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. 2024 Feb 8;24(4):1101.
doi: 10.3390/s24041101.

Multilingual Framework for Risk Assessment and Symptom Tracking (MRAST)

Valentino Šafran  1 , Simon Lin  2   3 , Jama Nateqi  2   3 , Alistair G Martin  2 , Urška Smrke  1 , Umut Ariöz  1 , Nejc Plohl  4 , Matej Rojc  1 , Dina Bēma  5 , Marcela Chávez  6 , Matej Horvat  7 , Izidor Mlakar  1
Affiliations

Multilingual Framework for Risk Assessment and Symptom Tracking (MRAST)

Valentino Šafran et al. Sensors (Basel). .

Abstract

The importance and value of real-world data in healthcare cannot be overstated because it offers a valuable source of insights into patient experiences. Traditional patient-reported experience and outcomes measures (PREMs/PROMs) often fall short in addressing the complexities of these experiences due to subjectivity and their inability to precisely target the questions asked. In contrast, diary recordings offer a promising solution. They can provide a comprehensive picture of psychological well-being, encompassing both psychological and physiological symptoms. This study explores how using advanced digital technologies, i.e., automatic speech recognition and natural language processing, can efficiently capture patient insights in oncology settings. We introduce the MRAST framework, a simplified way to collect, structure, and understand patient data using questionnaires and diary recordings. The framework was validated in a prospective study with 81 colorectal and 85 breast cancer survivors, of whom 37 were male and 129 were female. Overall, the patients evaluated the solution as well made; they found it easy to use and integrate into their daily routine. The majority (75.3%) of the cancer survivors participating in the study were willing to engage in health monitoring activities using digital wearable devices daily for an extended period. Throughout the study, there was a noticeable increase in the number of participants who perceived the system as having excellent usability. Despite some negative feedback, 44.44% of patients still rated the app's usability as above satisfactory (i.e., 7.9 on 1-10 scale) and the experience with diary recording as above satisfactory (i.e., 7.0 on 1-10 scale). Overall, these findings also underscore the significance of user testing and continuous improvement in enhancing the usability and user acceptance of solutions like the MRAST framework. Overall, the automated extraction of information from diaries represents a pivotal step toward a more patient-centered approach, where healthcare decisions are based on real-world experiences and tailored to individual needs. The potential usefulness of such data is enormous, as it enables better measurement of everyday experiences and opens new avenues for patient-centered care.

Keywords: chronic diseases; multilingual framework; patient-centered care; real-world data; risk assessment; symptom tracking.

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Conflict of interest statement

The authors declare no conflict of interest. The funders or Symptoma GmbH had no role in the design of the study; in the collection, analysis, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

Figures

Figure 1
Figure 1
The architecture of the MRAST framework.
Figure 2
Figure 2
Overall architecture of the MRAST framework.
Figure 3
Figure 3
ASR SPREAD: an end-to-end architecture.
Figure 4
Figure 4
From diary recording to updated patient profile.
Figure 5
Figure 5
MRAST framework basic flow.
Figure 6
Figure 6
DCD communication flow of real-world implementation.
Figure 7
Figure 7
Refined FHIR composition resource including the extracted symptoms.
Figure 8
Figure 8
Total response time of request batches between the UM REST API and SYM symptom extractor.
Figure 9
Figure 9
Response time for single request between UM REST API and SYM symptom extractor.
Figure 10
Figure 10
RAM usage per request batch on UM REST API side.
Figure 11
Figure 11
CPU usage per request batch on UM REST API side.
Figure 12
Figure 12
Network traffic per request batch on UM REST API side.
See this image and copyright information in PMC

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