Does an emergency access button increase the patients’ satisfaction and feeling of safety with the out-of-hours health services? A randomised controlled trial in Denmark

Ebert, J. F.; Huibers, Linda; Christensen, Bo; Christensen, Helle Collatz; Christensen, Morten Bondo

doi:10.1136/bmjopen-2019-030267

Cited by 6 publications

(5 citation statements)

References 31 publications

(29 reference statements)

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“…However, departments must first determine if they need AI to estimate wait times before proceeding. If the waiting time in the department is unusually short or the patient’s willingness for use AI is low, then there is no urgent need to predict the waiting time ( 26 ).…”

Section: Discussionmentioning

confidence: 99%

Prediction of outpatient waiting time: using machine learning in a tertiary children’s hospital

Li,

Liu,

Kong

et al. 2023

Transl Pediatr

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Background Accurately predicting waiting time for patients is crucial for effective hospital management. The present study examined the prediction of outpatient waiting time in a Chinese pediatric hospital through the use of machine learning algorithms. If patients are informed about their waiting time in advance, they can make more informed decisions and better plan their visit on the day of admission. Methods First, a novel classification method for the outpatient clinic in the Chinese pediatric hospital was proposed, which was based on medical knowledge and statistical analysis. Subsequently, four machine learning algorithms [linear regression (LR), random forest (RF), gradient boosting decision tree (GBDT), and K-nearest neighbor (KNN)] were used to construct prediction models of the waiting time of patients in four department categories. Results The three machine learning algorithms outperformed LR in the four department categories. The optimal model for Internal Medicine Department I was the RF model, with a mean absolute error (MAE) of 5.03 minutes, which was 47.60% lower than that of the LR model. The optimal model for the other three categories was the GBDT model. The MAE of the GBDT model was decreased by 28.26%, 35.86%, and 33.10%, respectively compared to that of the LR model. Conclusions Machine learning can predict the outpatient waiting time of pediatric hospitals well and ease patient anxiety when waiting in line without medical appointments. This study offers key insights into enhancing healthcare services and reaffirms the dedication of Chinese pediatric hospitals to providing efficient and patient-centric care.

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Section: Discussionmentioning

confidence: 99%

Prediction of outpatient waiting time: using machine learning in a tertiary children’s hospital

Li,

Liu,

Kong

et al. 2023

Transl Pediatr

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“…This finding is also common in other healthcare settings, where patients described how they felt safer knowing nurses were nearby, watching over them and having everything under control (Gettens et al, 2018; Lovink et al, 2015), or when they knew they could use the call button if they needed help (Watson et al, 2021). In this sense, Ebert et al (2020) studied the out‐of‐hours services in Denmark which provided telephone advice for citizens with acute but non‐life‐threatening health problems, and concluded that the option of bypassing the telephone queue increased satisfaction and feelings of safety in callers (Ebert et al, 2020).…”

Section: Discussionmentioning

confidence: 99%

“…In this sense, Ebert et al (2020) studied the out-of-hours services in Denmark which provided telephone advice for citizens with acute but non-life-threatening health problems, and concluded that the option of bypassing the telephone queue increased satisfaction and feelings of safety in callers (Ebert et al, 2020).…”

Section: Discussionmentioning

confidence: 99%

Factors influencing the perception of feeling safe in pre‐hospital emergency care: A mixed‐methods systematic review

Carrasco

Luque-Hernández

Rodríguez-Ruiz

et al. 2022

Journal of Clinical Nursing

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Aims: To systematically describe the factors influencing the perception of feeling safe among patients receiving pre-hospital emergency care. Background: Patient safety is a top priority worldwide. Little is known about how patients perceive safety during emergency health care and what the related factors are. Design: A mixed-methods systematic review.Methods: Six databases were searched to December 2021. Inclusion criteria were as follows: studies with patients of any age, framed in pre-hospital emergency care settings, referring to patient safety, addressing the perception of safety, and with a quantitative or qualitative research design. Exclusion criteria were as follows: conference records, abstracts, supplements, letters, editorials and reviews. The quality of the evidence was assessed with the Joanna Briggs Institute (JBI) checklists. A convergent integrated approach was followed according to the JBI methodology. The protocol was registered on PROSPERO (CRD42022306337). PRISMA 2020 guidelines were followed in reporting this review.Results: Among the 5223 records identified, the whole texts of 35 articles were reviewed. Ten studies were relevant to the research question. Seven followed a qualitative methodology, with interviews or focus group. Three involved a quantitative methodology, with questionnaires or validated measures. The 74 findings were organised into 11 categories and then grouped into four dimensions: 'receiving a satisfying response from healthcare professionals when I need it', 'EMS personnel's professional competence', 'the variable and unfamiliar settings of pre-hospital emergencies', and 'patients' personal characteristics'. Conclusions:The perception of safety is influenced by many factors, which can be grouped into four independent dimensions. Quantitative and qualitative studies can provide us with complementary findings. Scopus (emotions OR feeling OR perception OR experience) AND ("patient safety" OR safe OR safety) AND (ambulances OR "emergency medical services" OR "prehospital emergency care") Web of Science (WOS) (emotions OR feeling OR perception OR experience) AND ("patient safety" OR safe OR safety) AND (ambulances OR "emergency medical services" OR "prehospital emergency care") PsycInfo (emotions OR feeling OR perception OR experience) AND ("patient safety" OR safe OR safety) AND (ambulances OR "emergency medical services" OR "prehospital emergency care")

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“…Research methods used to write this article: economic-statistical analysis and synthesis, graphical method, analysis of series of dynamics [17]. Previously, it has been proved that research is more profitable in terms of financial and time aspects using electronic platforms [18]. Therefore, informational data for the article was taken from the website of the National Bank of the Republic of Kazakhstan.…”

Section: Metodologymentioning

confidence: 99%

Analysis Of The Dynamics Of Cashless Payments In Kazakhstan In The Context Of The Covid-19 Pandemic

Kredina

Akparova

Васа

2021

jour

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The COVID-19 pandemic has accelerated the development of FinTech and the transition to cashless payments of the population of various countries, including Kazakhstan. For provision of cashless payments there are created platforms which work in NFC & MFS systems, they protected by security protocols (Secure Element (SE)) and allow to store confidential user data. Changes in the business model of banks have led to the emergence of a new format of banking products and services that can be used through smartphones (the main operating systems Android and iOS). The goal of the study is to analyze the dynamics of the impact of the COVID-19 pandemic on the use of cashless payments through national payment systems in Kazakhstan. Based on the goal, a null and an alternative hypothesis were set, of which the second was confirmed in the result. Research methods used to write this article are economic and statistical analysis and synthesis, graphical method, analysis of the series of dynamics. The data for the study was taken from the NBK Statistical Bulletin: data on cashless payments through the Interbank System of Money Transfer (ISMT) and the Interbank Clearing System (ICS) for the period from 2002 to 2020. The impact of the COVID-19 pandemic on the dynamics of the transformation of cashless payments made by using financial technologies has been proved. Recommendations for further improvement of the financial system are given. The development of national payment systems makes it possible to reduce the level of fraud in the financial sector, to increase the speed of cashless transfers and level out transaction errors. Study limitation: only national payment systems ISMT and ICS were used.

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Does an emergency access button increase the patients’ satisfaction and feeling of safety with the out-of-hours health services? A randomised controlled trial in Denmark

Cited by 6 publications

References 31 publications

Prediction of outpatient waiting time: using machine learning in a tertiary children’s hospital

Prediction of outpatient waiting time: using machine learning in a tertiary children’s hospital

Factors influencing the perception of feeling safe in pre‐hospital emergency care: A mixed‐methods systematic review

Analysis Of The Dynamics Of Cashless Payments In Kazakhstan In The Context Of The Covid-19 Pandemic

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