Mobile Emergency, an Emergency Support System for Hospitals in Mobile Devices: Pilot Study
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".