Mobile Emergency, an Emergency Support System for Hospitals in Mobile Devices: Pilot Study

BACKGROUND: Hospitals are vulnerable to natural disasters, man-made disasters, and mass causalities events. Within a short time, hospitals must provide care to large numbers of casualties in any damaged infrastructure, despite great personnel risk, inadequate communications, and limited resources. Communications are one of the most common challenges and drawbacks during in-hospital emergencies. Emergency difficulties in communicating with personnel and other agencies are mentioned in literature. At the moment of emergency inception and in the earliest emergency phases, the data regarding the true nature of the incidents are often inaccurate. The real needs and conditions are not yet clear, hospital personnel are neither efficiently coordinated nor informed on the real available resources. Information and communication technology solutions in health care turned out to have a great positive impact both on daily working practice and situations. OBJECTIVE: The objective of this paper was to find a solution that addresses the aspects of communicating among medical personnel, formalizing the modalities and protocols and the information to guide the medical personnel during emergency conditions with a support of a Central Station (command center) to cope with emergency management and best practice network to produce and distribute intelligent content made available in the mobile devices of the medical personnel. The aim was to reduce the time needed to react and to cope with emergency organization, while facilitating communications. METHODS: The solution has been realized by formalizing the scenarios, extracting, and identifying the requirements by using formal methods based on unified modeling language (UML). The system and was developed using mobile programming under iOS Apple and PHP: Hypertext Preprocessor My Structured Query Language (PHP MySQL). Formal questionnaires and time sheets were used for testing and validation, and a control group was used in order to estimate the reduction of time needed to cope with emergency cases. First, we have tested the usability and the functionalities of the solution proposed, then a real trial was performed to assess the reduction in communication time and the efficiency of the solution with respect to a case without Mobile Emergency tools. RESULTS: The solution was based on the development of a mobile emergency application and corresponding server device to cope with emergencies and facilitate all the related activities and communications, such as marking the position, contacting people, and recovering the exits information. The solution has been successfully tested within the Careggi Hospital, the largest medical infrastructure in Florence and Tuscany area in Italy, thus demonstrating the validity of the identified modalities, procedures, and the reduction in the time needed to cope with the emergency conditions. The trial was not registered as the test was conducted in realistic but simulated emergency conditions. CONCLUSIONS: By analyzing the requirements for developing a mobile app, and specifically the functionalities, codes, and design of the Mobile Emergency app, we have revealed the real advantages of using mobile emergency solutions compared to other more traditional solutions to effectively handle emergency situations in hospital settings

Artificial Intelligence Agents and Knowledge Acquisition in Health Information System

This research work highlights the need for AI-powered applications and their usages for theoptimization of information flow processes in the medical sector, from the perspective of howAI-agents can impact human-machine interaction (HCI) for acquiring relevant and necessaryinformation in emergency department (ED). This study investigates how AI-agents can be applied to manage situations of patient related unexpected experiences, such as long waiting times,overcrowding issues, and high number of patients leaving without being diagnosed. For knowledge acquisition, we incorporated modelling workshop techniques for gathering domain information from the domain experts in the context of emergency department in Karolinska Hospi-tal, Solna, Stockholm, Sweden, and for designing the AI-agent utilizing NLP techniques. We dis-cuss how the proposed solution can be used as an assistant to healthcare practitioners and workers to improve medical assistance in various medical procedures to increase flow and to reduce workloads and anxiety levels. The implementation part of this work is based on the natural language processing (NLP) techniques that help to develop the intelligent behavior for information acquisition and itsretriev-al in a natural way to support patients/relatives’ communication with the healthcare organization efficiently and in a natural way

PICT-DPA: A Quality-Compliance Data Processing Architecture to Improve the Performance of Integrated Emergency Care Clinical Decision Support System

Emergency Care System (ECS) is a critical component of health care systems by providing acute resuscitation and life-saving care. As a time-sensitive care operation system, any delay and mistake in the decision-making of these EC functions can create additional risks of adverse events and clinical incidents. The Emergency Care Clinical Decision Support System (EC-CDSS) has proven to improve the quality of the aforementioned EC functions. However, the literature is scarce on how to implement and evaluate the EC-CDSS with regard to the improvement of PHOs, which is the ultimate goal of ECS. The reasons are twofold: 1) lack of clear connections between the implementation of EC-CDSS and PHOs because of unknown quality attributes; and 2) lack of clear identification of stakeholders and their decision processes. Both lead to the lack of a data processing architecture for an integrated EC-CDSS that can fulfill all quality attributes while satisfying all stakeholders’ information needs with the goal of improving PHOs. This dissertation identified quality attributes (PICT: Performance of the decision support, Interoperability, Cost, and Timeliness) and stakeholders through a systematic literature review and designed a new data processing architecture of EC-CDSS, called PICT-DPA, through design science research. The PICT-DPA was evaluated by a prototype of integrated PICT-DPA EC-CDSS, called PICTEDS, and a semi-structured user interview. The evaluation results demonstrated that the PICT-DPA is able to improve the quality attributes of EC-CDSS while satisfying stakeholders’ information needs. This dissertation made theoretical contributions to the identification of quality attributes (with related metrics) and stakeholders of EC-CDSS and the PICT Quality Attribute model that explains how EC-CDSSs may improve PHOs through the relationships between each quality attribute and PHOs. This dissertation also made practical contributions on how quality attributes with metrics and variable stakeholders could be able to guide the design, implementation, and evaluation of any EC-CDSS and how the data processing architecture is general enough to guide the design of other decision support systems with requirements of the similar quality attributes

Acceptability of artificial intelligence (AI)-enabled chatbots, video consultations and live webchats as online platforms for sexual health advice

Objectives Sexual and reproductive health (SRH) services are undergoing a digital transformation. This study explored the acceptability of three digital services, (i) video consultations via Skype, (ii) live webchats with a health advisor and (iii) artificial intelligence (AI)-enabled chatbots, as potential platforms for SRH advice. Methods A pencil-and-paper 33-item survey was distributed in three clinics in Hampshire, UK for patients attending SRH services. Logistic regressions were performed to identify the correlates of acceptability. Results In total, 257 patients (57% women, 50% aged <25 years) completed the survey. As the first point of contact, 70% preferred face-to-face consultations, 17% telephone consultation, 10% webchats and 3% video consultations. Most would be willing to use video consultations (58%) and webchat facilities (73%) for ongoing care, but only 40% found AI chatbots acceptable. Younger age (<25 years) (OR 2.43, 95% CI 1.35 to 4.38), White ethnicity (OR 2.87, 95% CI 1.30 to 6.34), past sexually transmitted infection (STI) diagnosis (OR 2.05, 95% CI 1.07 to 3.95), self-reported STI symptoms (OR 0.58, 95% CI 0.34 to 0.97), smartphone ownership (OR 16.0, 95% CI 3.64 to 70.5) and the preference for a SRH smartphone application (OR 1.95, 95% CI 1.13 to 3.35) were associated with video consultations, webchats or chatbots acceptability. Conclusions Although video consultations and webchat services appear acceptable, there is currently little support for SRH chatbots. The findings demonstrate a preference for human interaction in SRH services. Policymakers and intervention developers need to ensure that digital transformation is not only cost-effective but also acceptable to users, easily accessible and equitable to all populations using SRH services

Designing for Nurse-AI Collaboration in Triage

The Local Emergency Medical Communication Centers (LEMCs) play a crucial role in the Norwegian healthcare system by receiving calls for immediate medical assistance. Registered nurses operate the phone calls, and their task is to assess the situation and triage the caller into appropriate triage levels indicating when and how help should be provided. Telephone triage poses challenges due to the limitations of audio communication, time sensitivity, and complex decision-making. Additionally, nurses often face the burden of managing clinical tools across multiple interfaces. This thesis explored how to design a system to support nurses in telephone triage and how we can facilitate nurse-AI collaboration in the process. A Research through Design (RtD) methodology was employed, and an iterative design approach was utilized. The research investigated the design aspects of AI-based suggestions and the use of natural language when creating semi-structured documentation. Four prototype iterations were developed throughout the study, and researchers from RE-AIMED and telephone operators conducted evaluations of the prototypes. Designing a tool for telephone triage requires understanding the user's needs and workflow. It is, therefore, crucial to involve telephone operators in the design process. The prototype demonstrated how we could design for incorporating AI in the triage process, and this thesis explores the various considerations when designing for nurse-AI collaboration. One notable finding was the importance of enabling documentation in natural language, as relying solely on structured documentation may fail to capture the caller's specific situation. Additionally, it is important to design a system that facilitates documentation of patient-initiated information and questions initiated by the nurses or the system.Masteroppgave i informasjonsvitenskapINFO390MASV-INF