Electronic health records: Three decades of bibliometric research productivity analysis and some insights

Jabali, A. Karim; Waris, Abu; Khan, Diwan Israr; Ahmed, Shakil; Hourani, Rana

doi:10.1016/j.imu.2022.100872

Cited by 17 publications

(17 citation statements)

References 25 publications

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“…The date limit was chosen considering digital health stimulus initiatives and implementation plans, the overall state and use of eHealth data at international level, and research productivity levels in this field. 34–36…”

Section: Methodsmentioning

confidence: 99%

Using eHealth Data to Inform CPD for Medical Practitioners: A Scoping Review with a Consultation Exercise with International Experts

Pizzuti,

Palmieri,

Shaw

2023

J Contin Educ Health Prof

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Introduction: eHealth data analytics is widely used in health care research. However, there is limited knowledge on the role of eHealth data analysis to inform continuing professional development (CPD). The aim of this study was to collate available research evidence on the use of eHealth data for the development of CPD programs and plans for medical practitioners. Methods: A scoping review was conducted using the six-stage Arksey and O'Malley Framework. A consultation exercise (stage 6) was performed with 15 international experts in the fields of learning and practice analytics to deepen the insights. Results: Scoping review. The literature searches identified 9876 articles published from January 2010 to May 2022. After screening and full-text review, a total of nine articles were deemed relevant for inclusion. The results provide varied—and at times partial or diverging—answers to the scoping review research questions. Consultation exercise. Research rigor, field of investigation, and developing the field were the three themes emerged from analysis. Participants validated the scoping review methodology and confirmed its results. Moreover, they provided a meta-analysis of the literature, a description of the current CPD ecosystem, and clear indications of what is and should be next for the field. Discussion: This study shows that there is no formal or well-established correlation between eHealth data and CPD planning and programming. Overall findings fill a gap in the literature and provide a basis for further investigation. More foundational work, multidisciplinary collaborations, and stakeholders' engagement are necessary to advance the use of eHealth data analysis for CPD purposes.

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Section: Methodsmentioning

confidence: 99%

Using eHealth Data to Inform CPD for Medical Practitioners: A Scoping Review with a Consultation Exercise with International Experts

Pizzuti,

Palmieri,

Shaw

2023

J Contin Educ Health Prof

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“…Clinical time series data: Continuous patient monitoring leads to a magnitude of measurements captured and stored at discrete timestamps. Regardless of the disease type, a variety of temporal datasets such as Electronic health records (EHR), lab values, vital signs, diagnoses and treatments records can be used for patient monitoring (58).…”

Section: Medical Data Modalities For Intensive Patient Carementioning

confidence: 99%

Artificial intelligence for clinical decision support for monitoring patients in cardiovascular ICUs: A systematic review

Moazemi

Vahdati

et al. 2023

Front. Med.

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BackgroundArtificial intelligence (AI) and machine learning (ML) models continue to evolve the clinical decision support systems (CDSS). However, challenges arise when it comes to the integration of AI/ML into clinical scenarios. In this systematic review, we followed the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA), the population, intervention, comparator, outcome, and study design (PICOS), and the medical AI life cycle guidelines to investigate studies and tools which address AI/ML-based approaches towards clinical decision support (CDS) for monitoring cardiovascular patients in intensive care units (ICUs). We further discuss recent advances, pitfalls, and future perspectives towards effective integration of AI into routine practices as were identified and elaborated over an extensive selection process for state-of-the-art manuscripts.MethodsStudies with available English full text from PubMed and Google Scholar in the period from January 2018 to August 2022 were considered. The manuscripts were fetched through a combination of the search keywords including AI, ML, reinforcement learning (RL), deep learning, clinical decision support, and cardiovascular critical care and patients monitoring. The manuscripts were analyzed and filtered based on qualitative and quantitative criteria such as target population, proper study design, cross-validation, and risk of bias.ResultsMore than 100 queries over two medical search engines and subjective literature research were developed which identified 89 studies. After extensive assessments of the studies both technically and medically, 21 studies were selected for the final qualitative assessment.DiscussionClinical time series and electronic health records (EHR) data were the most common input modalities, while methods such as gradient boosting, recurrent neural networks (RNNs) and RL were mostly used for the analysis. Seventy-five percent of the selected papers lacked validation against external datasets highlighting the generalizability issue. Also, interpretability of the AI decisions was identified as a central issue towards effective integration of AI in healthcare.

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“…The successful and efficient deployment of EHRs is enabled by the combination of these identifiers and the provision of protected database services. The articles in [ 35 , 36 , 37 ] provide further information regarding the specific technologies and platforms necessary for the realization of EHR systems.…”

Section: Ehealth: An Overviewmentioning

confidence: 99%

eHealth: A Survey of Architectures, Developments in mHealth, Security Concerns and Solutions

Alenoghena

Onumanyi

Ohize

et al. 2022

IJERPH

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The ramifications of the COVID-19 pandemic have contributed in part to a recent upsurge in the study and development of eHealth systems. Although it is almost impossible to cover all aspects of eHealth in a single discussion, three critical areas have gained traction. These include the need for acceptable eHealth architectures, the development of mobile health (mHealth) technologies, and the need to address eHealth system security concerns. Existing survey articles lack a synthesis of the most recent advancements in the development of architectures, mHealth solutions, and innovative security measures, which are essential components of effective eHealth systems. Consequently, the present article aims at providing an encompassing survey of these three aspects towards the development of successful and efficient eHealth systems. Firstly, we discuss the most recent innovations in eHealth architectures, such as blockchain-, Internet of Things (IoT)-, and cloud-based architectures, focusing on their respective benefits and drawbacks while also providing an overview of how they might be implemented and used. Concerning mHealth and security, we focus on key developments in both areas while discussing other critical topics of importance for eHealth systems. We close with a discussion of the important research challenges and potential future directions as they pertain to architecture, mHealth, and security concerns. This survey gives a comprehensive overview, including the merits and limitations of several possible technologies for the development of eHealth systems. This endeavor offers researchers and developers with a quick snapshot of the information necessary during the design and decision-making phases of the eHealth system development life cycle. Furthermore, we conclude that building a unified architecture for eHealth systems would require combining several existing designs. It also points out that there are still a number of problems to be solved, so more research and investment are needed to develop and deploy functional eHealth systems.

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Electronic health records: Three decades of bibliometric research productivity analysis and some insights

Cited by 17 publications

References 25 publications

Using eHealth Data to Inform CPD for Medical Practitioners: A Scoping Review with a Consultation Exercise with International Experts

Using eHealth Data to Inform CPD for Medical Practitioners: A Scoping Review with a Consultation Exercise with International Experts

Artificial intelligence for clinical decision support for monitoring patients in cardiovascular ICUs: A systematic review

eHealth: A Survey of Architectures, Developments in mHealth, Security Concerns and Solutions

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