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      Experimental Implementation of NSER Mobile App for Efficient Real-Time Sharing of Prehospital Patient Information With Emergency Departments: Interrupted Time-Series Analysis

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          Abstract

          Background

          With the aging society, the number of emergency transportations has been growing. Although it is important that a patient be immediately transported to an appropriate hospital for proper management, accurate diagnosis in the prehospital setting is challenging. However, at present, patient information is mainly communicated by telephone, which has a potential risk of communication errors such as mishearing. Sharing correct and detailed prehospital information with emergency departments (EDs) should facilitate optimal patient care and resource use. Therefore, the implementation of an app that provides on-site, real-time information to emergency physicians could be useful for early preparation, intervention, and effective use of medical and human resources.

          Objective

          In this paper, we aimed to examine whether the implementation of a mobile app for emergency medical service (EMS) would improve patient outcomes and reduce transportation time as well as communication time by phone (ie, phone-communication time).

          Methods

          We performed an interrupted time-series analysis (ITSA) on the data from a tertiary care hospital in Japan from July 2021 to October 2021 (8 weeks before and 8 weeks after the implementation period). We included all patients transported by EMS. Using the mobile app, EMS can send information on patient demographics, vital signs, medications, and photos of the scene to the ED. The outcome measure was inpatient mortality and transportation time, as well as phone-communication time, which was the time for EMS to negotiate with ED staffs for transport requests.

          Results

          During the study period, 1966 emergency transportations were made (n=1033, 53% patients during the preimplementation period and n=933, 47% patients after the implementation period). The ITSA did not reveal a significant decrease in patient mortality and transportation time before and after the implementation. However, the ITSA revealed a significant decrease in mean phone-communication time between pre- and postimplementation periods (from 216 to 171 seconds; −45 seconds; 95% CI −71 to −18 seconds). From the pre- to postimplementation period, the mean transportation time from EMS request to ED arrival decreased by 0.29 minutes (from 36.1 minutes to 35.9 minutes; 95% CI −2.20 to 1.60 minutes), without change in time trends. We also introduced cases where the app allowed EMS to share accurate and detailed prehospital information with the emergency department, resulting in timely intervention and reducing the burden on the ED.

          Conclusions

          The implementation of a mobile app for EMS was associated with reduced phone-communication time by 45 seconds (22%) without increasing mortality or overall transportation time despite the implementation of new methods in the real clinical setting. In addition, real-time patient information sharing, such as the transfer of monitor images and photos of the accident site, could facilitate optimal patient care and resource use.

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          Most cited references24

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          Interrupted time series regression for the evaluation of public health interventions: a tutorial

          Abstract Interrupted time series (ITS) analysis is a valuable study design for evaluating the effectiveness of population-level health interventions that have been implemented at a clearly defined point in time. It is increasingly being used to evaluate the effectiveness of interventions ranging from clinical therapy to national public health legislation. Whereas the design shares many properties of regression-based approaches in other epidemiological studies, there are a range of unique features of time series data that require additional methodological considerations. In this tutorial we use a worked example to demonstrate a robust approach to ITS analysis using segmented regression. We begin by describing the design and considering when ITS is an appropriate design choice. We then discuss the essential, yet often omitted, step of proposing the impact model a priori. Subsequently, we demonstrate the approach to statistical analysis including the main segmented regression model. Finally we describe the main methodological issues associated with ITS analysis: over-dispersion of time series data, autocorrelation, adjusting for seasonal trends and controlling for time-varying confounders, and we also outline some of the more complex design adaptations that can be used to strengthen the basic ITS design.
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            The use of controls in interrupted time series studies of public health interventions

            Interrupted time series analysis differs from most other intervention study designs in that it involves a before-after comparison within a single population, rather than a comparison with a control group. This has the advantage that selection bias and confounding due to between-group differences are limited. However, the basic interrupted time series design cannot exclude confounding due to co-interventions or other events occurring around the time of the intervention. One approach to minimizse potential confounding from such simultaneous events is to add a control series so that there is both a before-after comparison and an intervention-control group comparison. A range of different types of controls can be used with interrupted time series designs, each of which has associated strengths and limitations. Researchers undertaking controlled interrupted time series studies should carefully consider a priori what confounding events may exist and whether different controls can exclude these or if they could introduce new sources of bias to the study. A prudent approach to the design, analysis and interpretation of controlled interrupted time series studies is required to ensure that valid information on the effectiveness of health interventions can be ascertained.
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              Overcrowding in emergency department: an international issue.

              Overcrowding in the emergency department (ED) has become an increasingly significant worldwide public health problem in the last decade. It is a consequence of simultaneous increasing demand for health care and a deficit in available hospital beds and ED beds, as for example it occurs in mass casualty incidents, but also in other conditions causing a shortage of hospital beds. In Italy in the last 12-15 years, there has been a huge increase in the activity of the ED, and several possible interventions, with specific organizational procedures, have been proposed. In 2004 in the United Kingdom, the rule that 98 % of ED patients should be seen and then admitted or discharged within 4 h of presentation to the ED ('4 h rule') was introduced, and it has been shown to be very effective in decreasing ED crowding, and has led to the development of further acute care clinical indicators. This manuscript represents a synopsis of the lectures on overcrowding problems in the ED of the Third Italian GREAT Network Congress, held in Rome, 15-19 October 2012, and hopefully, they may provide valuable contributions in the understanding of ED crowding solutions.
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                Author and article information

                Contributors
                Journal
                JMIR Form Res
                JMIR Form Res
                JFR
                JMIR Formative Research
                JMIR Publications (Toronto, Canada )
                2561-326X
                July 2022
                6 July 2022
                : 6
                : 7
                : e37301
                Affiliations
                [1 ] Department of Emergency Medicine Shonan Kamakura General Hospital Kamakura-shi, Kanagawa Japan
                [2 ] TXP Research TXP Medical Co Ltd Tokyo Japan
                Author notes
                Corresponding Author: Kiyomitsu Fukaguchi fukaskgh@ 123456gmail.com
                Author information
                https://orcid.org/0000-0003-2262-1898
                https://orcid.org/0000-0002-5880-2968
                https://orcid.org/0000-0001-9312-5352
                https://orcid.org/0000-0002-4224-8130
                Article
                v6i7e37301
                10.2196/37301
                9301553
                35793142
                004d0d08-b937-4ef2-87da-db7b3cb439ae
                ©Kiyomitsu Fukaguchi, Tadahiro Goto, Tadatsugu Yamamoto, Hiroshi Yamagami. Originally published in JMIR Formative Research (https://formative.jmir.org), 06.07.2022.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Formative Research, is properly cited. The complete bibliographic information, a link to the original publication on https://formative.jmir.org, as well as this copyright and license information must be included.

                History
                : 15 February 2022
                : 19 April 2022
                : 19 May 2022
                : 20 June 2022
                Categories
                Original Paper
                Original Paper

                emergency department,emergency medical services,mobile apps,interrupted time series analysis,emergency,patient record,implementation,patient care,app,implement,medical informatics,clinical informatics,decision support,electronic health record,ehealth,digital health

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