Paving the Way for AI, ML, and Real-Time Clinical Decision Support with Ontologies and Knowledge Graphs.

Healthcare is experiencing a transformative shift fueled by artificial intelligence (AI), machine learning (ML), and real-time clinical decision support (CDS) systems. These innovations hold enormous promise for improving patient outcomes, streamlining operations, and delivering personalized care at scale. But there’s a catch: these systems are only as good as the data they’re built on, and that’s where things often fall apart.

Proprietary electronic medical record (EMR) systems are a barrier to innovation because of their siloed and inconsistent data models. But we do have options. Ontologies and knowledge graphs can help—they can create a foundation for healthcare data across different systems, providing consistent structure, meaning, and connectivity. They bridge the gap between raw data and actionable insights, enabling AI, ML, and CDS to deliver on their potential.

Here’s why ontologies and knowledge graphs are critical—and what happens when we fail to use them.

The Foundation: AI, ML, and CDS Need Clean, Connected Data

Data is the fuel that powers AI, ML, and CDS engines. These technologies thrive on consistent, interoperable, and context-rich datasets. Additionally, real-time CDS depends on instant access to structured and actionable insights, often from live data streams. Without a strong foundation, these systems falter.

Ontologies: The Semantic Glue

Ontologies define and structure the relationships between data points, making raw data meaningful and machine-readable. Think of them as the dictionary and grammar rules that ensure everything aligns.

Ontologies enhance understanding by adding a "metadata" layer to raw data, which includes labels, classifications, and context, which algorithms require to reason. For example, they assist AI in recognizing that “glucose” is associated with “diabetes,” not “energy drinks.” Additionally, ontologies determine the relationships algorithms need to identify critical patterns, such as connecting a high heart rate with low blood pressure as a potential sepsis alert.

Knowledge Graphs: The Relational Map

Knowledge graphs take structured data and organize it into a network of relationships. This "grounding" makes it easy to query, navigate, and discover hidden insights that are not otherwise evident.

This grounding enables AI and ML algorithms to prevent morbidity and mortality by identifying and notifying caregivers of patterns, trends, and connections indicative of a brewing threat or one that instantly emerges for patients in an intensive care unit whose health is tenuous. Knowledge Graphs traverse and effortlessly integrate the relevant data points required to choose one course of action over another, delivering real-time "decision support" to healthcare providers, such as which antibiotic will be the most effective treatment for a patient based on their unique combination of relevant data - the result of a blood culture test, type of infection, the patient's allergies, and relevant vital signs. Without this "grounding," even the most advanced AI and ML technologies will struggle like a surgeon working with a dull instrument—technically possible but painfully inefficient and prone to error.

What's the Potential Payoff?

1. Ontologies and knowledge graphs help eliminate ambiguity in data labeling by reducing the number of ways data can be interpreted depending on which questions are asked and how they are asked. This results in more accurate and reliable training datasets. Accuracy and reliability affect predictive power. For example, an algorithm that predicts heart failure utilizes an ontology to comprehend the relational significance among high blood pressure, an enlarged heart, and relevant lifestyle factors predicting the level of risk this combination poses. Knowledge graphs enhance this process by enabling the calculation of the degree of relational significance among various data points. Understanding these relationships is crucial for identifying subtle patterns in large datasets that exceed human cognitive capabilities.
2. Ontologies and knowledge graphs enhance the speed and flexibility of artificial intelligence and machine algorithms. This improvement enables clinical decision-support tools to quickly identify, process, and respond to critical relationships in real-time. As a result, these tools can incorporate new real-time data and dynamically react to continually changing heart rate and blood pressure values, which, when combined with new lab results, for example, satisfy criteria that exceed specific red flag thresholds.
3. The connections and relational significance provided by ontologies and knowledge graphs can help improve the consistency and accuracy of interpreting data from different source systems, each with its own "shorthand." For example, analyzing relationships and their significance can determine that, for instance, "MI" means "myocardial infarction" in one system, while in another, it stands for a geographical location (MI—Michigan).

The Cost of Ignoring Ontologies and Knowledge Graphs

Skipping ontologies and knowledge graphs is like building a skyscraper without blueprints. You can try, but it will cost you more money and time than the alternative. Navigating the delicate balance between expedience and quality is vital.

Fragmented Data Hurts AI and ML

1. Without ontologies, algorithms struggle with inconsistent labels and formats, leading to errors. For example, a model may mistake “HbA1c” for a medication instead of recognizing it as a diabetes biomarker.
2. Without knowledge graphs, models miss relational insights. For example, failing to link medications to kidney disease leads to dangerous treatment recommendations.

Inflexible and Error-Prone CDS

1. Without ontologies, systems miss critical connections. For example, a drug interaction goes unnoticed because the system doesn’t recognize that "Coumadin" is equivalent to "Warfarin."
2. Without knowledge graphs, real-time decision-making breaks down. For example, a system may recommend a treatment without accounting for lab results indicating contraindications.

Bloated Costs and Delays

1. Without ontologies, every project begins with extensive data preparation. For example, hospitals spend millions each year cleaning and standardizing their data manually.
2. Without knowledge graphs, each system rebuilds relationships from scratch. For example, developing a drug interaction checker for every EMR vendor or instance increases development costs and adds months to timelines.

Falling Behind on Emerging Needs

1. Without ontologies, systems can’t adapt to new knowledge or standards. During COVID-19, symptom tracking was delayed because existing systems couldn’t accommodate new data fields.
2. Without knowledge graphs, systems can’t dynamically integrate new data sources, locking wearable device data in silos, limiting its use in real-time care.

The Future: Meeting Emerging Healthcare Needs

As healthcare evolves, new challenges like pandemics, rare diseases, and precision medicine demand flexible, adaptive systems. Ontologies and knowledge graphs are uniquely positioned to meet these needs.

How They Help:

Pandemics: Ontologies standardize symptoms, conditions, and treatments, while graphs link patient data to public health insights. During the COVID-19 pandemic, ontologies helped AI models identify "anosmia" as a significant symptom. This enabled knowledge graphs to track outbreaks of this unanticipated "loss of smell" symptom in real-time, enhancing the accuracy of incidence rate calculations.

Rare Diseases: Ontologies map relationships between symptoms and conditions, while graphs link genetic and clinical data for precision diagnostics. AI identifies Friedreich’s ataxia by connecting ataxia (muscle loss causing clumsiness) and vision loss using an ontology.

Precision Medicine: Ontologies define genetic markers and treatments, while graphs integrate patient-specific data for tailored care. Knowledge graphs suggest immunotherapy based on tumor markers and past responses.

Conclusion: The Blueprint for Healthcare’s Future

Ontologies and knowledge graphs aren’t just "technical" luxuries—they are foundational to realizing the promise of AI, ML, and CDS. They transform healthcare data from fragmented chaos into structured, actionable intelligence. Today's healthcare demands intelligent, adaptable, and connected systems, and for the foreseeable future, ontologies and knowledge graphs are the tools to make that vision a reality.