The latest medical research on Paramedicine

The aim of this study was to summarize the literature on the applications of machine learning (ML) and their performance in Emergency Medical Services (EMS).

Four relevant electronic databases were searched (from inception through January 2024) for all original studies that employed EMS-guided ML algorithms to enhance the clinical and operational performance of EMS. Two reviewers screened the retrieved studies and extracted relevant data from the included studies. The characteristics of included studies, employed ML algorithms, and their performance were quantitively described across primary domains and subdomains.

This review included a total of 164 studies published from 2005 through 2024. Of those, 125 were clinical domain focused and 39 were operational. The characteristics of ML algorithms such as sample size, number and type of input features, and performance varied between and within domains and subdomains of applications. Clinical applications of ML algorithms involved triage or diagnosis classification (n = 62), treatment prediction (n = 12), or clinical outcome prediction (n = 50), mainly for out-of-hospital cardiac arrest/OHCA (n = 62), cardiovascular diseases/CVDs (n = 19), and trauma (n = 24). The performance of these ML algorithms varied, with a median area under the receiver operating characteristic curve (AUC) of 85.6%, accuracy of 88.1%, sensitivity of 86.05%, and specificity of 86.5%. Within the operational studies, the operational task of most ML algorithms was ambulance allocation (n = 21), followed by ambulance detection (n = 5), ambulance deployment (n = 5), route optimization (n = 5), and quality assurance (n = 3). The performance of all operational ML algorithms varied and had a median AUC of 96.1%, accuracy of 90.0%, sensitivity of 94.4%, and specificity of 87.7%. Generally, neural network and ensemble algorithms, to some degree, out-performed other ML algorithms.

Triaging and managing different prehospital medical conditions and augmenting ambulance performance can be improved by ML algorithms. Future reports should focus on a specific clinical condition or operational task to improve the precision of the performance metrics of ML models.

Handheld ultrasound (US) devices have become increasingly popular since the early 2000s due to their portability and affordability compared to conventional devices. The Rapid Ultrasonography for Shock and Hypotension (RUSH) protocol, introduced in 2009, has shown promising accuracy rates when performed with handheld devices. However, there are limited data on the accuracy of such examinations performed in a moving ambulance. This study aimed to assess the feasibility and accuracy of the RUSH protocol performed by paramedics using handheld US devices in a moving ambulance.

The study aimed to examine the performability of the RUSH protocol with handheld US devices in a moving ambulance and to evaluate the accuracy of diagnostic views obtained within an appropriate time frame.

A prospective study was conducted with paramedics who underwent theoretical and practical training in the RUSH protocol. The participants performed the protocol using a handheld US device in both stationary and moving ambulances. Various cardiac and abdominal views were obtained and evaluated for accuracy. The duration of the protocol performance was recorded for each participant.

Nine paramedics completed the study, with 18 performances each in both stationary and moving ambulance groups. The accuracy of diagnostic views obtained during the RUSH protocol did not significantly differ between the stationary and moving groups. However, the duration of protocol performance was significantly shorter in the moving group compared to the stationary group.

Paramedics demonstrated the ability to perform the RUSH protocol effectively using handheld US devices in both stationary and moving ambulances following standard theoretical and practical training. The findings suggest that ambulance movement does not significantly affect the accuracy of diagnostic views obtained during the protocol. Further studies with larger sample sizes are warranted to validate these findings and explore the potential benefits of prehospital US in dynamic environments.

To quantify the burden of communicable diseases and characterize the most reported infections during public health emergency of floods in Pakistan.

The study's design is a descriptive trend analysis. The study utilized the disease data reported to District Health Information System (DHIS2) for the 12 most frequently reported priority diseases under the Integrated Disease Surveillance and Response (IDSR) system in Pakistan.

In total, there were 1,532,963 suspected cases during August to December 2022 in flood-affected districts (n = 75) across Pakistan; Sindh Province reported the highest number of cases (n = 692,673) from 23 districts, followed by Khyber Pakhtunkhwa (KP) (n = 568,682) from 17 districts, Balochistan (n = 167,215) from 32 districts, and Punjab (n = 104,393) from 3 districts. High positivity was reported for malaria (79,622/201,901; 39.4%), followed by acute diarrhea (non-cholera) (23/62; 37.1%), hepatitis A and E (47/252; 18.7%), and dengue (603/3245; 18.6%). The crude mortality rate was 11.9 per 10 000 population (1824/1,532,963 [deaths/cases]).

The study identified acute respiratory infection, acute diarrhea, malaria, and skin diseases as the most prevalent diseases. This suggests that preparedness efforts and interventions targeting these diseases should be prioritized in future flood response plans. The study highlights the importance of strengthening the IDSR as a Disease Early Warning System through the implementation of the DHIS2.

Māori (the Indigenous Peoples of Aotearoa New Zealand) are disproportionately represented in cardiovascular disease (CVD) prevalence, morbidity and mortality rates, and are less likely to receive evidence-based CVD health care. Rural Māori experience additional barriers to treatment access, poorer health outcomes and a greater burden of CVD risk factors compared to Non-Māori and Māori living in urban areas. Importantly, these inequities are similarly experienced by Indigenous Peoples in other nations impacted by colonisation. Given the scarcity of available literature, a systematic scoping review was conducted on literature exploring barriers and facilitators in accessing CVD health care for rural Māori and other Indigenous Peoples in nations impacted by colonisation.

The review was underpinned by Kaupapa Māori Research methodology and was conducted utilising Arksey and O'Malley's (2005) methodological framework. A database search of MEDLINE (OVID), PubMed, Embase, SCOPUS, CINAHL Plus, Australia/New Zealand Reference Centre and NZResearch.org was used to explore empirical research literature. A grey literature search was also conducted. Literature based in any healthcare setting providing care to adults for CVD was included. Rural or remote Indigenous Peoples from New Zealand, Australia, Canada, and the US were included. Literature was included if it addressed cardiovascular conditions and reported barriers and facilitators to healthcare access in any care setting.

A total of 363 articles were identified from the database search. An additional 19 reports were identified in the grey literature search. Following screening, 16 articles were included from the database search and 5 articles from the grey literature search. The literature was summarised using the Te Tiriti o Waitangi (Treaty of Waitangi) Framework principles: tino rangatiratanga (self-determination), partnership, active protection, equity and options. Themes elucidated from the literature were described as key drivers of CVD healthcare access for rural Indigenous Peoples. Key driver themes included input from rural Indigenous Peoples on healthcare service design and delivery, adequate resourcing and support of indigenous and rural healthcare services, addressing systemic racism and historical trauma, providing culturally appropriate health care, rural Indigenous Peoples' access to family and wellbeing support, rural Indigenous Peoples' differential access to the wider social determinants of health, effective interservice linkages and communication, and equity-driven and congruent data systems.

The findings are consistent with other literature exploring access to health care for rural Indigenous Peoples. This review offers a novel approach to summarising literature by situating the themes within the context of equity and rights for Indigenous Peoples. This review also highlighted the need for further research in this area to be conducted in the context of Aotearoa New Zealand.