging imaging data with artificial intelligence enables clinicians to achieve better outcomes by reducing duplicate imaging.
- Smoother workflow: Imagine insights in a patient’s radiology report or electronic health record driven by artificial intelligence, enabling a smoother workflow.
Comments from the healthcare industry professionals indicate why interoperability is a game-changer in medical imaging. It is the difference between manually reporting measurements and using artificial intelligence results versus auto-populating information in radiology reports.
Using interoperable systems to connect patients’ medical records and imaging data unlocks the true potential of artificial intelligence in radiology. It will enable more possibilities in the field.
Critical Standards That Enable Interoperability
If you have plans to leverage interoperability in your healthcare organization, you must also consider recognized standards. These standards are already in place.
They enable the interaction of disparate systems in one common language. These are the two foundational standards below:
- DICOM: One of the foundational standards is DICOM. The whole meaning is “Digital Imaging and Communications in Medicine.” DICOM governs two aspects – storage and transmission of medical images. It also stores and transmits their associated metadata between imaging modalities, PACS/RIS, including other systems. Now, let’s discuss the second one.
- HL7FHIR: It’s regarded as the Fast Health Interoperability Resources. This standard is modern and web-based. It focuses on electronic health record (EHR) data, including patient data, observations, encounters, imaging studies, and more.
Why adopting any of these standards — or merging them — can be a game changer: the application is what really matters. The implementation will determine whether your system will interoperate smoothly.
How Artificial Intelligence Models Use Interoperable Imaging And Patient Records Data
You may have explored artificial intelligence use cases in radiology, including disease classification, lesion detection, and segmentation. But to make the jump from the laboratory to the clinic, you must feed your models with multimodal data. That’s where interoperability comes in. Here is how it makes the connection.
- Imaging data: Example: CT scan. The data is stored in DICOM format. In the same vein, patients’ medical records contain data such as lab results, prior medical history, and demographics. These are stored in FHIR/HR-7 format.
- Imaging study linked to patient record: A system can use linkage services, identifiers, or metadata to align patients’ imaging studies with medical records.
- Imaging and structured patient data ingested: The AI model can ingest the patient’s imaging and structured data. An example is aligning MRI image features with other data, such as patient’s lab results, official age, including other records.
- The AI model output: It can be a risk score, a prediction, or an actionable insight. For example, it could be a tumor response prediction.
- Output flows back into the healthcare provider’s workflow: Interoperability automatically channels the output to the clinician’s workflow—EHR, dashboard, and radiology report.
Studies suggest that artificial intelligence orchestration performs better when the model is aligned with both imaging and non-imaging clinical data, including labs, patients’ medical history, and other data sources. Not leveraging integration can add complexity to your workflow rather than deliver actual value.
Must-know Benefits of Interoperability
Making your system interoperable offers the following benefits.
- Higher accuracy and relevance: artificial intelligence models arrive at their decisions with complete context, not isolated pictures acquired via imaging analysis.
- Workflow efficiency: You will find the insights you need in the right place, when you need them. You don’t need to toggle between systems to manually access essential insights.
- Scalability: Healthcare providers can add new and improved AI tools whenever they want. You don’t need to rewire everything from the start.
- Better patient care: The easy access to robust information of patients make the job a breeze for clinicians, including radiologists. It allows them to make better decisions faster.
Conclusion
Investing in an interoperable infrastructure is an important decision that can transform healthcare delivery. This system connects patients’ medical records and medical imaging data. It also has capacity to deploy intelligent artificial intelligence models. It will make you more efficient and faster.
Anchor your strategy in data standards, governance, and patient-centric workflows. Treat interoperability as the foundational element. In other words, in its absence, you can’t reliably align artificial intelligence models with patients’ medical records in a way that makes a tangible difference.
Using the right architecture is a step in the right direction. You’ll have the power to conduct more intelligent diagnostics, and make better decisions.
