BACKGROUND: Recording and coding of ageing syndromes in hospital records is known to be suboptimal. Natural Language Processing algorithms may be useful to identify diagnoses in electronic healthcare records to improve the recording and coding of these ageing syndromes, but the feasibility and diagnostic accuracy of such algorithms are unclear.
METHODS: We conducted a systematic review according to a predefined protocol and in line with Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines. Searches were run from the inception of each database to the end of September 2023 in PubMed, Medline, Embase, CINAHL, ACM digital library, IEEE Xplore and Scopus. Eligible studies were identified via independent review of search results by two coauthors and data extracted from each study to identify the computational method, source of text, testing strategy and performance metrics. Data were synthesised narratively by ageing syndrome and computational method in line with the Studies Without Meta-analysis guidelines.
RESULTS: From 1030 titles screened, 22 studies were eligible for inclusion. One study focussed on identifying sarcopenia, one frailty, twelve falls, five delirium, five dementia and four incontinence. Sensitivity (57.1%-100%) of algorithms compared with a reference standard was reported in 20 studies, and specificity (84.0%-100%) was reported in only 12 studies. Study design quality was variable with results relevant to diagnostic accuracy not always reported, and few studies undertaking external validation of algorithms.
CONCLUSIONS: Current evidence suggests that Natural Language Processing algorithms can identify ageing syndromes in electronic health records. However, algorithms require testing in rigorously designed diagnostic accuracy studies with appropriate metrics reported.

Background: The interfacility transfer places the patient at greater risk for poor outcomes due to outdated, inaccurate, or miscommunication of patient information at the time of transfer. Rural patients are at greater risk for poor outcomes due to experiencing a higher rate of transfers because of healthcare inequities related to limited access to specialty and critical care services. This paper systematically reviewed the literature to describe the current state of interfacility transfer communication practices and methods in the United States. Methods: The review followed the 2020 Preferred Reporting Items for Systematic Review and Meta-Analyses guidelines and checklist. PubMed, CINAHL, and Scopus were searched using MeSH terms and keywords. Inclusion criteria: peer-reviewd research articles published in English from 2013 to 2022 in the United States, and included both adult and pediatric patient transfers. It was noted that there is a lack of research on nursing-based interfacility transfer communication practices and methods. Results: A total of 763 articles were reviewed, and 24 met eligibility for inclusion. The following coded themes were identified in the research literature: transfer patient characteristics and geographic barriers, communication challenges, transfer process, interoperability, digital intervention, and standardized transfer tools. Conclusion: The development and implementation of an integrated standardized interfacility transfer communication tool are warranted to decrease miscommunication and improve patient outcomes. The integration of technologies such as telehealth, the use of health information exchanges, and improved interoperability between health systems can improve communication and outcomes for all transfer patients but specifically rural transfers. Additionally, healthcare workers, particularly those in rural areas, need adequate infrastructure and financial resources to achieve positive patient outcomes.

UNASSIGNED: Use of the electronic health record (EHR) has motivated the need for data standardization. A gap in knowledge exists regarding variations in existing terminologies for defining diabetic retinopathy (DR) cohorts. This study aimed to review the literature and analyze variations regarding codified definitions of DR.
UNASSIGNED: Literature review and quantitative analysis.
UNASSIGNED: Published manuscripts.
UNASSIGNED: Four graders reviewed PubMed and Google Scholar for peer-reviewed studies. Studies were included if they used codified definitions of DR (e.g., billing codes). Data elements such as author names, publication year, purpose, data set type, and DR definitions were manually extracted. Each study was reviewed by ≥ 2 authors to validate inclusion eligibility. Quantitative analyses of the codified definitions were then performed to characterize the variation between DR cohort definitions.
UNASSIGNED: Number of studies included and numeric counts of billing codes used to define codified cohorts.
UNASSIGNED: In total, 43 studies met the inclusion criteria. Half of the included studies used datasets based on structured EHR data (i.e., data registries, institutional EHR review), and half used claims data. All but 1 of the studies used billing codes such as the International Classification of Diseases 9th or 10th edition (ICD-9 or ICD-10), either alone or in addition to another terminology for defining disease. Of the 27 included studies that used ICD-9 and the 20 studies that used ICD-10 codes, the most common codes used pertained to the full spectrum of DR severity. Diabetic retinopathy complications (e.g., vitreous hemorrhage) were also used to define some DR cohorts.
UNASSIGNED: Substantial variations exist among codified definitions for DR cohorts within retrospective studies. Variable definitions may limit generalizability and reproducibility of retrospective studies. More work is needed to standardize disease cohorts.
UNASSIGNED: Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

BACKGROUND: Breast cancer is the most common disease in women. Recently, explainable artificial intelligence (XAI) approaches have been dedicated to investigate breast cancer. An overwhelming study has been done on XAI for breast cancer. Therefore, this study aims to review an XAI for breast cancer diagnosis from mammography and ultrasound (US) images. We investigated how XAI methods for breast cancer diagnosis have been evaluated, the existing ethical challenges, research gaps, the XAI used and the relation between the accuracy and explainability of algorithms.
METHODS: In this work, Preferred Reporting Items for Systematic Reviews and Meta-Analyses checklist and diagram were used. Peer-reviewed articles and conference proceedings from PubMed, IEEE Explore, ScienceDirect, Scopus and Google Scholar databases were searched. There is no stated date limit to filter the papers. The papers were searched on 19 September 2023, using various combinations of the search terms \'breast cancer\', \'explainable\', \'interpretable\', \'machine learning\', \'artificial intelligence\' and \'XAI\'. Rayyan online platform detected duplicates, inclusion and exclusion of papers.
RESULTS: This study identified 14 primary studies employing XAI for breast cancer diagnosis from mammography and US images. Out of the selected 14 studies, only 1 research evaluated humans\' confidence in using the XAI system-additionally, 92.86% of identified papers identified dataset and dataset-related issues as research gaps and future direction. The result showed that further research and evaluation are needed to determine the most effective XAI method for breast cancer.
CONCLUSIONS: XAI is not conceded to increase users\' and doctors\' trust in the system. For the real-world application, effective and systematic evaluation of its trustworthiness in this scenario is lacking.
UNASSIGNED: CRD42023458665.

UNASSIGNED: Since the 1970s, a plethora of tools have been introduced to support the medication use process. However, automation initiatives to assist pharmacists in prospectively reviewing medication orders are lacking. The review of many medications may be protocolized and implemented in an algorithmic fashion utilizing discrete data from the electronic health record (EHR). This research serves as a proof of concept to evaluate the capability and effectiveness of an electronic prospective medication order review (EPMOR) system compared to pharmacists\' review.
UNASSIGNED: A subset of the most frequently verified medication orders were identified for inclusion. A team of clinical pharmacist experts developed best-practice EPMOR criteria. The established criteria were incorporated into conditional logic built within the EHR. Verification outcomes from the pharmacist (human) and EPMOR (automation) were compared.
UNASSIGNED: Overall, 13 404 medication orders were included. Of those orders, 13 133 passed pharmacist review, 7388 of which passed EPMOR. A total of 271 medication orders failed pharmacist review due to order modification or discontinuation, 105 of which passed EPMOR. Of the 105 orders, 19 were duplicate orders correctly caught by both EPMOR and pharmacists, but the opposite duplicate order was rejected, 51 orders failed due to scheduling changes.
UNASSIGNED: This simulation was capable of effectively discriminating and triaging orders. Protocolization and automation of the prospective medication order review process in the EHR appear possible using clinically driven algorithms.
UNASSIGNED: Further research is necessary to refine such algorithms to maximize value, improve efficiency, and minimize safety risks in preparation for the implementation of fully automated systems.

Diagnostic delay in monogenic disease is reportedly common. We conducted a scoping review investigating variability in study design, results, and conclusions.
We searched the academic literature on January 17, 2023, for original peer reviewed journals and conference articles that quantified diagnostic delay in monogenic disease. We abstracted the reported diagnostic delay, relevant study design features, and definitions.
Our search identified 259 articles quantifying diagnostic delay in 111 distinct monogenetic diseases. Median reported diagnostic delay for all studies collectively in monogenetic diseases was 5.0 years (IQR 2-10). There was major variation in the reported delay within individual monogenetic diseases. Shorter delay was associated with disorders of childhood metabolism, immunity, and development. The majority (67.6%) of articles that studied delay reported an improvement with calendar time. Study design and definitions of delay were highly heterogenous. Three gaps were identified: (1) no studies were conducted in the least developed countries, (2) delay has not been studied for the majority of known, or (3) most prevalent genetic diseases.
Heterogenous study design and definitions of diagnostic delay inhibit comparison across studies. Future efforts should focus on standardizing delay measurements, while expanding the research to low-income countries.

UNASSIGNED: One vital strategy for the success of healthcare organisations currently resides in the capacity of the nurse managers (NMs) to develop informatics competencies in management.
UNASSIGNED: The aim of this review was to synthesise the evidence related to the informatics competencies of NMs in healthcare settings.
UNASSIGNED: The databases of PubMed, Web of Science, Scopus and Education Resources Information Center were searched. All publications were accepted if they had been published in English and full texts were accessible. The study was carried out using a systematic literature search of work published between January 2000 and December 2021. The Endnote v.17 software was used to facilitate reference management. Quality assessment of publications was carried out independently by two reviewers using the Critical Appraisal Skills Programme guidance. A descriptive summary with data tables was produced to summarise the literature.
UNASSIGNED: After screening the search results, 12 studies published between the period 2001 and 2019 met the inclusion criteria. Analysis of the included studies led to the classification of informatics competencies in three main themes: skills related to work with computers (four sub-themes), informatics skills of NMs (three sub-themes) and knowledge of NMs about informatics (five sub-themes).
UNASSIGNED: These systematic review results can lead to the identification of informatics competency gaps and recommend and provide solutions to address these gaps and increase competencies for nursing managers.

BACKGROUND: Despite the touted potential of artificial intelligence (AI) and machine learning (ML) to revolutionize health care, clinical decision support tools, herein referred to as medical modeling software (MMS), have yet to realize the anticipated benefits. One proposed obstacle is the acknowledged gaps in AI translation. These gaps stem partly from the fragmentation of processes and resources to support MMS transparent documentation. Consequently, the absence of transparent reporting hinders the provision of evidence to support the implementation of MMS in clinical practice, thereby serving as a substantial barrier to the successful translation of software from research settings to clinical practice.
OBJECTIVE: This study aimed to scope the current landscape of AI- and ML-based MMS documentation practices and elucidate the function of documentation in facilitating the translation of ethical and explainable MMS into clinical workflows.
METHODS: A scoping review was conducted in accordance with PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines. PubMed was searched using Medical Subject Headings key concepts of AI, ML, ethical considerations, and explainability to identify publications detailing AI- and ML-based MMS documentation, in addition to snowball sampling of selected reference lists. To include the possibility of implicit documentation practices not explicitly labeled as such, we did not use documentation as a key concept but as an inclusion criterion. A 2-stage screening process (title and abstract screening and full-text review) was conducted by 1 author. A data extraction template was used to record publication-related information; barriers to developing ethical and explainable MMS; available standards, regulations, frameworks, or governance strategies related to documentation; and recommendations for documentation for papers that met the inclusion criteria.
RESULTS: Of the 115 papers retrieved, 21 (18.3%) papers met the requirements for inclusion. Ethics and explainability were investigated in the context of AI- and ML-based MMS documentation and translation. Data detailing the current state and challenges and recommendations for future studies were synthesized. Notable themes defining the current state and challenges that required thorough review included bias, accountability, governance, and explainability. Recommendations identified in the literature to address present barriers call for a proactive evaluation of MMS, multidisciplinary collaboration, adherence to investigation and validation protocols, transparency and traceability requirements, and guiding standards and frameworks that enhance documentation efforts and support the translation of AI- and ML-based MMS.
CONCLUSIONS: Resolving barriers to translation is critical for MMS to deliver on expectations, including those barriers identified in this scoping review related to bias, accountability, governance, and explainability. Our findings suggest that transparent strategic documentation, aligning translational science and regulatory science, will support the translation of MMS by coordinating communication and reporting and reducing translational barriers, thereby furthering the adoption of MMS.

Computational analysis of RNA sequences constitutes a crucial step in the field of RNA biology. As in other domains of the life sciences, the incorporation of artificial intelligence and machine learning techniques into RNA sequence analysis has gained significant traction in recent years. Historically, thermodynamics-based methods were widely employed for the prediction of RNA secondary structures; however, machine learning-based approaches have demonstrated remarkable advancements in recent years, enabling more accurate predictions. Consequently, the precision of sequence analysis pertaining to RNA secondary structures, such as RNA-protein interactions, has also been enhanced, making a substantial contribution to the field of RNA biology. Additionally, artificial intelligence and machine learning are also introducing technical innovations in the analysis of RNA-small molecule interactions for RNA-targeted drug discovery and in the design of RNA aptamers, where RNA serves as its own ligand. This review will highlight recent trends in the prediction of RNA secondary structure, RNA aptamers and RNA drug discovery using machine learning, deep learning and related technologies, and will also discuss potential future avenues in the field of RNA informatics.

Clinical decision-making for diagnosing and treating oral and dental diseases consolidates multiple sources of complex information, yet individual clinical judgements are often made intuitively on limited heuristics to simplify decision making, which may lead to errors harmful to patients. This study aimed at systematically evaluating dental practitioners\' clinical decision-making processes during diagnosis and treatment planning under uncertainty. A scoping review was chosen as the optimal study design due to the heterogeneity and complexity of the topic. Key terms and a search strategy were defined, and the articles published in the repository of the National Library of Medicine (MEDLINE/PubMed) were searched, selected, and analysed in accordance with PRISMA-ScR guidelines. Of the 478 studies returned, 64 relevant articles were included in the qualitative synthesis. Studies that were included were based in 27 countries, with the majority from the UK and USA. Articles were dated from 1991 to 2022, with all being observational studies except four, which were experimental studies. Six major recurring themes were identified: clinical factors, clinical experience, patient preferences and perceptions, heuristics and biases, artificial intelligence and informatics, and existing guidelines. These results suggest that inconsistency in treatment recommendations is a real possibility and despite great advancements in dental science, evidence-based practice is but one of a multitude of complex determinants driving clinical decision making in dentistry. In conclusion, clinical decisions, particularly those made individually by a dental practitioner, are potentially prone to sub-optimal treatment and poorer patient outcomes.