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

Patient deep spatio-temporal encoding and medication substructure mapping for safe medication recommendation

Medication recommendations are designed to provide physicians and patients with personalized, accurate and safe medication choices to maximize patient outcomes. Although significant progress has been made in related research, three major challenges remain: inadequate modeling of patients' multidimensional and time-series information, insufficient representation of medication substructures, and poor balance between model accuracy and drug-drug interactions. To address these issues , a safe medication recommendation model SDRBT based on patient deep spatio-temporal encoding and medication substructure mapping is proposed in this paper. SDRBT has developed a patient deep temporal and spatial coding module, which combines symptom information, disease diagnosis information, and treatment information from the patient's electronic health record data. It innovatively utilizes the Block Recurrent Transformer to model longitudinal temporal information of patients in different dimensions to obtain the horizontal representation of the patient's current visit. A dual-domain mapping module for medication substructures is designed to perform global and local mapping of medications, fully learning and aggregating medication substructure representations. Finally, a PID LOSS control unit was designed, in which we studied a drug interaction control module based on the similarity calculation between the electronic health map and the drug interaction graph. This module ensures the safety of the recommended medication combination effectively improved the recommendation efficiency and reduced the model training time. Experiments on the public MIMIC-III dataset demonstrate SDRBT's superior accuracy in medication recommendation.

Acceptance of voice assistant technology in dental practice: A cross sectional study with dentists and validation using structural equation modeling

Voice assistant technologies (VAT) has been part of our daily lives, as a virtual assistant to complete requested tasks. The integration of VAT in dental offices has the potential to augment productivity and hygiene practices. Prior to the adoption of such innovations in dental settings, it is crucial to evaluate their applicability. This study aims to assess dentists' perceptions and the factors influencing their intention to use VAT in a clinical setting. A survey and research model were designed based on an extended Unified Theory of Acceptance and Use of Technology (UTAUT). The survey was sent to 7,544 Ohio-licensed dentists through email. The data was analyzed and reported using descriptive statistics, model reliability testing, and partial least squares regression (PLSR) to explain dentists' behavioral intention (BI) to use VAT. In total, 257 participants completed the survey. The model accounted for 74.2% of the variance in BI to use VAT. Performance expectancy and perceived enjoyment had significant positive influence on BI to use VAT. Perceived risk had significant negative influence on BI to use VAT. Self-efficacy had significantly influenced perceived enjoyment, accounting for 35.5% of the variance of perceived enjoyment. This investigation reveals that performance efficiency and user enjoyment are key determinants in dentists' decision to adopt VAT. Concerns regarding the privacy of VAT also play a crucial role in its acceptance. This study represents the first documented inquiry into dentists' reception of VAT, laying groundwork for future research and implementation strategies.

Electronic health records perception among three healthcare providers specialties in Saudi Arabia: A cross-sectional study

Worldwide, more health care facilities are adapting the use of electronic health record (EHR). Healthcare providers (HCP) have different perceptions toward the use of EHR. To investigate the perception of three classes of HCP in Saudi Arabia toward using EHR, a questionnaire (targeting satisfaction, easiness, and benefits of use as major perception indicators) was prepared. The questionnaire was assessed by an expert panel for content validity. The questionnaire internal consistency was examined using Cronbach's alpha. 108 physicians, physical therapists (PT) and respiratory care therapists (RT) from different hospitals in Saudi Arabia answered the questionnaire. Most of respondents perceived EHR systems as beneficial and made work easier. Most HCP were satisfied with the use of EHR, however, with the use of EHR more time was needed to finish the work. Age, experience, job, and job rank of HCP are of different importance in determining responses, perception, and obstacles of using EHR. Moreover, the perception of using EHR seems to be field specific. There is a positive perception among Saudi Arabia HCP about EHR use.

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

Background

Artificial 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.

Methods

Studies 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.

Results

More 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.

Discussion

Clinical 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.

Natural Language Processing in Electronic Health Records in relation to healthcare decision-making: A systematic review

BACKGROUND

Natural Language Processing (NLP) is widely used to extract clinical insights from Electronic Health Records (EHRs). However, the lack of annotated data, automated tools, and other challenges hinder the full utilisation of NLP for EHRs. Various Machine Learning (ML), Deep Learning (DL) and NLP techniques are studied and compared to understand the limitations and opportunities in this space comprehensively.

METHODOLOGY

After screening 261 articles from 11 databases, we included 127 papers for full-text review covering seven categories of articles: (1) medical note classification, (2) clinical entity recognition, (3) text summarisation, (4) deep learning (DL) and transfer learning architecture, (5) information extraction, (6) Medical language translation and (7) other NLP applications. This study follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines.

RESULT AND DISCUSSION

EHR was the most commonly used data type among the selected articles, and the datasets were primarily unstructured. Various ML and DL methods were used, with prediction or classification being the most common application of ML or DL. The most common use cases were: the International Classification of Diseases, Ninth Revision (ICD-9) classification, clinical note analysis, and named entity recognition (NER) for clinical descriptions and research on psychiatric disorders.

CONCLUSION

We find that the adopted ML models were not adequately assessed. In addition, the data imbalance problem is quite important, yet we must find techniques to address this underlining problem. Future studies should address key limitations in studies, primarily identifying Lupus Nephritis, Suicide Attempts, perinatal self-harmed and ICD-9 classification.

Identifying a Clinical Informatics or Electronic Health Record Expert Witness for Medical Professional Liability Cases

BACKGROUND

The health care field is experiencing widespread electronic health record (EHR) adoption. New medical professional liability (i.e., malpractice) cases will likely involve the review of data extracted from EHRs as well as EHR workflows, audit logs, and even the potential role of the EHR in causing harm.

OBJECTIVES

Reviewing printed versions of a patient's EHRs can be difficult due to differences in printed versus on-screen presentations, redundancies, and the way printouts are often grouped by document or information type rather than chronologically. Simply recreating an accurate timeline often requires experts with training and experience in designing, developing, using, and reviewing EHRs and audit logs. Additional expertise is required if questions arise about data's meaning, completeness, accuracy, and timeliness or ways that the EHR's user interface or automated clinical decision support tools may have contributed to alleged events. Such experts often come from the sociotechnical field of clinical informatics that studies the design, development, implementation, use, and evaluation of information and communications technology, specifically, EHRs. Identifying well-qualified EHR experts to aid a legal team is challenging.

METHODS

Based on literature review and experience reviewing cases, we identified seven criteria to help in this assessment.

RESULTS

The criteria are education in clinical informatics; clinical informatics knowledge; experience with EHR design, development, implementation, and use; communication skills; academic publications on clinical informatics; clinical informatics certification; and membership in informatics-related professional organizations.

CONCLUSION

While none of these criteria are essential, understanding the breadth and depth of an individual's qualifications in each of these areas can help identify a high-quality, clinical informatics expert witness.

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

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.

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

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 a quick snapshot of the information necessary during the design and decision-making phases of the eHealth system development lifecycle. 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.

Applied Clinical Informatics Journal: A Brief History

In 2009, Schattauer Verlag in Stuttgart, Germany first published the Applied Clinical Informatics (ACI) Journal. ACI has served since its inception as an official journal of the International Medical Informatics Association. Later, the American Medical Informatics Association and the European Federation for Medical Informatics named ACI as an official journal. This manuscript describes the history of the journal from its inception to present day including publication measures, challenges, and successes.