## 4 Conclusions
The TRISS scores form the basis for calculation of prediction of survival scores, and for predicting mortality and estimating mortality rate. Once the early warning and trauma scores were calculated, the EHR with the public health care service provider could be updated using the ICD and SNOMED/LOINC classification coding system using HL7 standards. Fast Health Interoperability Resources FHIR is a HL7 standard that provides interoperability specifications for web services and EHR databases, and by modelling the trauma scores into FHIR 'Observations' and 'Bundles' of information payload the trauma-specific information could be logged into EHR databases for future decision support, and to prepare for emergency procedures ahead of time [33]. It was observed that since the analysis was performed on datasets of patients that were admitted to the ICU wards, the NEWS score, the RTS scores, and the prediction of survival scores agreed with their health status. The NEWS and the RTS severity scores of less than 4 indicated the patients were under severe trauma, and this helped to accept the hypothesis. The MIMIC II Numerics database is a widely accepted database, and the trauma scoring hypothesis could be effectively tested with the datasets in the database as it contained all the vital signs required for calculating trauma scores. A very important application for any wearable IoT healthcare monitoring kit is to be able to locate the individual when the trauma related events take place. With the availability of low cost wearable GPS/GNSS, or the GSM/GPRS receivers, the location awareness could be embedded in the kit itself. The GPS receiver could provide location-specific information, and the composite sensor could provide physiological information and trauma scores. This composite payload could be transmitted to the healthcare service provider, which could enable them to get ready for emergency. With the preloaded GIS information, related to the rail and road routes and the traffic conditions, the shortest path/route between current location and the nearest healthcare centre could be calculated using QGIS network analysis tools [37]. By putting the data acquisition, signal processing tools and techniques along with the trauma scoring and standard clinical coding practices together and by embedding location awareness into the composite healthcare monitoring kit, it could be concluded that a real-time incident response system for trauma related events could be implemented. Such a system would prepare the critical care team in healthcare units to prepare for emergencies well ahead of time, and can reduce the mortality rates for severe injuries and trauma. This research was funded by the \"Knowledge Transfer Partnership, Innovate UK\", grant number \"KTP9562\" Year 2016, Innovate UK (formerly Technology Strategy Board) RCUK. [[https://info.ktponline.org.uk/action/details/partnership.aspx?id=9562](https://info.ktponline.org.uk/action/details/partnership.aspx?id=9562)]([https://info.ktponline.org.uk/action/details/partnership.aspx?id=9562](https://info.ktponline.org.uk/action/details/partnership.aspx?id=9562)). We thank PhysioBank and PhysioNet for providing access to MIMIC II datasets after passing the \"Protecting Rights for Human Subjects\" certification. The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.