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Purpose - Health is at the center of society concerns, being characterized by the dilemma of contributing to the population well-being, while demanding high financial investments at the same time. In this sense, information technology (IT) becomes essential for the progress of the sector, directly impacting on how care practices are performed. This study aims to analyze the adoption of mobile devices in the mobile emergency care service (MECS) of the state of Rio Grande do Sul, Brazil. Design/methodology/approach - The authors carried out a multi-method study with an initial qualitative exploration through a focal group, followed by a survey. Potential determinants and impacts of mobile device use on the work context of the MECS teams were identified. Following, we tested the proposed conceptual model applying a questionnaire to 350 professionals from a total of 160 bases throughout the State. Partial least squares structural equation modeling was used to test the hypotheses herein. Findings - The authors found that Satisfaction with the Use of Mobile PHC (PHC - Primary Health Care) is determined by the application compatibility with MECS work, followed by the performance expectancy with the use of the technology and the technical support provided to the users - acting as important facilitators of this process; while the technological complexity inherent in the use of the technology appears as the main barrier to the success of this technology. Besides, the authors found that both intensity of Use and Satisfaction with the Use of the technology provide different benefits to those involved (teams, patients and the organization). Research limitations/implications - As limitations of the study, the authors point out to the fact that the data are from a single Brazilian State, and therefore, its results cannot be generalized. Another limitation is that the study considered only the use of a specific mobile technology, which requires caution when using this information in contexts where the health information technology is different, besides the fact that the findings may not be compatible in environments where IT adoption is voluntary. Practical implications - The study can help managers of public and private organizations in the planning and implementation of different technologies, whether mobile or applied to the health context, as well as in the expansion of their use in their respective institutions. Social implications - The research contributes to other studies that realize that the adoption of IT can cause relevant changes to health being associated to productivity gains and improvement of the quality of service provided to society through different forms and solutions. Originality/value - The adoption and use of IT - such as mobile devices - impacts on how care practices are performed in the MECS, providing different benefits to those involved (teams, patients and the organization).

AbstractObjectiveTo determine whether a behavioral intervention delivered through a video game can improve the appropriateness of trauma triage decisions in the emergency department of non-trauma centers.DesignRandomized clinical trial.SettingOnline intervention in national sample of emergency medicine physicians who make triage decisions at US hospitals.Participants368 emergency medicine physicians primarily working at non-trauma centers. A random sample (n=200) of those with primary outcome data was reassessed at six months.InterventionsPhysicians were randomized in a 1:1 ratio to one hour of exposure to an adventure video game (Night Shift) or apps based on traditional didactic education (myATLS and Trauma Life Support MCQ Review), both on iPads. Night Shift was developed to recalibrate the process of using pattern recognition to recognize moderate-severe injuries (representativeness heuristics) through the use of stories to promote behavior change (narrative engagement). Physicians were randomized with a 2×2 factorial design to intervention (game v traditional education apps) and then to the experimental condition under which they completed the outcome assessment tool (low v high cognitive load). Blinding could not be maintained after allocation but group assignment was masked during the analysis phase.Main outcome measuresOutcomes of a virtual simulation that included 10 cases; in four of these the patients had severe injuries. Participants completed the simulation within four weeks of their intervention. Decisions to admit, discharge, or transfer were measured. The proportion of patients under-triaged (patients with severe injuries not transferred to a trauma center) was calculated then (primary outcome) and again six months later, with a different set of cases (primary outcome of follow-up study). The secondary outcome was effect of cognitive load on under-triage.Results149 (81%) physicians in the game arm and 148 (80%) in the traditional education arm completed the trial. Of these, 64/100 (64%) and 58/100 (58%), respectively, completed reassessment at six months. The mean age was 40 (SD 8.9), 283 (96%) were trained in emergency medicine, and 207 (70%) were ATLS (advanced trauma life support) certified. Physicians exposed to the game under-triaged fewer severely injured patients than those exposed to didactic education (316/596 (0.53) v 377/592 (0.64), estimated difference 0.11, 95% confidence interval 0.05 to 0.16; P<0.001). Cognitive load did not influence under-triage (161/308 (0.53) v 155/288 (0.54) in the game arm; 197/300 (0.66) v 180/292 (0.62) in the traditional educational apps arm; P=0.66). At six months, physicians exposed to the game remained less likely to under-triage patients (146/256 (0.57) v 172/232 (0.74), estimated difference 0.17, 0.09 to 0.25; P<0.001). No physician reported side effects. The sample might not reflect all emergency medicine physicians, and a small set of cases was used to assess performance.ConclusionsCompared with apps based on traditional didactic education, exposure of physicians to a theoretically grounded video game improved triage decision making in a validated virtual simulation. Though the observed effect was large, the wide confidence intervals include the possibility of a small benefit, and the real world efficacy of this intervention remains uncertain.Trial registrationclinicaltrials.gov; NCT02857348 (initial study)/NCT03138304 (follow-up).

Background Before patients are admitted into the emergency department, it is important to undertake a pre-hospital process, both in terms of treatment performance and a request for resources from an emergency unit. The existing system to triage patients in Thailand is not functioning to its full capacity in either the primary medical system or pre-hospital treatment with shortcomings in the areas of speed, features, and appropriate systems. There is a high possibility of issuing a false Initial Dispatch Code (IDC), which will cause the over or underutilisation of emergency resources, such as rescue teams, community hospitals and emergency medical volunteers. Methods A usability system design, together with a reliability test, was applied to develop an application to optimise the pre-hospital process, specifically to sort patients, using an IDC to improve the request for emergency resources. The triage mobile application was developed on both iOS and Android operating systems to support patient triage based on Criteria Based Dispatch (CBD). The 25 main symptom categories covered by CBD were used to design and develop the application, and 12 emergency medical staff, including doctors and nurses, were asked to test the system in the aspects of triage protocol correction, triage reliability, usability and user satisfaction. Results The results of testing the proposed triage application were compared with the time used to triage by experienced staff and it was found that, in non-trauma cases, it was faster and more effective to use the application for emergency operations and to correct the IDC code representation. Conclusions The triage application will be utilised to support the pre-hospital process and to classify patients’ conditions before they are admitted to the Emergency Department (ED). The application is suitable for users who are not medical emergency staff. Patients with non-trauma symptoms may be a suitable group to use the application in terms of time used to identify IDC for their own symptoms. The use of the application can be beneficial for those who wish to self-identify their symptoms before requesting medical services.

Symptom checkers are digital tools assisting laypersons in self-assessing the urgency and potential causes of their medical complaints. They are widely used but face concerns from both patients and health care professionals, especially regarding their accuracy. A 2015 landmark study substantiated these concerns using case vignettes to demonstrate that symptom checkers commonly err in their triage assessment. This study aims to revisit the landmark index study to investigate whether and how symptom checkers' capabilities have evolved since 2015 and how they currently compare with laypersons' stand-alone triage appraisal. In early 2020, we searched for smartphone and web-based applications providing triage advice. We evaluated these apps on the same 45 case vignettes as the index study. Using descriptive statistics, we compared our findings with those of the index study and with publicly available data on laypersons' triage capability. We retrieved 22 symptom checkers providing triage advice. The median triage accuracy in 2020 (55.8%, IQR 15.1%) was close to that in 2015 (59.1%, IQR 15.5%). The apps in 2020 were less risk averse (odds 1.11:1, the ratio of overtriage errors to undertriage errors) than those in 2015 (odds 2.82:1), missing >40% of emergencies. Few apps outperformed laypersons in either deciding whether emergency care was required or whether self-care was sufficient. No apps outperformed the laypersons on both decisions. Triage performance of symptom checkers has, on average, not improved over the course of 5 years. It decreased in 2 use cases (advice on when emergency care is required and when no health care is needed for the moment). However, triage capability varies widely within the sample of symptom checkers. Whether it is beneficial to seek advice from symptom checkers depends on the app chosen and on the specific question to be answered. Future research should develop resources (eg, case vignette repositories) to audit the capabilities of symptom checkers continuously and independently and provide guidance on when and to whom they should be recommended.

Only 2–5% of patients who have a stroke receive thrombolytic treatment, mainly because of delay in reaching the hospital. We aimed to assess the efficacy of a new approach of diagnosis and treatment starting at the emergency site, rather than after hospital arrival, in reducing delay in stroke therapy. We did a randomised single-centre controlled trial to compare the time from alarm (emergency call) to therapy decision between mobile stroke unit (MSU) and hospital intervention. For inclusion in our study patients needed to be aged 18–80 years and have one or more stroke symptoms that started within the previous 2·5 h. In accordance with our week-wise randomisation plan, patients received either prehospital stroke treatment in a specialised ambulance (equipped with a CT scanner, point-of-care laboratory, and telemedicine connection) or optimised conventional hospital-based stroke treatment (control group) with a 7 day follow-up. Allocation was not masked from patients and investigators. Our primary endpoint was time from alarm to therapy decision, which was analysed with the Mann-Whitney U test. Our secondary endpoints included times from alarm to end of CT and to end of laboratory analysis, number of patients receiving intravenous thrombolysis, time from alarm to intravenous thrombolysis, and neurological outcome. We also assessed safety endpoints. This study is registered with ClinicalTrials.gov, number NCT00153036. We stopped the trial after our planned interim analysis at 100 of 200 planned patients (53 in the prehospital stroke treatment group, 47 in the control group), because we had met our prespecified criteria for study termination. Prehospital stroke treatment reduced the median time from alarm to therapy decision substantially: 35 min (IQR 31–39) versus 76 min (63–94), p<0·0001; median difference 41 min (95% CI 36–48 min). We also detected similar gains regarding times from alarm to end of CT, and alarm to end of laboratory analysis, and to intravenous thrombolysis for eligible ischaemic stroke patients, although there was no substantial difference in number of patients who received intravenous thrombolysis or in neurological outcome. Safety endpoints seemed similar across the groups. For patients with suspected stroke, treatment by the MSU substantially reduced median time from alarm to therapy decision. The MSU strategy offers a potential solution to the medical problem of the arrival of most stroke patients at the hospital too late for treatment. Ministry of Health of the Saarland, Germany, the Werner-Jackstädt Foundation, the Else-Kröner-Fresenius Foundation, and the Rettungsstiftung Saar.

Background Elderly individuals with chronic conditions or acute illnesses are major drivers of hospitalization, with frail patients frequently utilizing emergency department (ED) services. To ease this burden, many countries offer home-based medical services. In Region Halland, Sweden, a mobile team intervention in municipal home care (MHC) was introduced to support frail elderly patients. This study aimed to assess whether the intervention reduced ED visits and hospitalizations among MHC recipients. Methods The study population consisted of all patients aged ≥ 65 years enrolled in MHC in Halmstad, Sweden, from October 2014 - April 2016. Healthcare utilization during the seven months prior to the initiation of the intervention (October 2014 - April 2015) constituted the pre-intervention group and were compared with healthcare consumption during a seasonally matched seven-month period after the launch of the intervention (October 2015-April 2016). The primary outcome was the number of adverse events, defined as unplanned ED visits or hospital admission. Negative binomial regression was used to assess the association between exposure and adverse events, presented as Incidence Rate Ratios (IRRs) with 95% confidence intervals (CIs). Results A total of 2163 patients were included in the pre-intervention group, and 2197 patients in the intervention group. Both groups had a mean age of 84 years, with no significant differences regarding sex. In the pre-intervention group, 64% had severe comorbidities, compared to 66% in the intervention group. Primary care home visits by physicians increased from an average of 0.9 in the pre-intervention group to 1.1 in the intervention group ( p  < 0.001). Risk for adverse events was elevated among patients with severe comorbidities (IRR = 3.14, 95% CI: 1.91–5.15, p  = < 0.001). There was a slight decrease in the incidence rate for the intervention group; however, this reduction was not statistically significant (IRR = 0.91, 95% CI: 0.82–1.01, p  = 0.09). Conclusion The mobile team intervention in MHC did not significantly reduce ED visits or hospitalizations among elderly MHC recipients, suggesting that physician-led interventions alone may be insufficient to lower acute care utilization in this population. This highlights the complexity of care needs among frail older adults and suggests that a more comprehensive, multidisciplinary approach may be required to achieve meaningful reductions in emergency care use. Keypoints • The intervention with a mobile team in municipality home care had limited impact on ED visits and hospitalization. • The mobile team intervention was associated with increased encounters with primary care physicians. • Patients with severe comorbidities exhibited the highest risk for adverse events.

Mobile Integrated Health Community Paramedicine (MIH-CP) programs are designed to increase access to care and reduce Emergency Department (ED) and Emergency Medical Services (EMS) usage. Previous MIH-CP systematic reviews reported varied interventions, effect sizes, and a high prevalence of biased methods. We aimed to perform a meta-analysis on MIH-CP effect on ED visits, and to evaluate study designs' effect on reported effect sizes. We hypothesized biased methods would produce larger reported effect sizes. We searched Pubmed, Embase, CINAHL, and Scopus databases for peer-reviewed MIH-CP literature from January 1, 2000, to July 24, 2021. We included all full-text English studies whose program met the National Associations of Emergency Medical Technicians definition, reported ED visits, and had an MIH-CP related intervention and outcome. We established risk ratios for each included study through interpreting the reported data. We performed a random-effects and cumulative meta-analysis of ED visit data, tests of heterogeneity, and a moderator analysis to assess for factors influencing the magnitude of observed effect. We identified 16 studies that reported ED visit data and included 12 in our meta-analysis. All studies were observational; 3 used matched controls, 6 pre-post controls, and 3 without controls. 7 studies' intervention were diversion/triage while 5 studies intervened with health education/home primary care services. Pooled risk ratio for our data set was 0.56 (95% confidence interval 0.42–0.74). Cumulative meta-analysis revealed that as of 2018 MIH-CP programs began to show consistent reductions in ED visits. Significant heterogeneity was seen among studies, with I-squared >90%. Moderator analysis showed reduced heterogeneity for matched-control studies. Our data revealed MIH-CP programs were associated with a reduced risk of ED visits. Study design did not have a statistically significant influence on effect size, though it did influence heterogeneity. We would recommend future studies continue to use high levels of control to produce reliable data with lower heterogeneity.

Efficient emergency patient transport systems, which are crucial for delivering timely medical care to individuals in critical situations, face certain challenges. To address this, CONNECT-AI (CONnected Network for EMS Comprehensive Technical-Support using Artificial Intelligence), a novel digital platform, was introduced. This artificial intelligence (AI)-based network provides comprehensive technical support for the real-time sharing of medical information at the prehospital stage. This study aimed to evaluate the effectiveness of this system in reducing patient transport delays. The CONNECT-AI system provided 3 key AI services to prehospital care providers by collecting real-time patient data from the scene and hospital resource information, such as bed occupancy and the availability of emergency surgeries or procedures, using 5G communication technology and internet of things devices. These services included guidance on first aid, prediction of critically ill patients, and recommendation of the optimal transfer hospital. In addition, the platform offered emergency department medical staff real-time clinical information, including live video of patients during transport to the hospital. This community-based, nonrandomized controlled intervention study was designed to evaluate the effectiveness of the CONNECT-AI system in 2 regions of South Korea, each of which operated an intervention and a control period, each lasting 16 weeks. The impact of the system was assessed based on the proportion of patients experiencing transfer delays. A total of 14,853 patients transported by public ambulance were finally selected for analysis. Overall, the median transport time was 10 (IQR 7-14) minutes in the intervention group and 9 (IQR 6-13) minutes in the control group. When comparing the incidence of transport time outliers (>75%), which was the primary outcome of this study, the rate was higher in the intervention group in region 1, but significantly reduced in region 2, with the overall outlier rate being higher in the intervention group (27.5%-29.7%, P=.04). However, for patients with fever or respiratory symptoms, the group using the system showed a statistically significant reduction in outlier cases (36.5%-30.1%, P=.01). For patients who received real-time acceptance signals from the hospital, the reduction in the percentage of 75% outliers was statistically significant compared with those without the system (27.5%-19.6%, P=.02). As a result of emergency department treatment, 1.5% of patients in the control group and 1.1% in the intervention group died (P=.14). In the system-guided optimal hospital transfer group, the mortality rate was significantly lower than in the control group (1.54%-0.64%, P=.01). The present digital emergency medical system platform offers a novel approach to enhancing emergency patient transport by leveraging AI, real-time information sharing, and decision support. While the system demonstrated improvements for certain patient groups facing transfer challenges, further research and modifications are necessary to fully realize its benefits in diverse health care contexts. ClinicalTrials.gov NCT04829279; https://clinicaltrials.gov/study/NCT04829279.