DOI: 10.5281/zenodo.18234539
title: Wirtualne narzędzia dedykowane medycynie – wyniki prac B+R (RPMA.01.02.00-14-d767/20) Mazowiecka Jednostka Wdrażania Projektów Unijnych

description: 1. Research objectives and scope

The objective of the project was to develop a technology that reduces operational costs and medical staff workload in remote patient monitoring (RPM) through the use of an intelligent virtual assistant — Smart Signal Bot (SSB) — which automatically handles a significant share of communication and incident management generated by telemedical devices.

The system was primarily designed for:





patients with chronic diseases (ICD-10: I10, E11, J44, I50),




elderly patients,




patients enrolled in telemonitoring procedures (IoMT – Internet of Medical Things).





2. Clinical problem scale

Analysis of telemonitoring system data showed that patients generate four main categories of events:








Event type
Share of total alerts




Missing measurement
Very high


Threshold exceedance
High


Symptom reports
Medium


Technical problems
Very high






Previously, each of these events required intervention by Contact Center medical staff, resulting in continuous 24/7 operational costs.



3. Technological solution

The project developed Smart Signal Bot (SSB), a system based on:





Natural Language Processing (NLP) algorithms,




deep neural networks,




integration with IoMT devices,




HL7 FHIR interoperability standard.



SSB automatically handles four core categories of events:








Category
Automated function




Missing measurement
reminders, dialogue, escalation


Technical issues
self-repair, guidance, escalation


Health symptoms
medical interview, AI report, transfer to Contact Center


Parameter alarms
trend analysis, alerts, dashboards








4. Algorithmic research results

4.1 Tested architectures

Three classes of neural networks were evaluated:





RNN (LSTM, GRU),




CNN (convolutional networks),




Transformer (fine-tuned GPT-2).










Model
Response accuracy
Stability




LSTM Seq2Seq
Low
Low


CNN
Medium
High


GPT-2 Transformer
Highest
Highest






The fine-tuned GPT-2 model (1.5 billion parameters) proved to be the best conversational model for medical dialogue.



5. Training data

The model was trained on:





more than 10 MB of medical dialogue data,




patient–system Q&A datasets,




symptom datasets,




technical IoMT data.



Training setup:





20 epochs,




Transformer architecture with attention mechanism,




transfer learning fine-tuning.





6. System performance








Metric
Result




Prediction accuracy for patient condition escalation
89%


Reduction of medical staff interventions
35%


Alarm response time
Reduced by more than 50%


Fully automated event handling
Dominant








7. Data visualization

The system generates:





therapy trend charts,




vital parameter dashboards,




warning and alarm signals,




AI-generated reports for medical staff.



Each alert contains:[parameter type] – [time] – [value] – [threshold]



8. Clinical relevance

Thanks to SSB:





patients receive 24/7 contact,




medical staff focus only on critical events,




telecare costs are significantly reduced,




scalable senior care becomes possible.



The system is applicable in:





cardiology,




diabetology,




pulmonology,




senior care,




community psychiatry.





9. Legal status of the technology

The developed algorithms are protected by a patent application:P.442425 (WIPO ST.10/C PL442425)Title: Method for optimizing the cost of patient alert handling…Filing date: 30 September 2022