Objective: To make data generated in routine clinical practice in the treatment of patients with movement disorders and deep brain stimulation available for clinical decision-making and research.

Background: Routine clinical practice constantly produces valuable data. However, documentation is often subjectively recorded and neither machine-readable nor interoperable. In clinical routine, the motivation for data entry will often be subjective short-term benefits rather than long-term goals. This is even more critical with the use of complex programmable devices, e.g. deep brain stimulation (DBS). An automatic multiaxial integration of these data together with patient reported outcomes on quality of life would provide highly valuable resources. Currently, this is only possible individually or within the framework of clinical studies and involves considerable manual effort. Enabling the clinician to document logically structured data directly into a machine-readable format is the obvious barrier. High utilization is only achievable if data entry facilitates rather than complicates the work of the clinical user.

Method: We aimed to develop an open source software that provides a simple graphical interface for the clinical user, while the data is automatically stored in Fast Healthcare Interoperability Resources (FHIR). To avoid the need for double documentation, synchronization with the hospital information system was required.

Results: Our software allows clinicians to visually adjust the way inputs are structured, while unnoticeably adapting a FHIR resource. Data structure and relationships known only to the documenting expert are now reflected within the data.  For complicated systems such as DBS, we developed a customizable code system in FHIR that supports all current and future vendor specifications. This again allows for an adaptive visual interface while maintaining a machine-readable format. In fact, for Medtronic devices, a fully automated import is already possible. As the storage is FHIR-based, it is easy to integrate with patient reported outcomes, interoperable, and usable for scientific research.

Conclusion: Enabling automatic data capture from clinical routine using FHIR Resources is likely more effective in providing relevant data than manually extracting and reintegrating. This open source software could dramatically improve data quality and ultimately facilitate clinical decision-making and research.

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MDS Abstracts - https://www.mdsabstracts.org/abstract/closing-the-gap-how-to-record-valuable-data-for-movement-disorders-using-fhir/