Accepted for/Published in: JMIR Mental Health
Date Submitted: Dec 21, 2020
Date Accepted: Apr 2, 2021
Design and Implementation of an Informatics Infrastructure for Standardized Data Acquisition, Transfer, Storage and Export in Psychiatric Clinical Routine: Feasibility Study
ABSTRACT
Background:
Empirically driven personalized diagnostic and treatment is widely perceived as a major hallmark in psychiatry. However, databased personalized decision making requires standardized data acquisition and data access, which is currently absent in psychiatric clinical routine.
Objective:
Here we describe the informatics infrastructure implemented at the psychiatric university hospital Münster allowing for standardized acquisition, transfer, storage and export of clinical data for future real-time predictive modelling in psychiatric routine.
Methods:
We designed and implemented a technical architecture that includes an extension of the EHR via scalable standardized data collection, data transfer between EHR and research databases thus allowing to pool EHR and research data in a unified database and technical solutions for the visual presentation of collected data and analyses results in the EHR. The Single-source Metadata ARchitecture Transformation (SMA:T) was used as the software architecture. SMA:T is an extension of the EHR system and uses Module Driven Software Development to generate standardized applications and interfaces. The Operational Data Model (ODM) was used as the standard. Standardized data was entered on iPads via the Mobile Patient Survey (MoPat) and the web application Mopat@home, the standardized transmission, processing, display and export of data was realized via SMA:T.
Results:
The technical feasibility was demonstrated in the course of the study. 19 standardized documentation forms with 241 items were created. In 317 patients, 6,451 instances were automatically transferred to the EHR system without errors. 96,323 instances were automatically transferred from the EHR system to the research database for further analyses.
Conclusions:
With the present study, we present the successful implementation of the informatics infrastructure enabling standardized data acquisition, and data access for future real-time predictive modelling in clinical routine in psychiatry. The technical solution presented here might guide similar initiatives at other sites and thus help to pave the way towards future application of predictive models in psychiatric clinical routine.
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