The next-gen healthcare platform powered by RGDB
Use Case : Multi-model Database, RGDB (Relational Graph DataBase)
For a recent few years, modern healthcare has been one of the major recipients of what the recent revolutionary advances in IT technology have delivered to our world. The incurred availability of big-data hit hard the healthcare domain, and like many others, brought dramatic changes to it. We’d like to tell you a story that almost all doctors and healthcare people may have experienced, and, the transitions.
When patients come by for a diagnosis, doctors normally attempt to collect every bit of information related to the patient’s symptom and physical condition. And the problem is that they just can’t.
Looking into the scenes, patients are keep telling that they can’t remember well or not sure of what they feel, or just quibble with something that is not accurate. So, charts have remained as a diagnosis record whose scope of information only clings to the specific visit from which it was generated on some specific point of time.
This leads to the fact that chart information is necessarily and normally bound to time and place and this hasn’t changed a lot even in the modern data environment of today, due to the obstacles that barricade quick and fluent interchange of information between hospitals, labs, and health insurance companies.
Then, we want to introduce a case that brought a significant turning around on this situation that maybe you or your organization have been waiting for so long. For more details, check out the use case : next-gen healthcare platform powered by graph database.
The wave of change from data-driven Healthcare
As the trend of modern medical science is moving toward a point called ‘Evidence Based Medicine,’ collecting and using the massive amount of data generated at every stage of healthcare service became more critical than it ever was. Evidence Based Medicine is a medical practice intended to optimize decision-making in the treatment process by emphasizing the use of evidence from well-designed and well-conducted medical research.
One can easily imagine from his/her daily experience of medical treatment that healthcare data may consist of many types and includes a huge volume.
Types of Healthcare data
Data Interoperability and Data Lineage*
(* Data Interoperability stands for the availability that data can be interchanged and used across multiple systems and Data Lineage refers to the comprehensive record that shows a patient’s medical history in the long term view)
It boggles one’s mind to imagine the plurality of health data that are available from a patient’s legacy medical history; administrative and claims data, disease registries, clinical trials and health surveys and so much more. Evidence Based Medicine requires all data to be immediately available to the physician when we walk into our healthcare provider. It’s easy to understand the problems that could occur without well-designed systems to manage this data given that its large volumes, scattered across multiple databases, and in different formats and compounding in complexity on a daily basis. And this kind of data environment made Data Interoperability, the ability of health information systems to work together within and across organizational boundaries, almost unachievable thing.
Patient Data Lineage is a data life cycle that includes the data’s origins and where it moves over time and describes what happens to data as it goes through diverse processes. It helps provide visibility into the analytics pipeline and simplifies tracing errors back to their sources so doctors can make better, more informed decisions.
And our problem has something to do with these concepts. The issue here is not that extracting the benefit from healthcare data is difficult to achieve; it is almost impossible to achieve, due to the lack of Data Interoperability and the outrageous complexity of personal Data Lineage management considering the current level of data management adopted by most medical institutions and organizations.
Every organization in the healthcare field needs to be able to share patient data with other organizations but the divergent healthcare technologies and the specialization of hospitals and clinics intruded the way for achieving Data Interoperability. And because the information of each person’s medical history is scattered over many organizations (multi-sourced data), maintaining a well-organized, streamlined Data Lineage for every person was doomed to fail, which then makes doctors cannot easily capture the patient’s status precisely.
Company A, a medical data platform provider in the U.S., was a challenger aiming to tear up this old picture.
Beyond the Challenges: A Breakthrough from RDB+GDB based solution
Company A planned to deploy a healthcare data platform of outstanding scalability and data interoperability that can collect, organize patient data distributed over multiple organizations’ systems. To do this, Company A found Graph Database for improving the existing platform.
AgensGraph & Apache AGE (incubating) are Multi-model database(Relational database + Graph database, RDGB) that seamlessly integrates graph and relational data model and support use of both models simultaneously
Company A’s new healthcare data platform was designed to change the game. It integrates the massive amount of data accumulated in the systems of healthcare organizations and extracts data from multiple sources to reach an optimum level of data interoperability. They adapted the graph model to handle the complexity of patient data lineage and became able to access and process data with a view based on the lifecycle of patient healthcare.
Company A was enabled to collect healthcare data that comply with the data standard for interoperability from many sources like hospitals, clinics, labs, and insurance companies. And they adopted an intuitive visualization of those to offer precise analyses.
Now, Company A offers a unique solution that leads the healthcare domain with the best interoperability ever existed in this industry, powered by RGDB.
[About Apache AGE}
Apache AGE is a PostgreSQL extension that provides graph database functionality. AGE is an acronym for A Graph Extension, and is inspired by Bitnine’s fork of PostgreSQL 10, AgensGraph, which is a RGDB. The goal of the project is to create single storage that can handle both relational and graph model data so that users can use standard ANSI SQL along with openCypher, the Graph query language.
