The healthcare system is no longer how it used to be. Now, there is a greater need to understand, act, and share patients’ data across diverse systems.
Patient care has undergone several transformations, thanks to the digitalization of the healthcare system via electronic health records (EHRs). The emergence of AI-powered diagnostic equipment, wearable health devices, and telemedicine is also driving the transformation.
While patient care has improved, the fact that the systems driving the growth are operating in silos presents several challenges. The healthcare sector is facing difficulty in moving and retaining the meaning of data from one platform to another.
This persistent issue is at the core of several healthcare interoperability challenges, slowing down the healthcare sector from achieving truly connected patient care.
Semantic interoperability can help the healthcare sector address this problem. It can enable healthcare providers to understand patient data exchanged across devices.
Here is how AI-powered semantic interoperability is driving the growth of connected medicine.
The Rise of Healthcare Interoperability
Interoperability has been a major challenge for the healthcare sector for years. The standards and diversity of healthcare systems, including data formats, even made the challenge more complex.
In the past, technical interoperability was the primary focus. Then, the idea was to ensure healthcare systems successfully establish connections and exchange data. The introduction of syntactic interoperability shifted the focus of the healthcare industry.
Syntactic interoperability focused majorly on data standardization. These include the formats FHIR or HL7.
The introduction of technical and syntactic interoperability showed the healthcare sector was making progress. Unfortunately, neither innovation addressed the major problem, which is ensuring that the data exchanged between systems maintains its meaning.
AI-powered semantic interoperability provided the needed solution. It ensures that the data transmitted from one system to another retains its meaning. With that, healthcare providers can use the patient data to make reliable decisions regarding patient treatment.
How Vital Is Semantic Interoperability?
Semantic interoperability is driving integrated healthcare and improving patient care. It empowers healthcare providers to understand and interpret shared data the same way. This helps prevent data loss and misinterpretation.
Here is what this means for healthcare providers:
- Better treatment for patients: It allows providers to make timely decisions regarding patient treatments. That’s because they can access a clear picture of patients’ medical history.
- Enhances collaboration: Hospitals, laboratories, researchers, and even insurers can act on data more decisively.
- Reduced administrative burden: The introduction of semantic interoperability eliminates manual data entry, which has been a burden for the administrative arm of healthcare providers. Semantic interoperability also eliminates repetitive data entry, which can be highly frustrating.
- Improves medical research: The availability and access to standardized datasets will make medical researchers more effective.
AI-Powered Semantic Interoperability: The Role of AI
What role is AI playing in semantic interoperability? How is it driving growth and reliable decision-making in the healthcare sector?
Artificial intelligence is making healthcare providers approach semantic interoperability in a different way. Using advanced algorithms, including natural language processing and machine learning, AI is making it easier for providers to curate and interpret data accurately and faster.
Examples of use cases of AI in semantic interoperability:
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Natural Language Processing (NLP):
AI is using NLP to turn unstructured data into usable and easy-to-understand standard medical terminologies. Unstructured clinical data include patient discharge summaries or physician notes.
By transforming unstructured data into standardized medical terminologies, AI is making patient data usable across healthcare systems.
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Automated data normalization:
Artificial intelligence can identify variations in data entry and quickly normalize them.
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Ontology mapping:
Clinic data relies on the use of codes. These include ICD, SNOMED CT, and LOINC. Artificial intelligence can align these ontologies, eliminating duplication and making data useful across systems.
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Predictive analysis for data gaps:
Aside from data standardization, artificial intelligence can predict missing values in a dataset or identify inconsistencies, thus improving data quality and reliability.
AI-Powered Semantic Interoperability: Real Use Cases
Public health surveillance:
With artificial intelligence, public health surveillance can become easier. It achieves this by aligning public health information from several sources.
Artificial intelligence can spot emerging health risks, treat, and monitor outbreaks, enabling the authorities to make informed decisions.
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Precision:
By merging data gathered from patients’ clinical records, imaging, and genomics, artificial intelligence can empower healthcare providers to create personalized treatment plans tailored to patients’ medical history and genetic profile.
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Cross-border healthcare:
AI-powered semantic interoperability will benefit EU patients, particularly those who seek care across borders. The fact that it can harmonize medical records between countries ensures continuous and effective patient care.
The Future of Artificial Intelligence-Powered Semantic Interoperability
AI is driving the future of connected patient care. It is providing an ecosystem where patient data moves securely from one healthcare provider to another, and across borders.
How is artificial intelligence driving this growth?
- Federated learning models: AI generates insights from patient data rather than making the raw data available, fostering reliable decision-making.
- Encouraging innovation: The introduction of artificial intelligence in semantic interoperability is creating pathways for personalized medical treatment and data-driven research.
Conclusion
AI-powered semantic interoperability is driving connected patient care, encouraging healthcare providers to share patients’ data within and across borders. Artificial intelligence ensures that the data exchanged between systems is understood and reliable.
Artificial intelligence helps reduce the administration burden on healthcare providers when applied correctly. It can help identify duplicate data, giving healthcare providers, researchers, and insurers access to quality data.
AI-powered semantic interoperability is making it possible for patients to receive personalized treatments, as healthcare providers can access their complete medical history before commencing treatment. Patients can receive personalized treatments, tailored to their medical history and genetic makeup.
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