Both MongoDB and CouchDB are regarded in similar fashion—as they are document databases—and have been used widely in healthcare applications. The similarity to relational database systems usually allows for an easier learning curve and integration with in-place systems. They have been tested against XML and relational databases (e.g., Freire et al., 2016) and used in conjunction with them (e.g., Groce, 2015).

With respect to electronic health record (EHR) management, Freire et al. (2016) tested CouchDB performance with millions of EHRs including both administrative and epidemiological data points. It was noted that CouchBase is specifically designed for distributed computing and is a strength in this case. A number of datasets were set up for benchmarking and specific queries were written in each database language to answer health-specific questions. Response times varied widely, but the XML-based solutions consistently underperformed both MySQL and CouchBase. Against MySQL, CouchBase delivered faster response times. Despite space and indexing time requirements, CouchBase emerged as the top performer in the test.

MongoDB may be used to supplement and scale up SQL-based deployments, as outlined by Groce (2015). In this case, MongoDB was used to cut down on latency and performance overhead in Doctoralia, a company that connects patients with medical providers. Prior to the deployment, a single SQL server in one geographic location was utilized to handle all the load. As the organizational needs expanded to different countries and data volume increased, it became clear that a scaled approach was needed.

MongoDB allowed Doctoralia to deploy servers to each geographic location (reducing geographic latency) and frontload queries and aggregates to these servers (reducing processing latency). This precompute process also took much of the load off the central SQL server. The distributed framework allows Doctoralia to scale hardware needs up or down as demand requires, and replication allows for high availability with little to no downtime or lack of response seen by end users. Deploying a new server to handle new load is done in a matter of minutes. Doctoralia measures the MongoDB deployment in terms of speed and availability, and has considered it a great success.

References

CouchBase (2017). NoSQL for healthcare. Retrieved from https://www.couchbase.com/solutions/nosql-for-healthcare

Freire, S. M., Teodoro, D., Wei-Kleiner, F., Sundvall, E., Karlsson, D., & Lambrix, P. (2016). Comparing the performance of NoSQL approaches for managing archetype-basedelectronic health record data. PLoS ONE, 11(3).

Groce, D. (2015). How MongoDB helped a healthcare firm scale horizontally. Retrieved from https://dzone.com/articles/leaf-in-the-wild-doctoralia-scales-patient-service

MongoDB. (2019). Healthcare. Retrieved from https://www.mongodb.com/industries/healthcare