Rapid digitization in the healthcare segment is establishing a modern health ecosystem, improving patient care and process efficiency. With cutting-edge technologies like AI, IoT, and telehealth platforms, the ecosystem delivers more speed and precision to address key medical issues and public health challenges.
The result is a well-established and interconnected health data framework that serves as the backbone for innovative solutions in healthcare. However, most of this data is complex and longitudinal. Hence, traditional relational databases fall short in capturing the nuances that define real-world clinical environments.
To unlock in-depth insights, organizations are turning to graph databases that excel at mapping these complex relationships, offering a seamless and innovative method to analyze healthcare data and networks.
In this post, we will discover how graph databases are being leveraged to drive more personalized care and improved collaboration in healthcare.
The Role of Graph Databases in Healthcare Technology
Traditional approaches use a tabular structure (relational databases) that has rigid tables and predefined schemas. These work for linear transactions. However, they fail when representing the dynamic relationships inherent to healthcare data.
On the other hand, graph databases offer a great solution by storing data in a graph as nodes (vertices) that are connected by links or edges. The underlying structure can be used for data analytics or graph learning.
For instance, these databases structure information using nodes like patients, diagnoses, or medications and edges like referrals, co-treatments, or test results. Thus, mapping real-time interactions becomes easy.
In graph databases, queries work with traversals that move from node to node along the connected edges. Say, we need to get a list of physicians treating patients. If the physician node is connected to the patient node, the traversal will begin with the patient node and collect all the edges that point to a physician node.
Several top HealthTech software development trends point to a future where graph databases will be used to retain data’s natural richness and complexity. For instance, graph databases can be used in population health analysis. Say, you want to analyze the incidence of diabetes in various geographies. Using this method, you can identify regions with higher rates of the disease and focus on preventive care, targeted interventions, and healthcare resource allocation.
How Graph Databases Are Being Used in HealthTech
As mentioned earlier, graph databases are ideal for managing interconnected data, offering complex relationship queries.
Let’s look at the perfect scenarios in healthcare where graph databases are being deployed to navigate and analyze extensive connections swiftly.
1. Patient Data Management
Graph databases offer an effective way to manage patient data by capturing the intricate web of relationships across medical records, lab results, treatments, provider interactions, and more. They help integrate this data for a complete health overview and precise patient care.
Thus, healthcare professionals get a holistic view of individual patients, identify gaps, and personalize interventions. Graph models also make it easier to track updates, ensure compliance, and maintain data integrity across care journeys.
2. Drug Discovery
Graph databases play a central role in modern drug discovery. They model complex relationships between data points related to genes, proteins, compounds, and diseases, allowing researchers to uncover hidden associations, predict drug-target interactions, and identify potential off-target effects.
By integrating diverse datasets from genomics, clinical trials, and more, graph datasets reduce the time and cost involved in bringing new drugs to the market.
3. Disease Tracking
Graph databases help in tracking disease outbreaks as they can map relationships between infected cohorts, locations, and more. During an epidemic, graph databases can visualize transmission patterns and spot spreader events by connecting patient data, timelines, and geospatial movement.
Health authorities can use this insight to predict outbreaks and implement containment strategies proactively. Graph databases allow real-time updates and rapid querying, thus supporting data-driven decisions to manage emerging health concerns.
4.Medical Imaging
Graph databases help with medical imaging analysis as they can link imaging metadata with clinical, genetic, and historical patient data.
For instance, in the oncology domain, graph databases can relate tumor images with biopsy results and patient outcomes, helping healthcare professionals in early diagnosis. These models also identify subtle correlations across cases, improving diagnostic accuracy.
5. Clinical Trial Management
Graphical databases can streamline clinical trial management by modeling complex relationships between the trial participants, protocols, adverse events, and trial outcomes.
Besides, these models help researchers track the eligibility criteria, monitor participant responses across trial phases, and identify patterns in trial data in real time.
For instance, these models can link the patient demographics with drug interactions to flag high-risk cohorts early. Thus, with graph databases, researchers can improve trial efficiency, support trial designs, and improve the accuracy of the outcome analysis.
6. Health Recommendation Systems
Since they can model complex relationships and preferences, graph databases are highly effective in powering recommendation systems in the healthcare industry. These models enhance the intelligence behind health and wellness apps by connecting user data with evidence-based recommendations.
For instance, they can connect user data like activity levels, sleep patterns, diet, and medical history.
With such interconnected data point analysis, apps can share personalized wellness plans for individual goals.
7. Healthcare Fraud
Healthcare fraud impacts individuals and businesses and causes billions of dollars in losses each year. Graph models have a central role in detecting fraudulent behavior as they can connect different entities and offer context to data.
For instance, if you have a graph of relationships between people and their social accounts, you can find out whether or not the 2 accounts are the same person.
Graph databases examine healthcare claims and interactions and spot billing inconsistencies. They can also identify prescription fraud and reduce system exploitation.
Summing Up
Graph databases are emerging as a powerful tool for analyzing complex and interconnected datasets. They possess this amazing ability to model relationships and manage interrelated data, increasing their scope in the healthtech domain. They offer an intuitive and effective way to represent relationships that mirror real-life scenarios.
We are sure the information shared in this post will help you get an in-depth understanding of graph databases and how it is changing the way healthcare professionals model patient journeys and clinical relationships.
The post Using Graph Databases to Model Patient Journeys and Clinical Relationships appeared first on Datafloq.
