Graph Database Use Cases in Healthcare

As the healthcare industry embraces digital transformation, there is an overabundance of information in this critical field. This has caused serious issues in numerous healthcare organizations as they are now inundated with data that they don’t know how to handle. Accessing and deciphering this intricate data is extremely inefficient due to the surplus of data. So what can be done about this problem? A graph database provides the solution. They’re ideal for managing highly connected data, precisely the kind of information that we find today in healthcare.

Let’s examine some of the exciting use cases of graph databases in healthcare, so you can comprehend the possibilities this technology brings.

Not familiar with graph databases? Have a look at this amazing article that provides an in-depth look into the difference between relational and graph databases.

Graph database use cases in healthcare

Healthcare is a complex field, especially due to the large amounts of data generated every day. This data could be valuable if it were used in meaningful ways. This is exactly what graph databases do and the healthcare industry is finding exciting use cases for the technology.

Here are the most popular use cases:

Use Case #1: Improving patient care

The most obvious use case for graph databases in healthcare is improving patient care through data. For example, a graph database can be used to detect patient deterioration earlier. This is done by tracking vital signs and other data to create a model of a healthy patient. When a patient’s vitals start to move away from the norm, the system will send an alert to hospital staff.

The ability of graph databases to integrate healthcare data from a variety of sources can also  help doctors, researchers and other healthcare professionals get a more complete view of a patient’s history and health. It also helps them find connections between different pieces of data that might not have been apparent before.

For example, by integrating data from different sources, doctors might be able to more quickly identify patients who are at risk for a certain disease. Or they might be able to better understand how different treatments are working for different patients.

Use Case #2: Drug research and development

A graph database can help researchers identify trends and relationships between different data points in a way that wouldn’t be possible with a traditional relational database.

One example is using a graph database to map out drug interactions. By understanding how different drugs interact with each other, researchers can develop new drugs that are more targeted and effective, and avoid dangerous interactions.

Another way graph databases are being used in research and development is to map out disease progression. By understanding how a disease progresses, researchers can develop new treatments that are more effective at treating the disease.

Researchers can also use graph databases to map out drug resistance. By understanding how diseases become resistant to drugs, researchers can develop new drugs that are more effective.

A key reason graph databases are so well-suited for this particular use case is because they can help research teams to quickly identify relationships between molecules, proteins, and cells. And what that means is that you can more quickly find new target proteins for drug development, and determine which molecules will interact with a given target protein.

Use Case #3: Electronic Health Records (EHR)

In the past, EHRs were often stored in a relational database, which made them difficult to query and access.

Graph databases can easily handle the complex relationships between different pieces of data in EHRs, making it easy to find the information you need. This can improve care by allowing doctors to quickly access the information they need about their patients.

Some of the ways that graph databases can power EHR include:

• Managing patient data: A graph database can be used to track a patient’s medical history and interactions with the healthcare system. This can help doctors and other healthcare professionals get a better understanding of the patient’s health and treatment history.
• Tracking medication interactions: A graph database can be used to track the interactions between different medications across several EHR systems and identify potential problems. This can help to ensure that patients receive safe and effective treatment.
• Generating care plans: A graph database can be used in EHRs to generate care plans for individual patients or groups of patients. This can help to ensure that patients receive the right care, at the right time, and from the right provider.
• Predictive modeling: In predictive modeling, the data in the EHR is analyzed to identify patterns that can be used to predict future outcomes. Graph databases are particularly well-suited for this type of analysis because they can handle the complex relationships between the data elements. This allows the EHR data to be analyzed in its entirety, resulting in more accurate predictions.One example of how predictive modeling is being used in EHR is the identification of high-risk patients. By analyzing the data in the EHR, it is possible to identify patients who are at risk for hospitalization or other adverse events. This information can then be used to develop interventions that can prevent these events from happening.
• Real-time risk monitoring: Healthcare organizations are under constant pressure to do more with less, and that includes ensuring patient safety. One way that graph databases can help with this is by providing real-time risk monitoring. With a graph database, you can create a logical patient profile within EHR. This profile can include all of their demographics, medical history, medications, and lab results. This profile can then be used to flag any potential risks in real-time, such as drug interactions or allergies.The information can be used to proactively prevent errors and adverse events. For example, if a patient is about to be given a medication that they’re allergic to, the system can alert the care team and make sure that the error is caught before it causes any harm.

Also Read: The difference between EHR, EMR and PHR

Use Case #4: Precision Medicine

As you probably know, precision medicine is all about treatments that are tailored specifically to one individual only, based on their genes, environment and lifestyle. In the past, medicine has been largely a one-size-fits-all approach. In contrast, precision medicine is a more personalized approach, where treatments are based on each person’s individual characteristics. Imagine if you could peer into the complicated relationships between genes, proteins, and illnesses. With a graph database, you can.

You could use a graph database to track a patient’s medical history, their family history, their lifestyle habits, and more. With all of this data in one place, you can start to see patterns and trends that might not be apparent with other methods. This can help you make more informed decisions about a patient’s unique medication requirements.

Use Case #5: Clinical decision support systems (CDSS)

Graph databases can help to develop CDSS that are more comprehensive, accurate, and up-to-date than the systems currently in use. By incorporating real-time data from EHRs, genomics, wearables, and other sources, CDSS can provide more personalized recommendations for treatment and care plans.

For example, a graph database could be used to develop a CDSS that takes into account a patient’s specific genetic makeup when making recommendations for medication. The CDSS could also consider the patient’s individual response to specific medications, as well as any allergies or contraindications.

In addition, a graph database-powered CDSS could be used to monitor a patient’s condition in real time and make recommendations for early intervention if necessary. For example, if the CDSS detects that a patient’s blood pressure is rising, it could recommend lifestyle changes or medication adjustments to prevent the development of hypertension.

Use Case #6: Population health management

In healthcare, population health management (PHM) is the process of improving the health of an entire group of people. And one way to do that is by using graph databases to track risk factors and interventions at the population level.

By storing data about patients, their conditions, and the treatments they receive in a graph database, you can get a better understanding of which interventions are most effective and which risk factors are most predictive of poor outcomes. This information can then be used to develop targeted PHM programs that improve the health of a specific population.

For example, when a clinician is trying to determine whether a treatment is effective for a particular patient population, they can use a graph database to quickly identify all of the patients who share commonalities (such as having a certain disease, being of a certain age, or living in a certain area). This can be incredibly helpful in identifying trends and developing better treatments for specific patient populations.

Use Case #7: Medical imaging analysis

Graph databases can be used to track image data and organize it in a way that makes it easier to find patterns and correlations. For example, imagine you’re a doctor and you’re trying to find all the patients in your database who have had a certain type of MRI.

With a graph database, you can easily query the database to find all the patients who have had that MRI, as well as any other relevant information about them (e.g. their age, gender, medical history, etc.). This can be incredibly useful for research purposes, as well as for diagnosing and treating patients.

Use Case #8: Genomics Data Analysis

Imagine if you could predict whether you would develop a certain disease based on your genes. Sounds like science fiction? It’s not. In fact, researchers are already using graph databases to map the interactions between genes, allowing them to unravel the secrets of genomics and disease-risk prediction. This is enabling scientists to see the complex web of genetic relationships that underlies diseases. The information is then used to create predictive models that can identify risk factors for diseases like cancer and Alzheimer’s.

In addition, graph databases are very flexible and can easily accommodate changes in the data. This is important in genomics, where the data is constantly changing as we learn more about the human genome. They are also great for managing and processing large amounts of data. In the world of genomics, this is critical, as the field is constantly expanding and new data is being generated all the time..

Graph databases are also very good at handling uncertainty. In genomics, there is always some uncertainty about the results, as scientists are constantly learning new things about genetics and disease. Graph databases can help manage this uncertainty, by providing a way to track the ever-changing relationships between genes and diseases.

Use Case #9: Social media mining in healthcare

The explosion of social media has had a profound impact on the way we communicate—and healthcare is no exception.

With the prevalence of sites like Twitter and Facebook, it’s easier than ever for patients to connect with each other and share their experiences. This has created a wealth of data that can be mined to help improve the quality of care.

Graph databases are particularly well suited for this task, as they can easily connect related data points and reveal patterns that would be difficult to discern with other methods.

For example, a graph database could be used to monitor mentions of specific drugs or side effects on social media, which could help identify previously unknown issues. Or it could be used to track the spread of infectious diseases by mapping out the relationships between people who have been affected.

Use Case #10: Drug retargeting

Imagine you’re a pharmaceutical company with a new cancer drug on the market. You’ve done your research, and you know that this drug is going to make a big impact. But you’re not the only one who knows it. Your competition is watching  your moves, and they’re not about to let you take the lead.

So what do you do? You start retargeting your drug to other cancers. You widen the net, and you start targeting other diseases. And you do it fast.

How do you do all of this? With the help of a graph database, of course. Graph databases are quickly becoming the go-to tool for drug retargeting, and for good reason. They’re fast, efficient, and accurate—and they allow you to target your drugs like never before.

By taking data from fragmented sources, such as research results, clinical trials and existing drug target pathways, and storing it in the schema-agnostic structure of a graph database, this powerful data structure helps researchers to connect the dots between seemingly unrelated pieces of information and identify novel drug targets that would have otherwise been hard to identify. Not only does this save time for researchers looking for areas where medicines are needed most, but can even lead to better drugs and treatments available faster in clinical settings.

Drug retargeting is an exciting new approach to health research and treatments, particularly for diseases like cancer. It uses existing drugs that were originally designed for other uses and modifies them to become effective treatments for cancer. This strategy also has the potential to shorten the time between discovering a new compound and bringing it to market, because the drug is already approved by regulatory authorities. Additionally, this approach can result in lower drug development costs, since much of the preliminary research on safety and efficacy has already been done. Drug Retargeting could change the landscape of cancer treatment as we know it and help bridge gaps in healthcare today so that more patients have access to life-saving treatments.

Use Case #11: Healthcare fraud detection

Because of the way it’s built, a graph database is an excellent tool for fraud detection in healthcare. It can help identify relationships and patterns that might not be detectable with other data structures. This makes it an essential tool for combating healthcare fraud.

Here are some of the main ways that a graph database can help with fraud detection in healthcare:

• Detecting false billing and insurance claims: Insurance companies can use graph databases to investigate and detect patterns of fraud, including false claims and billing for services that were never provided.
• Tracking prescription drug abuse: Pharmacists can use graph databases to track prescriptions and identify patients with cases of over-prescription or who are obtaining medication illegally.
• Tracking healthcare providers: Healthcare regulators can use graph databases to track healthcare providers and detect patterns of fraud such as kickbacks or other illegal activities.

Use Case #12: Regulatory compliance

Graph databases can be used for a variety of regulatory compliance tasks, including auditing. This way, healthcare organizations can easily ensure that they are constantly compliant with regulations such as HIPAA and GDPR.

So why are graph databases so well-suited for regulatory compliance in the healthcare industry? There are a few key reasons:

• Data lineage: Graph databases make it easy to track data lineage, meaning you can easily see where data came from and how it has been modified over time. You can keep track of compliance-related information such as clinical trial data, adverse events, and product complaints. By tracking this information in a graph database, you can more easily identify patterns and trends that could potentially be problematic. This is critical for meeting regulatory compliance standards, as it helps ensure data integrity.
• Data reporting: If you work in healthcare, you know that data reporting is a huge part of complying with regulations. And if you’ve ever had to compile a report, you also know that it can be a huge pain. With a graph database, you can streamline the data reporting process by storing data in a way that makes it easy to query and create real-time reports.  So if a regulator wants to see how a certain medication is impacting patients, you can easily generate a report that shows them the latest data.
• Access to patient records: With a graph database, you can give different users different levels of access to patient information. For example, you can allow doctors to see a patient’s full medical history, while only allowing nurses to see a limited amount of information. You can also control who has access to certain types of information. For example, you can use a graph database to control who has access to a patient’s sensitive information, such as their HIV status or their mental health history. This is critical for ensuring compliance with regulations like HIPAA and GDPR.
• Audit trails: In the healthcare industry, audit trails are essential to maintaining compliance with regulations. A graph database can be used to create an audit trail of every single action taken in an EHR system, providing a complete record that can be used to demonstrate compliance. For example, if a patient’s medical record is accessed, the graph database will help record when and by whom the record was accessed. This level of detail is not possible with traditional databases and is possible because a graph database stores data in a series of connected nodes, which makes it easy to track relationships between data points. In this case, the relationship is between the patient’s medical record and the person who accessed it.
• Real-time notifications: Graph databases make it possible to generate real-time notifications from complex data, alerting them to any potential risks, violations, or modifications in their surroundings. This ensures entities can take the required steps to keep compliance and prevent any disruption or harm that may arise from the absence of such a system.

Conclusion

So, what can we conclude here? It’s clear that graph databases are extremely powerful and can be applied in many ways within healthcare. At the most basic level, they have the ability to help us understand diseases, discover new treatments and enhance patient care.

But it’s worth noting that the use cases we’ve examined here are just a tip of the iceberg; graph databases have the power to revolutionize healthcare beyond what we might expect. Furthermore, they offer an exceptional basis to construct upon and use state of the art graph analytics to support superior quality of care.