Open online courses on basic life support: Availability and resuscitation guidelines compliance

Online platform for cardiopulmonary resuscitation and automated external defibrillator training in a rural area: a community clinical trial protocol

INTRODUCTION

Sudden death resulting from cardiorespiratory arrest carries a high mortality rate and frequently occurs out of hospital. Immediate initiation of cardiopulmonary resuscitation (CPR) by witnesses, combined with automated external defibrillator (AED) use, has proven to double survival rates. Recognising the challenges of timely emergency services in rural areas, the implementation of basic CPR training programmes can improve survival outcomes. This study aims to evaluate the effectiveness of online CPR-AED training among residents in a rural area of Tarragona, Spain.

METHODS

Quasi-experimental design, comprising two phases. Phase 1 involves assessing the effectiveness of online CPR-AED training in terms of knowledge acquisition. Phase 2 focuses on evaluating participant proficiency in CPR-AED simulation manoeuvres at 1 and 6 months post training. The main variables include the score difference between pre-training and post-training test (phase 1) and the outcomes of the simulated test (pass/fail; phase 2). Continuous variables will be compared using Student's t-test or Mann-Whitney U test, depending on normality. Pearson's χ2 test will be applied for categorical variables. A multivariate analysis will be conducted to identify independent factors influencing the main variable.

ETHICS AND DISSEMINATION

This study adheres to the tenets outlined in the Declaration of Helsinki and of Good Clinical Practice. It operated within the Smartwatch project, approved by the Clinical Research Ethics Committee of the Primary Care Research Institute IDIAP Jordi Gol i Gurina Foundation, code 23/081-P. Data confidentiality aligns with Spanish and European Commission laws for the protection of personal data. The study's findings will be published in peer-reviewed journals and presented at scientific meetings.

TRIAL REGISTRATION NUMBER

NCT05747495.

Large Language Model-based Chatbot as a Source of Advice on First Aid in Heart Attack

The ability of the cutting-edge large language model-powered chatbots to generate human-like answers to user questions hypothetically could be utilized for providing real-time advice on first aid for witnesses of cardiovascular emergencies. This study aimed to evaluate quality of the chatbot responses to inquiries on help in heart attack. The study simulated interrogation of the new Bing chatbot (Microsoft Corporation, USA) with the "heart attack what to do" prompt coming from 3 countries, the Gambia, India and the USA. The chatbot responses (20 per country) were evaluated for congruence with the International First Aid, Resuscitation, and Education Guidelines 2020 using a checklist. For all user inquiries, the chatbot provided answers containing some guidance on first aid. However, the responses commonly left out some potentially life-saving instructions, for instance to encourage the person to stop physical activity, to take antianginal medication, or to start cardiopulmonary resuscitation for unresponsive abnormally breathing person. Mean percentage of the responses having full congruence with the checklist criteria varied from 7.3 for India to 16.8 for the USA. A quarter of responses for the Gambia and the USA, and 45.0% for India contained superfluous guidelines-inconsistent directives. The chatbot advice on help in heart attack has omissions, inaccuracies and misleading instructions, and therefore the chatbot cannot be recommended as a credible source of information on first aid. Active research and organizational efforts are needed to mitigate the risk of uncontrolled misinformation and establish measures for guaranteeing trustworthiness of the chatbot-mediated counseling.

An expert consensus–based checklist for quality appraisal of educational resources on adult basic life support: a Delphi study

Objective

Given the lack of a unified tool for appraising the quality of educational resources for lay-rescuer delivery of adult basic life support (BLS), this study aimed to develop an appropriate evaluation checklist based on a consensus of international experts.

Methods

In a two-round Delphi study, participating experts completed questionnaires to rate each item of a predeveloped 72-item checklist indicating agreement that an item should be utilized to evaluate the conformance of an adult BLS educational resource with resuscitation guidelines. Consensus on item inclusion was defined as a rating of ≥7 points from ≥75% of experts. Experts were encouraged to add anonymous suggestions for modifying or adding new items.

Results

Of the 46 participants, 42 (91.3%) completed the first round (representatives of 25 countries with a median of 16 years of professional experience in resuscitation) and 40 (87.0%) completed the second round. Thirteen of 72 baseline items were excluded, 55 were included unchanged, four were included after modification, and four new items were added. The final checklist comprises 63 items under the subsections “safety” (one item), “recognition” (nine items), “call for help” (four items), “chest compressions” (12 items), “rescue breathing” (12 items), “defibrillation” (nine items), “continuation of CPR” (two items), “choking” (10 items) and “miscellaneous” (four items).

Conclusions

The produced checklist is a ready-to-use expert consensus–based tool for appraising the quality of educational content on lay-rescuer provision of adult BLS. The checklist gives content developers a tool to ensure educational resources comply with current resuscitation knowledge, and may serve as a component of a prospective standardized international framework for quality assurance in resuscitation education.

Large Language Model (LLM)-Powered Chatbots Fail to Generate Guideline-Consistent Content on Resuscitation and May Provide Potentially Harmful Advice

INTRODUCTION

Innovative large language model (LLM)-powered chatbots, which are extremely popular nowadays, represent potential sources of information on resuscitation for the general public. For instance, the chatbot-generated advice could be used for purposes of community resuscitation education or for just-in-time informational support of untrained lay rescuers in a real-life emergency.

STUDY OBJECTIVE

This study focused on assessing performance of two prominent LLM-based chatbots, particularly in terms of quality of the chatbot-generated advice on how to give help to a non-breathing victim.

METHODS

In May 2023, the new Bing (Microsoft Corporation, USA) and Bard (Google LLC, USA) chatbots were inquired (n = 20 each): "What to do if someone is not breathing?" Content of the chatbots' responses was evaluated for compliance with the 2021 Resuscitation Council United Kingdom guidelines using a pre-developed checklist.

RESULTS

Both chatbots provided context-dependent textual responses to the query. However, coverage of the guideline-consistent instructions on help to a non-breathing victim within the responses was poor: mean percentage of the responses completely satisfying the checklist criteria was 9.5% for Bing and 11.4% for Bard (P >.05). Essential elements of the bystander action, including early start and uninterrupted performance of chest compressions with adequate depth, rate, and chest recoil, as well as request for and use of an automated external defibrillator (AED), were missing as a rule. Moreover, 55.0% of Bard's responses contained plausible sounding, but nonsensical guidance, called artificial hallucinations, that create risk for inadequate care and harm to a victim.

CONCLUSION

The LLM-powered chatbots' advice on help to a non-breathing victim omits essential details of resuscitation technique and occasionally contains deceptive, potentially harmful directives. Further research and regulatory measures are required to mitigate risks related to the chatbot-generated misinformation of public on resuscitation.