By: Meirav Malkin

Here we present a short case-study describing how FUME: Outburn’s FHIR mapping engine – leverages the automation of the clinical study process, as seen in Rambam’s groundbreaking initiative “FHIR®-Based Data Sharing for Clinical Research”, co-sponsored by the Israel Innovation Authority and the Israeli Ministry of Health.

Enjoy!

Introduction

Rambam is the leading medical center in northern Israel. With over 1000 beds and close to 6000 employees, it serves over 2 million people per annum. It carries out thousands of medical studies on a huge variety of subjects, in cooperation with leading universities, such as Harvard, Johns Hopkins, Stanford, Weil Cornell and many more. As part of its mission statement, Rambam has been making a concerted effort to become fully digitized, in order to promote better healthcare outcomes, while bringing human error to a minimum. To this end, they have decided to utilize FHIR for data-sharing using the FHIR standard. The first immediate goal was to use FHIR’s abilities for clinical research.

The problems

Running thousands of research projects is no joke: there are literally thousands of possible failure points. No doubt, one of the major problems any research project is exposed to is how to manage data quickly and, more importantly, reliably. Some projects may utilize data from thousands of patients or more. The room for human-related data errors is huge, and if the data isn’t perfect, then the whole project and its results are suspect. The biggest problem stems from having to print out the entire medical history of each study participant and manual data entry – a leading cause for data incompleteness and errors and even not having the participants’ data in the system by the time the study begins. This process is resource and time intensive. To remedy this, they first partnered up with Israeli start-up - @YonaLink, enabling them to work with many resource sponsors through a single EDC interface.

The solution

They chose to opt for FHIR in order to standardize the data, such that terminologies would all be standard, and to ensure data completeness. YonaLink’s data-research platform receives input based on FHIR. This, in turn, led to another challenge for Rambam: having to map the data from the EMR to FHIR. This is a huge undertaking - both time and labor intensive, with a steep learning curve, and requires experts in the field.

In what Liat calls “basically a big shortcut”, Rambam utilized Outburn’s FUME mapping engine. FUME allows its users to easily map the data from or to any legacy system and effortlessly convert it to FHIR® resources, conformant to profiles. Once a map is created, it can be reused, adjusted, and shared with ease.

In addition, Outburn consulted Rambam with establishing an on-prem FHIR server, to handle all the research resources.

Results

Using FUME drastically cut down the FHIR learning curve, saving many man-hours and resources. This freed up the team to perform many other important tasks, lowered costs and greatly reduced the time required for the project.

Conclusion

This case study shows us that for any type of data requirement in the healthcare sector, especially if this requires not only transferring, but also transforming the data, FUME is a robust, scalable, and affordable solution. FUME proved to not only shorten the project’s time requirements but was also easy to learn and use.
Thus, for any healthcare organization looking to make a move into the world of FHIR, if for business reasons or looming regulation (ahem, Israel) – FUME has proven to be a fantastic solution. If time = money, FUME saves you money.

If you or your organization are planning on using FHIR in your data strategy, in a more strategic view, FUME helps organizations to become conformant to regulatory legislation, as well as to national Core profiles.